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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-261547154210.1186/1471-230X-4-26Case ReportAn unusual case of an ulcerative colitis flare resulting in disseminated intravascular coagulopathy and a bladder hematoma: a case report Suskind David L [email protected] Karen [email protected] Dennis [email protected] Department of Pediatrics, Seattle Children's Hospital and Regional Medical Center, University of Washington, 4800 Sand Point Way NE, Seattle, Washington, USA2004 7 10 2004 4 26 26 14 5 2004 7 10 2004 Copyright © 2004 Suskind et al; licensee BioMed Central Ltd.2004Suskind et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Disorders of coagulation have long been associated with inflammatory bowel disease. Children, as well as adults, with both active and inactive ulcerative colitis have been found to have abnormal coagulation and fibrinolysis. Disseminated intravascular coagulation arises from an overwhelming of the haemostatic regulatory mechanisms leading to an excessive generation of thrombin and a failure of the normal inhibitory pathways to prevent systemic effects of this enzyme. Ulcerative colitis has been associated with disseminated intravascular coagulation in conjunction with septicemia, toxic megacolon and surgery.
Case presentation
A fourteen-year-old boy with a history of poorly controlled ulcerative colitis presented with nonbilious emesis, hematochezia, and hematuria. Laboratory workup revealed disseminated intravascular coagulation. He was placed on triple antibiotics therapy. An infectious workup came back negative. A computerized tomography (CT) scan of the abdomen revealed a marked thickening and irregularity of the bladder wall as well as wall thickening of the rectosigmoid, ascending, transverse, and descending colon. Patient's clinical status remained stable despite a worsening of laboratory values associated with disseminated intravascular coagulation. Patient was begun on high dose intravenous steroids with improvement of the disseminated intravascular coagulation laboratory values within 12 hours and resolution of disseminated intravascular coagulopathy within 4 days. A thorough infectious workup revealed no other causes to his disseminated intravascular coagulation.
Conclusions
The spectrum of hypercoagulable states associated with ulcerative colitis varies from mild to severe. Although disseminated intravascular coagulation associated with ulcerative colitis is usually related to septicemia, toxic megacolon or surgery, we present a case of an ulcerative colitis flare resulting in disseminated intravascular coagulation and a bladder hematoma.
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Background
A wide variety of disorders are associated with the development of disseminated intravascular coagulation (DIC). Initiation usually involves mechanical tissue injury and or endothelial cell activation and injury. DIC arises from an overwhelming of the haemostatic regulatory mechanisms leading to an excessive generation of thrombin and a failure of the normal inhibitory pathways to prevent systemic effects of the enzyme leading to DIC [1]. Ulcerative colitis has been associated with DIC. In previously reported cases, DIC has arisen from active disease in conjunction with septicemia, toxic megacolon or surgery [2-5]. The authors report a pediatric case of DIC associated with a colitis flare resulting in a bladder hematoma.
Case presentation
A 14-year-old boy with a diagnosis of ulcerative colitis based on colonic histology, serology and a normal barium study of his small bowels was admitted with a five-day history of nonbilious vomiting and bloody diarrhea. Additional symptoms included recent onset hematuria, and low-grade fevers to 100.4 C over the prior four days. He had also sustained a 25 lb weight loss in the last six months, indicating a lack of disease control.
As an outpatient, his maintenance therapy included mesalamine (1 gram three times a day), and mercaptopurine (75 mg once per day). In addition, he had been started on prednisone approximately 7 weeks prior for treatment of an ulcerative colitis flare. His current dose of prednisone was 10 mg once a day. Soon after symptoms begun, he had been placed on ciprofloxacin as treatment for a presumptive flare.
Physical exam showed he was afebrile, with a heart rate of 130 beats per minute, respiratory 16 breaths per minute and blood pressure 115/67 mmHg. He was alert although with a sallow appearance. Abdominal exam revealed a soft nontender nondistended abdomen. Rectal showed normal external exam with grossly bloody stool. Initial blood work showed hemoglobin of 12.3, a normal white blood cell count, normal differential and normal platelet count with a mildly elevated prothrombin time of 16.2 with an international normalized ratio (INR) of 1.2. Urine analysis showed a specific gravity of 1.035, 3+blood, +ketones and > 100 RBC per high powered field and 0–5 WBC per high power field. Abdominal ultrasound revealed irregular shaped bladder wall.
Patient was placed on intravenous fluids (IV) as well as metronidazole (IV). Blood and urine cultures were sent for analysis. Stool was sent for culture and for Clostridium difficile toxin analysis.
Serial repeat lab works the following day revealed a dropping hemoglobin (7.4 g/dL) and platelet count (64 K/mm3) increasing PT/PTT (21.3/47 seconds) with an INR of 1.8. Blood smear showed moderate amount of elliptocytes, schistocytes, microcytes and fragmented red blood cells. Initial DIC panel revealed an elevated D-dimer of 4.9 mcg/mL with a normal thrombin time and fibrinogen. Thrombin time subsequently increased to > 120 seconds. D-dimers increased to 10.3 mcg/mL. A computerized tomography (CT) scan of the abdomen revealed a marked thickening and irregularity of the bladder wall as well as wall thickening of the rectosigmoid, ascending, transverse, and descending colon (Figure 1). Urology was consulted and felt that this represented a submucosal hematoma.
Patient was begun on broad-spectrum antibiotics because of concerns regarding possible bacteremia and a worsening DIC laboratory picture. Blood, stool and urine cultures returned negative. Viral cultures and monoclonal antibody staining for adenovirus detection in the urine was negative. Despite a worsening in the DIC panel, the patient remained clinically unchanged. IV steroids were begun approximately 36 hours into patient's hospital stay. Patient had a stabilization of PT/PTT/INR/thrombin time and D-dimer, and a subsequent normalization of labs over the following 4-day period ( Figure 2, 3, 4, 5, 6, 7, 8 ). Patient's diarrhea and hematuria resolved as well. Colonscopy revealed chronic colitis consistent with ulcerative colitis. Cystoscopy revealed a fibrin clot consistent with submucosal hematoma. Patient was discharged from the hospital on a steroid taper, and remains in remission to date.
Conclusions
Disorders of coagulation have long been associated with inflammatory bowel disease [6-11]. Children, as well as adults, with both active and inactive ulcerative colitis have been found to have abnormal coagulation and fibrinolysis[11]. It is unclear whether this is a direct or indirect result of inflammatory bowel disease.
Although hypocoagulable states have been noted in the literature, most studies indicate an associated hypercoagulable state. There appears to be an increase in thrombin-anti-thrombin complex and a decrease in antithrombin III activity, which causes an increase in thrombin generation[10,12,13]. Other studies have demonstrated an increase in fibrinogen content, increase Factor VIII, and Factor IX activity, platelet count and aggregation rate[9,12]. These hypercoagulable abnormalities return towards normal with therapy in direct correlation with sedimentation rate and clinical disease activity [12], but can still show mild abnormalities despite clinical remission[14].
The hypercoagulable state in ulcerative colitis is associated thromboembolic events; although uncommon, deep vein thrombosis, pulmonary embolisms and stroke have been associated with ulcerative colitis[6,15-18]. Disseminated intravascular coagulopathy is a rare occurrence in inflammatory bowel disease. When it occurs, it is usually associated with other co-founding problems such as septicemia, toxic megacolon or surgery.
Presented is a case of DIC associated solely with an ulcerative colitis flare resulting in a bladder hematoma. We presume that the occurrence of DIC in this patient resulted from an acute flare on top of a chronic unremitting course of ulcerative colitis. A thorough infectious work-up of this patient did not reveal any infectious etiology that would have predisposed him to develop DIC. The presumed cause of the DIC was damage to the endothelial wall of the colonic blood vessels, which exposed blood to excessive amounts of tissue factor. This in turn led to the excessive generation of thrombin and a failure of the normal coagulation inhibitory pathways. By treating the ulcerative colitis flare, we decreased the intestinal inflammation and thereby decreased the endothelial cell damage. This, theoretically, resolved the DIC. Patient's clinical symptoms and laboratory values normalized after treatment with intravenous steroids, completely resolving the disseminated intravascular coagulopathy.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DLS drafted the manuscript. KM and DC participated in the manuscript preparation. All authors approved the final manuscript.
Figure 1 Abdominal CT revealing a marked thickening and irregularity of the bladder wall consistent with bladder hematoma.
Figure 2 Graphic illustration of C-reactive protein throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 3 Graphic illustration of hemoglobin throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 4 Graphic illustration of platelets throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 5 Graphic illustration of prothrombin time throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 6 Graphic illustration of international normalized ratio throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 7 Graphic illustration of partial thromboplastin time C-reactive protein throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Figure 8 Graphic illustration of D-dimer throughout hospitalization: Day 1 (admission date) – Day 9 (day of discharge). Patient received intravenous steroids at approximately 36 hours into hospitalization.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15471542 | PMC526371 | CC BY | 2021-01-04 16:29:54 | no | BMC Gastroenterol. 2004 Oct 7; 4:26 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-26 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-451547655910.1186/1471-2458-4-45Research ArticleSurveillance of antimicrobial resistance at a tertiary hospital in Tanzania Blomberg Bjørn [email protected] Davis SM [email protected] Willy K [email protected] Samwel Y [email protected] Marcellina [email protected] Asbjørn [email protected] Stig [email protected] Nina [email protected] Centre for International Health, University of Bergen, N-5021 Bergen, Norway2 Institute of Medicine, University of Bergen, N-5021 Bergen, Norway3 Department of Microbiology and Immunology, Muhimbili University College of Health Sciences, Dar es Salaam, Tanzania4 Department of Microbiology and Immunology, the Gade Institute, Haukeland Hospital, N-5021 Bergen, Norway5 Department of Medicine, Haukeland University Hospital, N-5021 Bergen, Norway2004 11 10 2004 4 45 45 28 6 2004 11 10 2004 Copyright © 2004 Blomberg et al; licensee BioMed Central Ltd.2004Blomberg et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Antimicrobial resistance is particularly harmful to infectious disease management in low-income countries since expensive second-line drugs are not readily available. The objective of this study was to implement and evaluate a computerized system for surveillance of antimicrobial resistance at a tertiary hospital in Tanzania.
Methods
A computerized surveillance system for antimicrobial susceptibility (WHONET) was implemented at the national referral hospital in Tanzania in 1998. The antimicrobial susceptibilities of all clinical bacterial isolates received during an 18 months' period were recorded and analyzed.
Results
The surveillance system was successfully implemented at the hospital. This activity increased the focus on antimicrobial resistance issues and on laboratory quality assurance issues. The study identified specific nosocomial problems in the hospital and led to the initiation of other prospective studies on prevalence and antimicrobial susceptibility of bacterial infections. Furthermore, the study provided useful data on antimicrobial patterns in bacterial isolates from the hospital. Gram-negative bacteria displayed high rates of resistance to common inexpensive antibiotics such as ampicillin, tetracycline and trimethoprim-sulfamethoxazole, leaving fluoroquinolones as the only reliable oral drugs against common Gram-negative bacilli. Gentamicin and third generation cephalosporins remain useful for parenteral therapy.
Conclusion
The surveillance system is a low-cost tool to generate valuable information on antimicrobial resistance, which can be used to prepare locally applicable recommendations on antimicrobial use. The system pinpoints relevant nosocomial problems and can be used to efficiently plan further research. The surveillance system also functions as a quality assurance tool, bringing attention to methodological issues in identification and susceptibility testing.
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Background
Exaggerated and irrational use of drugs, availability of antibiotics without prescription, the use of pharmaceuticals of doubtful quality and the HIV epidemic may all contribute to the current worldwide surge in antimicrobial drug resistance. Emerging resistance to antimicrobial drugs increases morbidity and mortality by hampering the provision of effective chemotherapy, and makes treatment more costly [1-3]. The surge in antimicrobial resistance seen in many low-income countries is potentially disastrous because of the lack of resources for purchasing expensive second-line drugs [4].
It is widely held that surveillance of antimicrobial susceptibility is fundamental to combat the emergence of resistance [5]. Surveillance must be global since resistant bacteria can be transferred between countries, but it must also be local, since countries have very different resistance patterns and different treatment practices [6]. The primary task of a surveillance system is to provide locally applicable data to guide empiric therapy. Furthermore, surveillance may help assessing the magnitude of the resistance problem locally, nationally and internationally, monitoring changes in resistance rates and detecting the emergence and spread of new resistance traits. A well-functioning surveillance system is also necessary to measure the impact of any interventions. Surveillance systems also functions as a quality assurance tool and may help improving the quality of the susceptibility testing.
This paper describes the experience with the implementation of a computerized surveillance system for antimicrobial drug susceptibility at Tanzania's major referral hospital, and its use to analyze the susceptibility patterns of 7621 consecutively recorded clinical bacterial isolates.
Methods
Setting
The study was performed at Muhimbili National Hospital (MNH), Dar es Salaam, Tanzania. With more than 1000 beds, MNH is the largest hospital in the country and serves as a national referral and university teaching hospital, as well as a primary and referral hospital for a population of approximately 3.6 million in the Dar es Salaam area. The Department of Microbiology and Immunology at MNH examines specimens from inpatients and outpatients at MNH, and from a number of nearby hospitals. Bacteriological cultures are performed on more than 23,000 specimens per year.
The surveillance system
A free-of-charge software for the surveillance of antimicrobial resistance (WHONET, World Health Organization, Geneva, Switzerland) [7] was implemented at MNH in 1998. Currently a total of 880 microbiology laboratories in 76 countries use this software, however, among these are only 41 laboratories in four countries on the African continent (data from 2002, personal communication from John Stelling, author of the WHONET software). The software has three main parts, a laboratory configuration file which can be used to customize it to the particular laboratory, an interface for data entry and a part for analysis and reporting of resistance data. At our hospital, all bacterial isolates of clinical significance from specimens received during the period July 1st 1998 to December 31st 1999 were recorded and analyzed. The specimens examined included urine, pus/secretions (swabs from skin, surgical and traumatic wounds, burns, umbilical cords, throat, nose, eye and ear discharge and genital swabs), blood, cerebrospinal fluid, other body fluids, stools and other specimens. Mycobacteria and anaerobic bacteria were not included in the study. Apart from the WHONET software, we used Stata 8.0 for Macintosh (Stata Corporation, College Station, Texas, USA) to evaluate differences of proportions by Fisher's exact test (2-tailed, cut-off point for statistical significance at p-value of 0.05).
Laboratory methods
The specimens were cultured and the bacterial isolates identified using standard microbiological methods as described in Mackie & McCartney Practical Medical Microbiology [8]. Susceptibility testing was performed by Stokes' method [9] on Iso-Sensitest (Oxoid Limited, Basingstoke, UK) agar plates. This method, developed by Dr Joan Stokes half a century ago, was designed to monitor for both disc and agar quality in that both the clinical isolate and a control strain were tested on every plate. The clinical isolate is swabbed onto the middle of the agar plate and the control strain at the periphery. The antibiotic disk is placed precisely at the interface between the surface areas inoculated with the clinical isolate and the control strain. After overnight incubation, the relative size of the inhibition zones of the clinical isolate and the control strains are compared. The test results are classified as susceptible (S), intermediate (I) or resistant (R) by evaluation of the difference between the inhibition zones of the clinical isolate and the control strain. The control strains used in our lab are S. aureus NCTC 6571, E. coli NCTC 10418 or Pseudomonas aeruginosa NCTC 10662. The isolates showing intermediate resistance were few and were grouped together with sensitive isolates for the purpose of data analysis. Either methicillin or oxacillin disks were used to test for methicillin-resistance in S. aureus, the results being considered equivalent and interchangeable in the data analysis. ß-Lactamase testing was not routinely performed. The susceptibility of pneumococci to penicillin was examined by the use of penicillin 2 μg disks. Commercially produced antibiotic disks, mostly obtained from Oxoid Limited, were used, however, in some instances, antibiotic disks, prepared locally were used due to financial constraints. The Department of Microbiology and Immunology participates in an external quality assessment program in bacteriology led by the World Health Organization-collaborating centre, the National Institute for Communicable Diseases (NICD), Johannesburg, South Africa. The Department of Microbiology and Immunology at our hospital receives bacterial strains from NICD, performs species identification and antimicrobial susceptibility testing, and report the results back to NICD.
Evaluation of the surveillance system
We evaluated the strengths and shortcomings of the surveillance system in our setting, particularly in terms of how well it performed in its main application areas, providing locally applicable data to guide empiric therapy, monitoring antimicrobial susceptibility trends, detecting the emergence and spread of new resistance traits and as a tool for quality assurance. We also assessed the cost-implications of implementing the surveillance program in our setting. We considered direct costs, such as the purchase of equipment, and indirect costs, such as those related to the running of the laboratory, including human resources. We also comment on the benefits of the surveillance system related to both direct patient care and long-term implications of containing antimicrobial resistance.
Results
Bacterial isolates
A total of 7617 bacterial isolates were registered during the study period, of which 67.4% (n = 5134) were Gram-negative and 32.6% (n = 2483) Gram-positive. Table 1 shows the most frequently encountered bacteria, overall and from various specimen types. The majority of the isolates were obtained from pus (44.3%), urine (43.5%) and blood cultures (10.1%). Cerebrospinal fluid accounted for 0.4% of the isolates. Among the 2034 blood cultures, 15.9% (n = 323) yielded growth of a total of 326 pathogenic bacterial isolates and 447 Coagulase-negative staphylococci (CoNS) as shown in Table 1. CoNS are potential pathogens and are increasingly considered as a cause of blood-stream infections. However, in many cases they are merely contaminants, i.e. bacterial isolates present on the skin surface, which are introduced in the blood specimen and grow in the blood culture, but do not produce disease in the patient. For CoNS isolates to be considered a probable pathogen, it is commonly required that they are recovered from two separate blood cultures. Since multiple blood cultures were not routinely taken from the same patient in the hospital, the susceptibilities of these isolates were not evaluated further. CoNS and various other Gram-positive probable contaminants, mostly Bacillus spp. were recovered from 22.0% (n = 447) and 6.9% (n = 141) of the blood cultures, respectively. Furthermore, five Candida spp. isolates and one Cryptococcus neoformans were recovered. Among the 49 Salmonella isolates, two were identified as S. Typhi, 16 as S. Typhimurium, 16 as S. Paratyphi B and one each as S. Paratyphi C, S. Enteritidis and S. Arizonae. Twelve Salmonella isolates were not serotyped. Among the 41gonococcal isolates, 28 (68.3%) were from genital swabs. Eleven (26.8%) gonococcal isolates were obtained from the neonatal ward, out of which 4 were specified as from eye discharge.
Table 1 Frequency of pathogenica bacterial isolates from different specimen types at Muhimbili National Hospital, Tanzania
Organism Blood (%) Spinal fluid (%) Urine (%) Pusb(%) Other (%) Overall (%)
Gram-negative isolates
E. coli 27 (3.5) 0 (0.0) 1466 (44.2) 417 (12.3) 26 (21.8) 1936 (25.4)
Klebsiella spp. 91 (11.8) 8 (23.5) 1036 (31.3) 603 (17.9) 33 (27.7) 1771 (23.3)
Pseudomonas spp. 10 (1.3) 2 (5.9) 52 (1.6) 531 (15.7) 9 (7.6) 604 (7.9)
Proteus spp. 7 (0.9) 0 (0.0) 121 (3.7) 249 (7.4) 3 (2.5) 380 (5.0)
Enterobacter spp. 4 (0.5) 0 (0.0) 97 (2.9) 1 (0.0) 0 (0.0) 102 (1.3)
Salmonella spp. 37 (4.8) 2 (5.9) 6 (0.2) 0 (0.0) 4 (3.4) 49 (0.6)
N. gonorrhoeae 0 (0.0) 0 (0.0) 0 (0.0) 41 (1.2) 0 (0.0) 41 (0.5)
Haemophilus spp. 1 (0.1) 5 (14.7) 0 (0.0) 0 (0.0) 0 (0.0) 6 (0.1)
Other GNR 32 (4.1) 7 (20.6) 12 (0.4) 184 (5.4) 10 (8.4) 245 (3.2)
Subtotal, Gram-negative isolates 209 (27.0) 24 (70.6) 2790 (84.2) 2026 (60.0) 85 (71.4) 5134 (67.4)
Gram-positive isolates
Staphylococcus aureus 72 (9.3) 1 (2.9) 362 (10.9) 1120 (33.2) 12 (10.1) 1567 (20.6)
Streptococcus pyogenes 1 (0.1) 0 (0.0) 0 (0.0) 160 (4.7) 2 (1.7) 163 (2.1)
Other streptococcic 39 (5.0) 3 (8.8) 52 (1.6) 58 (1.7) 13 (10.9) 165 (2.2)
Enterococci 3 (0.4) 0 (0.0) 64 (1.9) 3 (0.1) 1 (0.8) 71 (0.9)
S. pneumoniae 2 (0.3) 6 (17.6) 0 (0.0) 11 (0.3) 6 (5.0) 25 (0.3)
CoNSa 447 (57.8) ... 45 (1.4) ... ... 492 (6.5)
Subtotal, Gram-positive isolates 564 (73.0) 10 (29.4) 523 (15.8) 1352 (40.0) 34 (28.6) 2483 (32.6)
Total 773 (100.0) 34 (100.0) 3313 (100.0) 3378 (100.0) 119 (100.0) 7617 (100.0)
GNR, Gram-negative rod-shaped bacteria, not further identified; CoNS, coagulase-negative staphylococci; "...", not applicable.a CoNS from blood and urine specimens are reported as possible pathogens, although many may be contaminants. CoNS from other specimen types are considered contaminants and not reported. b Pus includes swabs from skin, surgical and traumatic wounds, burns, umbilical cords, throat, nose, eye and ear discharge and genital swabs. c Streptococci other than S. pyogenes and S. pneumoniae, and streptococci not identified below genus level.
Specimens from inpatients and outpatients contributed to 53.2% and 31.9% of the isolates, respectively. A further 6.0% were obtained from specimens from other hospitals in Dar es Salaam, while 8.8% were obtained from other or unknown locations. Among the isolates from inpatients, 36.5% were obtained from the Department of Pediatrics, 28.4% from the neonatal section and 8.1% from the other pediatric wards. The other isolates came from the Departments of Surgery (22.4%), Internal Medicine (16.6%), Obstetrics and Gynecology (9.8%), the Intensive Care Unit (4.9%) and other locations (9,8%). For 4900 isolates, the age or the estimated age group of the patient was known. Of these, 23.6% (n = 1155) were from neonates (≤ 1 month old), 6.8% (n = 335) from children aged one month to seven years, and 69.6% (n = 3410) from adults or children older than 8 years.
Antimicrobial susceptibility
Tables 2 and Table 3 show the antimicrobial susceptibility patterns of the most frequently isolated Gram-negative and Gram-positive bacteria, respectively. There were no clear-cut differences in the antimicrobial susceptibilities among the various serotypes of Salmonella isolates (data not shown). The majority of Pseudomonas aeruginosa isolates was susceptibility-tested to gentamicin only, to which 4.3% (15/350) were resistant. Among the isolates of Neisseria gonorrhoeae, 70.0% were resistant to penicillin, 45.2% to tetracycline, 59.3% to trimethoprim-sulfamethoxazole, 5.9% to erythromycin and none was resistant to spectinomycin, fluoroquinolones or amoxicillin-clavulanate (data not shown).
Table 2 Percentage of Gram-negative bacterial isolates resistant to antimicrobial agents (number of tested isolates in brackets)
Drug E. coli Klebsiella spp. Proteus spp. Enterobacter spp. Salmonella spp. GNR
Ampicillin 80% (1761) 85% (1572) 60% (331) 72% (86) 70% (46) 56% (204)
Amoxicillin- clavulanate 28% (1292) 32% (1153) 17% (247) 32% (78) 52% (23) 31% (124)
Ceftazidime 5% (788) 6% (605) 2% (95) 10% (51) 0% (8) 14% (35)
Tetracycline 77% (1223) 66% (1016) 77% (211) 72% (54) 42% (12) 45% (153)
Gentamicin 8% (1634) 14% (1538) 7% (343) 15% (91) 9% (23) 8% (217)
Trimethoprim- sulfamethoxazole 76% (1313) 69% (1174) 57% (224) 70% (56) 73% (44) 51% (172)
Sulfonamides 84% (174) 84% (231) 74% (46) 100% (14) 95% (22) 62% (34)
Nitrofurantoin 32% (929) 53% (652) 72% (71) 48% (48) ... ...
Chloramphenicol 45% (250) 51% (372) 55% (132) ... 20% (41) 57% (138)
Fluoroquinolones 13% (432) 6% (343) 3% (65) 6% (32) 0% (20) 15% (40)
Nalidixic acid 28% (509) 16% (334) 18% (22) 31% (16) ... ...
GNR, Gram negative rod-shaped bacteria, not further identified; "...", not tested.
Table 3 Percentage of Gram-positive bacterial isolates resistant to antimicrobial agents (number of tested isolates in brackets)
Drug S. aureus CoNS Enterococci S. pneumoniae S. pyogenes Other strept.a
Penicillin 97% (1521) 93% (42) 67% (9) 4% (23) 0% (163) 23% (98)
Ampicillin ... ... 6% (66) ... ... 13% (83)
Methicillin/ cloxacillin 2% (1556) 21% (47) ... ... ... ...
Tetracycline 49% (1042) 90% (39) 76% (51) 8% (13) 47% (131) 61% (90)
Erythromycin 29% (1543) 69% (48) 26% (65) 6% (18) 7% (161) 26% (156)
CoNS, Coagulase-negative staphylococci; "...", not tested. a Streptococci other than S. pyogenes and S. pneumoniae, and streptococci not identified below genus level.
Comparison of resistance patterns of isolates obtained from inpatients and outpatients at MNH did not show large differences. However, ampicillin resistance was more frequent in urinary isolates of E. coli from inpatients than in those from outpatients as shown in Table 4. Likewise, urinary isolates of Klebsiella spp. from inpatients were more frequently resistant to gentamicin and trimethoprim-sulfamethoxazole than isolates from outpatients.
Table 4 Percentage of urinary E. coli and Klebsiella spp. isolates from inpatients and outpatients resistant to antimicrobial agents
E. coli Klebsiella spp.
Drug Inpatients Outpatients Pa Inpatients Outpatients Pa
Ampicillin 87.2 82.7 0.036a 92.2 91.1 0.624
Amoxicillin- clavulanate 31.4 28.3 0.344 37.7 33.9 0.327
Ceftazidime 4.9 5.6 0.731 7.6 6.0 0.577
Tetracycline 83.1 81.7 0.648 82.0 75.2 0.053
Gentamicin 8.6 7.7 0.572 14.9 5.4 <0.001a
Trimethoprim- sulfamethoxazole 86.0 81.3 0.067 82.7 74.2 0.012a
Sulfonamides 92.1 87.8 0.510 95.2 100.0 0.553
Nitrofurantoin 33.7 33.1 0.881 52.1 58.0 0.157
Fluoroquinolones 17.8 12.7 0.217 7.2 6.7 1.000
Nalidixic acid 29.0 28.2 0.913 14.0 18.8 0.334
a P < 0.05 (Fisher's exact test, 2-tailed) indicates statistical significance of the differences in resistance rates.
Comparison of resistance patterns in isolates blood cultures with those from other specimen types showed apparent great differences for some drugs, however, in most cases the number of blood culture isolates were few and did not show statistically significant differences. However, as shown in Table 5, blood culture isolates of Klebsiella spp. were indeed more frequently resistant to gentamicin than those from other specimen types. A significantly greater proportion of blood culture isolates of S. aureus were resistant to tetracycline than among those from other specimen types, whereas for penicillin the isolates from blood cultures were resistant in a lower proportion than the others.
Table 5 Percentage of bacterial isolates from different specimen types resistant to antimicrobial agents
E. coli Klebsiella spp. S. aureus
Drug Blood Other Pa Blood Other Pa Blood Other Pa
Penicillin ... ... ... ... ... ... 91.5 96.9 0.028a
Ampicillin 84.0 79.4 0.803 84.3 85.3 0.759 ... ... ...
Amoxicillin- clavulanate 40.0 27.8 0.383 29.8 31.9 0.873 ... ... ...
Methicillin ... ... ... ... ... ... 1.4 2.2 1.000
Ceftazidime 0.0 5.3 1.000 5.7 6.0 1.000 ... ... ...
Tetracycline 54.5 77.3 0.139 66.7 66.4 1.000 84.6 48.3 <0.001a
Erythromycin ... ... ... ... ... ... 21.1 29.0 0.179
Gentamicin 13.0 7.7 0.416 41.3 12.3 <0.001a ... ... ...
Trimethoprim- sulfamethoxazole 72.0 76.3 0.636 63.0 69.1 0.297 ... ... ...
Sulfonamides 83.3 84.0 1.000 86.8 83.4 0.809 ... ... ...
Chloramphenicol 58.3 43.8 0.199 57.9 49.3 0.200 ... ... ...
Fluoroquinolones 40.0 13.1 0.136 0.0 6.5 0.381 ... ... ...
"...", not applicable. a P < 0.05 (Fisher's exact test, 2-tailed) indicates statistical significance of the differences in resistance rates.
Evaluation of the surveillance system
A great number of bacterial isolates were recorded in the system. All age groups and both inpatients and outpatients were represented in the study. More than a third of the isolates were from outpatient populations from the Dar es Salaam area, however we cannot exclude the possibility of a selection bias in favour of patients with infections caused by resistant organisms, since many patients get treatment at primary health facilities before reaching MNH. We do not know how well the rural population is represented in this material, but we assume that the outpatients in the study are mostly from the Dar es Salaam area. Ten percent of the isolates represented systemic infections, i.e. isolates from blood cultures and spinal fluid. The susceptibility test results were recorded as interpreted values (i.e. "R" (resistance), "I" (Intermediate) or "S" (susceptible)) and not as inhibition zone diameters. In this study, no molecular techniques were available for the detection of resistance genotypes and evaluation of genetic relatedness of bacterial isolates.
The direct cost of implementing the surveillance system was limited to the purchase of a computer at approximately 1000 Euro. However, less expensive second-hand computers would be sufficient. The software was downloaded free of charge from the WHO website. The indirect costs of running this surveillance program are related to human sources for operating the software, including data entry and analysis, and the costs of the susceptibility testing activities. It is difficult to separate these indirect costs from the costs of running the daily laboratory activities. In our setting, a laboratory technologist from the department took on the task of operating the software in addition to her regular duties. In our experience, for a hospital of our size, it is recommendable to allocate approximately 50% of a laboratory technologist position to operating the surveillance software. In our setting, this would translate into a monthly cost of approximately 100 Euro for the department. The surveillance system is dependent on susceptibility testing of acceptable quality. The susceptibility testing incurs costs related to human resources and the purchase of laboratory reagents including antimicrobial disks and agar media. Implementing a surveillance system may increase these costs by focusing on the importance of quality reagents. However, since the susceptibility testing activities are an integral activity of the department, which would have been performed regardless of the surveillance system, we choose not to attribute their costs to the surveillance system in this context. The benefits of a surveillance system are difficult to quantify, but are of potentially great magnitude. Foremost, surveillance data may improve empiric therapy for infections and thus save lives and reduce suffering. It may reduce treatment costs by enabling the use of the least expensive effective drugs. Additionally, surveillance systems may contribute to containing or reducing antimicrobial resistance, which in the long term perspective may have great benefits in reducing morbidity and mortality, and diminish the need for expensive second-line antimicrobial agents.
The strengths and weaknesses are elaborated on in the Discussion part.
Discussion
Resistance patterns and implications for therapy
Experience from the World Health Organization's External quality assurance system for antimicrobial susceptibility testing has shown that disk diffusion testing is suitable for routine surveillance [10]. However, disk diffusion is not optimal for testing of certain important resistance traits, such as penicillin-resistance in pneumococci. The lack of international standardization of methods and interpretive criteria causes concern, but there are indications that routine susceptibility testing data are suitable for surveillance even if obtained with different methods [11].
Consistent with observations from a number of other countries in the region [12-15] and elsewhere [16], Gram-negative bacilli displayed high rates of resistance to common inexpensive antibiotics. Reasonably priced antibiotics such as ampicillin, tetracycline, trimethoprim-sulfamethoxazole and sulfonamides are now of limited benefit in the treatment of infections caused by important Gram-negative bacteria such as E. coli, Klebsiella spp., Proteus spp. and Salmonella. Chloramphenicol may fail to cure as much as a quarter of infections caused by Salmonella and half or more of infections caused by E. coli, Klebsiella spp. and Proteus spp. Fluoroquinolones appear to be the only reliable drugs for oral treatment of infections caused by common Gram-negative bacilli, whereas gentamicin and third-generation cephalosporins remain useful for parenteral therapy.
The study showed a very low prevalence of methicillin-resistant S. aureus, consistent with previous data from the same hospital [17,18]. While the results should be interpreted with some caution since confirmatory nucleic acid based techniques were not available, the data support the current use of isoxazolyl penicillins, such as cloxacillin for the treatment of staphylococcal infections at the hospital. There were few isolates of enterococci compared to studies from high-income countries [19]. It is reassuring that the current study showed a low rate of ampicillin-resistant enterococci, indicating that nosocomial infections caused by these micro-organisms is a minor problem compared with many high-income countries. Low consumption of broad-spectrum antibiotics such as third-generation cephalosporins, fluoroquinolones, imipenem and vancomycin may explain this finding [19-21]. While other countries in the region have been affected by penicillin-resistant pneumococci [22,23], the current study indicates that pneumococcal disease in Dar es Salaam can safely be treated with penicillin or erythromycin. However, the results should be interpreted with some caution since the number of isolates tested was small. More than a quarter of the gonococcal isolates (11/41) were obtained from the neonatal ward, and most or all of these isolates probably represent gonococcal conjunctivitis. Amoxicillin-clavulanate, spectinomycin, fluoroquinolones and erythromycin appear to be good alternatives for the treatment of gonococcal infections. An apparent increase in resistance to trimethoprim-sulfamethoxazole (from 18% to 59%) is noted since the study by Mbwana [24] from 1993 to 1995, however, this may be due to the use of different methodology for susceptibility testing.
Applicability of data to guide treatment of serious infections
Recommendations for antibiotic treatment of serious bacterial infections such as bloodstream infections and meningitis should preferably be based on knowledge of the prevalence and antimicrobial susceptibility patterns of pathogens isolated from blood and spinal fluid. While a fair number of bacterial isolates were tested in the current study, the number of blood culture isolates was limited (n = 329, excluding the CoNS isolates). As shown in Table 5, there appears to be differences in resistance between isolates obtained from blood cultures and those from other specimen types, but these are difficult to assess because of the low number of blood culture isolates. Thus, the data from the current surveillance should be interpreted with caution with regards to the treatment of serious infections. The CoNS isolates obtained from blood were recorded in the WHONET database, since they may represent clinically important infections such as bacteremia in patients with compromised immunity, patients with indwelling intravascular devices and the newborn [25]. The study showed that a high proportion (21.9%) of blood culture bottles yielded CoNS isolates. However, the conventional way to distinguish pathogenic isolates of CoNS from contaminants, by requiring growth of a similar CoNS isolate in a separate blood culture, could rarely be used, since follow-up cultures were seldom available. Consequently, the susceptibilities of these isolates were not evaluated further.
Relevance of data for outpatient and rural populations
It is important to specify for which population the surveillance data are valid. At our hospital, specimens from both inpatients and outpatients were examined. The hospital is to a great extent used as a primary hospital for the population in the Dar es Salaam area. However, among the cases coming to the hospital, there may be a degree of selection of patients with infections caused by resistant microbes, since many patients rely on health centers and pharmacies to cure simple ailments, and only come to the hospital when primary treatment fails. The study found that a few resistance traits, such as ampicillin resistance in E. coli and gentamicin and trimethoprim-sulfamethoxazole resistance in Klebsiella spp. were more frequent in urinary isolates from inpatients than from outpatients. Apart from that, there were no dramatic differences between isolates from inpatients and outpatients. The data from the study should be representative for both the hospital setting and to some degree the population in Dar es Salaam. However, the majority of the population of Tanzania lives in rural areas, where resistance patterns may be substantially different. Thus one should be cautious to extrapolate the results of the current study to be valid for populations in the countryside.
Ability to monitor trends of antimicrobial susceptibility
Certain trends in antimicrobial susceptibility could be identified by comparison with data from other studies. While resistance to ampicillin, tetracycline and sulfonamides in Gram-negative bacteria was frequent already in the seventies [26,27], it is worrying that resistance to trimethoprim-sulfamethoxazole, chloramphenicol, nitrofurantoin, nalidixic acid and amoxicillin-clavulanate appear to have increased compared to previous studies [27,28]. The extensive use of chloramphenicol for the treatment of presumed cases of typhoid fever and the use of trimethoprim-sulfamethoxazole for the ambulatory treatment of chest infections, malaria and other infections, may have contributed to the high prevalence of resistance to these two drugs. Although still low, it is of concern that the rate of gentamicin-resistance in E. coli has increased from zero in 1978–79 [27] to 2% in 1995 [28] and 8% in the current study. In neighboring Kenya, the rate of gentamicin-resistance in E. coli has increased from 2% in the late seventies [29] to 20% and above in recent studies [12].
Resistance to gentamicin is common in Gram-negative bacteria with extended-spectrum beta-lactamases (ESBL), sometimes in as much as 96% of isolates [30]. Such an association cannot be investigated in the current study, since less than half of the isolates of E. coli and Klebsiella spp. were tested for susceptibility to third-generation cephalosporins and other methods for detection of ESBL (double disk synergy test, Etest, PCR) were not available.
Also in P. aeruginosa the rate of gentamicin-resistance has increased, from zero in the seventies [27] to 4% in the current study. Resistance to penicillin and erythromycin was common among S. aureus isolates in this study. However, the rate of tetracycline resistance (49%) was lower than reported from the same hospital in 1979 (57%) and 1982 (74%) [17]. In the late seventies, tetracycline was used in great quantities in Tanzania to prevent and treat cholera; as much as 1788 kilograms of the drug were used during a period of only 5 months [31]. Due to the rapid emergence of tetracycline-resistant Vibrio cholerae, the use of the drug was subsequently greatly reduced, and this may have contributed to a concurrent decline in the rate of tetracycline-resistance in an unrelated species such as S. aureus.
For meaningful comparison of data from different studies, whether from the same or different laboratories, the same method of susceptibility testing should preferably be employed. In our laboratory, the same method has been used for a number of years. The WHONET software features a number of sophisticated ways to analyze susceptibility information based on the measurements of inhibition zone diameters. Recording the diameter of the inhibition zones in disk diffusion testing is generally recommended [32], and may increase the accuracy of results and enable the detection of gradual shifts in antibiotic susceptibility over time. It also makes the data independent of the current breakpoints. With the WHONET software, data can easily and rapidly be re-analyzed with reference to new breakpoints. However, the Stokes' method for susceptibility testing [9], which is used in our laboratory, is based on visual interpretation of the difference in inhibition zones between the clinical isolate and the control strain. The interpretation is recorded as interpreted values, i.e. either susceptible "S", intermediate susceptible "I" or resistant "R". The WHONET software also accepts susceptibility data to be entered and analyzed as "interpreted values", i.e. "S", "I" and "R". The use of such interpreted values enables most of the analysis features of WHONET, but not all. Foremost, analyzing data based on zone diameters (or MIC values) is superior for the early detection of subtle shifts in antimicrobial resistance over time, which may alert clinicians about emerging resistance trends at an early stage. However, one asset of the Stokes' method, particularly under tropical conditions, is that unsuspected poor antibiotic disk quality will be discovered quickly since a control strain is tested on every plate.
Furthermore, variations over time in the battery of antibiotics tested makes comparison of data less useful. Laboratories in low-income countries are sometimes vulnerable to this because of unreliable supplies of antibiotic discs.
Ability to detect emerging resistance traits
Disk diffusion testing may give indications of emerging resistance traits such as methicillin-resistance in S. aureus and ESBL in Gram-negative bacteria. The current surveillance indicated that methicillin-resistance is rare in S. aureus at the hospital. Ideally this should be confirmed with PCR-based methods to detect the mecA gene. Likewise, the disk diffusion testing showed the presence of resistance to ceftazidime in Gram-negative isolates, albeit at a low rate, which calls for further investigation with regard to the possible presence of ESBL. Our laboratory did not employ molecular methods for detection of resistance genes on a routine basis, but a recent study showed low prevalence of methicillin-resistant S. aureus (MRSA) [18]. Resistance surveillance should be coupled with awareness of signs of various resistance traits and, preferably, the possibility of using molecular methods to verify emerging resistance traits.
Ability to detect nosocomial problems
The WHONET software is well suited to analyze antibiograms in order to detect suspicious nosocomial outbreaks. These functions too are dependant on the use of a consistent battery of test drugs, and also works better when results are entered as actual values for MIC or zone diameters, as opposed to the interpreted value ("S", "I" or "R"). In our hospital, comparison of resistance rates did not show dramatic variation between isolates from inpatients and outpatients. The exception was a trend for more frequent gentamicin-resistance in inpatient isolates of Gram-negative bacteria, particularly Klebsiella spp., which may suggest possible nosocomial spread. The analysis of antibiograms did not produce convincing evidence of clonal patterns spread of bacterial isolates, possibly partly due to the variations in the battery of antibiotics tested. Molecular methods for the evaluation of the genetic relatedness of bacteria were not available in this study.
Suitability for international comparison of resistance data
In 2002 a total of 880 laboratories in 76 countries across the world used the software, including 41 laboratories in 4 African countries. The WHONET system has been implemented at MNH since 1998. Unfortunately, there is no international consensus on a recommended method for antimicrobial susceptibility testing. Worldwide at least twelve different in vitro methods are followed, and only in Europe the number is at least ten [5]. Furthermore, there are ongoing changes in the interpretive criteria for susceptibility testing [10]. In addition to this, there is an abundance of molecular methods to describe various genetic markers of resistance. In vivo clinical assessment is of great importance in understanding bacterial drug resistance and the gold standard for evaluating resistance in malaria parasite. The multitude of methods employed for antimicrobial susceptibility testing has to some extent hampered the meaningful sharing and comparison of resistance data among countries. Recently, much work has been done in Europe to harmonize resistance surveillance efforts across country borders [33,34]. While many laboratories record inhibition zones for disk diffusion results, interpretation is usually according to national guidelines. Thus, susceptibility patterns from different countries must be compared prudently. The lack of standardization in methods is a problem that must be addressed at an international level.
The surveillance system as a quality assurance tool
The implementation of the surveillance system brought focus on methodological issues, including microbial identification and susceptibility. The WHONET software has built-in functions to alert the operator if isolates with unexpected resistance patterns are entered. During the surveillance exercise in our laboratory, it was discovered that four isolates of Streptococcus pyogenes were reported as resistant to penicillin. This was subsequently double-checked, and consulting the laboratory bench-book we found that clerical errors were the explanation for this. The use of the surveillance software enabled the easy detection, investigation and correction of such errors, and consequently may contribute to increase the attention to quality issues and generally improve the performance of the lab. The current surveillance project highlighted some methodological issues, most of which were caused by budgetary limitations, such as the occasional use of locally made antibiotic disks and limitations in the identification of organisms due to lack of reagents.
Impetus for further research
Routine surveillance makes use of available large data sets at little additional cost and may be representative for a greater part of the population. However, often it is necessary to supplement the routine surveillance with ad hoc studies aimed at investigating particular problems. While ad hoc studies generally are more expensive to conduct, they allow for the use of more advanced and expensive laboratory methods and are better at targeting particular populations of interest. The current surveillance study identified a need for more data from bloodstream infections in order to provide reliable guidance for the treatment of serious bacterial infections. As a consequence of this, we started a study of bloodstream infections with the pediatric department at the hospital. Another laboratory-based research was started to ascertain the finding that methicillin-resistance in staphylococci is still relatively infrequent at this hospital.
Influencing popular opinion on antimicrobial resistance issues
Resistance surveillance is a platform from which to promote focus on antimicrobial resistance issues, both within the hospital and the medical community, but also among the general population. In conjunction with the surveillance exercise, we have highlighted issues regarding antimicrobials and resistance in local newspaper letters [35], and there is ongoing work to establish a chapter of APUA (Alliance for the prudent use of antibiotics, ) in Tanzania.
Cost considerations and human resources
The study suggests that laboratories, which perform susceptibility testing, can gain useful information on antimicrobial susceptibility with a minimal budget. As appropriate software can be obtained free-of-charge, the main cost of the surveillance system is associated with purchasing a computer. However, there are other, indirect costs, which may be attributed to the surveillance program depending on the situation of the laboratory, such as running costs for microbiologic procedures, including susceptibility testing. Particularly, it is important to ensure availability of antimicrobial discs of satisfactory quality. A susceptibility surveillance system also implies the need for some additional human resources for data entry and analysis. In our experience, it is recommendable to allocate approximately 50% of a laboratory technologist position to this task. While the WHONET program is excellent for entry, analysis and reporting of resistance data, the software is not intended to function as a complete patient management system for the laboratory. Data can be transferred from other databases into WHONET by the use of a complementary software called BacLink (also free-of-charge). However, in laboratories such as ours, where the management of patients' laboratory tests (i.e. receipt of specimens and laboratory forms, inscription in registers, return of test results, etcetera) is handled manually via register-books, the data must be punched into WHONET by hand. Since the WHONET database is not used directly for patient management, the surveillance activity tends to become less integrated in the clinical routine work than it should. Thus, although the program performs its task very well, in a long-term perspective, a surveillance system that is integrated with a patient management system might be more sustainable. It is difficult to quantify the potential benefits of a well-functioning surveillance system. However, we are fully convinced that the modest costs of the surveillance program are highly justified since the data generated may improve empiric therapy, help contain or prevent the further emergence of antimicrobial resistance, decrease the need for expensive second-line antimicrobial drugs and, ultimately, save lives and reduce suffering.
Conclusions
It is imperative to preserve the effectiveness of common antibiotics by promoting rational use of antibiotics based on sound knowledge of local resistance patterns. In a hospital with bacteriology services, the implementation of a computerized surveillance system is a low-cost tool to make use of available resistance data. In our hospital, the resistance surveillance system has generated information on resistance patterns that is useful as guidance for empiric therapy of infections. It can help alert clinicians of trends of antimicrobial resistance, guide drug-policy decisions and facilitate rational use of drugs to prevent the further emergence of antimicrobial resistance. The surveillance system has also served as a quality assurance tool and led to increased focus on antimicrobial resistance and prudent use of drugs. There is need for more data from blood cultures for reliable guidance for the treatment of severe, systemic bacterial infections. For antibiotic policy recommendations to be applicable for the general population, more information is needed from outpatients and rural areas.
There is limited information on antimicrobial resistance trends on the African continent. Only four African countries use the WHONET system for antimicrobial resistance surveillance, although some countries may use other similar software. Recently much work has been done to establish consensus and a more standardized approach to resistance surveillance in Europe [34]. Susceptibility data based on recorded zone diameters, instead of interpreted values ("S", "I" and "R"), would make the surveillance system more effective in detecting subtle changes in antimicrobial resistance. We believe there is a need for a standardized approach to antimicrobial resistance surveillance also in the African region, as well as globally. This would facilitate liaisons and sharing of information among countries.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BB was the principal investigator, participated in the planning and execution of the study, performed data entry and data analysis, and was the main responsible author. DSMM, WU, SYM and AD participated in the planning of the study and contributed to the writing process. MM contributed to designing the WHONET database, performed data entry and microbiological work, and contributed to the writing process. SH participated in the writing. NL was the project coordinator and participated in planning, data analysis and writing.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We sincerely thank the staff of the Department of Microbiology and Immunology at MNH whose work made the study possible. We also thank Dr John Stelling, the author of the WHONET software, who provided valuable comments on microbiological as well as software-related issues, and Dr Roland Jureen and Dr Faustine Ndugulile who critically reviewed the manuscript and provided constructive information. This study was supported by grant no 100675 from the Norwegian Research Council and by funding from NUFU (Norwegian Council for Higher Education's Program for Development Research and Education) project number: 44003 PRO 42.2.91.
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| 15476559 | PMC526372 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Oct 11; 4:45 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-45 | oa_comm |
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BMC EcolBMC Ecology1472-6785BioMed Central London 1472-6785-4-151547947210.1186/1472-6785-4-15Research ArticleLong-term patterns in European brown hare population dynamics in Denmark: effects of agriculture, predation and climate Schmidt Niels M [email protected] Tommy [email protected] Mads C [email protected] Department of Ecology, Zoology Section, Royal Veterinary and Agricultural University, Thorvaldsensvej 40, DK-1871 Frederiksberg, Denmark2 Department of Population Biology, Institute of Biology, University of Copenhagen, Universitetsparken 15, DK-2100 Copenhagen, Denmark3 Department of Wildlife Ecology and Biodiversity, National Environmental Research Institute, Grenåvej 14, DK-8410 Rønde, Denmark2004 12 10 2004 4 15 15 24 6 2004 12 10 2004 Copyright © 2004 Schmidt et al; licensee BioMed Central Ltd.2004Schmidt et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In Denmark and many other European countries, harvest records suggest a marked decline in European brown hare numbers, a decline often attributed to the agricultural practice. In the present study, we analyse the association between agricultural land-use, predator abundance and winter severity on the number of European brown hares harvested in Denmark in the years 1955 through 2000.
Results
Winter cereals had a significant negative association with European brown hare numbers. In contrast to this, root crop area was positively related to their numbers. Remaining crop categories were not significantly associated with the European brown hare numbers, though grass out of rotation tended to be positively related. The areas of root crop production and of grass out of rotation have been reduced by approximately 80% and 50%, respectively, while the area of winter cereals has increased markedly (>70%). However, European brown hare numbers were primarily negatively associated with the number of red fox. Finally, we also found a positive association between mild winters and European brown hare numbers.
Conclusion
The decline of Danish European brown hare populations can mainly be attributed to predation by red fox, but the development in agricultural land-use during the last 45 years have also affected the European brown hare numbers negatively. Additionally, though mild winters were beneficial to European brown hares, the increasing frequency of mild winters during the study period was insufficient to reverse the negative population trend.
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Background
In most countries in Western Europe, the landscape has undergone dramatic changes during the last century due to changes in the agricultural practices. In Denmark, both the uncultivated land and the semi-cultivated land, such as permanent grass areas, have been reduced dramatically, reflecting the general intensification of agriculture [1]. Additionally, fields have become larger, which has resulted in widespread fragmentation of remaining habitats, and today the landscape appears as a mosaic of natural habitats surrounded by cultivated land [1,2]. These changes in agriculture have affected a number of wildlife species living in this man-made landscape. For instance, the shift in agricultural practice has severely influenced the diversity and abundance of insects with concomitant consequences for the dynamics of a wide range of farmland birds [3,4], including the grey partridge (Perdix perdix) [5]. Among mammals, the European brown hare (Lepus europaeus) in particular has experienced a dramatic decline in many European countries [reviewed by [6]], including Denmark [7,8].
Despite its currently declining numbers, the European brown hare is still common and one of the most important game species throughout the country [8]. The dynamics of European brown hares seem resilient to even heavy hunting pressure [9], though local population dynamic data may be needed to ensure sustainable harvest [10]. In Denmark hunting of European brown hares is generally assumed to be without regulating effect [8]. The European brown hare is a typical grass steppe herbivore, and inhabits primarily open landscapes, including cultivated farmland [11], which is the predominant landform in Denmark [1]. The species is rather sedentary, and has generally small home ranges [e.g. [12]]. This site fidelity makes European brown hares highly susceptible to changes in their surrounding habitats, and the general decline in the European brown hare populations in Europe is mainly being attributed to changes in agriculture practice and land-use [reviewed by [6,12]].
European brown hares are important prey primarily for mammalian predators. In Northern Europe, the red fox (Vulpes vulpes) is the main predator on European brown hares, and foxes have been reported to influence the dynamics of several European brown hare populations substantially [13-17]. Also, infectious diseases such as the European brown hare syndrome virus, pseudotuberculosis, pasteurellosis and coccidiosis are present in many European brown hare populations [18-20]. Haerer et al. [19], however, concluded that diseases were not responsible for the decline of brown hare populations in Switzerland. Similarly, Frölich et al. [20] found that compared to red foxes, infectious diseases seemed to play a minor role in the dynamics of European brown hare populations in Germany.
An increasing number of papers have documented the importance of climate for a number of life history traits and abundance of many terrestrial vertebrates [21], and European brown hare populations are affected negatively by cold winters and cold springs [12,22-24]. Factors regulating vertebrate populations may, however, exhibit large spatial variation, and even among proximate populations spatial variation and gradients in vertebrate population dynamics may exist [e.g. [25]].
In this study, we analyse and contrast the simultaneous associations between agricultural land-use, the number of red fox harvested, winter severity and the population dynamics of European brown hares across 11 Danish districts during 45 years.
Results
In the period 1955–2000 the European brown hare harvest record declined steadily in all the Danish districts but one: On the island Bornholm the European brown hare population declined until the late 1980s, but has increased markedly since then, and has now reached a level higher than that of 1955 (Fig. 1).
Figure 1 Harvest records of European brown hare (bags per 1000 ha) from 11 Danish districts, 1955–2000.
We found statistical significant direct density-dependence (Xt-1) in the European brown hare time series (G = 281.4, df = 2, P < 0.0001). Additionally, the effect of district was also significant (G = 769.0, df = 1, P < 0.0001).
From 1955 to 2000, agricultural land-use changed markedly in Denmark, resulting in large temporal shifts in the areas covered by the different crop categories (Fig. 2). The areas covered with grass and green fodder in rotation and in particular the areas out of rotation have been reduced, the latter by approximately 50% since the mid 1950s. An even more dramatic decline has been observed for the root crops in the same period, a decline by more than 80% (Fig. 2). In Storstrøm, however, the area covered with root crops has been relatively stable over the years. In general, cereals were the dominant crop category in 1955 through 2000, with a shift from a predominance of spring cereals to winter cereals in the 1980s (Fig. 2).
Figure 2 The total areas of the seven crop categories in Denmark 1955–2000. Note that prior to 1960 no data on winter cereals exist.
The areas covered with winter cereals had a marked negative association with the number of European brown hares (Table 2), whereas root crops had a marked positive relation. Neither spring cereals, nor winter and spring rape seemed to be associated with European brown hare numbers (Table 2). Similarly, neither grass areas in or out of rotation were significantly related to European brown hare numbers, though the latter tended to have a positive association (Table 2).
Table 2 Summarised results from the analysis of the impact of agricultural land-use, red fox and winter climate on European brown hare harvest records from 11 Danish counties, 1955–2000. Also given is the change in model deviance (Δ deviance) explained by the variable when fitted last. Type 3 sums of squares.
Variable Coefficient SE F value P value Δdeviance
Winter cereals(t) -0.08322 0.03778 4.85 0.0282 37.7
Spring cereals(t) -0.06243 0.04675 1.78 0.1825 2.5
Grass in rotation(t) 0.03217 0.03562 0.82 0.3669 4.1
Grass out of rotation(t) 0.05766 0.03070 3.53 0.0612 1.7
Winter rape(t) -0.03272 0.03094 1.12 0.2909 4.0
Spring rape(t) -0.02704 0.03948 0.47 0.4938 4.2
Root crops(t) 0.10370 0.04753 4.76 0.0297 0.4
Fox(t) -0.01791 0.02024 0.78 0.3767 5.2
Fox(t-1) -0.15720 0.01971 63.62 <.0001 52.8
NAO(t) 0.03732 0.01110 11.31 0.0008 4.0
Note: Due to the standardization of model variables, regression coefficients and Δ deviance values are directly comparable.
The number of red foxes harvested in yeart-1 had a marked negative effect on the number of European brown hares harvested the following year (Table 2), whereas red fox number in yeart seemed unimportant for the European brown hare numbers.
Mild winters, i.e. high winter state of the large-scale climatic phenomenon the North Atlantic Oscillation [NAO; [26]], had a significant positive effect on the European brown hare numbers (Table 2).
Discussion
Despite its high reproductive potential [e.g. [27]], the Danish European brown hare has, according to annual harvest records, declined dramatically since 1955. Studies of Danish European brown hare populations indicate that its reproductive success is low compared to that of con-specifics in other countries [27], and has, in turn, declined from the 1950s to the 1990s [28]. Hansen [27] suggested that the low reproductive success observed in Danish European brown hares might be attributed to the agricultural practice. Using data covering almost half a century, our analyses suggest that the dramatic decline in the Danish European brown hares can be attributed mainly to the negative effect of red foxes, but also to the agricultural land-use. The area of winter cereal production has increased dramatically during the last century, and became the dominating crop in the early 1990s. We also found a significant positive association between root crops, a crop type that has declined dramatically in the second half of the last century, and European brown hare numbers.
European brown hares mainly forage on grasses and herbs [12], and cereals such as wheat are preferred during winter [29,30], which seems to contradict the results of our analyses. However, as European brown hares choose to feed on specific crops according to plant phenology [12,31], cereals, although important in winter, still occupy large areas when no longer of nutritional value to European brown hares, which may result in low availability of food especially during summer. Similarly, rape is avoided in the diet [29], but European brown hares may spend a substantial fraction of their time in rape fields during winter [30] prior to the development of glucosinulates [see [29]]. Apart from winter cereals and root crops, the crop categories did not affect the European brown hare numbers significantly. The lacking effect of grass and green fodder areas, especially those out of rotation, was unexpected as other studies have shown that hares prefer grass areas year-round [12,30]. This, however, may be attributed to the fact that we were unable to separate grass areas into those e.g. with and without cattle, factors that might affect European brown hare use of grass areas [12].
In a recent study, Fox [32] showed that farmland birds seemed to benefit from the reduced application of pesticides and inorganic fertilisers seen in Denmark since the early 1980s. The continuing decline in European brown hare numbers in that period therefore suggests that hares are not directly affected by the use of such substances, but rather respond to the loss of suitable habitat and space. European brown hares mainly move along field margins [12,33], and the increasing field size [34] combined with the general loss of suitable habitats possibly force hares to aggregate in the remaining patches of non-agricultural and non-urbanized land. Here, density-dependence may perpetuate the negative population development as European brown hares aggregate in the few, remaining pockets of suitable habitat. In line with this, Frylestam [23] reported an inverse relationship between fertility and density in European brown hares, which he attributed to shortage of food at least in some parts of the year, which also has been suggested in a number of other studies [12,24,27]. Hence, agricultural land-use, especially the increasing use of winter cereals and the marked reduction in root crops, but probably also grass areas out of rotation, seem to have contributed to the European brown hare decline in Denmark. However, the single-most important parameter for the number of European brown hares was the number of red foxes. Hence, our results are consistent with other studies reporting that red foxes may affect European brown hare populations negatively through predation [13-17]. This relationship is also particular evident from the positive development in the European brown hare population on the island Bornholm following the outbreak of sarcoptic mange there [35]. To elaborate the fox-hare interaction further, we reran the analyses including all variables but the red fox variables, and added the delayed AR term (i.e. Xt-2). In seven of the 11 districts the delayed density dependence was significant (P < 0.05), suggesting important inter-specific interactions [36,37]. The significant association with red fox numbers in yeart-1 (and not yeart) most likely reflects that compared to adults, juvenile European brown hares suffer more from predation [e.g. [19]]. Hence, high red fox numbers in yeart result in low harvest of European brown hares in yeart, which affects the reproductive potential of the populations, and, hence, the number of European brown hares the following year (yeart+1). Also, the significant effect of district may point to differences in habitat quality, but also differences in the history of the sarcoptic mange, i.e. time since the eruption of the mange, among districts [see [35]].
Both European brown hare over-winter survival [24] and reproductive rate [23] increases with temperature, which may be attributed to improved forage availability following mild winters [see [38,39]]. Our analyses revealed similar results as mild winters affected the European brown hares positively. There may, however, also be a negative effect of mild winters, namely through transmission of diseases and parasites, which may be enhanced under mild climatic conditions [18]. Nevertheless, the overall effect of mild winters seemed positive. The upward trend in the NAO since the 1960s [40], however, was not sufficient to reverse the European brown hare decline, but may have decelerated it.
Conclusion
Our analyses have provided important insight into the structure of the European brown hare dynamics in Denmark, and documented important patterns within the mechanisms regulating European brown hares. Hence, we have documented that the decline of European brown hares in Denmark mainly can be attributed to predation by red fox, but also to changes in agricultural land-use. Additionally, though mild winters were beneficial to European brown hares, the increasing frequency of mild winters during the study period was insufficient to reverse the negative population trend.
Methods
European brown hare and red fox density indices
As indices of European brown hare and red fox density, we used the harvest records from 14 Danish counties from 1955–2000. In 1970, the geographical borders of some of the counties were changed, which in two cases lead to substantial changes in area [7]. This, together with inconsistency in crop registration, forced us to lump together some counties, and we therefore present analyses of European brown hare harvest records from 11 districts (Fig. 1). European brown hare and red fox harvest records were expressed as the number of animals shot per hectare per year. Harvest records may seem rather crude indicators of density compared to e.g. spotlight surveys and line transects [see [41]]. Unfortunately, no alternative indices of European brown hare density in Denmark are available. Harvest records, however, may still be useful indicators of the long-term trends in European brown hare numbers [41].
Sarcoptic mange was first encountered in Denmark in the early 1980s, and is now present in seven of the 11 districts [35]. In one district (Bornholm; Fig. 1), the disease has practically eliminated the entire red fox population, and red fox hunting has been prohibited here since 1991. Consequently, we restrict our analyses of the Bornholm district to 1955 through 1990. In all other districts, red fox hunting is open from 1 September to 31 January, and for European brown hares from 1 October to 31 December. Neither red fox nor European brown hare hunting is quota-based.
Agricultural land-use
Time series quantifying agricultural land-use data, i.e. annual crop areas in hectares, covering the period 1955–81 were obtained from Statistics Denmark. Data for the period 1982–2000 were obtained from the official website of Statistics Denmark . The annual crop data were grouped into seven categories (Table 1).
Table 1 Description of model variables used in the analyses of European brown hare harvest records from Denmark, 1955–2000.
Variable Description
Winter cereals Winter cereals, including winter wheat, winter barley, and winter rye
Spring cereals Spring cereals, including spring wheat, spring barley, spring rye, and oat
Grass in rotation Grass and green fodder in rotation, including grass, clover, maize, and Lucerne
Grass out of rotation Grass areas permanently out of rotation
Winter rape Winter rape
Spring rape Spring rape
Root crops Sugar beets for sugar production and fodder, mangolds, turnips, and carrots for feed
Fox The number of red fox harvested
NAO The winter state of the North Atlantic Oscillation
No data on the fraction of winter and spring cereals, respectively, were available for 1955–61, and these data points were omitted in the analyses. Similarly, prior to 1982 no data on the fraction of winter barley and spring barley were available. However up until 1968, winter barley was only sown on a few thousand hectares in Denmark, when it was forbidden due to its function as reservoir for mildew attacking spring barley [42]. The use of mildew resistant winter barley was permitted in 1982. Hence, from 1955–81 barley was regarded as spring barley. Finally, no data on the fraction of winter and spring rye are available from 1955–61 and 1979–98, but in Denmark spring rye is generally sown on very few hectares only [42], and we regarded rye as winter rye unless otherwise specified [see also [32]]. Oat is sown as spring crop only. No data on the areas covered with grass and green fodder in or out of rotation were available for 1994, and the data point was interpolated, i.e. 1994 equalled the average of 1993 and 1995.
Climatic variability
As an index of winter severity, we used the winter state of the North Atlantic Oscillation [NAO; [26]]. The climatic phenomenon NAO is defined as the difference in sea-level pressure between Portugal and Iceland, affecting the temperature, precipitation and wind across the Northern Hemisphere [26,43,44]. The NAO, thus, integrates the effects of a number of abiotic factors, and therefore seems particularly useful when analysing the dynamics of larger, endothermic animals with relatively large buffer-capacity against climatic variability.
The relationship between the winter NAO (Decembert-1 through Marcht) and temperature, precipitation and wind is particularly strong in Northern Europe [45]. When NAO is in its high state, the winter climate in Northern Europe is warm, wet, and windy, while when in its low state winters are cold and dry [44]. The NAO winter index has shown an upward trend since the 1960s, which accounts for a substantial fraction of the general warming in the Northern Hemisphere [40]. The NAO index data are available from the Climate Analysis Section, National Center for Atmospheric Research, USA .
Data analyses
To remove heteroscedasticity in the system, data were loge-transformed, i.e. X(t) = ln(N(t) + 1), where N(t) is the number of European brown hares harvested in year(t). To allow the direct comparison of regression coefficients, variables were standardized prior to the analyses (i.e., [X(t)-µ(1955–2000)]/s(1955–2000)). Regression coefficients therefore express the rate of change in standard deviation units of the independent variable per one standard deviation unit of the dependent variable [46]. To obtain stationarity in the time series [see [47] for details], data were detrended following [48] by including year as covariate in all models.
For each district, we tested for the presence of multi-collinearity among parameters prior to the analyses by means of condition indices and variance proportions [49], but multi-collinearity was not observed in any districts (condition index < 12.47; [see [49] for details]).
The European brown hare time series were tested for non-linearity in X(t) vs. X(t-1) by applying the likelihood ratio test [50]. In all districts, linearity in X(t) vs. X(t-1) was not rejected λ< 7.76, P > 0.05). We analysed the data using a first-order autoregressive (AR(1)) mixed model approach with district as fixed factor:
X(t)~(µ,V),
where
and
where LAND_USE represents the seven crop categories (see Table 1). Because of the inclusion of red fox numbers, we did not include a delayed AR term (i.e. Xt-2), as inclusion of both delayed density dependence and predator abundance can be seen as redundant [37]. All analyses were performed in SAS 8.2, using the PROC MIXED procedure with restricted maximum-likelihood estimation of regression coefficients [49]. Model reduction was conducted using log-likelihood ratio tests [51].
Authors' contributions
NMS designed the study, participated in data preparation, carried out the statistical analyses, and drafted the manuscript. TA participated in data preparation. MCF supported the data analyses and contributed to the writing. All authors read and approved the final manuscript.
Acknowledgements
We extend our sincere thanks to Eric Post and two anonymous referees for valuable comments and improvements to the manuscript.
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| 15479472 | PMC526373 | CC BY | 2021-01-04 16:29:14 | no | BMC Ecol. 2004 Oct 12; 4:15 | utf-8 | BMC Ecol | 2,004 | 10.1186/1472-6785-4-15 | oa_comm |
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BMC SurgBMC Surgery1471-2482BioMed Central London 1471-2482-4-141546961110.1186/1471-2482-4-14Technical AdvanceExternal metallic circle in hepaticojejunostomy Göçmen Erdal [email protected] Mehmet [email protected] Mesut [email protected]ürsel Sebat [email protected]ç Mahmut [email protected]ılıç Mehmet [email protected] Department of 5th Surgery, Numune Training and Research Hospital, Sıhhiye, Ankara, Turkey2 Department of Plastic and Reconstructive Surgery, Social Insurances Foundation Hospital, Ankara, Turkey2004 6 10 2004 4 14 14 15 4 2004 6 10 2004 Copyright © 2004 Göçmen et al; licensee BioMed Central Ltd.2004Göçmen et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Biliary-enteric anastomosis especially Roux-en Y hepaticojejunostomy is frequently used for biliary diversion in benign biliary strictures. In this study, we present the results of hepaticojejunostomy with external metallic circle.
Methods
Hepaticojejunostomy with external metallic circle were performed in eight male Sprague-Dawley rats. At the end of the third month, anastomoses were analysed for patency and stability of external circles.
Results
Relaparotomy revealed that all the anastomoses were patent and circles were in original places.
Conclusion
To provide the patency of narrow hepaticojejunostomy anastomoses, external metallic circle can be a good alternative to use of internal stents in suitable cases.
==== Body
Background
Although the risk of late bile duct cancer complicating biliary-enteric anastomosis has been well documented [1,2], biliary-enteric anastomosis especially, Roux-en Y hepaticojejunostomy is frequently used for high biliary injuries and for biliary diversion in benign biliary strictures [3]. Among the surgical techniques hepaticojejunostomy yields the most favaroble results [4].
External metallic circle had been used for the end to end choledochocholedocostomy in rats by Tez et al [5]. The patency of anastomosis was higher than conventional primary anastomosis with this device.
The aim of this study was to examine applicability of external metallic circle in hepaticojejunostomy.
Methods
Eight male Sprague-Dawley rats (Laboratory of Experimental Animals, Hacettepe University Faculty of Medicine, Ankara, Turkey) weighing 250 to 300 g were used. The animals housed under environmentally controlled conditions at 21 ± 2°C and 30% to 70% relative humidity with a 12-hour dark and 12-hour light cycle. Free access to water and standard laboratory food was provided. Before the operations, the rats were fasted overnight and were only allowed free access to water. Guiding Principles in the Care and Use of Laboratory Animals was strictly adhered to at all times together with the recommendations from the Declaration of Helsinki.
Technique
A surgical microscope (Zeiss, Opmi99, Germany), Codman microsurgical instruments, jeweler's forceps, and 10-0 Ethilon suture were used. Rats were anaesthesized with intramuscular injection of ketamine hydrochloride 100 mg/kg and xylazine 10 mg/kg. Under sterile conditions, a midline abdominal incision was made, and the peritoneal cavity was opened. After the traction of duodenum towards the left, the common bile duct was identified and a complete transection midway between the portal hilus and the duodenum was performed by means of sharp dissection. Proximal end was used for hepaticojejunostomy and distal end was closed by a tie. An opening was made on the wall of the jejunum, wide enough to match the size of the duct at a distance of 4–5 cm from the pylorus. Hepaticojejunostomy was performed by the help of surgical microscope with a silver made external metallic circle. All anastomoses were performed by the same investigator (S.K.). The principle of the technique was to tie the sutures over an external metallic circle 20 to 50 percent greater than the original outer diameter of the bile duct. The circle was handmade from a round-bodied silver wire 0.1 to 0.2 mm thick and 1.0 to 1.2 mm in diameter. The external metallic circle was incorporated at the anastomotic line without any effort to slip it over the cut end of the bile duct. The first suture was placed passing inside the circle and tied over the circle, passing through all layers of duct wall and intestinal wall. The remaining sutures were placed and tied according to the same principles. After completion of sutures, the circle was automatically exteriorised (Figure 1). In a preliminary work, we have performed relaparotomy at the end of third month. Eventhough all the anastomosis were patent, interestingly, we were not able to find any metallic circle around the anastomosis or anywhere else inside the abdomen. Therefore, we modified our technique in this study and placed 2–3 supporting sutures between the circle and jejunal serosa following hepaticojejunostomy anastomosis. These supporting sutures were passing only through the inside of circle and jejunal serosa 4 to 5 mm distant to anastomotic line. At the end of the third month, the rats underwent relaparotomy to investigate the patency of hepaticojejunostomy and stability of circles.
Figure 1 View of hepaticojejunostomy. (Arrows indicates External Metallic Circle)
Results
All anastomoses were completed with five or six sutures. Mean operation time was 30 minutes.
One rat died in the postoperative fourth day. In necropsy, there was anastomotic disruption on the anterior surface of anastomosis and external circle was on the original place.
At the end of third month, relaparotomy was performed on the remaining seven rats. There were no anastomotic dehiscense or biliary leakage. In all animals, there was a good connective tissue mass between the bile duct and jejunum. Dissection of the anastomosis region revealed that all the anastomosis were patent and all the circles were staying in original places.
Discussion
For the past 10 to 15 years, hepaticojejunostomy has been the method of choice for the treatment of benign biliary stricrures [6,7]. In this study, our aim was to examine the applicability of external circle in hepaticojejunostomy, not comparing the hepaticojejunostomy with or without external metallic circle. Since the first description of injured bile duct repair, many stenting techniques have been used [8]. In clinical practice, there is arguement about the use of internal stents in hepaticojejunostomy. Some authors recommend internal stents when unhealty (ie, ischemic, scarred) and small bile ducts (<4 mm) are found [9]. Braasch [10], Saypol [11] and Cameron [12] have reported high long-term results. when biliary-enteric anastomosis was complimented with internal stent; 80%, 80% and 88% success rates respectively. On the other hand, some authors suggest that biliaryenteric anastomosis can be performed without anastomotic stents. Aust [13], Bismuth [14] and Innes [15] have reported 84%, 86%, 95% success rates respectively when biliary-enteric anastomosis was performed without using any stents and they suggest that a stent may promote fibrosis of the anastomosis due to constant irritation of ductal mucosa. Thus, transanastomotic stents appear to have little impact on outcome and probably should not be used routinely. However, stents still may be useful in selected cases in which poor outcome is considered preoperatively or intraoperatively.
In a previous study of us [5], we showed that end to end biliary anastomosis with an external metallic circle had the advantage of shorter operating time and lower bile leakage rate compared to primary microsurgical anastomosis. And alkaline phosphatase levels were also found to be significantly lower for end to end biliary anastomosis with external metallic circle. This results directed us to search the applicability of external metallic circle in narrow hepaticojejunostomy anastomoses. Therefore, we designed a study to perform end to side hepaticojejunostomy with external metallic circle. During the relaparotomy performed at the end of third month, we found all the anastomosis were patent but we were not able to find circles in original places except in one rat. Later on, in this study, we modified our technique and added 2–3 supporting sutures between the circle and jejunal serosa. Relaparotomy revelad all the anastomosis were patent and circles were still in place.
Conclusion
We think that external metallic circles are also applicable to end to side hepaticojejunostomy anastomosis, should the extra sutures were placed between the circle and jejunal serosa neighbouring the anastomotic line following the completion of anastomosis. To provide the patency of narrow hepaticojejunostomy anastomoses, external metallic circle can be an alternative to use of internal stents in suitable cases.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
EG designed the study, performed the operations and prepared the manuscript. MK, MT, and MKı participated in performing the operations. MKo participated in the design of study and coordination. SK performed the microsurgical anastomosis. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
Authors thank to Selim Celebioglu from Department of Plastic and Reconstructive Surgery, Social Insurances Foundation Hospital, Ankara for his assistance during the research.
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| 15469611 | PMC526374 | CC BY | 2021-01-04 16:28:03 | no | BMC Surg. 2004 Oct 6; 4:14 | utf-8 | BMC Surg | 2,004 | 10.1186/1471-2482-4-14 | oa_comm |
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BMC Dev BiolBMC Developmental Biology1471-213XBioMed Central London 1471-213X-4-131547390610.1186/1471-213X-4-13Research ArticleCdc42 Effector Protein 2 (XCEP2) is required for normal gastrulation and contributes to cellular adhesion in Xenopus laevis Nelson Karen K [email protected] Richard W [email protected] Biology Department, Wabash College, 301 W. Wabash Ave., Crawfordsville, IN 47933, USA2004 8 10 2004 4 13 13 4 6 2004 8 10 2004 Copyright © 2004 Nelson and Nelson; licensee BioMed Central Ltd.2004Nelson and Nelson; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Rho GTPases and their downstream effector proteins regulate a diverse array of cellular processes during embryonic development, including reorganization of cytoskeletal architecture, cell adhesion, and transcription. Changes in the activation state of Rho GTPases are converted into changes in cellular behavior by a diversity of effector proteins, which are activated in response to changes in the GTP binding state of Rho GTPases. In this study we characterize the expression and function of one such effector, XCEP2, that is present during gastrulation stages in Xenopus laevis.
Results
In a search for genes whose expression is regulated during early stages of embryonic development in Xenopus laevis, a gene encoding a Rho GTPase effector protein (Xenopus Cdc42 effector protein 2, or XCEP2) was isolated, and found to be highly homologous, but not identical, to a Xenopus sequence previously submitted to the Genbank database. These two gene sequences are likely pseudoalleles. XCEP2 mRNA is expressed at constant levels until mid- to late- gastrula stages, and then strongly down-regulated at late gastrula/early neurula stages. Injection of antisense morpholino oligonucleotides directed at one or both pseudoalleles resulted in a significant delay in blastopore closure and interfered with normal embryonic elongation, suggesting a role for XCEP2 in regulating gastrulation movements. The morpholino antisense effect could be rescued by co-injection with a morpholino-insensitive version of the XCEP2 mRNA. Antisense morpholino oligonucleotides were found to have no effect on mesodermal induction, suggesting that the observed effects were due to changes in the behavior of involuting cells, rather than alterations in their identity. XCEP2 antisense morpholino oligonucleotides were also observed to cause complete disaggregation of cells composing animal cap explants, suggesting a specific role of XCEP2 in maintenance or regulation of cell-cell adhesion in early embryos. This loss of cell adhesion could be rescued by co-injection with a morpholino-insensitive version of the XCEP2 mRNA.
Conclusions
XCEP2 appears to be an essential component in the early developmental program in Xenopus laevis. XCEP2 is involved in maintenance of cell-cell adhesion, and as such may constitute a regulatory component that could help to balance the need for tissue integrity and plasticity during the dynamic cellular rearrangements of gastrulation.
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Background
Vertebrate gastrulation depends upon exquisite regulation of diverse morphogenetic processes including changes in cell shape, cellular adhesion and migration. For example, in Xenopus laevis coordinated changes in cell shape and motility guide the initial formation of the dorsal lip, the initial site of mesodermal involution [1,2]. Later, the completion of involution of prospective mesodermal cells is strongly influenced by biomechanical forces generated by convergent extension of dorsal mesodermal cells [3]. Convergent extension involves mediolateral elongation of trunk mesoderm cells, followed by medially-converging intercalation. While convergent extension of dorsal mesoderm is a major driving force for the extension and completion of involution movements, epiboly of the non-involuting ectoderm (driven by radial intercalation of cells of the blastocoel roof) is also necessary for efficient gastrulation [4]. The coordinated changes in cell shape and cell migration associated with gastrulation are influenced by diverse processes, including large-scale remodeling of cytoskeletal architecture and precise modulation of cell-cell and cell-matrix associations.
The molecular basis for initiating and regulating the complex cellular movements of vertebrate gastrulation is only beginning to be discerned. Recent work has shown Wnt proteins as key upstream regulators in gastrulation movements. In Xenopus laevis and zebrafish, non-canonical Wnt signaling on the dorsal side of developing embryos directly initiates the cellular rearrangements and migration that contribute to convergent extension of involuting mesoderm [5,6]. Other Wnt-mediated signals activate a distinct protein kinase C-dependent signaling pathway, which appears to affect Cdc42 signaling and to provide cues that maintain tissue separation during gastrulation [7,8]. Inactivation of this PKC-dependent pathway causes ineffective separation of involuting mesoderm from overlying ectoderm in Xenopus, resulting in severe gastrulation defects [8]. As with the planar cell polarity (PCP) pathway in Drosophila [9], vertebrate Wnt signals during gastrulation can involve signaling through the strabismus and JNK (JUN N-terminal Kinase) proteins [10,11], and affect cellular morphology and migratory behaviors through the activities of a diversity of Rho family GTPases [10,12-14].
Experiments perturbing the activity of Rho GTPases have suggested important regulatory roles for these proteins during gastrulation in Xenopus [7,10,12,14]. Upon activation, Rho GTPases are known to interact with a variety of downstream effector proteins which in turn mediate changes in actin and microtubule cytoskeletal architecture [15], cell adhesion [16], cell migratory behaviours [17], vesicular transport [18] and signalling [19-21]. Regulation of the coordinated cellular and tissue level processes involved in gastrulation appears to involve multiple Rho GTPases (including Rho, Rac and Cdc42) and multiple downstream effector proteins. However, we are only currently beginning to discern the identities and functions of the repertoire of effector proteins that specifically relay Rho GTPase signals involved in the initiation and coordination of gastrulation movements.
Regulation of cell-cell adhesion is thought to be of particular importance during gastrulation movements in Xenopus, when the integrity of the elongating mesodermal sheet must be maintained at the same time that major cellular rearrangements are occuring. Downregulation of C-cadherin-mediated cellular adhesion has been observed to correlate with convergent extension movements of mesodermal cells [22]. This change in C-cadherin mediated adhesion between blastomeres occurs with no detectable change in cell surface expression of the C-cadherin protein. While the precise mechanism of regulation in this developmental context remains unclear, it is known that lateral clustering of cadherins (mediated through interactions between cytoplasmic tails of cadherin proteins) is capable of strengthening adhesive function [23]. In addition, it is also known that Rho GTPases can act through the IQGAP protein to regulate cadherin-mediated adhesion [24-27]. Recent chararacterization of the developmental pattern of IQGAP expression in Xenopus embryos suggests that analogous upstream regulatory mechanisms may operate during Xenopus gastrulation [28].
In this study, the embryonic expression pattern and function of a Xenopus gene encoding a member of the recently characterized CEP/BORG family of RhoGTPase effector proteins [29,30] is explored. These proteins have been shown to have distinct effects on cytoskeletal architecture, cellular morphology, adhesion and migratory behaviours [29,30], and have also been shown to regulate septin function during cytokinesis [31,32].
Xenopus Cdc42 Effector Protein 2, hereafter referred to as XCEP2, is shown here to be developmentally regulated during early embryonic stages, with diffuse expression of mRNA in the animal region that diminishes during late gastrula to neurula stages. Experiments employing morpholino-mediated inhibition of translation suggest that expression of this protein is essential for normal gastrulation movements, and that its activity is required for maintenance of cell-cell adhesion between blastomeres of animal cap explants.
Results
Xenopus laevis cdc42 effector protein 2 (XCEP2) is present in two allelic forms, and its expression is developmentally regulated during early embryogenesis
A differential display approach was employed to reveal genes that are developmentally regulated during the transition from blastula to neurula. This screen allowed for the isolation of numerous cDNA fragments corresponding to differentially regulated mRNAs present in the Xenopus embryo. The sequence of one of these fragments was found to be highly homologous to the translational start site region of database cDNA sequences encoding Cdc42 effector protein 2. A full length cDNA corresponding to our isolated fragment was amplified using 3' RACE PCR, cloned and sequenced. An alignment of the predicted amino acid sequence for our isolated full length cDNA sequence with human [29] and Xenopus (accession number BC045241) CEP2/BORG1 genes from Genbank revealed significant homology (see Figure 1), particularly within and immediately surrounding the conserved CRIB, CI and CII domains [29,30,33]. When comparing the two Xenopus sequences, there were a significant number of positions where the amino acid sequence derived from our isolated cDNA sequence differed from that found in the Genbank sequence (15% of the amino acids were non-identical). These differences were found to occur almost exclusively in regions of the protein outside the three conserved domains (CRIB, CI and CII) that define the CEP family of effector proteins. Given the allotetraploid genetic ancestry of Xenopus laevis, and the degree of similarity between the sequences, it is likely that these two sequences constitute pseudoalleles of the same gene. We choose to refer to our isolated pseudoallele as XCEP2A and the database allele as XCEP2B.
Figure 1 XCEP2 Nucleic Acid and Amino Acid Sequences. (A) The nucleic acid sequence of the XCEP2A cDNA is shown. Sequences encoding the start methionine (green), the stop codon (red) are indicated. The protein coding region is shown in blue. (B) Amino acid sequences of the two Xenopus pseudoalleles of CEP2, along with the mammalian (human) sequence, are shown. Asterisks indicate highly conserved residues within the CRIB domain (black asterisks, lines), the CI domain (red asterisks, lines) and the CII domain (blue asterisks, lines) in the CEP-2 protein. Dark gray shading indicate residues conserved in all three species; light gray areas indicate residues that are conserved between two of the three species listed. Vertically oriented pairs of dots (:) indicate positions where amino acid identity is conserved in all three sequences.
Semi-quantitative RT-PCR was conducted on staged RNA samples using primers specific to the 5' untranslated sequence of the isolated XCEP2A cDNA. This analysis revealed the presence of maternally-derived XCEP2A mRNA prior to the onset of zygotic transcription (embryonic stage 7.5), maintenance of this expression level through mid-gastrula stages, with a downregulation of mRNA expression starting at stage 10.5 and becoming more marked by stage 12.5 (see Figure 2). RT-PCR analysis of the expression of the other pseudoallele (XCEP2B) revealed an expression pattern similar to XCEP2A (see Figure 2, lower panel), with an almost complete loss of detectable expression by stage 12.5. Both patterns correspond to that observed for the amplified fragments originally isolated from the original differential display gels (data not shown). In situ hybridization revealed that XCEP2A mRNA (corresponding to our isolated pseudoallele) is distributed uniformly over the animal hemisphere of the embryo at blastula and early gastrula stages (see Figure 3A and 3B), with an animal to vegetal gradient in staining evident (Figures 3B and 3C). Over time, there is a diminution of the intensity but no change in the general expression pattern as the embryo transitions from blastula to gastrula stages (data not shown).
Figure 2 Temporal regulation of XCEP2 during pre-neurula stages of embryonic development. Semi-quantitative RT-PCR was conducted on equivalent amounts of RNA derived from the indicated developmental stages. XCEP2A was amplified using XCEP2A forward and reverse primers (see Methods). Ornithine Decarboxylase (ODC) serves as a loading control (middle panel). RT-PCR amplification to reveal expression of the alternative pseudoallele (XCEP2B), using XCEP2B forward and reverse primers, is shown in the lower panel.
Figure 3 Spatial Localization of XCEP2 mRNA. (A) Animal pole view in situ hybridization of embryos fixed at late blastula stage with digoxygenin-labeled antisense XCEP2A antisense probe exhibit diffuse staining across the animal region of the embryo (right). An XCEP2A sense probe served as a negative control, and showed minimal background staining (left). (B) A blastula stage embryo, cut to reveal a cross-section, reveals the animal/vegetal gradient of antisense XCEP2 probe. The blastocoel cavity is visible in the upper portion of this embryo. (C) A side view of an antisense XCEP2A probed embryo, revealing an animal (top) to vegetal (bottom) gradient of staining.
Morpholino oligonucleotides directed against XCEP2 cause gastrulation defects
In order to assess the functional role of XCEP2 in early embryogenesis, microinjections were conducted with antisense morpholino oligonucleotides, MO1 and MO2 directed to the translational start site region of one or both of the two XCEP2 pseudoalleles. MO1 is targetted to XCEP2A, and MO2 is targetted to the XCEP2B sequence found in the Genbank database. For this experiment, a total of 100 ng of morpholino oligonucleotide was injected into the animal region of one-cell embryos. At time points just prior to blastopore closure (~embryonic stage 12), MO1 antisense oligonucleotides pseudoalleles elicited significant gastrulation delay as compared to an identical dose of control morpholino oligonucleotide (See Figure 4A). A comparison of the effects of each morpholino, and the combined effects of co-injection of both morpholinos is shown in Figure 4B. The severity of gastrulation delay was quantified at late gastrulation by comparing the ratio of blastopore diameter to embryo diameter in antisense-injected, control injected and non-injected embryos. The mean ratio, and the distribution of ratios differed significantly between control injected and antisense morpholino injected groups (See Figure 4B and 4C). MO2 was slightly less effective than MO1 in producing gastrulation delay. Small differences between the effects of the various injection conditions suggest that either the MO2 morpholino is less efficient at translational inhibition, or that the pseudoallele targeted by MO2 may be expressed at comparatively lower levels. The combined MO1+MO2 condition did not produce more severe defects than the individual morpholino injections, perhaps because the dose of each morpholino in the combined condition was half that in either of the single morpholino conditions (all injections were 100 ng, the combined condition included 50 ng of each morpholino). Many of the more severely affected embryos at this dose did not complete blastopore closure, and showed lethal defects that precluded normal neurulation. Embryos that survived treatments of between 75 and 115 ng of antisense morpholino (MO1) were found to have phenotypes consistent with gastrulation defects, including significant shortening of the AP axis and multiple instances of spina bifida (see Figure 4D). The phenotypes we observe share significant similarity to defects caused by specific inhibition of convergent extension in mesoderm [34]. At a higher dose (100 ng injected into each blastomere of 2-cell stage embryos, 200 ng total injection), embryonic lethality was observed consistently in practically all treated embryos prior to neurulation (see Figure 8A, lower panel).
Figure 4 Effects of antisense XCEP2 morpholino oligonucleotide on gastrulation. (A) Injected embryos received 100 ng of morpholino, delivered to the animal region of 1-cell embryos. Typical gastrula stage embryos for each of three experimental conditions (non-injected, control morpholino and MO1 antisense injected) are shown. (B) The distribution of blastopore-to-embryo diameter ratios for each experimental condition. 18–20 embryos were assessed for each condition. In the combined "MO1+MO2" condition, 50 ng of each morpholino were co-injected. (C) Average blastopore to embryo diameter ratios for each of the control and experimental conditions, +/- 1SEM, are shown. (D) Effects of antisense morpholinos on post-neurulation embryonic development are shown. Antisense XCEP2-injected embryos exhibit dose-dependent abnormalities suggestive of gastrulation and/or convergence extension defects. Representative viable embryos, at Stage 26–27, from each control and experimental condition are shown. Several embryos in the MO1 treated groups exhibited varying degrees of spina bifida. Embryonic viability for the water, control morpholino, and the three doses of antisense XCEP2 morpholino (75 ng, 95 ng and 115 ng) were 90%, 90%, 81%, 63%, and 26%, respectively (20–30 embryos injected for each condition).
The antisense morpholino oligonucleotides used in these studies were found to elicit specific translational inhibition of XCEP2. Injection of antisense morpholino against our isolated pseudoallele resulted in largescale inhibition of translation of a myc-tagged fusion protein from co-injected mRNA (see Figure 5B). A modified mRNA (XCEP2A*-myc) encoding a C-terminal myc tagged XCEP2A fusion protein, with seven point mutations in the 5' untranslated region and at the 3rd base of selected codons in the translated region (see Figure 5A), was found to be insensitive to inhibition by antisense morpholino oligonucleotide MO1 (See Figure 5B), demonstrating sequence specificity of the translational inhibition effect. XCEP2A* myc migrates as a doublet, which probably arises due to inefficient translational initiation at the true start codon of the mutated mRNA and some initiation at the second AUG codon, encoding methionine at position 29 (see Figure 1B). While MO1 does not inhibit translation from the multiply mutated XCEP2A* myc mRNA, we did find that MO2, which is designed to be complementary to the database pseudoallele, was capable of significantly reducing translation from mRNA corresponding to our isolated pseudoallele (XCEP2A-myc). This suggests that under these conditions, the two-nucleotide mismatch between MO2 and our XCEP2A-myc mRNA does not preclude translational inhibition (to compare sequences, see Methods, "Morpholino Oligonucleotides"). Given this observation, it is possible that injection of either antisense XCEP2 morpholino significantly reduces the endogenous expression of both pseudoalleles. In addition to the dosage issue in the combined condition (noted above), cross-inhibition of this type could contribute to the observed lack of additive effects in the combined MO1 + MO2 condition.
Figure 5 Antisense XCEP2 morpholino oligonucleotides specifically inhibit translation from XCEP2 mRNA. (A) Schematic diagram indicating mutations (designated by asterisks) in the morpholino target site of XCEP2*-myc mRNA that would be expected to inhibit strong interactions with the antisense XCEP2 morpholino (MO1). (B) 0.7 ng of mRNA encoding myc-tagged XCEP2 (either the normal XCEP2-myc or the mutated XCEP2*-myc) was injected alone or in combination with 80 ng of antisense XCEP2 morpholino oligonucleotide (MO1) into the animal pole of 1-cell embryos. XCEP2 protein was detected in embryo extracts (prepared at stage 8.5–9) by Western Blotting using anti-myc monoclonal antibody 9E10 (upper panels). Equivalency in protein loads in the Western Blot lanes are indicated by the similarity in staining intensity of protein bands in the corresponding SYPRO Ruby-stained gels in the lower panels.
The blastopore closure delay phenotype observed in antisense morpholino injected embryos could be completely rescued by co-injection with 0.7 ng of the XCEP2A*mRNA, which is insensitive to translational inhibition (see Figure 6). This result suggests that the observed effects of antisense morpholino oligonucleotides on gastrulation are not due to inhibition of a non-XCEP2 gene product, or to non-specific toxicity of the antisense oligonucleotides. This observation strongly suggests that the observed effects of the antisense oligonucleotides on embryogenesis are attributable to a specific knockdown of translation of the endogenous XCEP2 mRNA, and a concomitant reduction in XCEP2 protein.
Figure 6 Rescue of the antisense XCEP2 morpholino phenotype by XCEP2 mRNA co-injection. Injected embryos received 100 ng of morpholino oligonuceotide in the animal region of both blastomeres in two-cell stage embryos. MO1 and MO2 oligonucleotides were mixed at a molar ratio of 3:1, respectively. 20–30 embryos were assessed for each condition. (A) The distributions of blastopore to embryo diameters for the indicated experimental conditions are shown. The rescuing XCEP2 mRNA (XCEP*-myc) sequence was altered such that antisense morpholino oligonucleotides would be expected to bind inefficiently. (B) Average blastopore to embryo diameter ratios, +/- 2 SEM, are indicated for each experimental condition.
In contrast to the morpholino knockdown effects, overexpression of XCEP2 in embryos by injection of up to 1 ng XCEP2 mRNA was found to result in no detectable abnormalities in embryonic development (data not shown). The absence of effects may not be unexpected, given current models of RhoGTPase effector protein activation. RhoGTPase effector proteins are activated by direct binding to GTP-bound RhoGTPases. As such, the number of activated RhoGTPase molecules may constrain the number of effector proteins bound and activated, regardless of the heightened expression levels of the effector protein.
XCEP2 morpholinos do not interfere with mesoderm induction
The observed effects of antisense XCEP2 morpholinos in Xenopus embryos suggests that XCEP2 may play a direct role in directing the morphogenetic movements of gastrulation. This interpretation would be consistent with the limited characterization that have thus far been conducted on functional properties of this class of proteins [29,30]. However, it is also conceivable that XCEP2 antisense morpholinos may indirectly inhibit gastrulation by interfering with mesodermal induction. In order to assess this possibility, the level of expression of the pan-mesodermal marker, brachyury, and the dorsal mesodermal marker, goosecoid, was assessed in control and antisense morpholino injected embryos at gastrula stage using semi-quantitative RT-PCR (see Figure 7). Expression of mesodermal markers was similar in non-injected, control morpholino injected and antisense XCEP2 injected embryos, suggesting that it is the behavior of mesodermal cells during gastrulation (rather then the presence or absence of mesodermal cells) that is affected by XCEP2 antisense morpholino oligonucleotides.
Figure 7 Antisense XCEP2 morpholino oligonucleotides do not inhibit mesodermal induction. Semiquantitative RT-PCR analysis was conducted on duplicate RNA samples using primers for Brachyury (a pan-mesodermal marker), goosecoid (a dorsal mesodermal marker) and ornithine decarboxylase ("ODC", used as loading control). For control and morpholino-injected conditions, 100 ng of morpholino oligonucleotide was injected into the animal region of 1-cell stage embryos.
Figure 8 Effects of antisense XCEP2 Morpholino oligonucleotides on animal cap explant cell adhesion. (A) 100 ng morpholino oligonucleotide MO1 was injected into the animal region of each blastomere of 2-cell stage embryos. Animal cap explants from antisense MO1-injected embryos exhibited a marked loss of integrity (upper right panel) 24 hours after explantation, as compared to animal caps from non-injected or control morpholino injected embryos (upper left panels). Effects of these treatments on whole sibling embryos at Stage 22–23 are shown in the lower panels. In (B), embryos were injected with 75 ng of MO1 antisense morpholino in each blastomere at the 2-cell stage. Animal explants derived from non-injected embryos (left panel), MO1-injected embryos (middle panel), or embryos co-injected with 0.7 ng XCEP2*myc mRNA (right panel) are shown 24 hours after explantation.
XCEP2 morpholinos Interfere with cell-cell adhesion in animal cap explants
In order to assess the mechanism by which XCEP2 influences gastrulation, the effects of XCEP2 morpholinos on cultured animal cap explants were evaluated. While the initial intent of these experiments was to discern whether XCEP2 morpholinos prevented normal activin-induced convergent extension of animal caps, it soon became clear that morpholino-treated explants had a more fundamental deficiency. Within a 24 hour period, animal cap explants derived from XCEP2 antisense morpholino (MO1) -injected embryos were found to completely lose their integrity (see Figure 8A, upper right panel), collapsing into piles of dissociated cells. This contrasts with control injected and non-injected caps, which remained tightly associated at 24 hours (see Figure 8A, upper left panels). This result was evident with or without activin treatment (data for activin treated caps is not shown). The observed defect in XCEP2 MO1 treated caps suggested a loss of cell-cell adhesion, as cells of disintegrated explants appeared to be completely dissociated, with few if any adherent clusters of cells. The observed loss of integrity followed a reproducible pattern. At approximately 18 hours after explantation, dissociated cells from the interior of the explant could often be observed discharging from the healed wound site (or, in some cases, at other localized sites), causing the explants to become progressively reduced in size. The cells of the outer pigmented epithelium of the explant remained adherent until the latest stages of the 24 hour time course, by which time they often also dissociated.
The animal cap dissociation caused by the MO1 morpholino could be largely rescued by co-injection with a XCEP2*myc mRNA (see Figure 8B), which has modifications that make it insensitive to translational inhibition by our morpholinos (see Figure 5B). This suggests that the dissociation observed is not a non-specific toxicity or cross-inhibition effect. XCEP2*myc mRNA rescued explants showed little difference from non-injected controls at and beyond the 24 hour time point.
Discussion
The Rho GTPases and their associated effector proteins are known to play diverse roles in the regulation of cytoskeletal remodelling, cellular adhesion and cell motility. The complex morphogenetic movements associated with gastrulation in Xenopus, including changes in the morphology and polarity of mesodermal cells, and their later mesolateral intercalation of these cells during convergent extension, are now known to be dependent upon RhoGTPase functions [5,12,14] (for review, see [35,36]). Currently, however, there is little known regarding the identity or role of specific effector proteins that are utilized to convert changes in the GTP binding state of RhoGTPases into gastrulation-associated changes in cytoskeletal architecture, cell morphology, and cellular adhesion and migration.
The data presented here indicate a role for the Xenopus Cdc42 effector protein 2 (XCEP2) in gastrulation movements. XCEP2 is a member of the recently characterized CEP family of Rho GTPase effector proteins [29,30], which include the previously characterized mouse protein, MSE55 [33]. Effector proteins of the CEP family share a conserved expanded CRIB domain, which binds Rho GTPases, and two other highly conserved protein domains (CI and CII) [29,30]. By analogy to other Rho GTPase effector proteins, it has been proposed that Cdc42 binding to the CRIB domain of CEP proteins leads to a conformational change that exposes the previously inaccessible CI or CII domains (reviewed in [37]). The exposed domains of the effector protein would then be free to interact with downstream components of the signaling/regulatory pathway. When overexpressed in cultured cells, members of this family of effector protein induce marked pseudopodial/ lamellipodial extensions, membrane ruffling, alterations in actin and vinculin organization, and a reduction of E-cadherin staining at adherens junctions [29,30].
Consistent with a potential role for this class of proteins in embryonic morphogenesis, we have shown that XCEP2 expression is temporally regulated at gastrulation stages, when major modulations of cellular morphology, cytoskeletal organization, and cellular adhesion are occurring. mRNA for XCEP2 is present prior to mid-blastula transition, persists through mid gastrulation, and is strongly down-regulated by the time the blastopore closes and neurulation begins. This pattern would suggest that XCEP2 protein would be present through the period when active gastrulation movements are occurring. The diffuse spatial pattern of mRNA and, presumably, protein expression of XCEP2 may suggest that XCEP2 functions broadly in cells of the animal and equatorial regions. However, the observed broad spatial distribution of XCEP2 mRNA does not preclude the possibility that the XCEP2 protein may be functionally activated or inactivated at discreet times and locations during early embryonic development.
Furthermore, we show that antisense morpholino oligonucleotides capable of blocking translation of the XCEP2 message interfere with Xenopus gastrulation, delay the closure of the blastopore and inhibit embryonic elongation. The observed rescue with XCEP2 mRNA is strong evidence for the specificity of the antisense morpholino effect. These effects are not due to a loss of mesodermal induction, as brachyury and goosecoid expression do not change in response to antisense XCEP2 morpholinos. This is particularly relevant given recent reports demonstrating a direct link between brachyury expression and control of cellular migration [38].
The effects we report require relatively high, although not unprecedented, doses of morpholino antisense oligonucleotide. This dosage requirement may reflect the difficulty inherent in morpholino-mediated translational knockdown of maternally expressed genes. XCEP2 mRNA and (presumably) XCEP2 protein are present prior to midblastula transition. Given this situation, the timing morpholino induced protein downregulation is dependent both upon the effectiveness of translational blockade, and the half life of the protein in the cytoplasm. In this context, it may be essential to impose close to complete translational inhibition in order to reduce protein levels rapidly enough to affect early embryonic events, such as gastrulation.
Clearly, specific probes to assess endogenous XCEP2 protein expression will be necessary for fuller characterization of the role of this protein during gastrulation. For this reason, antibodies are currently being raised against the XCEP2 protein. These antibodies will be important in the characterization of the developmental time course of endogenous XCEP2 protein expression, assessment of the subcellular localization of the XCEP2 protein, isolation of potential XCEP2 binding partners, and in assessing and further optimizing the extent of protein down-regulation in morpholino injected embryos.
Currently, the specific mechanism by which XCEP2 exerts its role in gastrulation is unknown. However, our preliminary data suggest that XCEP2 may either contribute to a required "ground state" of cellular adhesion or play a role in modulations in the strength of cadherin-mediated cell-cell adhesion that are known to occur during gastrulation [22,39,40]. More detailed work will be necessary to clearly distinguish between these possibilities.
In embryos Wnt-mediated signals have been shown to activate Cdc42, a process that is required for normal gastrulation movements [7,12]. In future work, it will be important to discern whether XCEP2 plays an important role in transducing these upstream signals into changes in cellular behaviour during gastrulation.
The known functional properties of the CEP class of effector proteins, and the characteristics of CRIB domain effector proteins in general, suggest some interesting possibilities relating to the control of cell adhesion during gastrulation. Consistent with the observed functions of the XCEP2 homologs in cultured cells, XCEP2 in embryos may impinge on regulatory circuits downstream of Cdc42 that control actin filament assembly, which in turn may affect diverse cellular processes, including assembly of adherens junctions. Alternatively, XCEP2 may more directly impinge upon cadherin functional activity, perhaps by influencing the association of IQGAP or other molecules with cadherin complexes. In future studies, it will also be important to establish whether embryonic activation of whether there are links between Wnt-mediated activation of Cdc42 and functional activation of XCEP2 and to characterize the mechanism(s) by which XCEP2 contributes to cell adhesion between cells of gastrulating embryos.
Conclusions
It has become clear in recent years that an integrated network of signals involving Rho GTPase proteins and their effector proteins help to control and regulate the diverse and intricate morphogenetic processes that occur during embryonic development. Less clear are the specific modes of functional interaction between the multiple Rho GTPases and the diversity of potential effector proteins. We have shown that XCEP2 is one component in the complex regulatory puzzle contributing to morphogenetic processes during Xenopus gastrulation. For this reason it will be interesting and important to discern further the role of XCEP2, with regard both to its relationship to intracellular signalling pathways and its effects on cellular behavior during gastrulation.
Methods
Restriction fragment differential display primer and adaptor sequences
Adaptor 1-
5'-ATGAGTCCTGAC-3' (upper strand)
5'-PO4-CGGTCAGGACTCAT-3'(lower strand).
Adaptor 2 (the 3' phosphate group of the lower strand inhibits DNA polymerase extension)-
5'-ACTGGTCTCGTAGACTGCGTACC-3'(upper strand)
5'-PO4-CGGGTACGCAGTC-PO43' (lower strand).
"Universal" RFDD-PCR primer (complementary to Adaptor 2)-
5'-ACTGGTCTCGTAGACTGC-3'
"Selection" Primer 4 (complementary to Adaptor 1, with a 3-nucleotide 3' extension)
5'-ATGAGTCCTGACCGAAAG-3' (3-nucleotide extension is underlined)
Primers used for construction of full-length XCEP2 cDNAs
(sequences encoding start codon shown in bold, PCR-induced mutation sites are underlined)
XCEP2 cDNA forward-
5'-ATTGCAAAGATGTCCGCCAAG-3'
XCEP2* cDNA forward-
5'-CGGGATCCTAGATGTCGGCGAAAGCGCCGATATACCTAAAGAG AAG-3'
XCEP2 cDNA reverse-
5'-AACGTATCCCCTTCCCCA-3'
RT-PCR primer sequences
XCEP2A forward (complementary to 5' untranslated region of the cDNA sequence)
5'-AACGTATCCCCTTCCCCA-3'
XCEP2A reverse (complementary to 5' untranslated region of the cDNA sequence)
5'-AAAGAGAAGTAGCCGTAAAGGA-3'
XCEP2B forward
5'-GCCAAGGCCCCGATATAC-3'
XCEP2B reverse
5'-CCAATAGCAGGTAGGGAA-3'
Brachyury forward
5'-GGATCGTTATCACCTCTG-3'
Brachyury reverse
5'-GTGTAGTCTGTAGCAGCA-3'
Goosecoid forward
5'-ACAACTGGAAGCACTGGA-3'
Goosecoid reverse
5'-TCTTATTCCAGAGGAACC-3'
ODC forward-
5'-GTCAATGATGGAGTGTATG-3'
ODC reverse
5'-TCCATTCCGCTCTCCTGA-3'
Morpholino oligonucleotides
Antisense morpholino oligonucleotides, which specifically block translation of targeted mRNAs [41] were synthesized by GeneTools, LLC (Philomath, OR) and were designed to interact with both characterized pseudoalleles of the XCEP2 gene. Antisense oligos were targeted to the region upstream and downstream of the translational start site of the XCEP2 mRNA. The target sequences that were chosen were compared to the Genbank database to confirm that the XCEP2 antisense oligonucleotides would not be expected to interfere with the expression of other known gene products. The morpholino sequences utilized in these studies were as follows, with morpholino sequence complementary to the start codon of the cognate mRNA shown in bold:
Antisense XCEP2 MO1 (specific to the XCEP2A, characterized in this study)-
5'-GGGCCTTGGCGGACATCTTTGCA-3'
Antisense XCEP2 MO2 (specific to XCEP2B, Xenopus Genbank sequence accession # BC045241)-
5'-GGGCCTTGGCTGACATCTTTCCA-3'
Control Morpholino oligonucleotide
5'-CCTCTTACCTCAGTTACAATTTATA-3'
Restriction fragment differential display PCR, gene identification and isolation
Restriction Fragment Differential Display PCR (RFDD-PCR) procedures were patterned closely after the commercially available DisplayProfile kit from QBiogene (Carlsbad, CA), with minor modifications. In this differential display procedure, Adaptors 1 and 2 (see RFDD-PCR primer and adaptor sequences, above) are ligated to restriction digested, double stranded cDNA. Amplification of this cDNA is conducted with the "universal primer", complementary to one of the adaptor sequences, and one of 64 "selection" primers that bind largely to the to the second adaptor sequence but have a 3-nucleotide 3' extension that is complementary only to a subset of cDNA inserts. An extension-inhibiting chemical modification of the lower strand Adaptor 1, consisting of a 3' phosphate group in our modified procedure, and the 3-nucleotide extension of the selection primer largely restricts amplification to cDNA fragments that have different adaptor sequences ligated on opposite ends. Furthermore, any particular selection primer will selectively amplify only a subset of cDNA fragments (in theory, approximately 1/64th of the total).
Total RNA from staged, duplicate batches of Xenopus laevis embryos was purified using Trizol reagent (Invitrogen, Carlsbad, CA). RNA from Xenopus laevis embryonic stages 7, 8.5, 9.5, 10.5 and 12.5 were prepared. First strand cDNA was synthesized using oligo dT primer and Superscript III reverse transcriptase (Invitrogen), followed by second strand synthesis catalyzed by DNA polymerase I (Roche, Indianapolis, IN). Double stranded cDNA was purified using GeneClean silica resin (Qbiogene, Carlsbad, CA), and then digested with TaqI restriction enzyme (Roche, Indianapolis, IN). An annealed mix of RFDD-PCR of Adaptor 1/Adaptor 2 oligonucleotides was added, and ligated to the TaqI digested cDNA ends using T4 DNA Ligase (Roche). Touchdown PCR using the universal RFDD-PCR primer, 35S-labelled dCTP, and "Selection" Primer 4 was conducted using Accuprime PCR mix (Invitrogen), with the following cycling parameters:
Pre-dwell: 94°C 4 minutes
10 cycle touchdown PCR, with a 0.5 temperature decrement each cycle:
94°C 30 seconds
60°C→ 55°C 30 seconds
72°C 1 minute
30 cycle standard amplification:
94°C 30 seconds
55°C 30 seconds
72°C 30 seconds
Post dwell: 72°C, 5 minutes
Samples were run on a standard 6% denaturing DNA sequencing gel, and the gel was dried and subjected to autoradiography. Bands showing differential expression were excised from the gel and eluted by heating at 95°C for 15 minutes. Eluted fragments were re-amplified with 30 cycles of standard PCR, using the original primers (above). Direct cycle sequencing of isolated product (Thermosequenase; United States Biochemical, Cleveland, OH) revealed that one of the fragments corresponded to the 5' end (overlapping the translational start site) of a Xenopus homologue of the Cdc42 Effector Protein 2, which we refer to as "XCEP2". The full length cDNA was amplified using gene-specific primers and poly-dT primers using standard 3' RACE procedures [42]. The sequence of the full-length cDNA was determined by commercial automated sequencing (MWG Biotech, High Point, NC).
Plasmid construction and RNA preparation
The RACE amplified full-length fragment isolated from the RFDD-PCR procedure (see above) was cloned into the pCR2.1 vector, using the pCR2.1 TA cloning kit (Invitrogen), and commercially sequenced (MWG Biotech). To construct a vector encoding XCEP2A fused to a C-terminal 6xmyc tag, PCR was conducted using primers directed to the 5' and 3' end of the XCEP2A cDNA sequence (XCEP2A cDNA forward and XCEP2A cDNA reverse) with the cloned, full length XCEP2A as template. The primers used in this PCR mutated the normal stop codon and introduced a BamHI and ClaI site at the 5' and 3' ends of the amplified product, respectively. BamHI/ClaI digested full length cDNA was directionally cloned in the sense orientation into the corresponding into BamHI/ClaI digested pCS2-myc vector, producing the plasmid pCS2-XCEP-myc. In this context, the full length X-CEP2A cDNA encodes a fusion protein with a C-terminal 6x myc tag. A second construct, containing introduced mutations in the XCEP2A antisense morpholino target region, was also derived by PCR. To construct this vector, PCR was conducted using XCEP2* forward and XCEP2 reverse primers with the full-length XCEP2 DNA as template. XCEP2* forward introduced multiple changes in the sequence of the cDNA encoding the translational start site region (see Figure 5A). These changes do not change the predicted amino acid sequence of the encoded protein, but would be expected to significantly reduce or eliminate binding of the antisense morpholino oligonucleotides used in this study.
For transcription of antisense probe for in situ hybridization, the full length X-CEP2A cDNA was subcloned into the pCS2+ plasmid in an antisense orientation (relative to the SP6 promoter), producing the plasmid pCS2-XCEP2-anti. In a similar fashion, an RNA expression vector containing the XCEP2 insert in the sense orientation in pCS2+ was also constructed.
Capped mRNAs were synthesized in vitro using the mMessage mMachine kit (SP6) from Ambion (Austin, TX), and were dissolved in distilled water prior to injection.
Semi-quantitative RT-PCR
Total RNA was isolated from embryos using Trizol (Invitrogen) according to manufacturer's instructions. Reverse transcription was carried out using SuperscriptIII (Invitrogen). Assessment of relative levels of gene expression over developmental time, or under different experimental conditions, was carried out using standard semi-quantitative RT-PCR methods [43], with the primers previously described (see RT-PCR Primers, above). 25 cycles of PCR were carried out using Accuprime PCR mix (Invitrogen) in 25 ul reactions containing 2 ul of 200 ug/ml reverse transcribed cDNA, 0.3μM of each primer, and a trace of radiolabelled nucleotide to allow for autoradiographic visualization. Parallel reactions containing primers specific to the metabolic gene, ornithine decarboxylase (ODC), were used as a loading control in all semi-quantitative RT-PCR experiments. RT-PCR products were separated electrophoretically on 5% acrylamide gels, which were dried and subsequently subjected to autoradiography. RT-PCR of the XCEP2A allele resulted in stronger amplification than with the XCEP2B primer set. XCEP2B bands required correspondingly longer exposure times (2–4 fold) for detection.
Embryo manipulations
Adult Xenopus laevis were purchased from Nasco (Fort Atkinson, WI). Eggs were obtained and fertilized according to standard methods. Staging was determined according to Nieuwkoop and Faber [44]. Antisense morpholino oligonucleotides and in vitro transcribed mRNA's were injected into two-cell stage embryos, with injected volumes of approximately 13 nl in doses as described. All injections were carried out using the Nanoject positive displacement microinjector (Drummond Scientific, Broomall, PA).
Measurement of blastopore to embryo diameter ratios was accomplished by dividing the measured blastopore diameter (the distance between dorsal and ventral poles of the blastopore) by the embryo diameter. Diameter measurements were obtained using digital images of individual embryos, and the "Ruler" tool of the Adobe Photoshop software.
To assess the effects of morpholino antisense oligonucleotides on animal cap explants, morpholino injected and control embryos were allowed to develop until stage 8.5. At stage 8.5 animal caps were dissected, and then cultured in 1xMMR for 24 hours. Non-dissected sibling embryos were allowed to develop until post-neurula stages in order to asses the effectiveness of the morpholino treatment.
Detection of myc-tagged protein expression
Proteins from embryos were extracted in 10 volumes of isotonic buffered saline containing 1% NP40. Extracts were cleared by a brief centrifugation, and supernatants were denatured by a 5-minute incubation at 95°C after diluting samples with SDS-PAGE sample buffer. Proteins were separated on 8–16% gradient polyacrylamide gels, and transferred to nitrocellulose. After blocking, blots were probed with monoclonal antibody 9E10 [45], which recognizes the myc epitope, and AP-conjugated goat-anti-mouse IgG (Biorad, Hercules, CA). Antibody binding was detected using Enhanced Chemiluminescence (ECL) reagents (Amersham-Pharmacia, Piscataway, NJ). Equivalency of protein loads was confirmed by running equivalent volumes of protein extracts on SDS-PAGE, and staining overnight with SYPRO Ruby (BioRad), with detection and documentation of protein bands conducted using UV transillumination.
Wholemount in situ hybridization
Digoxygenin-labelled antisense RNA probe was transcribed in vitro from linearized pCS2-XCEP2anti plasmid using a digoxygenin nucleotide labeling mix (Roche) and the mMessage mMachine in vitro transcription kit (SP6) from Ambion. In situ hybridization was carried out essentially as described [46], using non-hydrolyzed X-CEP2 digoxygenin-labeled probe. Embryos were subjected to a hybridization temperature of 62°C. AP-conjugated anti-digoxygenin FAb fragments (Roche) and BM Purple substrate (Roche) were used to detect hybridized probe.
Authors' contributions
KKN played a critical role in establishing in situ hybridization methodology in the laboratory, and conducted in situ hybridizations. RWN conceived of the study, designed and conducted molecular biological, embryological and biochemical experiments, and wrote the manuscript. Both authors read and approved of the final manuscript.
Acknowledgements
This work was supported by generous support from Byron K. Trippett new faculty research funds and the Treves Trust Fund of Wabash College. Thanks to L. David Polley at Wabash College for critical reading of the manuscript.
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| 15473906 | PMC526375 | CC BY | 2021-01-04 16:30:29 | no | BMC Dev Biol. 2004 Oct 8; 4:13 | utf-8 | BMC Dev Biol | 2,004 | 10.1186/1471-213X-4-13 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1553853810.1371/journal.pbio.0020384Research ArticleEvolutionGenetics/Genomics/Gene TherapyYeast and FungiConvergent Evolution of Chromosomal Sex-Determining Regions in the Animal and Fungal Kingdoms Evolution of Complex Sex DeterminationFraser James A
1
2
Diezmann Stephanie
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3
Subaran Ryan L
1
Allen Andria
1
3
Lengeler Klaus B
1
2
¤Dietrich Fred S
1
3
Heitman Joseph [email protected]
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4
5
1Department of Molecular Genetics and Microbiology, Duke University Medical CenterDurham, North CarolinaUnited States of America2Howard Hughes Medical Institute, Duke University Medical CenterDurham, North CarolinaUnited States of America3Duke Institute for Genomics Sciences and Policy, Duke University Medical CenterDurham, North CarolinaUnited States of America4Department of Medicine, Duke University Medical CenterDurham, North CarolinaUnited States of America5Department of Pharmacology and Cancer Biology, Duke University Medical CenterDurham, North CarolinaUnited States of America12 2004 9 11 2004 9 11 2004 2 12 e38410 6 2004 10 9 2004 Copyright: © 2004 Fraser et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Fungus Holds Clues to the Evolution of Sex Chromosomes
Sexual identity is governed by sex chromosomes in plants and animals, and by mating type (MAT) loci in fungi. Comparative analysis of the MAT locus from a species cluster of the human fungal pathogen Cryptococcus revealed sequential evolutionary events that fashioned this large, highly unusual region. We hypothesize that MAT evolved via four main steps, beginning with acquisition of genes into two unlinked sex-determining regions, forming independent gene clusters that then fused via chromosomal translocation. A transitional tripolar intermediate state then converted to a bipolar system via gene conversion or recombination between the linked and unlinked sex-determining regions. MAT was subsequently subjected to intra- and interallelic gene conversion and inversions that suppress recombination. These events resemble those that shaped mammalian sex chromosomes, illustrating convergent evolution in sex-determining structures in the animal and fungal kingdoms.
A comparative genomic analysis of the sex determining region in fungi reveals a remarkable similarity between its evolution and the events which shaped mammalian sex chromosomes
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Introduction
Elucidating mechanisms by which sex chromosomes evolved from autosomes has been accelerated by the revolution in genomic sciences. In humans, the male-specific approximately 50–60 Mb Y chromosome evolved via chromosomal rearrangement, gene conversion, duplication, and degeneration over approximately 300 million years to give rise to four distinct evolutionary temporal groupings, or strata (Lahn and Page 1999; Skaletsky et al. 2003). In contrast, fungi have much less extensive sexually dimorphic chromosomal regions; in the budding yeast Saccharomyces cerevisiae, the a and α mating types are established by the mating type (MAT) locus, which spans only 642 bp or 747 bp, respectively, and encodes only one or two cell type factors (Figure 1) (Herskowitz 1989). Recent studies of the evolution of ascomycete MAT loci have shown that, despite significant changes in both content and structure, small size has remained a common feature (Tsong et al. 2003; Butler et al. 2004). In contrast to mammals and other obligate diploid organisms, fungi are viable as both haploids (the equivalent of gametes in other systems) and diploids. This has influenced the evolution of genes in the sex-determining regions in the two systems, as it enables those in obligate diploids to degenerate to nonfunctional alleles in one of two sex-determining chromosomes.
Figure 1 Fungal MAT Locus Paradigms
Interaction of mating partners during the fungal sexual cycle is directed by bipolar or tetrapolar mating systems. The budding yeast S. cerevisiae is an ascomycete with bipolar mating (graphic at upper left). The maize pathogen U. maydis is a tetrapolar basidiomycete with multiple mating types conferred by two mating type loci (graphic at upper right). One (a) is biallelic and encodes pheromones and pheromone receptors, while the second (b) is multiallelic and encodes homeodomain transcription factors. In contrast, the human pathogen C. neoformans (lower graphic) is a basidiomycete with a bipolar system with only two mating types (a and α). The C. neoformans MAT locus encodes homeodomain transcription factors, pheromones and pheromone receptors, other elements of the pheromone activated MAPK cascade, and many genes whose role in mating, if any, is at present unknown.
Unlike the exclusively bipolar mating in ascomycetes, basidiomycete fungi usually have more complex tetrapolar mating, in which two unlinked genomic regions establish cell identity, and both must differ for sexual reproduction (Kahmann et al. 1995; Kronstad and Staben 1997; Casselton and Olesnicky 1998). One locus encodes pheromones and pheromone receptors, while the second encodes homeodomain transcription factors (Figure 1). However, like S. cerevisiae, some haploid basidiomycete species, such as the human fungal pathogen Cryptococcus
neoformans, exhibit bipolar mating, in which a single locus establishes mating type. Unlike S. cerevisiae and Schizosaccharomyc pombe, which are homothallic fungi that switch mating type via recombination between silent and active MAT cassettes, Cryptococcus is a heterothallic fungus that has never been observed to switch mating type and lacks any silent MAT cassettes. In contrast to the more restricted ascomycete MAT loci, the C. neoformans MAT locus is unusually large (spanning over 100 kb) and contains more than 20 genes (Figure 1) (Lengeler et al. 2002). Previous studies of this region of the genome have shown the MAT region to be recombinationally suppressed, with meiotic segregants from a cross each receiving a single intact, nonrecombined locus of either the MATa or MATα type (Hull and Heitman 2002; Lengeler et al. 2002). The MAT locus orchestrates sexual development involving cell fusion, formation of dikaryotic hyphae, and subsequent nuclear fusion, meiosis, and sporulation to produce the suspected infectious particles. The α allele of the C. neoformans MAT locus has been linked to environmental prevalence, virulence, differentiation capacity, and unusual fecundity in a recent outbreak (Kwon-Chung et al. 1992; Wickes et al. 1996; Fraser et al. 2003).
C. neoformans exists as two subspecies—Cn. var. grubii and Cn. var. neoformans—that diverged approximately 20 million years ago (mya), and the MAT locus has been characterized in both (Xu et al. 2000; Lengeler et al. 2002). The C. neoformans MAT locus encodes the pheromones, pheromone receptors, and homeodomain factors that are usually distributed between the two tetrapolar loci in this phylum, as well as additional pheromone response pathway elements and proteins from many other functional categories (Lengeler et al. 2002), including essential genes (Figure 2). As in the multicellular eukaryotes, this sex-determining structure is large; the impending completion of the Cn. var. neoformans genome reveals that the MAT locus occupies 6% of a 1.8-Mb chromosome in a genome of approximately 20 Mb. Analogous to the human Y chromosome, the sex-determining genes are scattered among others seemingly unrelated to sex. Here we show, on the basis of comparative genomic analysis using new sequences isolated from a primary pathogenic sibling species Cryptococcus
gattii, that sequential evolutionary events that fashioned this large, highly unusual region of the Cryptococcus genome can be reconstructed. The Cryptococcus MAT locus therefore provides insights into how complex, dimorphic sex-determining regions evolved from simpler loci containing only one or two genes.
Figure 2 Genes of the MAT Locus
Comparison of a and α alleles of the MAT genes in the three Cryptococcus lineages based on percent nucleotide identity between the coding sequences of the a and α alleles within that species. Ks values were calculated from comparison of the a and α alleles in each species. Genes with unusual Ks values are shown in red, and genes from regions flanking MAT are shown in grey. Phylogenetic trees are based on maximum likelihood analysis (scale bar = 0.05 substitutions per site), and are labeled with the phylogenetic class they represent. Further details are presented in Figures S1 and S2. *The Average K
s for CAP1 was calculated after excluding the indicated unusual values.
Results
The closest known relative to C. neoformans is the sibling species C. gattii, a primary human pathogen which diverged approximately 40 mya (Xu et al. 2000, Kwon-Chung et al. 2002). C. gattii therefore provides a unique vantage point from which to analyze MAT evolution, via comparative genomics, from a species cluster of human fungal pathogens.
The a and α alleles of the MAT locus were cloned and sequenced from two representative C. gattii strains (AY10429, AY10430). Figure 3 shows the structures of the six known MAT alleles. Four features are prominent. First, the mating type-specific sequences span more than 100 kb in all six alleles. Second, the MAT-specific sequences are separated from the genome by sharply demarcated borders; the flanking regions share over 99% nucleotide sequence identity and syntenic gene order, whereas the sequences within MAT are divergent (Figures 4 and S1). Third, comparison of the MAT alleles reveals few genes unique to one mating type (encoding factors Sxi1α and Sxi2a, the only MAT homeodomain proteins) with the locus composed almost entirely of divergent alleles of a common gene set. Fourth, the MAT gene cohort has been dramatically rearranged during evolution (Figure 4). Whole-genome analysis of Cn. var. neoformans at Stanford and The Institute for Genomic Research (TIGR), Cn. var. grubii at Duke and the Broad Institute, and C. gattii at the University of British Columbia and the Broad Institute, reveal that these rearrangements at MAT are highly atypical compared to the non-MAT regions of the genome in all three species (B. J. Loftus, unpublished data; J. W. Kronstad, personal communication). In addition to the original set of shared genes (Lengeler et al. 2002), comparative analysis employing the new C. gattii sequences revealed that an additional five novel genes are present in all six characterized alleles, including one predicted noncoding gene with no apparent open reading frame. All five were confirmed by RT-PCR analysis to be expressed (unpublished data). In total, genic sequences comprise approximately 50% of MAT (see Figure 3).
Figure 3 The Structure of MAT Is Highly Rearranged, with Divergent Gene Alleles Embedded in Syntenic Genomic Regions
The nonrecombining α (blue) and a (yellow) MAT alleles from the divergent but related species are depicted, spanning more than 100–130 kb and including 10 kb of common flank regions on the left and right demarcated by sharp borders with MAT. The original locations of ancient tetrapolar loci proposed to have given rise to MAT are shown in red (ancestral homeodomain locus) and green (ancestral pheromone/receptor locus), with the most ancient genes (encoding homeodomain transcription factors, pheromones and pheromone receptors) bulleted. Genes that show mating type-specific phylogeny are shown in black, and genes with species-specific phylogeny are white. Synteny between the genes with species-specific phylogeny is indicated with grey boxes. Pseudogenes are labeled in blue, and grey bars represent repeated elements in Cn. var. neoformans. Red arrows represent pheromone amplicons.
Figure 4 MAT Is Highly Rearranged between Species and Mating Types
The genomic region spanning the nonrecombining α (blue) and a (yellow) MAT alleles from the divergent but related species is depicted, with pink and green colored bars representing regions of synteny, and black lines the relative positions of genes whose position is not conserved. Black arrows depict mating type-specific genes. White arrows represent genes with a species-specific phylogeny. Red arrows represent pheromone amplicons.
Our model for the evolution of this unusual structure is that two unlinked sex-determining regions of the genome expanded by acquiring genes of related function, and these two novel gene clusters were then captured into a common genomic region by a chromosomal translocation, entrapping still further genes. This resulted in a tripolar transitional intermediate mating system that collapsed via gene conversion to result in the contiguous, linked MAT alleles and a bipolar system. MAT was then subject to inversions that suppressed recombination, punctuated by ongoing rounds of inter- and intra-allelic gene conversion. Below, we summarize the analyses that support this model.
Both maximum likelihood and parsimony analyses reveal that MAT is constructed from genes with different phylogenetic histories; comparing the topologies of the phylograms for each protein-coding gene showed four major classes (Figures 5A, S2, and S3). The first class, which we call “ancient,” contains genes in which the alleles share a low level of nucleotide identity and cluster into distinct a and α clades. This pattern represents the most ancient genes contained within this recombinationally suppressed region of the genome, and includes those encoding pheromones, pheromone receptors, and elements of the pheromone-sensing mitogen-activated protein kinase (MAPK) pathway. The second and third classes (which we term “intermediate I” and “intermediate II,” respectively) represent intermediate genes that have progressively higher nucleotide identity between the a and α alleles, and less discrete, although still MAT-specific, phylogenetic patterns (Figures 2 and 5A). This pattern reflects genes that have been contained within this recombinationally suppressed region for shorter periods of time. Finally, the last group (“recent”) comprises the five most recently acquired genes that exhibit a species-specific, but not MAT-specific, phylogenetic pattern, similar to genes outside MAT (see Figure 2). These distinct patterns mirror the relative length of time each gene has spent within the largely nonrecombining MAT locus (ranging from ancient to recent acquisitions), and provide insight into how this large genomic structure was fashioned. Stated differently, the divergence times for the ancient and intermediate classes in C. gattii and C. neoformans are equivalent (represented by STE20,
ZNF1, and SPO14 in Figure 5), as these genes entered MAT prior to speciation, while the recent class (represented by RPO41) began diverging after this time. The tree topologies of STE20, ZNF1, SPO14, and RPO41 were shown to be statistically different (p < 0.0001) by the Shimodaira-Hasegawa test (Shimodaira and Hasegawa 1999).
Figure 5 MAT Genes Have Different Phylogenetic Histories
(A) The genes of the MAT locus can be separated into four distinct groupings based on phylogenetic class, synonymous substitution rate, and nucleotide identity. The C. gattii alleles in each phylogram are encircled in red.
(B) The LPD1 gene defines an ancient border of MAT. The 5′ end of the coding region is species-specific, while the 3′ region is mating type-specific. The ancient homeodomain locus is shown in red, and the ancient pheromone/pheromone receptor locus in green. Maximum likelihood trees are shown. Scale bar represents 0.05 substitutions per site. Further details are provided in Figures S2 and S3.
All six MAT alleles contain the same set of five genes that exhibit the unusual species-specific phylogenetic pattern, suggesting that each allele has recruited the same cohort of genes by a common mechanism. One hypothesis we initially considered to explain acquisition of a common gene set is that the locus expanded by recruiting flanking genes. The IKS1 gene is an integral component of the MAT locus in two lineages (Cn. var. grubii and C.
gattii), but is a flanking gene in the third (Cn. var. neoformans), providing a unique opportunity to test this model (see Figure 4). Phylogenetic analysis reveals that the IKS1 gene tree resembles phylograms for an ancient MAT-specific gene, with the exception of the Cn. var. neoformans lineage, in which gene conversion has fixed the α allele in both mating types with concomitant loss of the ancestral a-specific allele (Figures S2 and S3). The ETF1, BSP3, and NCM1 genes share a similar evolutionary history. These genes are therefore not recently acquired, and instead provide examples of gene eviction from MAT by interallelic recombination to result in a reduction in MAT size and complexity in the Cn. var. neoformans lineage.
An alternative, more parsimonious model to explain why an identical set of five species-specific genes is present in all six MAT alleles is that these genes were acquired once in the progenitor MAT locus. However, their high level of synteny suggests recent acquisition; the GEF1, CID1, and LPD1 genes are clustered in five of the six MAT alleles, the BSP2 and RPO41 genes are adjacent in all six, and synteny of the entire five-gene cluster has been maintained in three alleles (see Figure 3). Our model is that the two ancestral unlinked sex-determining regions were juxtaposed by a chromosomal translocation, entrapping this set of recently acquired common genes which were then subjected to more recent gene conversion.
An indication of the relative time over which alleles have resided within MAT and diverged can be inferred from the rate of synonymous mutations (Ks), which accumulate over time and are nearly neutral with respect to selection in organisms such as Cryptococcus, in which strong codon bias is absent (Li 1993; Lahn and Page 1999; Nakamura et al. 2000). If we compare the alleles of genes outside MAT from a and α strains of the same species, their Ks values are close to zero, reflecting freely recombining regions of the genome (see Figure 2). By contrast, genes embedded in MAT are largely nonrecombining, and therefore have Ks values based on their divergence since the time of acquisition to the locus. The higher the Ks value, the longer the genes have been diverging due to entrapment in MAT. This analysis reveals four major classes of genes, which correspond to the ancient (Ks > 2.00), intermediate class I (2.00 > Ks > 0.70), intermediate class II (0.60 > Ks > 0.35), and most recently acquired genes (Ks < 0.17) (see Figure 2). These gene classes are analogous to those shown by phylogenetic analysis. The MAT locus genes therefore partition into four primary groupings based on phylogeny, nucleotide identity, and synonymous substitution rates.
In the human Y chromosome, analysis of substitution rates reveals four temporal clusters, or strata, representing the sequential acquisition of genes to the male-specific region (Lahn and Page 1999). The genes in the Cryptococcus MAT locus are no longer stratified by age, because the members of the four identified classes have been heavily shuffled during speciation and mating type divergence, and this rearrangement appears ongoing. However, an analysis of relative gene locations in MAT reveals two alleles in which higher levels of synteny are evident: Cn. var. grubii MATα and C. gattii MATa (see Figure 4). The Ks values of genes distributed in these alleles were analyzed to reconstruct the genomic architecture of the common ancestral structure. In both cases, genes from each Ks-defined class can be clustered via a single inversion, creating identical groupings in both the a and α alleles—and we hypothesize that this represents the genomic architecture of an ancestral MAT locus (Figure 6). Assuming the components of the ancient tetrapolar system are represented by the pheromone and pheromone receptor genes in one group and homeodomain-encoding genes in the other, the ancient pheromone/pheromone receptor cluster is further divisible into two groups based on Ks values (Figures 2 and 6). This implies two major expansion events of this component. First, an ancient recruitment of genes including pheromone-sensing cascade components previously implicated in fertility (Ks > 2.00), followed by more recent acquisition of the intermediate class I genes, whose role in mating has not yet been studied (2.00 > Ks > 0.70). In constrast, the intermediate class II genes (0.60 > Ks > 0.35) were recruited by expansion of the ancient tetrapolar homeodomain-containing locus. In this model, the expanding ancestral loci are separated by the five genes that exhibit species-specific phylogenies (Figure 6).
Figure 6 Reconstructing the Ancient MAT Alleles by Inversion-Mediated Rearrangement
Plotting the synonymous mutation rate (Ks) of each protein coding gene in MAT reveals that the different classes of genes in the two least rearranged loci (Cn. var. grubii MATα and C. gattii MATa; see Figure 4) can be clustered by a single inversion. This may represent an ancient linked tetrapolar system—one cluster contains the pheromone and pheromone receptor genes (green bars), and the other a homeodomain-encoding gene (red bar). Transposon remnants are present at the extrapolated inversion breakpoint regions in Cn. var. grubii, as indicated (Tn). Ks cannot be calculated between the SXI1α and SXI2a genes, because these are unrelated and not alleles, in contrast to other genes in the locus.
We hypothesize that the species-specific genes were then incorporated into the MAT locus by inversions that fused and rearranged the ancient tetrapolar structures. This model is supported by two of the species-specific genes, LPD1 and RPO41, which exhibit an unusual hybrid phylogeny; although the majority of their coding region exhibits a species-specific phylogeny, the 3′ regions are mating type-specific. In two of the three lineages (Cn. var. grubii and Cn. var. neoformans) the LPD1 gene exhibits this hybrid phylogeny, but in C. gattii the 5′ region in both mating types resembles the α alleles, and a Ks value of zero for this region is indicative of recent gene conversion (Figures 2, 5B, S2, and S3). Analogous to the Amelogenin locus of primates, which spans an ancient pseudoautosomal boundary (Iwase et al. 2003), this phylogeny suggests that the five entrapped genes were integrated into MAT while the boundary genes were in a state of transition from species to mating type-specificity. Furthermore, it supports models in which these genes maintain species-specific phylogeny via gene conversion.
Rearrangement of MAT may be driven by recombination between transposable elements. In the Cn. var. grubii α allele, the breakpoints of the postulated inversion lie in intergenic regions that contain remnants of the Tcn760 mariner-type transposon (Figure 6) (Lengeler et al. 2002). The Cn. var. neoformans MAT locus is rich in transposable elements and remnants (17.3% α and 13.2% a; see Figure 1), a feature shared with the sex chromosomes of humans and mice (Waterston et al. 2002). Comparison with the completed Cn. var.neoformans genome sequence revealed that this represents a greater than 5-fold enrichment relative to the rest of the genome, when the transposon-rich presumptive centromeric regions are excluded. In S. cerevisiae, transposons and their remnants may be principal sites at which chromosomes rearrange in response to growth selection (Dunham et al. 2002), and transposons have been implicated as drivers of genome evolution in a number of eukaryotes, including humans (Kazazian 2004). Similarly, repeated elements may have driven stochastic MAT rearrangements to produce its current structure and efface the vestiges of the ancestral evolutionary strata, as successive random small inversions appear to be a major evolutionary mechanism shaping the locus. Although inversions have previously been implicated in transposing gene order during S. cerevisiae evolution (Seoighe et al. 2000), they have occurred at an unprecedented level in MAT while sparing adjacent regions (see Figure 4).
In the human Y chromosome, gene decay has been competing with ongoing gene acquisition and conservation (Skaletsky et al. 2003). In Cryptococcus, which is viable as either a haploid or diploid, suppression of meiotic recombination in MAT has not led to loss of any genes, with two minor exceptions. First, unique 5′ truncated pseudogenes exist in three of the alleles (ΨNCP1 in Cn. var. neoformans MATa, ΨNAD4 in Cn. var. grubii MATα, and ΨVPS26 in C.
gattii MATα). Second, the number of pheromone genes varies between alleles. The a alleles contain three unlinked 130-bp MFa pheromone genes embedded in 900–5,000 bp amplicons identical within an allele but not between species. In contrast, the alleles contain three or four MFα pheromone genes embedded in approximately 500-bp conserved repeats, usually flanking the syntenic PRT1/ZNF1 gene pair in inverted orientation. These repeats are likely maintained by intra-allelic gene conversion, ensuring maintenance of these important fertility genes in the absence of meiotic recombination. This is a striking difference to the MAT locus of S. cerevisiae, where gene conversion plays the unrelated and very distinct role of driving mating type switching (Strathern and Herskowitz 1979; Wu and Haber 1996; Haber et al. 2004). In the C. gattii MATa allele one MFa gene repeat has expanded into the adjacent IKS1 gene, duplicating the IKS1 3′ region in a second amplicon. In Cn. var. neoformans, gene conversion has duplicated a retrotransposon fragment adjacent to MFα1 into the MFα2 repeat, while the fourth pheromone gene has been replaced by a retroelement, representing the only clear example of gene loss within MAT. We note that our gene disruption studies reveal that the MAT locus contains five essential genes (see Figure 2), and their presence likely constrains MAT to only those rearrangements that ensure their retention.
Discussion
Our studies reveal that the MAT locus of C. neoformans is strikingly divergent from that of the model budding yeast S. cerevisiae, other ascomyctes, and even related basidiomycetes. Whereas the budding yeast MAT locus is quite small, and encodes only one or two key cell fate determinants that are sequence unrelated, the C. neoformans MAT locus spans over 100 kb, contains more than 20 genes, and with the exception of the SXI1α and SXI2a genes, is otherwise composed of divergent alleles of a common gene set.
The similarity between the MAT locus of S. cerevisiae and that of C. neoformans is restricted to the presence of related homeodomain proteins, suggesting that these may represent the most ancient components of the ancestral MAT locus that was shared between the ascomycete and basidiomycete lineages. One of several unusual features that the C. neoformans MAT locus does not share with the more restricted S. cerevisiae counterpart is the presence of several predicted essential genes, which we have confirmed by gene disruption studies (see Figure 2). Given the evidence for rampant inversions and translocations in MAT, the presence of these essential genes, which are spaced throughout the locus, may have served as an evolutionary brake to ensure that large regions of the MAT locus were not lost in haploid recombinants produced by the sexual cycle. We note that essential genes are represented both in the expanded pheromone signaling and homeodomain clusters of the ancestral tetrapolar mating system and within the set of five newly acquired, entrapped genes. The presence of these essential genes embedded within each component of the MAT locus may have thereby contributed to the expansion of the locus from the much smaller MAT loci common in ascomycetes and other basidiomycetes. Another marked distinction is that C. neoformans is a heterothallic yeast that has never been observed to undergo mating type switching, whereas S. cerevisiae is a homothallic yeast in which Ho endonuclease-mediated cleavage effects mating type switching by promoting recombination between the active and silent MAT cassettes. We find no evidence for silent mating type cassettes in C. neoformans, consistent with its classification as a heterothallic fungus. Furthermore, recent studies (Butler et al. 2004) have revealed the acquisition of silent mating type cassettes and both Ho-independent and Ho-dependent switching is restricted to ascomyete fungal lineages related to S. cerevisiae and Sc. pombe.
We propose a model of MAT evolution that addresses the four distinct evolutionary classes in which the genesis of a bipolar system occurred in the progenitor of the three Cryptococcus lineages described here (Figure 7). Our evidence suggests that the ancient tetrapolar loci expanded to incorporate additional genes; this process began with acquisition of components of the pheromone signaling MAPK cascade (STE20, STE11, andSTE12) into the ancestral pheromone/pheromone receptor locus (ancient class). This was followed by a second round of acquisition of genes with an unknown role in mating (intermediate class I). Next, the ancestral homeodomain locus acquired genes that we hypothesize function in the dikaryon or meiosis (SPO14, RUM1; intermediate class II) based on their known roles in S. cerevisiae and Ustilago maydis (Honigberg et al. 1992; Quadbeck-Seeger et al. 2000).
Figure 7 A Model for the Evolution of MAT
Our evidence indicates that the ancient loci of a canonical tetrapolar system expanded to incorporate additional genes, beginning with two rounds of expansion of the pheromone/receptor locus: first to acquire genes including components of the pheromone-signaling MAPK cascade (ancient), and second to acquire genes whose role in mating is unknown (intermediate I). Next, the ancestral homeodomain locus acquired genes hypothesized to function in the dikaryon or meiosis (intermediate II). The tetrapolar loci in one mating type fused by chromosomal translocation, entrapping the most recently acquired species-specific gene set (recent) and creating a tripolar intermediate. A second locus fusion event then occurred, to link the two regions from the opposite mating type and create the bipolar ancestors of MAT. Subsequent inversion-mediated rearrangements have erased the discrete evolutionary strata.
Subsequent chromosomal translocation fused the pheromone signaling and homeodomain cluster in one mating type, entrapping the set of most recently acquired species-specific genes (recent class). This created an intermediate tripolar mating stage in which one mating type had one large contiguous MAT locus and the partner retained the ancestral gene clusters formed from the unlinked tetrapolar loci. During this transitional tripolar intermediate stage, only half of the meiotic progeny would be fertile. The incipient alleles of the opposite mating type were then either gene converted onto the newly-formed bipolar chromosome or, more likely, were linked via dual recombination events (Figure 7), collapsing the tetrapolar system to a bipolar one via a transitional tripolar intermediate. Evolutionary pressure for this event enabling production of a higher proportion of fertile progeny would then have swept the population, fixing the linked bipolar α and a alleles and leading to extinction of the tetrapolar system. More recent inversions facilitated by repetitive sequence elements then produced more homogeneous bipolar structures resulting in suppression of recombination between MAT alleles. The high degree of identity shared by α and a alleles of the recent gene class suggests that the evolution of MAT is still occasionally punctuated by both inter- and intra-allelic recombination. Our model involving collapse of a tetrapolar system to a bipolar one is supported by studies of the basidiomycete U. hordei; this organism is closely related to the tetrapolar fungus U. maydis, but its tetrapolar loci have been linked and recombination suppressed between the two, resulting in the formation of a bipolar mating type system (Lee et al. 1999).
Thus, we hypothesize that a chromosomal translocation juxtaposed the two previously unlinked sex-determining regions and produced a bipolar mating type system in which the two distinct regions are linked by a common block of sequence information. Subsequently, inversions occurred that obscured these boundaries and, as a consequence, suppressed recombination between the two regions. What was the evolutionary pressure to suppress recombination between the two linked loci, leading to their rearrangement in not only one but all three lineages? If the ancestral tetrapolar system was not multiallelic, and the four mating types in the population were in equal proportions, then any given individual in the population could mate with only 25% of the other members. However, in a population in which the two loci are linked, any individual can mate with 50% of the other population members. A recombination event in the common spacer region would result in two new mating types in which SXI1α is now linked to the STE3a pheromone receptor gene, and in which SXI2a is linked to the STE3α pheromone receptor gene. These recombinants could mate with each other, but would not complete the sexual cycle with the original MATa and MATα members of the population, because while cell-cell fusion would occur, two copies of only one of the homeodomain-encoding genes would be present, and both are required for completion of the sexual cycle (C. M. Hull, M. J. Boily, and JH, unpublished data). Thus, whereas the parental strains could mate with 50% of the population, the recombinants could not and would have a disadvantage. This selective pressure for fecundity could have driven the inversions that we hypothesize occurred to prevent recombination between the two linked sex-determining regions.
While we can reconstruct a likely model for the evolutionary events that drove the formation of the large MAT locus of Cryptococcus, the events that led to the initial formation of the homeodomain- and pheromone/pheromone receptor-based gene clusters involved in sexual processes are less clear. How can recombinationally suppressed sites such as the original tetrapolar loci initiate expansion to create a larger nonhomologous region? One likely hypothesis involves the presence of the large number of transposable elements in the genome. It has been suggested that the spread of a mobile element through a population can be facilitated by increasing the probability of sex in its host (Hickey 1982). Therefore, a transposon insertion adjacent to either the locus that controls initiation of the sexual stage (the pheromone/pheromone receptor locus) or its completion (the homeodomain locus), and that leads to a significant sexual advantage, would be subject to positive selection. These events, as linked to specific alleles of each locus, could therefore be expected to increase the size of the nonhomologous region. Furthermore, local transposition events have been shown to cause small rearrangements, including inversions, which would further expand the clusters (Daboussi 1997). These transposons may then have contributed to the original expansion of the locus, and would provide additional support for the proposed role of mobile elements in the evolution of sex (Hickey 1982).
Another striking parallel with the human Y chromosome is the coherence of genes with common functions. Eight of the ten most ancient genes encoded by or recruited to the fungal ancestral pheromone/pheromone receptor locus mediate pheromone production and sensing (see Figure 2), and the remaining two (IKS1, MYO2) may play related roles. Finally, Y-specific genes are maintained by intrachromosomal recombination and repair of genes embedded within palindromes in an inverted orientation (Rozen et al. 2003). This mirrors the Cryptococcus pheromone genes in a striking example of convergence to a common genomic configuration that ensures that genes required for fertility are preserved by intrachromosomal recombination in the absence of homologs on the opposite sex chromosome. While gene conversion has played an essential role in maintaining the multiple pheromone genes, it has also decorated the locus with multiple other examples that would provide no apparent fertility advantage.
An interesting feature in the evolutionary history of many genes encoded by sex chromosomes in mammals is the degeneration and loss of one functional copy, as has occurred dramatically on the mammalian X and Y chromosomes. This is in stark contrast with the fungal MAT locus, in which functional copies of each allele have been retained. The basis for this difference is that mammals are obligate diploids, and the haploid form occurs only as the gamete stage (sperm and egg) during the mammalian life cycle. By contrast, in fungi such as C. neoformans, the organism is viable as both a haploid and a diploid, and thus gene degeneration or loss of essential genes cannot occur in either sex-determining region, since the organism most commonly occurs as haploid cells in the environment. By comparing the gene composition of the a and α alleles of the Cryptococcus MAT locus, we see that each essential gene has been retained in both alleles, and that with the exception of the MAT-unique SXI1α and SXI2a genes, each other nonessential gene has also been maintained as a pair of alleles diverged to different degrees based on their date of acquisition to the locus and evolutionary constraints on sequence divergence. The nonessential genes are presumably maintained in a functional form because they serve a role in mating, and their loss would lead to sterile isolates that would be lost from the population, or they function in other roles that provide a survival benefit to the organism.
Another difference between the MAT locus alleles and the mammalian sex chromosomes is the size disparity between the X and Y chromosomes compared to a similar size in Cryptococcus for both the a and α MAT alleles, and therefore for their host chromosome. The MAT locus occupies 6% of the 1.8 MB chromosome on which it resides, and thus has not expanded to occupy nearly the entire chromosome, in contrast to the mammalian sex chromosomes. A more similar analogy to the fungal MAT locus is the sex chromosomes of the plant papaya (Carica papaya), in which the sex-determining region occupies only about 10% of the 41-MB primitive Y chromosome (Liu et al. 2004). In that example, the chromosomes are defined as sex chromosomes, and yet the sex-determining region has not yet expanded to capture the entire host chromosome on which it resides. Thus, there are two issues at play in determining the size of sex-determining regions: expansion, and gene degeneration and loss. Comparison of these divergent sex-determining systems in fungi, plants, and animals reveals both shared principles and unique features as these organisms have specialized to their particular environmental niche and survival strategy.
In summary, the Cryptococcus MAT locus resembles the structures hypothesized for the ancient human Y chromosome, in which recombination suppression was limited to a small portion of the chromosome around SRY (Skaletsky et al. 2003). Furthermore, this type of structure has been identified in the plant kingdom in studies that defined the sex chromosomes of the papaya (Liu et al. 2004). These parallels reveal that similar mechanisms drive the evolution of sex-determining regions in all three eukaryotic kingdoms, and establish Cryptococcus as a paradigm to elucidate molecular principles governing cell identity and sex chromosome dynamics.
Materials and Methods
Strains and media
The strains used for construction of bacterial artificial chromosome (BAC) libraries and analysis of the mating type alleles were C. gattii serotype B isolates WM276 (α) and E566 (a) from the Australian environment. E. coli DH5α was used as the library host strain. LB or FB media supplemented with the appropriate antibiotic was used for E. coli culture (Sambrook et al. 1989).
BAC and sequencing libraries
Large-insert libraries (insert sizes 100–120 kb) for the candidate C. gattii serotype B strains WM276 and E566 were constructed in pBACwich (Choi et al. 2000), a derivative of pBeloBAC11 (Cai et al. 1995), using HindIII partially digested genomic DNA (Lengeler et al. 2002). Clones from the BAC library were arrayed on nylon membranes (Sambrook et al. 1989) and hybridized to mating type-specific gene probes from Cn. var. neoformans to identify overlapping sequences that span the MAT locus (Lengeler et al. 2002). BAC DNA from two clones for each isolate (3K12 and 3O16 for WM276, and 3E18 and 1C03 for E566) was prepared using the NucleoBond BAC Maxi Kit (Clontech, Palo Alto, California, United States), and random insert libraries were constructed for each using randomly sheared 1.5- to 3-kb DNA fragments (GeneMachine HydroShear; Genomic Solutions, Ann Arbor, Michigan, United States) (Oefner et al. 1996) that were cloned into pUC18 (Yanisch-Perron et al. 1985). 1,100 clones were picked for each BAC random insert sequencing library.
Sequencing and assembly
Sequencing reactions were performed with an MJ Research (Reno, Nevada, United States) thermal cycler using standard BigDye chemistry (Applied Biosystems, Foster City, California, United States) and analyzed on an Applied Biosystems PE3700 96-capillary sequencer. Sequence reads were assembled using the PHRED/PHRAP/CONSED package (Ewing and Green 1998; Gordon et al. 1998). Additional analysis of the data was performed using BLAST (Altschul et al. 1990) and the GCG software suite (Wisconsin Package; Genetics Computer Group [GCG], Madison, Wisconsin, United States). Based on the initial assembly of the end sequences, oligonucleotides were selected to close gaps in the sequence coverage by primer walking.
MAT locus annotation
Genes were annotated in the C. gattii sequences based on homology to the existing annotation in C. neoformans; in some cases, this led to revision of the C. neoformans annotation. Additional genes that did not have previously defined homologs (BSP1, BSP2, BSP3, GEF1, and NCM1) were found by comparing the six available MAT allele sequences and by identifying large regions of identity that were unassigned to genes. Based on ClustalW v1.4 (Thompson et al. 1994) alignment and identification of intron consensus sequences, primers were designed and RT-PCR employed to characterize gene structures using total RNA and the Ready-To-Go RT-PCR Bead system (Amersham Biosciences, Piscataway, New Jersey, United States). RT-PCR products were directly sequenced using primer walking. Repeated elements in the Cn. var. neoformans locus were defined using the BLASTn algorithm to search the JEC21 genome generated at TIGR (www.tigr.org, 10/21/03 release).
Phylogenetic analysis
Protein-coding DNA sequences in FASTA format were automatically aligned using ClustalW 1.81 (Thompson et al. 1994). Each gene alignment was imported as a Nexus file into MacClade 4.05 (Maddison and Maddison 1997) and manually edited according to the superimposed amino acid sequences. Aligned data sets ranged in length from 130 to 5,600 bp. In each alignment, the start and the stop codon were excluded from the phylogenetic analysis, as were regions that could not be unambiguously aligned. Exhaustive searches under maximum parsimony and maximum likelihood criterion were conducted using PAUP* 4.0b10 (Swofford 1999) on each single gene data set. Model parameter estimates for the maximum likelihood analysis were obtained from Modeltest 3.06 (Posada and Crandall 1998). Statistical support was calculated using 1,000 bootstrap replicates under maximum parsimony and maximum likelihood. Ks values for comparison of the a and α alleles of all MAT protein-coding genes in each lineage were calculated using DnaSP 3.51 (Rozas and Rozas 1999). The tree topologies of STE20, ZNF1, SPO14, and RPO41 were compared with each other as representatives of the different strata by applying the Shimodaira-Hasegawa test (Shimodaira and Hasegawa 1999). Comparisons within a group were performed between two genes of the same stratum.
Genome-wide analysis of transposon content
To determine the relative frequency of transposons in the MAT locus alleles of Cn. var. neoformans compared to the rest of the genome, the locations of 35 previously identified transposable elements (Lengeler et al. 2002; Goodwin and Poulter 2001; Goodwin et al. 2003) were mapped on the TIGR JEC21 genome assembly. The relative transposon contents of the MAT and non-MAT regions were calculated as a percentage of total sequence occupied by both complete and partial transposons, yielding a 5.17-fold enrichment of transposons in the MATα allele and a 5.30-fold enrichment in MATa. The locations of each element have been submitted to TIGR for inclusion in the imminent release of the Cn. var. neoformans serotype D genome paper. In this analysis the transposon-rich presumptive centromeric regions were excluded.
Supporting Information
Figure S1 The Boundaries of the MAT Locus Are Sharply Defined by Loss of Sequence Identity
The entire MATa and MATα alleles plus an additional 10 kb flanking sequence from each species were subjected to a pairwise comparison using a window size of 100% identity over 30 bp. Sequence identity is indicated by dots, which join to form diagonal lines in regions of high sequence identity. Diagonal lines seen in the upper left and lower right corners represent the flanking sequences (greater than 99% identity) and those in the central portion correspond to those genes hypothesised to have entered the MAT locus most recently. The additional region of identity present in Cn. var. neoformans due to the fixation of the α alleles of the NCM1, BSP3 and IKS1 genes in the MATa allele is circled in blue. Scale is given in kb.
(64 KB EPS).
Click here for additional data file.
Figure S2 Genes of the Cryptococcus MAT Locus Define Four Discrete Maximum Parsimony Phylogenetic Groupings
Phylograms were generated for each protein-coding gene in MAT under the maximum parsimony criterion in an exhaustive search. Numbers next to branches indicate statistical support as calculated in 1,000 bootstrap replicates. Shaded trees indicate genes with an unusual phylogenetic pattern, due to either gene conversion or a hybrid phylogeny pattern.
(218 KB EPS).
Click here for additional data file.
Figure S3 Genes of the Cryptococcus MAT Locus Define Four Discrete Maximum Likelihood Phylogenetic Groupings
The phylograms represent the single most likely trees for each protein-coding gene in the MAT locus. Trees were generated under the best-fitting evolutionary model in an exhaustive search. Numbers besides branches indicate statistical support as calculated in 1,000 maximum likelihood bootstrap replicates. Shaded trees indicate genes with an unusual phylogenetic pattern, due to either gene conversion or a hybrid phylogeny pattern.
(225 KB EPS).
Click here for additional data file.
Accession Numbers
GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers for the MAT locus alleles discussed in this paper are WM276 (AY10430) and E566 (AY10429). GenBank accession numbers for other genes discussed in this paper are Cn. var. grubii MATa (AF542528), Cn. var. grubii MATα (AF542529), Cn. var. neoformans MATa (AF542530), and Cn. var. neoformans MATα (AF542531).
We thank Robin Wharton, Danny Lew, Blanche Capel, Tim James, and Hunt Willard for critical reading of the manuscript. This work was supported by National Institutes of Allergy and Infectious Diseases/National Institutes of Health (NIAID/NIH) grant AI50113 and the Howard Hughes Medical Institute. We acknowledge the C. neoformans Genome Project, Stanford Genome Technology Center, funded by the NIAID/NIH under cooperative agreement AI47087, and The Institute for Genomic Research, funded by the NIAID/NIH under cooperative agreement U01 AI48594 for genome information prior to publication.
Conflicts of interest. The authors have declared that no conflicts of interest exist.
Author contributions. JAF and JH conceived and designed the experiments. JAF, RLS, AA, and KL performed the experiments. JAF, SD, FSD, and JH analyzed the data. JAF and FSD contributed reagents/materials/analysis tools. JAF and JH wrote the paper.
Academic Editor: David Page, Massachusetts Institute of Technology
¤Current address: Institut für Mikrobiologie, Heinrich-Heine-Universität, Düsseldorf, Germany
Citation: Fraser JA, Diezmann S, Subaran RL, Allen A, Lengeler KB, et al. (2004) Convergent evolution of chromosomal sex-determining regions in the animal and fungal kingdoms. PLoS Biol 2(12): e384.
Abbreviations
BACbacterial artifical chromosome
MAPKmitogen-activated protein kinase
MATmating type
myamillion years ago
TIGRThe Institute for Genomic Research
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| 15538538 | PMC526376 | CC BY | 2021-01-05 08:28:08 | no | PLoS Biol. 2004 Dec 9; 2(12):e384 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020384 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020435SynopsisEvolutionGenetics/Genomics/Gene TherapyYeast and FungiFungus Holds Clues to the Evolution of Sex Chromosomes Synopsis12 2004 9 11 2004 9 11 2004 2 12 e435Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Convergent Evolution of Chromosomal Sex-Determining Regions in the Animal and Fungal Kingdoms
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It's a basic biological principle that living things share certain fundamental traits. That's why understanding the mechanisms of cell division in single-celled yeast, say, can offer insight into cell division in humans. Now Joseph Heitman and colleagues report that the evolutionary events that spawned sex chromosomes in yeast resemble those that shaped sex chromosomes in animals.
Strictly speaking, yeast—the common name for single-celled fungi—don't have sex chromosomes; they have sex-determining regions within chromosomes, called mating type, or MAT, loci. In a comparative genomic analysis of the MAT locus in three species of the human pathogenic fungus Cryptococcus, Heitman and colleagues found that this fungal sex-determining region arose via a series of discrete events that echo those that shaped mammalian sex chromosomes.
A primary benefit of sexual reproduction is the genetic diversity gained from reshuffling genetic material during meiosis, which creates gametes. Yeast sex, such as it is, accomplishes the same thing. Of course, sexual identity for a fungus does not take the form of sperm or egg but of mating type a, for example, and mating type alpha. Still, yeast manage a measure of complexity and considerable elegance in the systems they deploy to sexually reproduce.
In ascomycetes, like baker's yeast, the MAT locus is small and includes just a few genes. The genes that determine a cell's a or alpha mating status are alleles (variants) of a single MAT locus. Cells with the MATa allele are mating type a, while cells with the MATalpha allele exhibit mating type alpha. A cell can switch its mating type when genetic exchange, or recombination, between two mating loci occurs.
Human pathogenic fungus Cryptococcus
In basidiomycetes, like the corn smut Ustilago maydis—a maize pathogen that some consider a culinary delicacy—mating is more complex, and sexual identity is determined by two unlinked genomic regions with distinct classes of genes. Cells must be of different mating types at both loci to allow sexual reproduction. To their surprise, Heitman and colleagues discovered that the mating locus of Cryptococcus neoformans—a basidiomycete fungus that infects humans and is associated with transplant recipients, patients with AIDS, and other immune-compromised patients—exhibits several unique features, common to neither ascomycetes or their basidiomycete relatives.
Unlike most basidiomycetes, the C. neoformans locus occupies a single region and is unusually large, spanning more than 100 kilobases and containing over 20 genes, including those typically segregated in separate locations in other basidiomycetes. Like on the human Y chromosome, the sex-determining genes of C. neoformans are interspersed with non-sex-related genes. And unlike ascomycetes, which also have a single active MAT locus and two mating types, no mating type switching occurs as there are no silent mating type cassettes in the genome.
Heitman and colleagues sequenced the a and alpha alleles of C. neoformans' closest relative, C. gattii, and compared these variants to four already characterized variants derived from two C. neoformans subspecies. All six MAT alleles share characteristic features, including a fairly large size, a common gene set, and dramatic genomic migration during evolution (which is unusual compared to other genomic regions in the three strains). Each MAT allele has genes with different evolutionary histories, ranging from ancient to recent, that fall into distinct patterns based on shared nucleotide composition and mating type. The patterns correlate with how long the genes have occupied the MAT locus, suggesting how it evolved.
The authors hypothesize that this novel structure was formed by chromosomal rearrangements that linked two unrelated genomic regions into a single region. Recombination between these sex-determining regions was suppressed after other events blurred their boundaries. Specific genes in the once separated loci then attracted mobile elements in the genome to their sites, thus precipitating expansion of the locus. Because the Cryptococcus MAT locus resembles the evolution and structure proposed for the ancient Y chromosome, the authors argue that Cryptococcus can serve as a valuable model to study the molecular dynamics of sex chromosomes.
| 0 | PMC526377 | CC BY | 2021-01-05 08:28:08 | no | PLoS Biol. 2004 Dec 9; 2(12):e435 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020435 | oa_comm |
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BMC Dev BiolBMC Developmental Biology1471-213XBioMed Central London 1471-213X-4-141547390410.1186/1471-213X-4-14Research ArticleGenome-wide microarray analysis of TGFβ signaling in the Drosophila brain Yang Maocheng [email protected] Don [email protected] Yoko [email protected] Richard W [email protected] Waksman Institute, Department of Molecular Biology and Biochemistry, Cancer Institute of New Jersey, Rutgers University, Piscataway, NJ 08854-8020, USA2 Biological Sciences, University of the Cariboo, Kamloops, Britsh Columbia, (V2C 5N3), Canada2004 8 10 2004 4 14 14 20 8 2004 8 10 2004 Copyright © 2004 Yang et al; licensee BioMed Central Ltd.2004Yang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Members of TGFβ superfamily are found to play important roles in many cellular processes, such as proliferation, differentiation, development, apoptosis, and cancer. In Drosophila, there are seven ligands that function through combinations of three type I receptors and two type II receptors. These signals can be roughly grouped into two major TGFβ pathways, the dpp/BMP and activin pathways, which signal primarily through thick veins (tkv) and baboon (babo). Few downstream targets are known for either pathway, especially targets expressed in the Drosophila brain.
Results
tkv and babo both affect the growth of tissues, but have varying effects on patterning. We have identified targets for the tkv and babo pathways by employing microarray techniques using activated forms of the receptors expressed in the brain. In these experiments, we compare the similarities of target genes of these two pathways in the brain. About 500 of 13,500 examined genes changed expression at 95% confidence level (P < 0.05). Twenty-seven genes are co-regulated 1.5 fold by both the tkv and babo pathways. These regulated genes cluster into various functional groups such as DNA/RNA binding, signal transducers, enzymes, transcription regulators, and neuronal regulators. RNAi knockdown experiments of homologs of several of these genes show abnormal growth regulation, suggesting these genes may execute the growth properties of TGFβ.
Conclusions
Our genomic-wide microarray analysis has revealed common targets for the tkv and babo pathways and provided new insights into downstream effectors of two distinct TGFβ like pathways. Many of these genes are novel and several genes are implicated in growth control. Among the genes regulated by both pathways is ultraspiracle, which further connects TGFβ with neuronal remodeling.
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Background
TGFβ pathways are conserved between primitive animals, such as sponges and sea anemone [1,2] and vertebrates, thus representing an ancient signal transduction pathway. In both vertebrates and invertebrates, TGFβ family members play fundamental roles in proliferation, pattern formation, apoptosis, and specification of cell fate. Mutations of various TGFβ signaling components are associated with human diseases including cancer [3].
In recent years, the core signaling components of the TGFβ pathways have been elucidated by a combination of genetics and biochemical approaches. Unique to these signaling pathways are transmembrane receptor serine-threonine kinases that are novel in animals. Signaling is initiated when dimeric ligands bind to the type I receptor or a complex of the type I and type II receptors. The type II receptor phosphorylates the type I receptor, which renders it active. R-Smads are phosphorylated by the type I receptor, the complex with a co-Smad, and translocate to the nucleus. Smads bind DNA promoter elements weakly and require co-factors for efficient regulation of target genes.
In Drosophila, seven ligands have been identified from the genomic sequence [4-6]. These ligands act through a receptor complex comprised of heterodimeric combinations of type I and type II receptors. Three type I receptors, thick veins (tkv), saxophone, baboon (babo) and two type II receptors, punt and wishful thinking (wit), interact with either of two R-Smads, mothers against dpp (mad) or dSmad2 [7-12]. Although different heteromeric combinations of receptors exist, in general, tkv transmits a dpp/BMP signal through mad, and babo transmits an activin signal through dSmad2.
The dpp and activin pathways have known functions in the brain, although our understanding of it role is rudimentary. dpp is expressed in two areas adjacent to the outer proliferation center (OPC), where it modulates wingless expression [13]. To acquire the adult pattern of projections, extensive remodeling occurs in neurons of the larval neural circuits during metamorphosis [14]. Proper neuronal remodeling is important for transformation of the larval mushroom bodies (MBs) to the adult MBs [15,16]. babo and dSmad2 (activin pathway components) are involved in neuronal remodeling, which occurs in the larval-pupal transition [17]. One target of the activin pathway identified in these studies is a subunit of the ecdysone receptor, EcR-B. Neuronal remodeling is essential for brain development in most animals and this result raises the question of possible conservation of neuronal targets in vertebrates.
In spite of intense study using classical genetic approaches and biochemical methods, very few targets of the pathway have been identified. A better understanding of the growth and patterning properties of the pathway require a more complete list of target genes. Using activated receptors (tkv and babo), we have used microarray technology to identify common targets of the BMP and activin pathways in the Drosophila brain.
Results and discussion
Identifying targets of babo and tkv in Drosophila brains
Few targets of tkv signaling and even fewer targets of babo signaling are known in Drosophila. Though multiple ligands and type II receptors may interact with these type I receptors, the use of ligand/receptor combinations is not yet established with certainty. However, in a simplified view, tkv and babo send dpp (BMP) and activin signals. These pathways and receptors are conserved through evolution, but few downstream targets are known for these pathways in any organism. To learn more about the growth regulatory and pattering properties of these signals in the fly brain, we used microarray technology to identify downstream targets. In these experiments, Affymetrix™ chips containing the entire protein coding capacity of the Drosophila genome (about 13,500 genes) were screened. Genomic-wide microarray analysis allows us to examine similarities and differences between two signaling pathways in a tissue where both are known to function.
Constitutively active forms of the receptors were made by single amino acid substitutions [18], rendering them active in the absence of ligand. Transformants were generated which could be transcriptionally expressed using the heat shock GAL4 driver (hs-GAL4) [19,20]. To assay for the best induction protocol, animals were heat shocked and monitored for the presence of a UAS-gfp reporter. Since additional time is required for the induction of downstream signaling targets versus the time required for appearance of the GFP reporter, we collected RNA samples from third instar larvae in a broad time period roughly 30 minutes after the peak of GFP expression. Data resulting from induced ectopic expression of tkv, and babo were compared with each other and to the control (UAS-gfp; hs-GAL4).
Three independent replicates for each treatment were generated. Hierarchical clustering (Fig 1A) and Principal Component Analysis (Fig 1B) indicated that the microarray data is highly reproducible. Only transcripts that show an expression level above 1.5 fold change at significance values of P < 0.05 (Anova) were considered to be differentially expressed. These experiments identified genes that are either regulated by both tkv and babo pathways or by one of the pathways only.
Figure 1 Clustering of microarray data. A. Agglomerative Hierarchical Clustering of microarray data (P < 0.05, Group T, C, B represent individual samples with ectopic expression of tkv, control, and babo respectively); B. Principal Component Analysis (PCA) of microarray data (P < 0.05, Spheres in red, blue and yellow represent individual samples with ecotopic expression of babo, tkv, and control respectively).
To verify the differential expression levels in response to ectopic expression of tkv and babo on microarrays, semi-quantitative real-time RT-PCR was performed on selected genes. Six among the 27 genes were picked for validation. Real time RT-PCR showed similar results similar to the microarray results for four of the six genes (Fig 2). These PCR results are consistent with those of other reported microarray experiments [21,22]. This data, with the reproducibility of the individual samples analyzed, establish the validity of our microarray data and provide a comparison of two signaling pathways in the Drosophila brain.
Figure 2 Validation of microarray data by real time RT-PCR. The X-axis indicates fold changes (FC) of gene expression levels between tkv/babo ectopic expression and control (Positive values indicate that the relative expression level of a gene is increased and negative values indicate a decrease). Array /PCR (tkv, babo) represents the fold changes of transcripts with ectopic expression of tkv, babo in microarray /Real time PCR respectively.
Overview of gene expression following ectopic expression of babo and tkv
Upon ectopic expression of tkv, 91 transcripts are detected with differential expression values in brain tissues when compared with the control (Fig 3). This corresponds to about 0.7% of the transcripts on the array. More transcripts are down regulated (n = 60) than up regulated (n = 31) in abundance levels, indicating that ectopic expression of tkv causes both repression and activation of downstream genes. Induction of activated babo results in 216 genes with differential expression values in brain tissues. Interestingly, expression levels of more transcripts are decreased (n = 126) than increased (n = 90) (Fig 3). This corresponds to about 1.6% of the transcripts on the array. Most importantly, there are 27 genes co-regulated by induction of both babo and tkv – 17 of these transcripts are down regulated and 10 of them are up regulated (Fig. 3).
Figure 3 Distribution of differential regulated genes by ecotopic expression of tkv and babo. Upon ectopic expression of tkv, 60 transcripts are downregulated and 31 are upregulated. Similarly, at ectopic expression of babo, we detected 216 genes with differential expression values in brain tissues and expression levels of 126 transcripts are decreased and 90 transcripts are increased. There are 27 genes coregulated at ectopic expression of both babo and tkv. 17 of these transcripts are downregulated and 10 of transcripts are upregulated.
Role for TGFβ signaling in neuronal remodeling
The fact that both DPP and activin signaling pathways share some common features in differentiation and growth control in various tissues suggests that both pathways might share some downstream target genes. Microarray experiments identified 27 genes (Table 1) co-regulated by the induced expression of both tkv and babo. Among these 27 co-regulated transcripts, there are transcription factors, enzymes, transporters, signal transducers, miscellaneous proteins and four unknown genes (Fig 4). The transcription factor ultraspiracle (usp) gene has the highest expression level increase (8.1-fold for babo, 27.3-fold for tkv), which is a subunit of a nuclear receptor [15]. USP forms a heterodimer with the nuclear ecdysone receptor (EcR) and participates in neuronal remodeling [15,23].
Table 1 Changes in transcript levels of the coregulated genes by both tkv and babo pathways after ectopic expression of tkv and babo. (*FC represents the fold changes in gene expression levels between tkv/babo ectopic expression and control. Positive values indicate that the relative expression level of a gene is increased (upregulation) and negative values indicate a decrease (downregulation)).
Gene/synonym Signal FC* Molecular function P
babo tkv babo tkv
Transcription factors
usp 41 137 8.1 27.3 transcription factor, DNA binding, ligand-dependent nuclear receptor, ecdysteroid hormone receptor 0.0001
CG7839 36 34 1.7 1.6 transcription factor 0.0007
TfIIFβ 208 249 1.2 1.5 RNA polymerase II transcription factor 0.0000
CG14422 9 12 -3.8 -2.9 RNA binding /nucleic acid binding/transcription regulator 0.0115
Antp 24 25 -1.5 -1.4 specific RNA polymerase II transcription factor 0.0228
Enzymes and enzyme regulators
ia2 351 276 7.3 5.8 protein tyrosine phosphatase 0.0000
CG1827 32 26 2.3 1.8 N4-(beta-N-acetylglucosaminyl)-L-asparaginase 0.0010
ninaC 22 22 1.8 1.9 myosin ATPase, protein serine /threonine kinase 0.0127
G-iα65A 322 259 1.8 1.4 heterotrimeric G-protein GTPase 0.0028
Sucb 148 142 1.6 1.5 succinate-CoA ligase 0.0096
CG7288 117 125 1.5 1.6 ubiquitin-specific protease 0.0040
CG8913 50 69 1.1 1.5 peroxidase 0.0000
CG9236 7 5 -2.6 -3.4 calcium-dependent protein serine/threonine phosphatase 0.0199
Transporters
CG8533 16 12 -2.6 -3.5 glutamate-gated ion channel 0.0000
CG6293 28 49 -2.5 -1.4 L-ascorbate:sodium symporter 0.0004
Atpa 105 126 -1.5 -1.3 sodium/potassium-exchanging ATPase 0.0003
Fatp 240 219 -1.4 -1.5 long-chain fatty acid transporter 0.0003
Signal transducers
usp 41 137 8.1 27.3 transcription factor, DNA binding, ligand-dependent nuclear receptor, ecdysteroid hormone receptor 0.0001
ninaC 22 22 1.8 1.9 myosin ATPase, serine/threonine kinase, calmodulin binding 0.0127
CG8533 16 12 -2.6 -3.5 glutamate-gated ion channel 0.0000
Structural protein
CG14889 50 50 -2.8 -2.8 extracellular matrix /structural molecule 0.0023
Miscellaneous proteins
CG2807 78 105 -2.3 -1.7 pre-mRNA splicing factor 0.0034
CG32423 431 428 -2.1 -2.1 RNA binding 0.0000
XRCC1 56 20 -1.4 -3.8 DNA repair protein 0.0000
Cyp9f2 121 104 -1.5 -1.8 cytochrome P450 0.0012
Cyp9f3 77 70 -1.5 -1.7 pseudogene 0.0038
Unknown
CG3857 237 186 -2.4 -3.0 NA 0.0000
CG7986 130 117 -1.5 -1.7 NA 0.0009
CG31150 66 69 -1.5 -1.4 NA 0.0160
CG33187 148 125 -1.3 -1.6 NA 0.0035
Figure 4 Gene ontology of coregulated genes by both DPP and activin signaling pathways. The X-axis indicates the number of genes in each group.
Previous studies have shown that the Drosophila activin signaling pathway partially mediates neuronal remodeling through regulating EcR-B1 expression [17]. Two independent mutations that block neuronal remodeling in the mushroom bodies (MBs) during pupation were found to reside in babo and dSmad2 [17], both of which have been shown to participate in the activin signaling pathway [7,9]. Further, mutations in these signaling components reduce the expression of EcR-B1, and restoration of EcR-B1 expression rescues neuronal remodeling defects. These observations led to the model that the Drosophila activin signaling results in induction of the EcR-B1 isoform. Upon binding of ecdysone to the EcR-B1/USP heterodimeric receptors, neuronal remodeling is initiated via transcriptional activation of downstream target genes [17]. Our microarray analysis shows that high level expression of usp is also induced by ectopic expression of tkv and babo. In addition, we find that EcR-A expression is repressed by the induction of babo. Using real-time PCR, we confirmed that EcR-B1 is induced by ectopic expression of babo (1.5 fold), a more modest change than the increases on usp by tkv and babo. These finding suggest that Drosophila activin signaling mediates neuronal remodeling by regulation of both EcR-B1 and usp expression, while inhibiting EcR-A induction.
BMP-like pathways, as well as activin pathways, have been implicated in neuronal remodeling [13,17]. PUNT and WIT have been shown to have a redundant function in inducing EcR-B1 expression during brain development. In mutant clones, levels of EcR-B1 were unaffected, unless both receptors were mutant. These results are consistent with our findings that activated tkv and babo both induce EcR-B1, although it is not known which receptor combinations or ligands are responsible for these effects.
dpp (and presumably tkv) has other known roles in organizing the visual center of the brain [13]. It has been shown that wingless, acting through dpp, is an important participant in organizing the optical centers of the brain [13]. wingless is expressed at the tips of the crescent shaped OPC. Fourteen hours later, wingless induces dpp expression in adjacent cells, in two spots in each brain hemisphere. These dpp expressing cells also express fasciclin II. BrdU staining shows that wingless, dpp, and fasciclin II expressing cells proliferate throughout larval development. However, a reduction of wingless or dpp results in a reduction in the rate of proliferation in the OPC, resulting in smaller optic lobes of the brain. Loss of wingless also results in a severe reduction of the medulla, where the photoreceptor axons R7 and R8 migrate. Another defect noted in wingless mutant animals is that the OPC derived precursor cells had failed to assume their proper neuronal fate.
Transcription factors regulated by both DPP and activin pathways
Besides usp, two other transcription factor genes, CG7839 and TfIIFβ, are up regulated by tkv and babo. Both are implicated in growth processes. CG7839 has 30% homology over 1016 residues to C. elegans F23B12.7, which shows a slow growing phenotype in RNAi experiments [24]. TfIIFβ is part of the RNA transcriptional machinery, and 28% of glioblastomas and 80% of astrocytomas show amplification of this gene. Perhaps part of the growth potential of the tkv and babo TGFβ pathways operate through these transcription factors.
Two transcription factors, CG14422 and Antennapedia (antp), are down regulated by both pathways during brain development. antp is a well-studied Hox gene in Drosophila, which controls many developmental decisions, most notably, the differentiation of the antennae and legs from homologous structures [25]. The enormous diversity of body plans in animals is partially due to the variations that Hox transcription factors regulate gene expression. Most animals have one or more clusters of Hox genes, and each Hox gene controls the development of a specific region of the body plan [26]. In Drosophila, differences between segments, such as the presence or absence of appendages, are often controlled by Hox transcription factors. The role of antp in brain development is not known, but it is tempting to speculate that both dpp and activin might regulate brain development, at least partially, through interaction with the Hox gene antp. Determining the mechanisms by which Hox proteins regulate gene expression will be important for understanding animal development and pattern formation.
Other genes regulated by tkv and babo pathways
Many of the other genes that are significantly regulated by tkv and babo are evolutionarily conserved throughout animal phyla. Quantitative analysis of transcript levels indicates that TGFβ controls some genes that encode kinases and phosphatases that might be involved in signaling pathways. For example, ia2, a transmembrane receptor protein phosphatase [27], has the highest level of transcriptional change among these kinases and phosphatases. Antibodies to the human version of the gene are often indicative of diabetes [28-30]. NinaC is a protein serine/threonine kinase [31] with calmodulin binding activity [32]. CG9236 is a calcium-dependent protein serine-threonine phosphatase, which is down regulated. It is strongly related to C. elegans F30A10.1, which is involved in negative regulation of body size. If the function of the protein has also been conserved, then down-regulation by the TGFβ-like pathways would allow growth in the developing brain. Other kinases and phosphatases co-regulated by both TGFβ pathways are G-iα65A (G-ialpha65A), a G-protein coupled receptor protein involved in neuroblast cell division and cell size control [33,34], and CG 9236, a calcium-dependent protein serine/threonine phosphatase [27].
CG3857 and CG7986 are two novel proteins that have homologs in C. elegans and in vertebrates. While their molecular functions are not currently known, the C. elegans CG7986 homolog F41E6.13 is involved in positive regulation of growth. RNAi experiments with the C. elegans homolog of CG3857, Y54E2A.2, revealed no mutant phenotype. Four transporters (Atpa, Fatp, CG8533, CG6293) are transporters regulated by the tkv and babo pathways. Fatp is a long-chain fatty acid transporter. Atpa is a sodium/potassium-exchanging ATPase, while CG8533 is a glutamate-gated ion channel and CG6293 is a L-ascorbate:sodium symporter.
Conclusions
Microarray experiments revealed that 27 genes are co-regulated in both tkv and babo signaling pathways in the developing Drosophila brain. One of the most striking developmental events in the fly brain is neuronal remodeling. These results indicate usp is positively regulated by tkv and babo, and thus adds another important link to their roles in brain remodeling. Many of the 27 genes are strongly conserved in other species. If their biological functions are also conserved, then the RNAi experiments in their C. elegans counterparts show that several of them are involved in growth regulation. This is particularly useful since few downstream targets of BMP or activin signaling pathways are known, particularly the targets that execute their growth regulatory properties. Not surprisingly, mutational analysis of several of these genes has not been done, but the genetic tools in Drosophila make this relatively straightforward. Further characterization of these downstream genes may provide insights into the integration of tkv and babo signaling pathways in Drosophila brain development, and provide hints into their functions in other organisms.
Methods
Fly stocks
For over-expression of constitutively activated tkv, virgin females from UAS-CA-tkv were crossed to hs-Gal4 males. For over-expression of constitutively activated babo and the control, UAS-CA-babo and UAS-gfp were crossed to hs-Gal4 flies. The larvae were raised in standard medium at 25°C.
Heat shock treatment and RNA purification
Wandering third-instar larvae were heat-shocked to induce ectopic expression of tkv, babo, and the gfp control (UAS-gfp; hs-GAL4). Animals were heat shocked at 37°C for 1 hour, followed by cooling to room temperature for 30 minutes, and then kept at 25°C for one hour to allow expression before dissection. Approximately 150-200 larvae were dissected and the brains were collected in a drop of PBT (PBS, 0.01% Tween-20, pH 7.4) on Sylgard (Dow Corning). Total RNA was extracted from the tissue using the Trizol™ reagent (Invitrogen, Carlsbad, CA) according to the manufacturer's protocol.
Preparation of labeled cRNA
Total RNA from each of nine independent samples (three tkv, three babo and three gfp) was prepared for hybridization according to the Affymetrix GeneChip® Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA). The Superscript Choice System kit (Invitrogen, Gaithersburg, MD) was used to make complementary DNA (cDNA) from 5 μg. First strand synthesis was primed with a T7-(dT)24oligonucelotide primer containing a T7 RNA polymerase promoter sequence on the 5' end (Genset Oligos, La Jolla, CA). Second strand products were cleaned with the GeneChip® Sample Cleanup Module (Affymetrix, Santa Clara, CA) and used as a template for in vitro transcription (IVT) with biotin-labeled nucleotides (Bioarray High Yield RNA Transcript Labeling Kit, Enzo Diagnostics, Farmindale, NY). The copy RNA (cRNA) product was cleaned with the GeneChip® Sample Cleanup Module (Affymetrix, Santa Clara, CA) and a 20 μg aliquot was heated at 94°C for 35 min in fragmentation buffer provided with the Cleanup Module (Affymetrix, Santa Clara, CA).
Microarray hybridization
Fifteen μg of adjusted cRNA from each sample was hybridized for 16 hr at 45°C to an Affymetrix (Santa Clara, CA) Drosophila Genechip 1 array. After hybridization, each array was stained with a streptavidin-phycoerythrin conjugate (Molecular Probes, Eugene, Oregon), washed and visualized with a Genearray™ Scanner (Agilent Technologies, Palo Alto, CA). Images were inspected visually for hybridization artifacts. In addition, quality assessment metrics were generated for each scanned image and evaluated based on empirical data from pervious hybridizations and on the signal intensity of internal standards that were present in the hybridization cocktail. Samples that did not pass quality assessment were eliminated from further analyses.
Generation of expression values
Microarray Suite version 5 (Affymetrix, Santa Clara, CA) was used to generate *.cel files. Probe Profiler™ version 1.3.11 software (Corimbia Inc, Berkeley, CA) was used to convert cel file intensity data into quantitative estimates of gene expression for each probe set. For each probe set, a probability statistic is generated. Genes not significantly expressed above background in any of the samples (P > 0.05) were considered absent. Absent genes were removed from the data set and not included in further analyses.
Data analysis
Tests of Significance
Gene expression levels were subjected to a 1-way analysis of variance (Anova) for 3 treatments (B, C, T) and 3 replications using AnalyzeIt Tools, a custom software program developed by the Interdisciplinary Center for Biotechnology Research (ICBR, University of Florida), for the analysis of microarray data. In this software, the statistical package, R, serves as the backend for Anova. Genes were considered to have a significant treatment effect if P-level was less than 0.05.
The expression values of those genes that were considered to have a significant treatment effect were normalized by performing a Z- transformation [35], thereby generating a distribution with mean 0 and standard deviation of 1 for each gene. Hierarchical clustering, K-Means clustering and Principal Component Analysis were performed on normalized values using GeneLinker™ Gold 3.1 (Predictive Patterns, Kingston, Ontario).
To eliminate noise from low-level expression, spots quantified less than 5 were replaced by value 5. The following criteria were used to filter the data. Only transcripts with the fold change difference over 1.5 (tkv (or babo) average/Control average or Control average/tkv (or babo) average) and statistically significant (P <= 0.05, analysis of variance (Anova)) were considered as differentially expressed. AnalyzeIt Tools and notations in Flybase were used for classification of genes by gene ontology in molecular function and biological process categories.
Real time RT-PCR
Two independent total RNA samples were generated for each of the three experimental conditions (two tkv, two babo and two gfp). Each of the samples were analyzed three independent times, resulting in six repeats. These six repeats were averaged and the tkv and babo samples were compared with the gfp controls. Approximately 1 μg of the each total RNA was used for first strand cDNA reaction using Superscript First Strand Synthesis kit (Invitrogen, Carlsbad, CA) according to the manufacturer's protocol. For real-time PCR, the reaction consisted of cDNA first strand template, primer mix, Rox (Invitrogen, Carlsbad, CA) and SYBR Green PCR Master Mix (Invitrogen, Carlsbad, CA) in a total volume of 25 μl. Three reactions per template were performed in parallel. Actin 42F was used as an internal standard to generate a standard curve and to normalize the amount of cDNA samples. The fold change (as presented in Fig 2) was calculated from the average real time PCR data: (tkv or babo) average/Control average or Control average/(tkv or babo) average. The experiments were performed using a Rotor Gene 3000 (Corbett Research, Sydney, Australia). To validate the specificity of PCR reaction, a melting curve was produced by denaturation of PCR end products from 60 to 99°C at 0.5°C/min steep and the end products were also assayed with 1.5% agarose gel electrophoresis after cycling.
Authors' contributions
MY and DN carried out experiments in the project and YF assisted in the experimental design. RWP implemented and supervised the project. RWP and MY prepared the manuscript.
Acknowledgements
We thank Mick Popp and Li Liu of UFRCB (University of Florida) for microarray and data analysis. Fly stocks were received from the Bloomington Stock Center. We thank members of the Padgett lab for comments on the manuscript. MY was a Busch postdoctoral fellow and YF was a postdoctoral fellow for research abroad from Japan Society for Promotion of Science. This work was supported by grants from the National Institutes of Health to RWP.
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| 15473904 | PMC526378 | CC BY | 2021-01-04 16:30:29 | no | BMC Dev Biol. 2004 Oct 8; 4:14 | utf-8 | BMC Dev Biol | 2,004 | 10.1186/1471-213X-4-14 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1581550423810.1186/1471-2105-5-158Research ArticleA computational approach for ordering signal transduction pathway components from genomics and proteomics Data Liu Yin [email protected] Hongyu [email protected] Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA2 Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, New Haven, CT, 06520, USA2004 25 10 2004 5 158 158 6 7 2004 25 10 2004 Copyright © 2004 Liu and Zhao; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Signal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.
Results
We have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.
Conclusions
As demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms.
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Background
Signal transduction is the primary means by which eukaryotic cells respond to external signals from their environment and coordinate complex cellular changes. It plays an important role in the control of most fundamental cellular processes including cell proliferation, metabolism, differentiation, and survival [1]. Extracellular signal is transduced into the cell through ligand-receptor binding, followed by the activation of intracellular signaling pathways that involve a series of protein phosphorylation and dephosphorylation, protein-protein interaction, and protein-small molecules interaction.
Recently, with the accumulation of genome sequence information, large-scale genomic and proteomic techniques have offered insights into the components of signal transduction pathways and the molecular and cellular responses to cell signaling. For example, large-scale yeast two-hybrid screening methods and Co-IP technique have been used to identify physical interactions between proteins [2-5]. Synthetic lethal screens are used to identify genetic interactions [6]. The protein chip is an advanced in vitro technique for analyzing protein functions [7]. In addition, microarray experiments can simultaneously measure the transcript abundance of thousands of genes in different conditions. These experimental approaches have generated enormous amounts of data and provide valuable resources for studying signal transduction pathways. However, our understanding of the signal transduction processes underlying these data lags far behind data accumulation. Therefore, there is a great need to develop computational methods to direct biological discovery, enabling biologists to discover the mechanisms underlying complex signaling pathways and interactions among them.
Given the fact that signal transduction is achieved by a cascade of protein interactions and activations, one major challenge in dissecting signal transduction pathways is to determine the order in which the signal is transduced. Traditionally, genetic epistasis analysis is used to address this question. In such analysis, the order of gene function can be determined by comparing the phenotype of a double mutant ab to that of a single mutant a, or a single mutant b. However, this analysis is time-consuming, expensive and sometimes the results can be misinterpreted [8]. Computational methods using large-scale genomics and proteomics information can expand the scope of experimental data and reduce the number of experiments required to detect the order of pathway components. Although it is important, little research has been performed in this field, with a major obstacle being the lack of completeness and accuracy of the data. Here we present a computational approach that integrates different types of information to predict the order of the pathway components assuming all the pathway components are known.
Results
Because the yeast MAPK pathways involved in pheromone response, filamentous growth, maintenance of cell wall integrity and hypertonic shock response are among the most thoroughly studied pathways, we use them to develop and test our method (Fig. 1). As protein-protein interaction plays an important role in achieving the signal transduction process, useful prediction of the order of the pathway components will require knowledge of the interacting partners of these pathway components. Here, we utilize the Database of Interacting Proteins (DIP) that is based on curated collection of all functional linkages of proteins obtained by experimental methods, including yeast two-hybrid experiments, immunoprecipitation, and affinity purification [9]. Although important, the usefulness of the interaction information is limited, as the presence of a physical interaction may not indicate the activation of the interacting proteins. The protein kinases analysis based on protein chip technique provides direct information about protein phosphorylation and activation, but it only presents a very small fraction of the complete picture of protein activation. Compared to the protein chip data, gene expression data from DNA microarray provide an overall picture of whole-cell response under different conditions. Therefore, we utilize this data source as the indirect information about protein activation to complement protein-protein interaction data. Our goal is to develop a computational method for integrating these data sources for ordering yeast MAPK pathway components.
Two expression datasets are used in our analysis, one is composed of 56 conditions relevant to the behavior of MAPK signal transduction and another is the "compendium" set which is composed of 300 diverse mutations and chemical treatments [10,11]. To incorporate the gene expression data, we hypothesize that the genes encoding the proteins on the same signaling pathway, especially the adjacent pathway components, have similar gene expression profiles. In order to test the hypothesis, we calculated the correlations between each pair of genes using the two expression datasets, and performed a hypergeometric test on the similarity of gene expression pattern of the adjacent pathway components. The hypergeometric distribution is given by
where N represents the total number of protein pairs, M represents the number of protein pairs in adjacent positions on a specific MAPK pathway, n is the total number of protein pairs that have an absolute value of correlation coefficient above a given threshold, e.g. 0.7, and k is the number of adjacent protein pairs having an absolute value of correlation coefficient above this threshold. The p-value obtained from the test is 2 × 10-4 when the threshold is set to 0.7, indicating that protein pairs in adjacent position on a pathway tend to have a higher correlation coefficient value than random protein pairs. This fact is applied in developing our score function that incorporates the gene expression information.
For each MAPK pathway, we examine all permuted orders of the pathway components with the starting point (membrane receptor) and the ending point (transcription factor) of each MPAK pathway fixed and calculate the score for each permutation according to the score function defined as in "Method" section. Then, we rank each permutation based on its corresponding score, with the high-ranking orders being the more likely pathway orders.
For the pheromone response pathway, the scores based on each individual data set and the scores based on integrating both data sets are shown in Fig. 2. Based on protein-protein interaction data alone, the "true" pathway is assigned a score of 0.75, ranking 241 among all the 5040 possible pathways, while based on gene expression data alone, it is assigned a score of 0.96, ranking 25 among all the 5040 possible pathways. However, after we integrate the scores obtained from two different sources together, the "true" pathway obtain a score of 1.71, with a rank of 2, which is a much higher-ranking than the ranking based on either data type alone. Similar results are shown for the other three yeast MAPK pathways (Table 1). Therefore, our score function that integrates protein-protein interaction data and gene expression data seems to provide more accurate prediction of the order of the pathway components than methods based on either data source alone. This prediction can be used to guide hypothesis-driven research and significantly reduce the number of required experiments.
Discussion
The rapid accumulation of genomics and proteomics information and the development of large-scale experiment techniques motivate us to develop computational approaches to dissecting different pathways. Arkin et al. described a time-lagged correlation analysis to infer the interactions among the components on the first few steps of the glycolytic pathway, thus the order of the components on the glycolytic pathway could be deduced [13]. Schmitt Jr. et al. applied this method to identify the cause-effect relationships among genes in the organism Synechocystis in response to different light conditions [14]. The limitation of this time-lagged correlation analysis is the requirement of high resolution of time-scales for sampling. That is, if the level of gene expression or the amount of the pathway components is not measured in a small sampling interval, the great resolution into the orderings of pathway components cannot be achieved. Gomez et al. used known protein-protein interactions of Saccharomyces cerevisiae as training data and represented the proteins as collections of domains to predict links within the human apoptosis pathway [15]. However, not all proteins have a defined domain composition. In principle, these two approaches use either gene expression data or protein-protein interaction data to infer pathways. However, neither method can be applied to jointly analyze data of different sources. Although protein-protein interaction data provide key information to reveal the relationships between components in a singnal transduction pathway, they are subject to many biases (e.g. high false positive and false negative rates) and are not able to capture the dynamic nature of the pathways that are condition dependent. DNA microarray data offer information about whole-cell responses in different conditions but only provide indirect information on the ordering of genes in a specific pathway. These two different data types offer complementary information, and our approach infers the order of the pathway components based on the integration of these two data types and can significantly increase our ability for pathway inference. We note that, despite great improvements over the results based on single data type, our approach is not able to put the correct order as the top one among all possible orders. This is largely due to the imperfectness of current data sources. To further improve our method, we may require data of higher quality or incorporate more types of data, such as protein chip data.
We note that the utility of integrating yeast protein-protein interaction map and gene expression profiles to predict signal transduction network has previously been described by Steffen and colleagues [16]. In their approach, the interaction data were used to create "candidate" pathways and infer the orders between the pathway components, and then the "candidate" pathways were scored according to the number of pathway members that were clustered together based on the expression profiles. However, as many interactions are currently not identified, some links between pathway members may be missing at the very first step and cannot be recovered in the following inference. In addition, the prediction results are highly dependent on the clustering method and the number of clusters into which the genes were grouped. In contrast, our starting point is that we assume that all pathway components are known and use gene expression data to calculate the correlation coefficients between genes and incorporate the results into our score function directly. While our overall objective is somewhat more modest than that of Steffen and colleagues, the motivation of our work was to test whether there is any information in the current data sources to infer the correct order of pathway components. If the goal could not be achieved when all pathway components are known, then it is very unlikely that any method starting from scratch to reconstruct signal transduction pathway will succeed. Fortunately, our results indicate that this modest task can be accomplished and suggest the usefulness of genomics and proteomics information.
We have shown our method can lead to a good prediction for well-known yeast MAPK signaling pathways. In addition, we have tested our approach on the DNA damage checkpoint pathway that is involved in cell-cycle progression. The "true" pathway ranks 4 among all the 750 possible pathways based on our integrated approach, while it has a rank of 46 and 60 based on protein-protein interaction data alone and gene expression data alone, respectively. Therefore, we conjecture that our approach may be applicable to many other pathways including less well-understood ones.
It is worth to note that signaling pathways are not limited to one-dimensional sequence of genes, as our focus in this study. Instead, they should be depicted as multidimensional networks. To make further complicated prediction and modeling of the networks, we need to incorporate more biological information and apply more elaborate statistical approaches.
Conclusions
We have demonstrated that our integrated approach can significantly improve the performance of predicting the order of signaling pathway components, without detailed knowledge of all the genes in the pathway or the molecular nature of the gene products. It may be important to incorporate other valuable sources of data, including protein chip data, genomic sequence information and protein domain information if we want to make the transition from a linear one dimension pathway to a multidimensional model of signaling networks, which represents a great challenge in the field of systems biology.
Methods
For protein-protein interaction data, the score function is defined as follows:
where n is the total number of proteins on the pathway, and Xi, i+1 = 1 if there is an observed interaction between the ith and the (i+1)th proteins on the pathway and Xi, i+1 = 0 otherwise. Here p represents the false negative rate of the interaction data. In this study, we fixed the false negative rate as 0.4. It was estimated that the total number of interactions between all yeast proteins or the size of yeast interactome is about 20000~30000 [17,18]. In this study, the interaction data we obtained from DIP includes 15118 pairwise protein-protein interactions, which covers more than 50% of the total number of estimated protein interactions assuming all of the interactions in DIP are true interactions. Indeed, this assumption should be valid as DIP is manually curated and it provides high quality interaction data by minimizing the total number of false positive interactions. Therefore, the false negative rate of the interaction data in DIP may well be less than 0.5. As our method is based on the ranking the of calculated scores, the ranking of all possible orderings are not affected with any false negative rates below 0.5. However, the interaction data availability is limited for some species, for example, only 1379 interactions among about 900 human proteins are included in DIP. In such cases, the performance of our approach may not be as informative as that in yeast.
For gene expression data, the score function is defined as:
where ri, i+1 represents the correlation coefficient between the ith and the (i+1)th proteins on the pathway.
The two data sources are considered with equal importance, so we rescale the score Si of all the possible pathways to [0, 1] by
where Smin and Smax are the minimum and the maximum scores of all the possible pathways respectively for either protein-protein interaction data or gene expression data. The rescaling procedure is performed on both data sets. The integrated score is the sum of the rescaled scores for each individual data set.
Authors' contributions
YL designed the study, performed the pathway analysis, and drafted the manuscript. HZ conceived and guided the study. Both authors read and approved the final manuscript.
Acknowledgements
HZ acknowledges support by the NSF grant DMS-0241160. YL is supported by the NIH Institutional Training Grants for Informatics Research. The authors thank Nanxin Li, Liang Chen, Ning Sun and Baolin Wu for helpful advice and discussion.
Figures and Tables
Figure 1 MAPK signaling pathways in Saccharomyces cerevisiae. Membrane receptors are marked in blue, and transcription factors are marked in red. The figure is adapted from KEGG pathway database [12], and the scaffold proteins and proteins on the pathway branches are omitted for simplicity.
Figure 2 Distribution of the scores for permuted pheromone response pathways. (a) Scores based on protein-protein interaction data, (b) Scores based on microarray gene expression data, (c) the integrated scores based on both protein-protein interaction data and gene expression data.
Table 1 A comparison of the prediction results based on using different data types. PPI stands for protein-protein Interaction. The percentile rank of the true pathway is defined as the ratio between the number of pathways having a higher score than the "true" pathway to the number of all permuted pathways.
MAPK pathway PPI Expression PPI + Expression
The number of pathways having a higher score Percentile rank of the true pathway The number of pathways having a higher score Percentile rank of the true pathway The number of pathways having a higher score Percentile rank of the true pathway
Pheromone Response 240 0.05 24 0.005 1 10-3.7
Filamentous Growth 7 0.06 4 0.006 0 0
Cell Wall Integrity 70 0.10 80 0.11 17 0.02
High Osmolarity 0 0 34 0.28 0 0
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| 15504238 | PMC526379 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 25; 5:158 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-158 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-381548814810.1186/1471-2121-5-38Research ArticleTwo Drosophila suppressors of cytokine signaling (SOCS) differentially regulate JAK and EGFR pathway activities Rawlings Jason S [email protected] Gabriela [email protected] Susan MW [email protected] Rongwen [email protected] Douglas A [email protected] Dept. of Biology, University of Kentucky, 101 Morgan Bldg. Lexington, KY, 40506, USA2004 15 10 2004 5 38 38 1 6 2004 15 10 2004 Copyright © 2004 Rawlings et al; licensee BioMed Central Ltd.2004Rawlings et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The Janus kinase (JAK) cascade is an essential and well-conserved pathway required to transduce signals for a variety of ligands in both vertebrates and invertebrates. While activation of the pathway is essential to many processes, mutations from mammals and Drosophila demonstrate that regulation is also critical. The SOCS (Suppressor Of Cytokine Signaling) proteins in mammals are regulators of the JAK pathway that participate in a negative feedback loop, as they are transcriptionally activated by JAK signaling. Examination of one Drosophila SOCS homologue, Socs36E, demonstrated that its expression is responsive to JAK pathway activity and it is capable of downregulating JAK signaling, similar to the well characterized mammalian SOCS.
Results
Based on sequence analysis of the Drosophila genome, there are three identifiable SOCS homologues in flies. All three are most similar to mammalian SOCS that have not been extensively characterized: Socs36E is most similar to mammalian SOCS5, while Socs44A and Socs16D are most similar to mammalian SOCS6 and 7. Although Socs44A is capable of repressing JAK activity in some tissues, its expression is not regulated by the pathway. Furthermore, Socs44A can enhance the activity of the EGFR/MAPK signaling cascade, in contrast to Socs36E.
Conclusions
Two Drosophila SOCS proteins have some overlapping and some distinct capabilities. While Socs36E behaves similarly to the canonical vertebrate SOCS, Socs44A is not part of a JAK pathway negative feedback loop. Nonetheless, both SOCS regulate JAK and EGFR signaling pathways, albeit differently. The non-canonical properties of Socs44A may be representative of the class of less characterized vertebrate SOCS with which it shares greatest similarity.
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Background
The vertebrate JAK signaling pathway is an essential component of cellular response to a wide array of cytokines and growth factors. The JAK cascade is reutilized for signaling events in numerous tissues and at multiple stages of mammalian development [reviewed by [1-3]]. Many interleukins, interferons, and growth factors are among the ligands that stimulate signaling through the JAK pathway. The pathway can also be stimulated through activation of some receptor tyrosine kinases, including epidermal growth factor receptor (EGFR). As a result of its broad utilization, JAK signaling is essential for many developmental events.
Though the JAK pathway is vital to many developmental processes, strict control of JAK signaling is equally important. As with other signaling pathways, mechanisms must be in place to balance the activation of JAK pathway activity. Regulation serves to "reset" the pathway so that it will be responsive to subsequent signals and it restricts the level or duration of the signal so that it is properly interpreted by the cell. Inappropriate JAK activation is the direct cause of a specific form of acute lymphocytic leukemia (ALL) [4-6]. In addition, JAK/STAT activation has been strongly correlated with a variety of cancers, including many blood cell and immune cell transformations [reviewed by [7-9]]. Furthermore, in cell culture, constitutive activation of c-Eyk, v-src, or v-abl results in the constitutive activation of specific STATs or JAKs [10-13]. These examples highlight the necessity of regulating JAK/STAT activation.
Because of the need to limit JAK activity, it is not surprising that there are several conserved protein families that regulate JAK activation [reviewed by [3,14,15]]. These include phosphatases, Protein Inhibitors of Activated STATs (PIAS), and, the best characterized, the suppressors of cytokine signaling (SOCS) family. In mammals, eight different SOCS genes have been found [16]. These SOCS proteins have a distinctive modular architecture: a central SH2 domain followed by a carboxyl terminal SOCS domain, while the amino termini are quite divergent. Biochemical investigations have revealed that SOCS proteins use multiple mechanisms to regulate activity of the JAK pathway [see reviews, [3,9]]. First, the SOCS SH2 domain can bind to the phosphorylated receptor, thereby prohibiting access to positive effectors of the pathway. Second, at least some SOCS can specifically inhibit the catalytic activity of JAKs. Lastly, SOCS binding to activated JAK pathway components may target those proteins for degradation. The SOCS motif interacts with the elongins B and C, which bind to cullins and are E3 ubiquitin ligases [17,18]. Addition of ubiquitin to the bound proteins would target them for proteasomal degradation. Therefore, the negative influence of SOCS on its substrates may be due to multiple distinct mechanisms.
Use of the JAK signaling pathway for developmental processes is not restricted to mammals. Indeed, the JAK cascade is evolutionarily conserved, and can be found as an intact signaling pathway even in insects [3,19-21]. In Drosophila, the JAK pathway is involved in embryonic patterning, sex determination, blood cell development, patterning of adult structures, planar polarity of photoreceptor clusters, maintenance of stem cells in spermatogenesis, and follicle cell patterning and function [see reviews [19,21]]. Furthermore, the fly JAK pathway must also be properly regulated to avoid deleterious effects. As in vertebrates, hyperactive JAK signaling has also been shown to directly cause neoplastic cell growth in Drosophila. Two dominant gain-of-function alleles of hopscotch result in hypertrophy of the larval lymph glands, the hematopoietic organ, and melanotic masses [22-24]. Excess activity in the blood system causes overproliferation and differentiation of the macrophage-like blood cells, creating leukemia-like effects. Inappropriate activity in the developing tissues of the adult fly can also cause alteration of the development of the adult thorax, wing veins, head, eyes, and ovaries [22,25-27].
Of the eight mammalian SOCS, four have been studied extensively (CIS, SOCS1-3). These genes have been shown to respond to JAK pathway activation and subsequently are able to downregulate its activity as described above, completing a classical negative feedback loop. In comparison, very little is known of the remaining four. Here we present the identification and characterization of Drosophila Socs44A. It contains the same modular domain architecture as mammalian SOCS and shows greatest sequence similarity to the relatively uncharacterized SOCS6 and SOCS7. We show that, unlike the previously studied Drosophila Socs36E [28,29], Socs44A expression in embryogenesis is independent of JAK pathway activity. However, Socs44A is able to regulate the JAK cascade in embryogenesis, but not in oogenesis. Finally, Socs44A genetically interacts with and upregulates the EGFR/MAPK pathway. The characteristics of Socs44A that distinguish it from the canonical Socs36E may be representative of features that are shared with the class of less-defined mammalian SOCS genes.
Results
The Drosophila genome encodes three putative SOCS genes
Based on the consensus protein sequence for a SOCS box derived by Hilton and colleagues [30], a tBLASTn search of the Berkeley Drosophila Genome Project (BDGP) database [31] was conducted to examine all possible reading frames. Three putative loci containing both a SOCS box and an SH2 domain were identified using this strategy. All three match the arrangement of mammalian SOCS genes in that the SOCS box is at the carboxyl terminus with the SH2 domain directly preceding it. Each of these putative homologues also overlaps with a predicted gene from the BDGP. We named these three genes Socs16D (overlapping with CG8146), Socs36E (overlapping with CG15154), and Socs44A (CG2160), based upon their cytological location. Comparison of these three fly SOCS genes with vertebrate SOCS reveals that Socs36E is most similar to the mouse SOCS5, while Socs16D and Socs44A are less similar to specific mouse SOCS (see Fig. 1). While the amino termini are quite different, SOCS5 and Socs36E are 62% identical (71% similar) at the carboxy terminus from the region just before the SH2 domain to the end of the SOCS domain (region shown in Fig. 1A). Within that same C-terminal region, Socs44A is most similar to SOCS6 and SOCS7 (46% and 39% similar, respectively). Socs16D also has highest similarity to SOCS6 and SOCS7 (47% to each) over the same carboxyl region. These similarities suggest that the ancestral versions of Socs36E and a common predecessor of Socs16D and Socs44A existed as two separate SOCS genes at the time of divergence of mammals and dipterans (Fig. 1B).
Figure 1 Protein sequence comparison of Drosophila and mouse SOCS. (A) The predicted carboxyl terminal protein sequences of Drosophila (d), mouse (m), and C. elegans (ce) SOCS genes, including the SH2 and SOCS box domains, are aligned and shaded to indicate similarities and identities. (B) Based on the protein alignments, the neighbor-joining method was used to construct a phylogenetic tree of these SOCS.
Socs44A expression is not regulated by JAK pathway activity
In mammals, regulation of JAK signaling through SOCS proteins is based on a simple negative feedback mechanism. Specifically, the activity of the JAK pathway stimulates the expression of SOCS genes, because activated STATs bind to enhancers for the SOCS genes and induce transcription. Socs36E is similarly regulated during embryogenesis by Drosophila JAK signaling [29]. Socs36E is expressed dynamically, in a striped pattern that later becomes restricted predominantly to the tracheal pits [[28,29], and Fig. 3], very similar to upd, the gene encoding the embryonic ligand for the JAK pathway [32]. Indeed, activation of JAK signaling is both necessary and sufficient for Socs36E expression in embryogenesis [29]. Furthermore, the expression of Socs36E during oogenesis matches the known activation of JAK signaling. The expression of upd in the ovaries is restricted to the two polar follicle cells at either end of the egg chambers of the vitellarium [[26] and Fig. 2C]. Socs36E is expressed in a larger number of follicle cells centered at the two poles of the egg chamber (Fig. 2D). Given that secreted Upd protein is produced in the polar follicle cells and activates JAK signaling in neighboring cells [33], this suggests that Socs36E expression is controlled by JAK activity in oogenesis, as well as embryogenesis.
Figure 3 Loss of JAK activity does not affect Socs44A expression. As compared with wild-type at various embryonic stages (A and B), germline clone derived embryos from hopc111 mothers (C-H) display dramatically reduced or eliminated expression of Socs36E (C and D). Only a stripe of mesodermal staining in germ band extended embryos (D) remains at nearly normal intensity in the mutant embryos. In contrast, expression of Socs44A in trachea persists in hopc111 germline clone-derived embryos that are unrescued (E) or paternally rescued (F). However, the trachea are morphologically altered and drastically reduced in unrescued (G) and paternally rescued (H) animals, as compared with wild-type (I), as evidenced by a trachealess enhancer trap (G-I).
Figure 2 Socs36E and Socs44A are expressed in different spatio-temporal patterns. The embryonic expression patterns of upd and Socs36E are dynamic from early blastoderm throughout embryogenesis [see 28, 29 and Fig. 3]. Socs44A expression is not detected until very late stages in the trachea (A). Although such staining can be artifactual, sense strand probe never showed any staining (B). In the ovary, upd is expressed specifically in the polar follicle cells at each end of the chamber (C). Socs36E expression encompasses the anterior and posterior follicular epithelium, with highest expression at the poles (D). This is consistent with activation of Socs36E transcription due to reception of the Upd ligand which is secreted from the polar follicle cells and diffuses toward surrounding cells. Socs44A expression is restricted to the germline and only during later stages of oogenesis (E)
The Socs44A gene that was predicted based on protein homology is identical to hypothetical gene CG2160. A single cDNA corresponding to the locus (LP02169) was isolated by the BDGP, has been completely sequenced and encodes the expected SH2 and SOCS domains at the carboxyl terminus (gb AF435923). To determine whether Socs44A is similarly regulated by JAK pathway activity, in situ hybridization to embryos and ovaries was performed. No specific expression of Socs44A was detected until very late in embryogenesis. The only striking staining pattern observed was in the trachea of late embryos (Fig. 2A). Non-specific tracheal staining is sometimes seen with probes to late embryos, however this pattern was never observed when sense probe was used (Fig. 2B). Unfortunately, embryos homozygous for any available deletions that remove Socs44A die prior to formation of trachea, therefore we cannot conclusively determine whether the late tracheal staining reflects RNA expression. Nonetheless, because the JAK pathway is activated in a segmentally repeated pattern during embryogenesis, the lack of Socs44A expression suggests that it is not responsive to JAK signaling. Consistent with this conclusion, expression of Socs44A in the ovary is restricted to only germline expression late in oogenesis, with no detectable RNA in the follicular epithelium (Fig. 2E).
To directly test whether Socs44A expression is regulated by JAK pathway activity, in situ hybridization to Socs44A RNA was performed in embryos that lack JAK pathway activity. The product of the hop gene is required in early embryogenesis and must be provided maternally for proper segmentation of the embryo. The dominant female sterile (DFS) technique was used to generate females that fail to produce hop in the germline [34]. In situ hybridization of hop germline clone embryos using Socs36E as probe demonstrates a strong reduction in Socs36E expression in the mutant embryos as compared with wild-type (Fig. 3). Similar results have been reported in embryos lacking upd activity [29]. These data demonstrate that hop is required to stimulate the normal segmentally-repeated Socs36E expression in the embryo. However, expression of Socs44A does not appear to be affected by maternal loss of hop. Although the trachea are malformed and dramatically reduced in embryos lacking JAK pathway activity [[35,36] and Fig. 3G,3H], the remaining segments of trachea continue to express Socs44A at apparently normal levels (Fig. 3E,3F). Thus the failure of endogenous Socs44A to be expressed in the normal pattern of JAK pathway activation and of Socs44A expression to be eliminated by loss of JAK activity indicate that Socs44A expression is not stimulated by the pathway.
Activity of the JAK pathway is both necessary and sufficient for the expression of Socs36E. The ectopic activation of the JAK pathway by misexpression of upd results in expression of Socs36E in the same pattern [[29] and data not shown]. In contrast, similar misexpression of UAS-upd with the paired-GAL4 driver failed to stimulate any detectable expression of Socs44A in the embryo (not shown). We conclude that Socs44A expression is not responsive to JAK pathway activity, therefore cannot function via a traditional auto-regulatory feedback loop.
Ectopic SOCS activity suppresses JAK signaling in the wing
The lack of transcriptional regulation by JAK signaling does not preclude a role for Socs44A in the control of JAK activity. To test whether it can attenuate JAK signaling, Socs44A was misexpressed using the GAL4/UAS system. Similar experiments performed with Socs36E have demonstrated that expression in the developing wing reproducibly results in the production of ectopic wing vein near the posterior crossvein [Fig. 4C and [28]]. This phenotype is quite similar to that noted for viable mutants of hop or Stat92E [Fig. 4B and [37]], suggesting that Socs36E misexpression may cause a reduction in JAK signaling in the wing. But unlike observed JAK mutations, the anterior crossvein was also completely missing from Socs36E misexpression wings, perhaps suggesting an additional role for Socs36E that is independent of the JAK pathway. Callus and Mathey-Prevot [28] demonstrated that the additional influence on wing venation may be due to the suppression of the EGFR pathway.
Figure 4 Socs44A misexpression reduces JAK signaling in the wing. Wild-type venation (A) is compared with a viable hop mutant, hopmsv/hopM38 (B). hop reduction causes ectopic vein (arrow) near the posterior crossvein. (C) Expression of UAS-Socs36E using the engrailed-GAL4 driver (e16E-GAL) produces a similar ectopic vein phenotype, plus the loss of the anterior crossvein (arrowhead). (D) Similar misexpression of Socs44A causes ectopic wing vein production near the posterior crossvein (arrow) and arching of vein L3 (arrowhead). (E) Reduction of the dosage of hop enhances the Socs44A misexpression phenotype. (F) Misexpression of hop in the posterior compartment causes dramatic vein loss, but that loss is restored by the simultaneous expression of Socs44A (G).
Using the engrailed-GAL driver, GAL-e16E, expression of Socs44A in the posterior compartment of the wing caused mild venation defects similar, but not identical, to Socs36E (Fig. 4D). Expression of Socs44A caused production of ectopic wing vein near the posterior crossvein, but unlike Socs36E, the ectopic vein was seen predominantly posterior to L5, not between L4 and L5. Furthermore, the anterior crossvein was not reduced or eliminated by Socs44A expression, but a substantial arching of L3 was noticed. Both the ectopic vein and arching of L3 were enhanced in animals heterozygous for a null allele of hop (Fig. 4E), indicating that the phenotype is sensitive to a reduction in JAK pathway activity. Misexpression of hop activates JAK signaling and causes reduction of wing venation in the posterior of the wing, somewhat the opposite of Socs44A misexpression (Fig. 4F). The simultaneous misexpression of hop and Socs44A results in a phenotype similar to expression of Socs44A alone (Fig. 4G). Therefore, the activity of Socs44A is capable of negating the influence of ectopic JAK activity in the wing.
Loss of JAK function in embryos is lethal, but various combinations of weak alleles of hop show some viability (Table 1). If Socs44A were negatively regulating the JAK pathway, misexpression of Socs44A in a hop mutant background would be expected to further reduce viability. The ability of Socs44A misexpression to enhance the lethality of weak heteroallelic combinations of hop was tested. For all alleles examined, expression of Socs44A in the engrailed pattern caused complete lethality. For the weakest hop allelic combination, hopmsv/hopM75, misexpression of Socs44A caused viability to drop from 62% to 0% (Table 1). These data are consistent with the hypothesis that ectopic Socs44A acts to further reduce pathway activity in these JAK activity depleted animals, causing lethality.
Table 1 Misexpression of Socs44A exacerbates the reduced viability of hop heteroallic mutants.
Genotype hopM38 (n = 213) hopGA32 (n = 332) hopM75 (n = 172)
A- hopx/FM7; en-GAL; TM3 33 52 21
B- hopx/FM7; en-GAL; UAS-socs44A 25 33 28
C- hopx/hopmsv; en-GAL; TM3 11 20 13
D- hopx/hopmsv; en-GAL; UAS-socs44A 0 (E = 8.33) 0 (E = 12.69) 0 (E = 17.33)
Misexpression of Socs44A in a range of hop heteroallelic mutants resulted in lethality. For each mutant combination, x is the allele of hop designated in the column heading, n represents the total number of progeny scored in the cross. E represents the expected number of progeny of that genotype if Socs44A misexpression were to have no effect on viability. The expected value is calculated using the formula A/B=C/D, which takes into account the change in viability imparted by homozygosity for hop relative to heterozygosity and the change in viability for misexpression of Socs44A relative to the GAL4 alone. The progeny scored here are derived from the cross: hopX/Y Dp(1;Y)v+y+hop+; en-GAL4/CyO males mated to hopmsv/FM7; UAS-socs44A/TM3 females.
While the above data indicate that ectopic Socs44A is capable of downregulating JAK activity, they do not address whether Socs44A has an endogenous role in JAK pathway regulation. To determine if endogenous Socs44A downregulates JAK activity, we assayed the effect of a Socs44A deficiency on hop mutant phenotypes. The hopM38/msv heteroallelic mutant exhibits wing vein material at the posterior crossvein (Fig 4B) that is 98% penetrant. Removal of a single copy of Socs44A using either of two deficiencies in the region reduced the penetrance of the hop phenotype by as much as 52% (Table 2). An overlapping deficiency that did not remove the Socs44A locus had little effect on penetrance of the phenotype. These results suggest that regulation of JAK activity in the wing is a normal endogenous function of Socs44A.
Table 2 Endogenous Socs44A regulates JAK pathway activity.
+/+ CA53/+ (n = 237) NCX10/+ (n = 292) Drl/+ (n = 242)
hopM38/hopmsv 98% (of 89) 46% (of 13) 58% (of 12) 87% (of 15)
hopmsv/hopM38 heteroallelic females have a wing spur phenotype (Fig. 4B) that is 98% penetrant (n = 89). The penetrance of the spur phenotype is dramatically reduced by removal of one copy of Socs44A, as seen for heterozygotes of Df(2)CA53 (CA53)and Df(2)NCX10 (NCX10). Rescue of the phenotype was not seen with Df(2)Drlrv18 (Drl), an overlapping deficiency that does not include Socs44A. Total number of animals is indicated by n, and number of animals of the indicated genotype is in parentheses.
Socs44A upregulates EGFR pathway activity
In mammals, there are multiple points of cross-talk between the JAK and EGFR/MAPK signaling pathways [3,38-40]. EGFR signaling plays a prominent role in many developmental processes in Drosophila, including wing venation [41,42]. As mentioned above, expression of Socs36E has been reported to suppress EGFR signaling in the wings [28]. To determine the relationship of Socs44A to EGFR/MAPK signaling, wing phenotypes due to misexpression of Socs44A were examined in the background of heterozygous mutations for components of the EGFR signaling pathway. Engrailed-GAL4 driven misexpression phenotypes of Socs44A were suppressed in the background of heterozygous mutations for Ras85D, Son of sevenless (Sos), and Egfr (Fig. 5A,5B,5C,5D). Consistent with these observations, reduction in the dosage of the EGFR negative regulator argos enhanced the Socs44A misexpression phenotype (Fig. 5E). In contrast, concurrent misexpression of Socs44A and argos had antagonistic effects. Misexpression of two copies of an argos transgene under the engrailed-GAL4 driver resulted in wings lacking the 4th lateral vein (L4) as well as both cross-veins (Fig. 5H). Concurrent misexpression of a single copy of the Socs44A transgene in this background was able to rescue this phenotype, restoring the posterior crossvein and both the most proximal and distal portions of L4 (Fig. 5I). The resulting wing phenotype mimicked that seen when only a single copy of argos was used in the misexpression assay (Fig. 5J) or what is seen in heteroallelic Egfr mutants (Fig. 5G). Finally, concurrent misexpression of a single copy of the argos and Socs44A transgenes produced a nearly wildtype wing (Fig. 5K). These data indicate that Socs44A expression is able to suppress argos misexpression phenotypes in a dose-dependent manner. It should be noted that concurrent misexpression of UAS-GFP did not affect the UAS-argos phenotype (not shown), indicating that the suppression by UAS-Socs44A was not merely a consequence of titrating GAL4.
Figure 5 Socs44A increases activity of EGFR signaling. The ectopic wing vein phenotype of Socs44A misexpression (A) is rescued by reduction of Egfr (B), Sos (C) or Ras85D (D), positive effectors of EGFR signaling. In contrast, reduction of argos, a negative regulator of EGFR signaling, enhances the Socs44A misexpression phenotype (E). The argos allele combined with en-GAL have no effect on venation without the UAS-Socs44A transgene (F). Certain heteroallelic Egfr mutants possess a distinct wing vein phenotype, whereby the anterior crossvein and the central portion of L4 is missing (G, arrows). Engrailed-driven misexpression of argos has a similar phenotype (H and J). Concurrent misexpression of Socs44A antagonizes argos misexpression to restore near normal wing venation (I and K). The designation "2xUAS-argos" refers to presence of 2 total copies of the transgene in the genome.
Although these misexpression data indicate that Socs44A can enhance EGFR signaling, they do not necessarily demonstrate that this is a normal function of Socs44A. To address whether this is an endogenous function of Socs44A, we assayed the influence of a deficiency that removes Socs44A in the argos misexpression background. Engrailed-GAL4 misexpression of argos produces a range of phenotypic classes in which parts or all of L4 and/or the posterior cross-vein are missing (Fig. 6A). Addition of a single copy of a deficiency that removes Socs44A shifted the distribution of phenotypes to the more severe classes (Fig. 6B). In contrast, addition of an overlapping deficiency that does not include the Socs44A locus did not show such a shift. While it cannot be unambiguously stated that this effect is due to loss of Socs44A specifically, these results are consistent with the misexpression analyses and suggest that Socs44A normally plays a role in enhancing EGFR signaling in the Drosophila wing.
Figure 6 Socs44A deficiencies enhance argos misexpression phenotypes. (A) The engrailed-GAL4 driven misexpression of argos produces a range of phenotypes which were classified based on severity. The combination of en-GAL and Df(2)CA53 had no effect on venation. (B) In flies that were also heterozygous for Df(2)CA53, which removes the Socs44A locus, the distribution of phenotypes was significantly shifted to more severe classes as compared to animals heterozygous for Df(2)Drlrv18, an overlapping deficiency that does not remove Socs44A or for Sco, a chromosome wild-type for the 44A region.
Socs36E and Socs44A have different effects on oogenesis
Evidence presented here and elsewhere indicates that Socs36E and Socs44A can downregulate JAK signaling in the wing [28]. However, the ability of specific mammalian SOCS to regulate JAK activity has been observed to differ, depending upon the tissue examined [43]. To determine whether there is a similar context specificity for the Drosophila SOCS, regulation was examined in another tissue in which JAK and EGFR functions have been well characterized. Both pathways are required for proper patterning of the follicular epithelium surrounding developing egg chambers during oogenesis [26,33,44-47]. One of the distinct cell populations requiring these pathways is the posterior terminal follicle cells [33]. These cells are molecularly identified by the expression of the ETS domain transcription factor, pointed [47-49]. In clones of cells that lack hop activity (Fig. 7C,7D,7E) or egfr activity (not shown), there is a loss of pnt-lacZ expression, indicating failure to specify the posterior terminal follicle cells.
Figure 7 Socs36E and Socs44A have different activities during oogenesis. In wild-type ovaries (A, B), pnt-lacZ (red) is expressed in a gradient in the posterior terminal cells. Cells that lack hop activity (marked by a lack of green, see outline), also fail to express pnt-lacZ (C-E). Similarly, UAS-Socs36E misexpressed in clones (marked by presence of green, see outline), lack pnt-LacZ expression (F-H, see insets). In contrast, UAS-Socs44A misexpressed in clones (marked by presence of green, see outline), had no effect on pnt-LacZ expression (I-K). DAPI nuclear staining is shown in blue.
To test whether Socs36E and Socs44A can downregulate JAK or EGFR activity during oogenesis, clones of cells misexpressing these genes in developing egg chambers were examined. In clones misexpressing Socs36E at high levels in posterior cells of the developing egg chamber, there was a dramatic loss of the pnt-LacZ marker (Fig. 7F,7G,7H). This loss was restricted to only those cells that misexpressed Socs36E and did not influence neighboring cells. These results indicate that JAK and/or EGFR signaling was attenuated by Socs36E activity. In contrast, for cells in which Socs44A was misexpressed in a similar fashion, there was no reduction of pnt-LacZ expression (Fig. 7I,7J,7K). We conclude that Socs44A is unable to attenuate JAK activity in the follicle cells. This ability of Socs44A to regulate JAK signaling in the wing, but not in the ovary, indicates that SOCS activity in invertebrates can also be context specific. Furthermore, the differential ability of the fly SOCS to attenuate JAK and EGFR signaling in the ovary demonstrates distinct functions for these two proteins.
Discussion
The Drosophila genome encodes three homologues of the vertebrate SOCS. Each homologue contains the hallmark modular architecture, with a central SH2 domain followed by a carboxy-terminal SOCS domain. The genes are dispersed in the genome and are referred to by their cytological locations as Socs16D, Socs36E, and Socs44A. These fly SOCS genes are most similar to the vertebrate SOCS5, 6, and 7, none of which has been functionally characterized to date. Socs36E is the most similar in protein sequence to a vertebrate SOCS, SOCS5, but shares many characteristics with the extensively studied mammalian SOCS genes, SOCS1-3 and CIS. Each of these has been shown to be transcriptionally responsive to JAK pathway stimulation and act to downregulate JAK activity in a classical negative feedback loop [reviewed by [9]]. On the other hand, Socs44A is most similar to the less studied vertebrate genes, SOCS6 and 7. In this study, we demonstrated that Socs44A has properties that distinguish it from Socs36E and the canonical mammalian SOCS (compared in Table 3). First, the expression of Socs44A was not dependent on JAK pathway activity. Nevertheless, Socs44A was able to downregulate the JAK cascade in some, but not all tissues. In addition to regulating JAK pathway activity, Socs44A genetically interacts with the EGFR/MAPK pathway, acting to enhance its activity.
Table 3 Comparison of Drosophila SOCS.
Socs36E Socs44A
Expression-Embryogenesis Matches known pattern of JAK activation, including pair-rule stripes, gut, and tracheal pits Distinct from JAK activation, with possible exception of trachea very late
Expression- Oogenesis Matches known pattern of JAK activation, with graded expression highest at anterior and posterior poles Distinct from JAK activation, with expression only in nurse cells
Requirement for expression Requires JAK signaling for embryonic expression Does not require JAK signaling for embryonic expression
Inducibility Inducible by JAK activity in embryos Not inducible by JAK activity in embryos
Regulation of JAK activity Can repress JAK signaling in wing and possibly in follicle cells of ovary Can repress JAK signaling in wing, but cannot in follicle cells
Regulation of EGFR activity Can repress EGFR signaling in wing and possibly in follicle cells of ovary Can enhance EGFR signaling in wing
The contrasting properties of Socs36E and Socs44A are summarized.
The Drosophila genome encodes three SOCS genes
Phylogenetically, SOCS fall into three general clades. The first includes the best studied vertebrate SOCS, CIS and SOCS1-3. Interestingly, there are no representatives of this group found in the fly genome. Vertebrate SOCS of the remaining two clades have yet to be fully characterized with regard to their physiological roles, as well as mechanistic roles in JAK/STAT signaling. Socs36E is most similar to the vertebrate SOCS of the second clade, containing SOCS4 and SOCS5. It shares similarity not only in the SH2 and SOCS domain, but also in the region upstream of the SH2 domain. Mutational analysis has shown that SOCS5 inhibits IL-6 [50], whereas nothing is known about the activity of SOCS4. Socs44A falls into the third clade occupied by vertebrate SOCS6 and SOCS7, as well as the only C. elegans homologue. SOCS6 has been shown to downregulate the insulin receptor [51,52]. Very little is known about SOCS7, other than its ability to interact with Nck, Ash, and PLCγ [53]. Because of the relative lack of information about these latter two clades, study of the Drosophila SOCS may identify general properties of these homologues that span each clade.
Although mammalian genomes encode large families of specific JAK pathway components, Drosophila has only one characterized receptor, domeless, one Janus kinase, hop, and a single STAT, stat92E. Despite the simplicity of the transduction machinery for the JAK pathway, there are three SOCS genes in flies. Furthermore, there is only one Drosophila homologue of the PIAS negative regulatory family, zimp, and it is also capable of inhibiting JAK pathway activity [54,55]. In an organism with few functionally redundant genes, why are there three Drosophila SOCS? Two possible explanations for the apparent abundance of SOCS are that the different Drosophila SOCS may be expressed differently or they may differently regulate signaling through pathways other than JAK. Indeed, we presented evidence for both of these distinctions for Socs36E and Socs44A.
Socs44A does not participate in an auto-regulatory negative feedback loop
It has been demonstrated that, like the classical vertebrate SOCS genes, Socs36E is transcriptionally responsive to JAK pathway activity [[29] and this work]. In both embryos and ovaries, the expression of Socs36E mirrors the known pattern of JAK activation and, indeed, altered JAK activation in the embryo elicits a transcriptional alteration in Socs36E. Unlike Socs36E, the expression of Socs44A did not match that of JAK induction. In the embryo, detectable Socs44A expression was absent until late stages of embryogenesis, when it was restricted to the developing trachea. JAK activation does occur in the tracheal pits and has been implicated in tracheal morphogenesis [35,36], but Socs44A expression was lacking in the other tissues of the early embryo where JAK activation has been described. More telling was the finding that neither reduction nor expansion of JAK activation in the embryo had any effect on Socs44A expression. This disparity between Socs44A and Socs36E support the hypothesis that these genes are not redundant.
Despite the difference in expression of the two SOCS genes, both are able to downregulate JAK activity in some tissues. Misexpression of Socs36E is able to suppress JAK activity in the developing adult (imaginal) wing and thorax [28]. Similarly, misexpression of Socs44A reduced JAK activity in the imaginal wing, as illustrated by the enhancement of that phenotype by reduction of endogenous hop. Furthermore, misexpression of Socs44A rescued wing vein loss resulting from misexpression of hop. Perhaps most importantly, introduction of deficiencies that remove Socs44A rescued a hop wing vein phenotype. Taken together, these data strongly suggest that Socs44A downregulates JAK pathway activity during normal wing development. However, misexpression of Socs44A had no effect on expression of a marker for JAK pathway activity during oogenesis. This indicates that there is context specificity to SOCS action in Drosophila, a phenomenon that has been observed in the study of mammalian SOCS [43]. In contrast, misexpression of Socs36E was able to downregulate expression of the pnt-lacZ marker in follicle cells, although it cannot be distinguished whether this is due to reduction of signaling through JAK or EGFR. However, because Socs36E is expressed in the pattern of JAK activation in follicle cells, it is likely that it has a function in regulating JAK signaling in the ovary.
Socs44A upregulates EGFR/MAPK signaling
Another distinction we noted between the Drosophila SOCS was in their abilities to regulate signal transduction cascades in addition to JAK/STAT. Precedence for such additional roles for vertebrate SOCS include regulation of Tec, Vav, TCR, c-kit, and FAK mediated signaling [56-60]. It has been previously shown that Socs36E can suppress signaling not only through the JAK pathway, but also through the EGFR/MAPK pathway [28]. Socs44A was also able to regulate EGFR/MAPK signaling, but acted in the opposite manner. Socs44A was able to rescue misexpression of the EGFR negative regulator argos in a dose-dependent manner. Furthermore, mutations in EGFR pathway components rescued Socs44A misexpression phenotypes. Importantly, a reduction of endogenous Socs44A activity enhanced the argos phenotype. Taken together, these data suggest that a normal function for Socs44A is to enhance the EGFR pathway. A potential mechanism for this genetic interaction can be found in a recent report describing physical interaction between SOCS3 and the p120 RasGAP [61]. p120 RasGAP, a GTPase-Activating Protein, is an antagonist of MAPK signaling that is responsible for inactivating Ras. It does so by stimulating Ras GTP hydrolytic activity, leaving Ras in a GDP-bound, inactive configuration. Upon interaction with SOCS3, p120 RasGAP is unable to inactivate Ras, resulting in an upregulation of the EGFR/MAPK pathway. Perhaps Socs44A is acting in an analogous manner. Indeed, there are three candidate RasGAP genes in the fly genome. Biochemical analyses will be required to address this hypothesis.
Conclusions
There are three Drosophila SOCS, all of which have greatest homology to the two classes of vertebrate SOCS that are least well characterized. One of these, Socs36E, is a member of the vertebrate SOCS4/5 class and has been previously characterized [28,29]. It is similar to classical SOCS in that its expression is regulated by activity of the JAK pathway and that it functions to suppress JAK activity. Here we provided the initial characterization of Socs44A, a member of the vertebrate SOCS6/7 class. In contrast to Socs36E, activation of the JAK pathway was neither necessary nor sufficient for the expression of Socs44A. We conclude that Socs44A is unlike classical SOCS because it does not participate in a JAK pathway negative feedback loop. Still, Socs44A was capable of repressing JAK signaling, but that activity was limited to certain tissues. This context specificity is a feature that is shared with classical SOCS. Finally, Socs44A and Socs36E had opposite effects on EGFR/MAPK signaling. The enhancement of MAPK signaling that was seen for Socs44A is reminiscent of the influence of SOCS3 on this pathway, which is exerted through physical interaction of SOCS3 with p120 RasGAP. Perhaps a similar mechanism explains the enhancement of MAPK activity due to Socs44A. The differences observed here between Socs36E and Socs44A strongly suggest that they have distinct functions in the fly. Furthermore, the differences between Socs44A and the well studied class of canonical vertebrate SOCS may be representative of undiscovered distinctions amongst the three classes of vertebrate SOCS.
Methods
Comparison of SOCS sequences
Putative Drosophila SOCS genes were identified using a simple tBLASTn 2.0 query with a consensus sequence for the vertebrate SOCS domains [30] used to probe the complete genome contig sequences available from the BDGP. Identified homologies were compared with the predicted gene structures reported as "CG" sequences in the annotations of the genomic contigs. The translated sequences of the three putative SOCS gene genomic regions were scanned manually for possible alternative structures. The sequences surrounding the SOCS and SH2 domains were used to generate primers for the amplification of DNA corresponding to each putative gene. Amplification products were cloned and used to generate probes for the identification of cDNAs as described below. Phylogenetic comparison of SOCS proteins was performed using AlignX (VectorNTI 9.0), based on the ClustalW algorithm, to generate protein alignments and a neighbor-joining algorithm to create a phylogenetic tree.
Identification of cDNAs
A cDNA library constructed from RNA of 12–24 hr old embryos [62] was screened using 800bp of genomic DNA derived from the 3' end of the Socs36E coding region, including the SOCS box and SH2 domain. Two independent clones (Genbank accessions AF435838 and AF435839) were recovered, with the former being structurally similar to an EST from the BDGP (clot #7147). The BDGP also recovered two cDNA clones representing socs44A which have been designated as clot #8463. We have determined the complete sequence of the longer clone, LP02169 (Genbank AF435923).
In situ hybridizations
In situ hybridizations to embryos were performed as previously described [32]. Digoxigenin labeled probes for Socs36E and Socs44A were generated from the 5' ends of the respective cDNAs and did not include the coding region for the conserved SH2 and SOCS domains. Germline clone mutants for the hopc111 null allele were generated using the ovoD1 dominant female sterile technique [34]. Embryos derived from mutant mothers were collected overnight and prepared for hybridizations as previously indicated. Embryos misexpressing upd in a specific pattern were generated by crossing females carrying a UAS-upd transgene with males heterozygous for paired-GAL4, which expresses GAL4 in the seven stripe pair-rule pattern of the paired gene. Progeny were collected and hybridized as above. Trachea in germline clone-derived hopc111 embryos were visualized with the trh10512 enhancer trap [63] using anti-β-gal antibody (Cortex Biochemical, at 1:1000) as previously described [26].
Misexpression studies
To express Socs36E and Socs44A under control of GAL4, the full-length cDNAs described above were cloned into the pUAST vector [64]. Germline transformations were performed [65] and transgenic lines established. For wing phenotypes, engrailed-GAL4 (e16E-GAL) was used to drive expression of the transgenes in the posterior compartment. Wings were dissected and mounted in Hoyer's medium [66] for photography.
Ovarian clones of the null allele, hopc111, were generated by hsFLP mediated mitotic recombination as previously described [26,33]. Misexpression clones of Socs36E and Socs44A were generated using a GAL4 flip-out cassette [67]. Genotypes of those animals were w [hsFLP]1; [Act5C>y>GAL4] [UAS-GFP.S65T]/ [UAS-socs36E]11.2 and w [hsFLP]1; [Act5C>y>GAL4] [UAS-GFP.S65T]/+; [UAS-socs44A]/ pnt-LacZ, respectively. For each, ovaries were fixed and stained with anti-β gal and anti-GFP as previously described [26,33].
Microscopy
All in situ hybridization and wing images were acquired using a Spot Camera (Diagnostic Instruments) on a Nikon E800 microscope using differential interference contrast (DIC). A Leica TCS-SP laser scanning confocal microscope was used to capture all fluorescence micrographs. All images were then exported to Adobe Photoshop for manipulation and annotation.
Authors' contributions
JR performed experiments with Socs44A and participated in drafting the manuscript. GR performed most experiments with Socs36E. SH performed some experiments with Socs36E. RX performed immunofluorescence experiments in ovaries, except for Socs44A. DH conceived of the study, participated in its design and coordination, and participated in drafting the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank the Berkeley Drosophila Genome Project and FlyBase for genome and EST sequences. We also thank T. Schupbach, M. Freeman, and the Bloomington Stock Center for fly strains. This work was supported by the NSF (IBN-0091535 to DAH), the University of Kentucky Biology Graduate Program (JSR), and a University of Kentucky Women's Fellowship (GR).
==== Refs
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| 15488148 | PMC526380 | CC BY | 2021-01-04 16:39:07 | no | BMC Cell Biol. 2004 Oct 15; 5:38 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-38 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1451547154710.1186/1471-2105-5-145Research ArticleExtending the mutual information measure to rank inferred literature relationships Wren Jonathan D [email protected] Advanced Center for Genome Technology, Department of Botany and Microbiology, The University of Oklahoma, 101 David L. Boren Blvd, Rm 2025, Norman, OK, 73019 USA2004 7 10 2004 5 145 145 4 6 2004 7 10 2004 Copyright © 2004 Wren; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scale-free networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations.
Results
Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology categories) found within MEDLINE are used to create a network of associations for evaluation.
Conclusions
Mutual information calculations can be effectively extended into implied relationships and a significance cutoff estimated from analysis of random word networks. Of the models tested, the shared minimum MIM (MMIM) model is found to correlate best with the observed strength and frequency of known associations. Using three test cases, the MMIM method tends to rank more specific relationships higher than counting the number of shared relationships within a network.
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Background
Most scientific fields are data-intensive, but perhaps even more so for biology and medicine. Sequencing efforts have generated billions of base pairs of genetic information across hundreds of thousands of species, and ushered in the relatively recent completion of the Human Genome Project[1]. Microarrays enable thousands of transcriptional measurements per experiment [2], and high-throughput chemistry enables the simultaneous screening of thousands of molecules at a time for activity[3]. New discoveries among research areas (e.g. genetics, medicine, chemistry) lead to a necessarily increasing amount of specialization as more objects (e.g. genes, diseases, phenotypes, chemical compounds, etc.) are discovered to be of research interest. This is reflected by the growth in the number of scholarly journals published every year as well as the number of total records indexed in biomedical literature reference databases such as MEDLINE[4]. In any field, the gain in our cumulative scientific knowledge has the unfortunate effect of narrowing our perspectives as individuals – providing us with far too much information to assimilate, and far too many variables to analyze. Yet the most valuable type of information is often what is not known or apparent to others – information implied by a set of data, facts or associations. History is replete with examples of insights into scientific problems coming from a series of observations from apparently unrelated fields, discoveries or events. But how could one retrieve or compile such information in cases where one is not certain what to look for and the search space is vast? This is the primary reason that methods of data-mining and knowledge discovery are becoming increasingly important in handling this explosion of information.
Previous research
Most scientific knowledge comes from peer-reviewed articles and is written in free-form text, which is difficult to analyze algorithmically. However, the idea that novel relationships within text could be computationally identified based upon existing relationships has its roots in an approach developed by a researcher named Don Swanson, who used software to identify words shared between article titles [5]. Using their software, called Arrowsmith, Swanson and Smalheiser were able to identify common intermediates between Raynaud's Disease (a circulatory disorder restricting blood-flow to the extremities) and the dietary effects of fish oil, leading to the hypothesis and subsequent proof [6] that compounds within dietary fish oil could alleviate the symptoms of Raynaud's Disease [5,7]. To explain why such a sensible hypothesis had gone unnoticed by researchers in either field alone, the term "non-interactive literatures" was coined. This term, in essence, implies that increasing specialization among all fields results in a relative lack of awareness of the findings in other, less related fields. These entities that do not have known or documented associations, yet share intermediate relationships, have been referred to as "transitive", "implicit", "indirect" or "inferable" relationships. Deciding that no relationship exists when no co-mentions exist is somewhat of an over-simplification, but a necessary one. Realistically, several co-mentions between terms could be observed without a definitive relationship present. However, if one uses a greater-than-zero cutoff to define when a relationship exists, false-negatives become a problem: Some co-mentions below the cutoff will constitute a real relationship. Using zero co-mentions as a cutoff is a convenience to avoid this problem even if the end result is that some relationships are declared "known" when they really are not.
While pioneering, a keyword-based method such as Swanson and Smalheiser's is both limiting and highly burdensome, especially where a large body of literature is concerned, because the number of unique keywords grows quickly per record analyzed. Neither is the method amenable to open-ended querying – that is, telling a user what is implicitly related to a query term. Rather, one must essentially begin by postulating a relationship between a query term, A, and another term, C, where a set of intermediate terms, B, can be found that connect the two. Even improvements in visualizing or exploring records that share commonalities and/or define entities of interest [8,9] are limited because they require manual user navigation and analysis of results. Other approaches have attempted to utilize Medical Subheadings (MeSH)[10] or the Unified Medical Language System (UMLS) [11] to engage in open-ended discovery by pairing concepts, counting the number of relationships shared by two terms as a means of judging its implicit significance. However, these approaches do not take into account the fact that the more general the nature of the relationship is, the more connections are likely to be shared by two terms.
It was previously demonstrated that, because the number of associations between terms follows a scale-free, or inverse power-law, distribution, the number of inferable associations with any given term rapidly approaches the maximum number of possible associations as the number of direct associations grows[12]. That is, even if one starts with a term that is only associated with several others, at least one of these is likely to be associated with a very large number of terms. Thus, the starting term will be implicitly associated with most of the network (the "small world" phenomenon). Therefore, the issue is not identifying implicit associations, but somehow judging which of the many implicit associations are worth further examination.
Previous work demonstrated that it was feasible to identify pertinent implicit relationships by ranking inferred relationships and preferentially examining those at the top of the list[12]. One of its shortcomings, though, was that associations between terms are assigned based upon co-occurrence of terms within an abstract. This is a fairly well accepted means of assigning tentative relationships between terms, but when considering the scale-free distribution of objects within the literature, it is apparent that some frequently mentioned objects could be co-mentioned many times with other terms without any actual biological association being implied. Figure 1 uses an analysis of terms related to the term "capsaicin" to illustrate this point. Although the MIM may have drawbacks in identifying broad relationships (for example, see Table 1 – some very pertinent relationships receive modest MIM scores if the terms are common), it is a very straightforward and well-established means of measuring information content between two terms. Such a measure would enable us to pursue more specific relationships – those with high information content. MIM, however, can only be calculated using direct (A-B) or (B-C) relationships rather than implicit (A-C). Thus, the goal here is to test methods of extending the MIM calculation to include implied relationships such that a statement can be made about the implied mutual information content of two unrelated terms.
Identifying literature-based associations
The general approach to associating objects by searching for their co-occurrence within text has been used in many fields as a simple, yet comprehensive way to identify potential associations. In biology and medicine, co-occurrence has been used to identify potential relationships between genes [13,14], proteins [15] and drugs [16]. The disadvantage of this approach is that associations are very general – that is, no specifics on how two objects are related or associated are obtained by this method. False-positives can also be a problem, as terms far apart within the abstract with no apparent association may be included as "relationships". The advantages are that it is easy to implement and comprehensive.
To begin a search for novel, inferable associations within the literature, relevant "objects" of interest in scientific research were first defined by assimilating database entries from relevant databases into one central database. By doing this, both words and phrases can be identified within text, and it permits synonymous terms to be mapped to primary terms. All electronically available literature was then analyzed for associations between objects of interest by searching for their co-occurrence within MEDLINE records (titles & abstracts), summing the total number found. The significance of this collective set of co-occurrences is evaluated using the mutual information measure (MIM), which was originally based upon Shannon's Entropy theory [17], but has also been successful in identifying lexical dependencies [18]. By processing a body of literature that comprehensively covers a topic, field or area, it can be asserted that the current state of knowledge has been approximated, at least on the level of broad object-object associations. All available literature was processed, creating a network of associations for each object. This network can in turn be analyzed for associations shared by two unassociated objects. That is, we can use the network to identify objects that share associations but are not themselves associated. Such objects are said to be implicitly associated with each other, and new associations can be potentially inferred by evaluation of their shared associations. Since there are many implicitly associated objects, the relevance of each one is also evaluated using the MIM. However, a MIM can be calculated to evaluate the relevance of an association between A and B and between B and C, but it is not clear how each of these individual scores extends to the inference of an association between A and C. Therefore, we explore and evaluate different methods.
Methods and algorithms
Code was written in Visual Basic 6.0 (SP5) using ODBC extensions to interface with an SQL-based database, with database queries written in SQL. Programs were executed on a Pentium 4 3.06 GHz machine with 1 GB of RAM and two ultra-fast SCSI hard drives. The National Library of Medicine graciously provided an electronic archive of MEDLINE records in XML format. To obtain a set of common words for analysis, the Merriam-Webster dictionary was parsed into individual words and each word summed by the number of times it was observed within the dictionary. 10,000 words were chosen with dictionary frequencies ranging from 322 to 28. This range was selected so that no extremely common or rare words would be within the list. To create a database of random word associations, only 100,000 titles/abstracts were used. This was done to avoid network saturation (i.e. having a significant number of objects related to every other object) and to ensure that the distribution in the number of associations between words resembled the same power-law distribution observed for biomedical objects.
The occurrence of such objects within scientific text is identified by comparing phrases within MEDLINE records to entries in the object recognition database (ORD). This ORD is built by inputting terms found in several different biomedical databases, all freely available for download. Objects classified as diseases, disorders, syndromes or phenotypes were obtained from Online Mendelian Inheritance in Man (OMIM) [19]; chemical compounds and small molecules were obtained from the Medical Subject Headings (MeSH) database [20]; approved drug names from the Food and Drug Administration; genes were obtained from Locuslink [21], and ontological classifications for genes were obtained from the Gene Ontology consortium [22]. Assimilation of terms is done automatically, but a table within the ORD contains additional biomedical terms to be added or deleted as deemed necessary (e.g., some databases contain vague or uninformative terms such as "survey" or "extended", useless information such as "deleted entry" or errors such as "#NAME?"). Compared to the overall size of the ORD, this table is small (1,007 entries versus over 223,000 terms assimilated) and designed primarily to reduce clutter.
Acronyms for entries, if not explicitly stated within the assimilated database, were obtained from an acronym database[23]. Similarly, spelling variants were also obtained from this database where possible. This database can be accessed online[24]. As an example of spelling variants detected, the user can go to this URL, enter the acronym "ICAM-1" and note the many subtle variations. The acronym resolving heuristic used to construct this database was also used to resolve acronyms within text when they occurred.
The Mutual Information Measure
A scoring scheme based upon the Mutual Information Measure (MIM) [17] is used to estimate strength of association between co-occurring terms within the literature. It should be noted that other statistical methods of association such as chi-square tests, log-likelihood ratios, z-scores or t-scores could be used as well – these are all means of judging the statistical significance of a relationship. In this paper, however, we will focus on the MIM only as a proof of principle that mutual information calculations can be extended into implicit relationships as well. The MIM has been widely used to quantify dependencies between variables, including co-occurring terms in text [25], and is shown in equation (1):
PAB is the measured probability that A and B will be observed together in the same abstract, while PA and PB are the probabilities of observing A or B, respectively, in a given abstract. Furthermore, because scientific research and discovery is a time-dependant process, prior information can be incorporated to refine the probabilities in Equation (1). The describing of a disease or discovery of a gene, for example, will occur at a given point in time (illustrated in Figure 2) within the history of publications. Regardless of an object's overall frequency in the database, the probability it will appear in the literature prior to its discovery is zero. Thus PA and PB are calculated from their time of first appearance. PAB is then calculated using the later of these two dates. PAB, PA and PB are thus calculated as:
Where TA and TB are the total number of records A and B are independently mentioned in, respectively, and TAB is the total number of records co-mentioning A and B. Af and Bf represent how many records were read in before the first occurrences of A and B were observed, respectively. Max(Af, Bf) represents the larger of the two values between Af and Bf. And At is the total number of records processed.
As an example of how the MIM score is used, assume that the probability A will appear in any given record within a database of records is 10% and the probability of B appearing is the same. If the appearance of A is completely independent of the appearance of B then no information about one can be gained by observing the appearance of the other. The probability both A and B will be observed in the same record is thus 0.1*0.1 = 0.01. The value of MIM in Equation 1 then evaluates to 1 and the log value to zero – the information gained on one object by observing the other. If the probability of observing A increases when B is mentioned, then MIM > 0. If A and B are rarely mentioned together, then MIM < 0. When considering scientific writing style with reference to biomedical objects such as genes, diseases and chemical compounds, there is a probability that two of them might be mentioned together in the same record without having an established association. For example, one of the objects may be very commonly used in many studies (e.g. the gene LacZ is used for staining assays, luciferase is used for luminescence, etc), or one of the objects may be of great scientific/medical interest and authors may make an extra effort to speculate how their results might relate to such objects (e.g. cancer, diabetes, heart disease, apoptosis). The MIM provides a way of quantifying literature-based object dependencies. However, taking the log value can provide a negative weighting to an association when two frequent terms are mentioned together. Optimally, irrelevant or uninformative associations (i.e. those with little mutual information) would be ignored entirely rather than penalized. Therefore, the log function is removed and the equation becomes:
The possibility remains that rare associations might receive a very high MIM score [26], but it is hoped that the fact that many MIM scores are being summed and compared will ameliorate this effect when it occurs.
Inferring new associations based upon commonalities
Figure 3 shows the general conceptual approach undertaken here. Call the primary research object node "A" in a network constructed of MIM scores between objects. For each A there is a set of other objects, or nodes, associated with it by virtue of co-occurrence in the literature. We'll call this set "B" and assuming a total of t objects in this set, each individual object can be given the symbol "Bn", where 0 <n <t. For each Bn, there is another set of objects related to it by literature co-occurrence, called "C". Each object in the set C may or may not be connected to the primary object, A. That is, an association may consist of A↔B↔C where an object in the set C also belongs to the set B. The symbol "↔" is used here to represent the existence of a non-directional association between two objects.
Objects in the set C having no literature-based association with the primary object, A, represent associations that have not been previously made, or at least documented, by others. These represent new associations that can potentially be inferred by virtue of their shared associations. Because the number of implicit associations rapidly increases with each established association, the goal here is to provide a quantitative measure of the strength of an implicit association based solely upon the associations shared by two objects. After all, if no known relationship is documented, then these shared associations will be the only way to understand the nature of an relationship between A and C. Since directly associated objects also share associations with other objects, it is reasoned that the strength of known associations can be used to benchmark how well the scores from implicit associations correlate with the relative importance of an association. However, it is not clear how A-C relationships are best evaluated given a set of component A-Bn and Bn-C associations. Two models are thus proposed and evaluated, the numeric score obtained by any one of them will only be relevant in terms of how well it assigns a relative importance to each A-C connection within a list.
Scoring inferred associations
The first model to be tested assumes that the total information content of an implied A-C association can be approximated by the mutual information measure of each component connection. Thus, the MIM scores for each A-Bn and Bn-C MIM association is averaged over a total of t shared connections and then finally divided by t to normalize the total score by the total number of connections. The function for the normalized averaged MIM (AMIM) model is:
As model by which A-B and B-C values were summed was also considered, but it would be functionally indistinguishable from the AMIM model in terms of ranking implicit relationships, so it was not included.
The second model views the process of inferring an A-C connection as function of each of its component processes, limited in its potential by the mutual information in each step of the inference process. That is, inferring an A-C connection depends upon how much information is in the A-Bn association as well as the Bn-C connection, and the information potential an A-C connection will be no greater than the least mutual information given by A-Bn or Bn-C. This is equivalent to assuming that a chain can be no stronger than its weakest link. The equation for the normalized minimum MIM (MMIM) model is:
Results
A total of 12,899,016 MEDLINE records recorded from 1967 to May 2003 were processed in chronological order to create a network of 10,873,926 associations between a total of 112,805 unique objects assimilated from the databases mentioned. When including synonyms, the total number of recognizable phrases for these unique objects was 223,540 (e.g. "IL-6" is a synonym for "Interleukin-6", and the two are treated equivalently).
The distribution of objects found in MEDLINE ranges from more general categories (e.g. "blood", "tumor", "stress", "lesions") that are found in a higher percentage of records ("blood" was the most abundant, being found in 17.5% of all records analyzed) to the more specific. The frequency of objects when plotted follows a power-law distribution and resembles that of a scale-free network, which is reasonable given that new objects are typically studied in terms of their relationship to known objects (law of preferential attachment).
Records were chosen for analysis due to their electronic availability and are also because they are a good source of pertinent information due to their brief, focused nature that presumably contains a summary of the most important findings in each report. Several objects were examined to see if associated objects with high MIM scores correlated with the relative importance of the association. This was done by obtaining summary descriptions of an object from various authoritative sources such as review articles, glossaries or biomedical databases. Table 1 shows an example of associations to an object that were found by scanning all MEDLINE records. Note here that objects with higher MIM scores tend to be objects found in fewer MEDLINE records. Initially this was thought to be problematic because objects highly germane to the biological activity of another object could be down-weighted solely because of their relative abundance. However it was found that when analyzing sets of shared associations in both AMIM and NMIM models, these abundant objects that initially receive low MIM scores subsequently receive much higher scores because they share many high-information content associations with the primary object of analysis, and their cumulative score rises with each one.
Table 1 can be said to reflect the current state of knowledge, as obtainable from scientific abstracts and with reference to biomedically relevant associations to capsaicin. From what is known, a list of what can be inferred is constructed. Each of these secondary associations is used to identify and score implicit relationships as illustrated in Figure 3. As mentioned earlier, a subset of the objects in (C) identified by their associations to the secondary objects (B), will be other secondary objects themselves. That is, they will also be in the set B.
Model evaluation
When ranking inferred associations, the goal is for the score assigned to an inference to correlate well with the amount of mutual information gained from any given association. The only reference basis for this is the mutual information contained within established associations. Since they too will frequently contain shared associations, they can be evaluated independently using only their shared associations (Figure 4). Several different methods of ranking inferred associations were evaluated together using the object capsaicin for comparison. Because time must be spent analyzing shared associations to determine the nature of an inferred association, one inference ranking method could be considered superior to another if it yielded a high ratio of relevant to irrelevant associations during the analysis phase. For analysis purposes, the "relevance" of an association will be equivalent to its MIM score – the higher the MIM score, the more relevant the association. Thus, the higher that known, relevant associations are ranked within the set of all inferable associations, the better the ranking method is.
To evaluate this, a graph is drawn to reflect the rate that established relationships are discovered within the set of all objects analyzed. The total of all MIM scores for known relationships is added together, in order from highest MIM score to lowest, to reflect the fastest rate by which they could be discovered. When plotted, this curve is what would be observed were mutual information preserved exactly (the "exact" curve). Because it's neither expected that all possible relationships are known, nor that mutual information is static as the scientific discovery progresses, it is not anticipated that this curve would or even should be followed exactly (if it were, then that would imply future discoveries could not be more informative than what is already known). However, it is reasonable to expect established relationships with high mutual information content to retain a relatively high mutual information content when evaluated on the basis of its shared relationships. Thus, it is expected that the implicit MIM curve follow the "exact" MIM curve.
Figure 5 illustrates what percent of all established associations are identified by each scoring method. The mutual information of associations shared by two objects is ranked by several methods, including the Minimum MIM (MMIM – equation 6) and Averaged MIM (AMIM – equation 7). Objects are ranked here by their shared associations and included in this set are associations that have already been established within MEDLINE as well as those that are implicitly associated. When an established association is encountered within this ranked list, its MIM is added as a percentage of the sum of all MIM scores. When all established associations have been ranked by each method, the total will add to 100%. The faster an inference ranking method approaches 100%, the better it scores objects with high mutual information. Shown for comparison is what the curve would look like if each established association were ranked in the exact order of its highest to lowest MIM scores ("Exact"). Also shown is how quickly established associations would be found by guessing at random ("Random"), and how quickly established associations would be found when counting the number of intermediates ("Count of B").
To gain a better quantitative estimate of performance, 50 objects were chosen at random from both the MEDLINE and random word databases. Each object was analyzed to identify and rank other objects that shared relationships with it as described and the area under the curve (AUC) was taken for each of the ranking methods shown in Figure 5. For the MEDLINE network, the average AUC for the MMIM was 43% ± 9%, for the AMIM it was 42% ± 8%, and using the count of shared relationships was 9% ± 7%. The difference between the MMIM and AMIM was not large (p < 0.29 using a 2-tailed paired t-test) but was slightly biased by a relatively few examples where AMIM performed very well. Out of the 50 trials, MMIM performed better 35 times, equally 11 times and worse 4 times. What is most pertinent is that both MIM methods ranked objects with high mutual information content significantly higher (p < .000001) than counting the number of shared relationships.
A peculiar effect was noted with the average MIM-based scoring model: Some implicitly associated objects received higher MIM scores than the primary object itself, which is also analyzed as a control. There tend to be relatively few, sometimes none, such instances per analysis, but it occurs when a relatively rare object shares several or more associations with the primary object. This effect was not present in the minimum MIM model.
Using a random word network to estimate significance intervals
The scores assigned by inference methods so far have no meaning by themselves, but only as a means of ranking the potential relevance of an inference. Because the majority of database objects will be present in the list of implicit connections, the question naturally arises as to where a significance cutoff can be drawn. A range of significance for a given MIM score can be estimated by analysis of a random word network in which we would expect that meaningful relationships are only encountered by chance.
Since the MMIM model performed slightly better than the AMIM model, we evaluated it using the random words database. Words in the random network were effectively chosen at random from the Merriam-Webster dictionary (see Methods and algorithms), and so relevant associations between these words co-occurring within MEDLINE records should occur predominantly by chance. An uninformative association (i.e. chance alone could explain the number of term co-occurrences) would have an average MIM score of 1 (e.g. see Equation 5). Thus, summing a set of t random associations and dividing by t would also be expected to have an average (normalized) MIM score of 1. This is true for any set of A-B associations as well as a corresponding set of B-C associations, thus an average value of 1 should still be obtained when calculating the average minimum MIM score. Figure 6 shows a plot of the average minimum MIM score (with standard deviation) by the number of shared associations of words in a random network. The average minimum MIM score trends towards a value below 1 (average value from 500 to 1000 shared connections = 0.7), which is not surprising given the nature of writing: It is not random, so two words would not necessarily co-occur together with a probability that is proportional to their relative frequencies. This also suggests that a log value of zero for a MIM score may not be the most effective dividing line between informative and non-informative associations. The values obtained from this analysis provide us with a way of estimating a significance cutoff for implied mutual information analysis. As Figure 6 also shows, the fewer shared associations between two objects, the higher the average normalized MMIM score is as well as its standard deviation.
Evaluating capsaicin
Using the example of capsaicin brought up earlier, we analyzed it using the methods described to identify and rank objects sharing relationships with it (Table 2). When ranked by counting the number of shared relationships, the more general relationships (as mentioned in Table 1) tend to rank towards the top, such as calcium and neurons. This seems a good means of identifying general relationships, but each of the objects on this list is hardly specific to capsaicin. Ranking by MMIM, however, changes the nature of the types of objects that are ranked highly to those that share molecular/physiological relationships with capsaicin by their effects upon nerves and transmission of impulses. For example, the ileum is frequently used to test capsaicin effects because of its contractile response. Tachykinins Substance P and Neurokinin A as well as the neurotransmitter acetylcholine[27] are responsible for afferent nerve transmission in response to capsaicin, the response to which can be blocked by antagonists such as tetrodotoxin[28] or atropine[29]. These implicit objects share relationships with B objects of all different types mentioned in the methods & algorithms section, but the ones that tend to score highest are the ones that share several highly informative relationships with the A object. In general, these informative relationships tend to be objects that are mentioned much more frequently with the A object than any other object within the literature. Acetylcholine, for example, is associated with many neurological processes, but has relatively high MIM scores with other objects related to capsaicin such as bradykinin, atropine, neuropeptide Y and substance P, which are all molecules that affect the transduction of sensory signals.
Re-evaluating Swanson's original discoveries
Finally, we also sought to re-evaluate some of Swanson's original hypotheses as has been done in other text-mining studies [10,11,30]. It makes less sense, however, to attempt to judge performance based upon whether or not Swanson's studies or hypotheses could be replicated, per se. To do so presumes that Swanson's initial study was the "correct" way of finding relevant implicit relationships and Swanson did not employ the open-discovery model in these examples anyway. It would be useful to know, however, where Swanson's predictions rank among others using models in which implicit relevance is judged by counting the number of shared relationships and where it is evaluated by the MMIM. Both Raynaud's and Migraine headaches were analyzed as starting objects (A), the goal being to find all C objects that share relationships and rank them by their relevance. Both known and implicit relationships were displayed. The top 10 results are summarized in Table 3, and the entire dataset is available by request.
When ranking implicit relationships by the number of shared relationships, fish oil scored #1025 in the Raynaud's list and magnesium (the link Swanson found with migraines [31]) scored #166 in the Migraine list. When ranked by MIM, fish oil scored #1512 and magnesium was ranked #458, lower in both cases. The scores for Raynaud's Syndrome<->Fish oil were lower than expected. Upon examination, Swanson's discovery of this link, although validated experimentally [6], has apparently not generated a lot of continued experimental research interest in this area in the 15 years since then. A search via Ovid on "(raynaud or raynauds or raynaud's) and (eicosapentaenoic or docosahexaenoic or fish oil)" yielded only 5 papers, three of which were text mining papers including Swanson's original study [5,30,32], the fourth was the 1989 validation study [6] and the fifth was a study showing that fish oil did not have a significant effect upon Raynaud's phenomenon in mixed cryoglobulinemia (a syndrome in which Raynaud's is one of many symptoms)[33].
Examining the relationships that tend to rank highly in both models it is apparent that, when ranking by the number of shared relationships, the higher-scoring entries tend to be more general and vague in nature (e.g., links to "blood", "development", "females" and "males"). When ranked by the MMIM, their relevance to the object in question is more readily apparent. For example, sumatriptan is a drug used to treat migraines and other items ranking highly on the list such as nausea, vomiting, and dizziness typically accompany migraines. Notably, one of the important links that Swanson used to surmise the role of magnesium is also on this list: Seizures, which cause migraines.
Discussion
Information retrieval (IR) methods are limited to querying what is known; yet often the most valuable information is what is not directly known. Mutual information measures have been used successfully in many IR applications, and a method has been presented here to extend it to inferable associations. We find that the normalized MMIM method of ranking inferences based upon their shared associations correlates best the level of currently established mutual information. A good correlation is suggestive that mutual information is being captured even though evaluation proceeds indirectly, through intermediates. For simplicity, we have used a cutoff of zero co-occurrences to suggest that no association between objects has been made, but it is quite possible that a number of co-occurrences could be noted between two objects yet no specific relationship between them documented. Or additionally, a certain relationship may be known between the two, but other important relationships still remain to be inferred. At this point, however, it is not clear how this would effectively and quantitatively be taken into account.
The method reported was applied to biomedical research, but could ostensibly be applied to any domain in which the goal is to identify undiscovered relationships. Importantly, this method of automated inference ranking provides a quantitative way of prioritizing inferred associations when available literature is growing rapidly in size and scope.
Acknowledgements
The author would like to thank Le Gruenwald for a helpful review of this manuscript and the National Library of Medicine for providing MEDLINE records in XML format. This work was funded by NSF-EPSCoR grant # EPS-0132534.
Figures and Tables
Figure 1 The number of records that two objects co-occur in is loosely correlated with their mutual information. Objects co-occurring in MEDLINE records with "capsaicin" are shown in this graph sorted in descending order by their log2 MIM score (see Equation 1), which is plotted on the central axis. The number of records they co-occur within is displayed as vertical bars. Note that a good proportion of objects can co-occur many times, yet at the same time have a negative mutual information measure.
Figure 2 Time-dependency of discovery. All genes, drugs, phenotypes and chemical compounds first appeared at defined times within the literature, even if they were known before then. This prior information is incorporated into mutual information calculations. Shown is a timeline proceeding left to right from the first MEDLINE record indexed to the most recent, with approximate times of first appearance for hypothetical objects A, B and A-B together within a record (title + abstract). Tick marks roughly correspond to 1 million records.
Figure 3 Conceptual illustration of how an inferred association between two objects is identified and evaluated using the methods in Equations 6–7. A primary object of interest (A – black node) co-occurs in records with other objects (B – gray nodes). Each association (A-B and B-C) is assigned a mutual information score, the higher the score the stronger the association between the two. These intermediate associations (B) can then be used to infer an association between A and C (white nodes). Example values are given above the lines. Note that, depending upon whether these values are averaged or the minimum value taken, the rank order of the implicit connections changes. If averaging, the bottom C node receives a higher score (11.5 vs. 9). If taking the minimum, the top node receives a higher score (8 vs. 6).
Figure 4 Established associations also share associations with a primary object of interest and can be evaluated purely in terms of their shared associations.
Figure 5 Performance of several approaches to ranking the mutual information contained in inferred relationships for the object "capsaicin"
Figure 6 The average normalized minimum MIM (ANMMIM) score trends towards a value slightly less than one when a network of random words is analyzed for the relevance of implicit associations. The dashed trend line is a power-law fit to the standard deviation of the ANMMIM.
Table 1 An example of co-occurring objects found in MEDLINE with a primary object of interest, capsaicin. A brief summary of what capsaicin is shown at top, with database objects of associated biomedical research interest in bold. Below are the MIM scores for each of these co-occurring objects, along with their relative rank in the list when sorted by score. A total of 2,069 objects co-occurred with capsaicin in the body of literature analyzed. Capsaicin is the active compound in chili peppers that causes their burning sensation. It acts upon a small family of capsaicin receptors, which have been found in sensory and vagal neurons, and allows a calcium influx into these cells causing them to fire and send heat-related signals to the CNS. Capsaicin can cause neurogenic inflammation upon application, and in high enough concentrations it is a neurotoxin.
Primary Object (A) MIM Score Secondary Objects (B) Relative rank # of records containing B
Capsaicin 0.24 Calcium 1132 303,041
Capsaicin 10.42 Neurotoxin 66 7,612
Capsaicin 35.33 Neurogenic Inflammation 20 2,258
Capsaicin 89.62 Capsaicin Receptor 11 914
Capsaicin 0.96 Neurons 509 589,031
Table 2 Analysis of objects that share relationships with capsaicin, ranked both by the number of relationships they share and by their minimum mutual information measure (MMIM). Frequency (Freq) is the number of co-mentions the two objects share in the literature.
Ranked by # of shared relationships Ranked by Minimum MIM
Query term (A) Implicit relationship (C) Shared rels MMIM Score Freq. Implicit relationship (C) Shared rels MMIM Score Freq.
capsaicin Capsaicin 844 2623 - Capsaicin 844 2623 -
capsaicin development 800 135 250 Tachykinin 348 595 351
capsaicin Neurons 776 217 1835 Atropine 601 564 327
capsaicin Brain 769 174 118 Substance P 676 495 1513
capsaicin membrane 769 172 194 Tetrodotoxin 456 448 246
capsaicin Secretion 759 219 260 Neurokinin A 279 440 229
capsaicin Skin 749 157 591 Acetylcholine 681 436 167
capsaicin intracellular 746 224 158 Neuropeptide Y 640 417 536
capsaicin Calcium 743 189 190 Hexamethonium 312 392 130
capsaicin nervous system 743 189 138 Ileum 578 383 119
Table 3 Swanson's implicit discoveries analyzed either by counting the number of shared relationships or by the MMIM score. Frequency (Freq) is the number of co-mentions the two objects share in the literature. Notice that the type of relationship that ranks higher with the MMIM tends to be more specific and informative, while the terms that share the most relationships tend to be more general and vague.
Ranked by # of shared relationships Ranked by Minimum MIM
Query term (A) Implicit relationship (C) Shared rels MMIM Score Freq. Implicit relationship (C) Shared rels MMIM Score Freq.
migraine Migraine 1442 4149 - migraine 1442 4149 -
migraine development 1362 216 348 Headache 1140 1519 4032
migraine Lesions 1291 264 164 Sumatriptan 270 1038 1152
migraine nervous system 1280 311 227 Nausea 899 886 323
migraine pathogenesis 1253 375 475 Vomiting 889 732 242
migraine Muscle 1251 201 184 Dizziness 696 669 136
migraine Blood 1236 63 272 Ataxia 674 562 62
migraine Tumor 1236 93 158 Vertigo 467 511 208
migraine Females 1223 243 200 Somnolence 389 497 19
migraine Males 1219 238 169 Seizures 1057 493 198
Query term (A) Implicit relationship (C) Shared rels MMIM Score Freq. Implicit relationship (C) Shared rels MMIM Score Freq.
Raynaud's Raynaud's syndrome 200 2088 - Raynaud's syndrome 200 2088 -
Raynaud's development 198 45 11 Raynaud's phenomenon 183 1204 45
Raynaud's Artery 195 86 36 Raynaud's disease 118 936 30
Raynaud's Muscle 193 53 2 mixed connective tissue disease 121 870 13
Raynaud's pathogenesis 192 127 18 Scleroderma 161 862 48
Raynaud's Lesions 192 71 26 connective tissue diseases 151 842 31
Raynaud's Vessels 191 108 10 Arthralgia 133 761 6
Raynaud's Blood 190 16 26 overlap syndrome 81 619 1
Raynaud's Skin 190 24 18 Calcinosis 104 592 7
Raynaud's Cutaneous 189 200 13 progressive systemic sclerosis 124 587 14
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| 15471547 | PMC526381 | CC BY | 2021-01-04 16:02:46 | no | BMC Bioinformatics. 2004 Oct 7; 5:145 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-145 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1351538315610.1186/1471-2105-5-135Research ArticleProfiled support vector machines for antisense oligonucleotide efficacy prediction Camps-Valls Gustavo [email protected] Alistair M [email protected]ópez Antonio J [email protected]ín-Guerrero José D [email protected] Erik LL [email protected] Grup de Processament Digital de Senyals, Universitat de València, Spain. C/ Dr. Moliner, 50. 46100 Burjassot, València, Spain2 Center for Genomics and Bioinformatics (CGB), Karolinska Institutet, S-17177, Stockholm, Sweden2004 22 9 2004 5 135 135 7 5 2004 22 9 2004 Copyright © 2004 Camps-Valls et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1) feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE), and (2) AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions.
Results
In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE) using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278) and predicted high (>75% inhibition of gene expression) and low efficacy (<25%) AOs with a success rate of 83.3% and 82.9%, respectively, which is better than by previous approaches. A web server for AO prediction is available online at .
Conclusions
The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.
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Background
The expression of a gene can be inhibited by antisense oligonucleotides (AOs) targeting the mRNA. However, if the target site in the mRNA is picked randomly, typically 20% or less of the AOs are effective inhibitors in vivo [1]. The sequence properties that make an AO effective are not well understood, thus many AOs need to be tested to find good inhibitors, which is time-consuming and costly.
Antisense oligonucleotides contain 10–30 nucleotides complementary to a specific subsequence of an mRNA target, which are designed to bind to targets by standard Watson-Crick base pairing rules. The bound duplex can knockdown gene expression through a number of mechanisms. These are RNase-H mediated cleavage, inteference with translation or splicing and destabilization of the target mRNA [2-4]. The AO inhibits gene expression in a specific and reversible manner, a process termed 'Gene knock-down' and all mechanisms leave the AO intact to induce further knock-down. For a comprehensive review of the topic see [5].
There are many laboratory-based strategies for selecting AOs. A classical approach is the 'gene-walk' approach, in which 15 or more AOs are evaluated for a gene in order to find a sufficiently effective AO. Methods with higher reliability experimentally determine mRNA regions that are accessible to RNase-H clevage and therefore more likely to be an e'ective site for AOs [6-8]. In general, the experimental approaches are time consuming and expensive.
There are many examples in the literature of experimental groups attempting to correlate AO sequence properties with efficacy. A correlation between binding energy (AO-RNA) and efficacy has been observed [6,9]. Particular target secondary structures have been shown to correlate with efficacy [10]. However, the correlations are not consistently detected across studies. This variation can be due to many factors including biases in the selection of the AOs, varying experimental conditions, or, in cases where computational RNA folding prediction was used, limitations in the structure prediction methods. In [11], published AOs were examined and recomended values for dimer, hairpin and ΔG to increase the proportion of higher efficacy AOs were given.
AO selection can be based on either experimental or theoretical approaches (for a review, see [12]). Computational approaches to AO design have so far focused on prediction of the structure of the target mRNA and from this deriving the accessibility of target regions (e.g. [12-19]). Perhaps the most successful method is that of Ding and Lawrence [19], using a statistical sampling of secondary structures to predict accessible regions to find effective AOs for rabbit β-globin. In general, methods have not been evaluated on a broad range of gene targets. Another method is to look for motifs that occur more often in effective AOs. Ten sequence motifs have been identified with a correlation to AO efficacy in [20], and recently, motifs have been used as the input to neural network models [21,22] with reasonable success.
In this context, the challenge is hence to discover general principles that hold across all AO studies. One approach to discover such principles is to explore a diverse range of sequence properties and incorporate the factors that affect AO efficacy into a computational model for AO design. This requires both a database of tested AOs, such as that produced by [21,23], and machine learning methods of model building. The database should be based on large AO screening experiments to ensure comparability. In this context, the use of advanced pattern recognition methods such as neural networks or Support Vector Machines (SVMs) is becoming very popular because of their good capabilities for classification, function approximation and knowledge discovery. In particular, the use of SVMs in bioinformatics has found a natural match because they work efficiently with high input dimension spaces and low number of labeled examples. As a consequence, many biological problems have been solved in this field. The interested reader can visit [24] for a collection of SVM applications in bioinformatics. However, the use of the SVM has been traditionally attached to the classification problem, and few efforts have been made to tackle the regression (or function approximation) problem.
This paper proposes the use of SVMs for prediction and analysis of AO efficacy. The collected database comprises 315 AO molecules including 68 features each, which induces a priori a well-suited problem to SVMs, given the low number of samples and high input space dimension [25]. Nevertheless, the problem of feature selection becomes crucial because the number of examples in the database (AO molecules) is low compared to the number of features for each of them and, therefore, overfitting is likely to occur, reducing the performance of the model [26,27]. Additionally, being able to explain the obtained solution (in terms of the selected input features) can be as relevant as obtaining the best possible predictor. This is of particular interest in bioinformatics in general and for AO efficacy prediction in particular, as was previously illustrated in [21,22]. The issue of feature selection in the SVM framework has received attention in the recent years [28-32]. The fact that SVMs are not drastically affected by the input space dimensionality has sometimes led to the wrong idea that a feature selection is not necessary at all. The guiding principle of SVMs ensures certain robustness to outliers or abnormal samples in the distribution inherently, but the selection of the optimal subset of features is still an unsolved problem in the literature. We can state that in most applications, the success of machine learning is strongly affected by data quality (redundant, noisy or unreliable information) and thus a feature selection is not only recommendable but mandatory.
In this paper, we propose a two-stage strategy to tackle the problem:
1. Feature selection. This task is carried out using three techniques: correlation analysis, the mutual information feature selection (MIFS) method, and the SVM-based recursive feature elimination (SVM-RFE).
2. AO efficacy prediction. We develop standard and profiled SVMs to accomplish this task. Several measures of accuracy of the estimations and two cross-validation methods are used in order to attain both significant and robust results.
Methods
Data collection
In the present work, we have extended the database used in [21] by including 68 features for each AO. The so-called AO database (AOdb) was assembled from a selection of AO publications. Published data was incorporated for which: (a) at least 6 AOs were tested under the same experimental conditions, although more than one gene target were allowed; (b) efficacy of the AOs were presented as a percentage of the control level of the target gene expression, either as RNA or protein. No papers were reported matching these criteria before 1990, as is consistent with [23]. Accompanying this data is the full RNA sequence and accession number (where available) together with positional coordinates of the AOs and the position of the coding sequence. Publication details, cell line used and the chemistry of the AOs were also recorded in the database. The database consists of 315 oligonucleotides from 15 studies testing AO efficacy on 13 genes. The essential information in the database is AO sequence and efficacy expressed as (100% - [% of control expression])/100. For the cases where the same AO is tested in two different laboratories, or twice by the same laboratory the average efficacy is used.
A set of a priori representative parameters was derived from the information contained in the AO sequence collection, including values for: (1) base composition (Number of A/C/G/T, % GC content): (2) RNA-AO binding properties (binding energy, enthalpy, entropy): (3) RNA-AO terminal properties (3' binding energy, 5' binding energy); (4) AO-AO binding properties (Hairpin energy and quality, Dimer energy); and (5) 9 of the 10 verified sequence motifs correlated with efficacy from [20]. Binding energy calculations were completed using thermodynamic parameters from [33]. The calculation of dimer energy was made using an ungapped alignment with stacking energies taken from [34] and a uniform penalty 0.5 for mismatches. Hairpin energy was calculated using both Mfold [35] and the Vienna package [36]. Parameters describing cellular uptake and protein interactions were not included, as we have no explicit way of modeling them. A number of additional features were included to complete the AOdb: motifs, AO position, predicted conformation of the target structure, single-strandedness, binding energies from [14]. For brevity, the complete list and more information on the database can be obtained at [37]. The database is available under request.
The feature selection problem
The Feature Selection Problem (FSP) in a "learning from samples" approach can be defined as choosing a subset of features that achieves the lowest error according to a certain loss functional [28]. Following a general taxonomy, the FSP can be tackled using filter [38] and wrapper [26] methods. Filter methods use an indirect measure of the quality of the selected features, e.g. evaluating the correlation function between each input feature and the observed output. A faster convergence of the algorithm is thus obtained. On the other hand, wrapper methods use as selection criteria the goodness-of-fit between the inputs and the output provided by the learning machine under consideration, e.g. a neural network. This approach guarantees that, in each step of the algorithm, the selected subset improves performance of the previous one. Filter methods might fail to select the right subset of features if the used criterium deviates from the one used for training the learning machine, whereas wrapper methods can be computationally intensive due to the learning machine has to be retrained for each new set of features. In this paper, we evaluate the performance of SVMs for different subsets of relevant features, which are selected using both filter and wrapper approaches.
Correlation analysis and mutual information
A common practice to evaluate the (linear) relationship between each of the n input features and output , or among pair-wise inputs ( and ) is the use of the correlation function. This is a good method to remove redundant features and to evaluate relationships, but fails when working with low number of samples, or when the assumed linear relationship is not present. When data is considered as the realization of random processes, it is possible to compute the relevance of variables with respect to each other by means of the mutual information (MI) function, which is defined as the difference between entropy of and the conditional entropy of given . The MI function is suitable for assessing the information content of features in tasks where methods like the correlation are prone to mistakes. In fact, the MI function measures a general dependence between features, instead of a linear dependence offered by the correlation function. In [39], an algorithm called Mutual Information Feature Selection (MIFS) was successfully presented. The method greedily constructs the set of features with high mutual information with the output while trying to minimize the mutual information among chosen features. Thus, the ith input feature included in the set, maximizes over all remaining features for some parameter β ∈ (0,1]. The feature selection procedure is performed iteratively until a desired number of features is reached. We will use the correlation function and the MIFS method as filter methods, i.e. a feature ranking will be provided and only the most important features will be accounted for modeling.
Support vector regressor (SVR)
Support Vector Machines are state-of-the-art tools for nonlinear input-output knowledge discovery [40]. The Support Vector Regressor (SVR) is its implementation for regression and function approximation, which has been used in time series prediction with good results [41]. Basically, the solution offered by the SVR takes the form , where xi is an input example, φ is a nonlinear mapping, w is a weight vector and b is the bias of the regression function. In the SVR, a fixed desired accuracy ε is specified a priori and thus one tries to fit a "tube" with radius ε to the training data. The standard SVR tries to minimize two factors: the norm of the squared weight vector, ||w||2, and the sum of permitted errors. These two factors are traded-off by using a fixed penalization parameter, C. We can formally state the SVR method as follows: given a labeled training data set {(xi, yi), i = 1,..., n}, where xi ∈ ℝd and yi ∈ ℝ, and a nonlinear mapping to a higher dimensional space φ: ℝd → ℝH where d ≤ H, find the minimum of the following functional with respect to w, ξi, and b:
subject to:
where and C are, respectively, positive slack variables to deal training samples with a prediction error larger than ε (ε > 0) and the penalization applied to these ones. These two parameters are tuned by the user.
The usual procedure for solving the SVR introduces the linear restrictions (2)-(4) into (1) by means of Lagrange multipliers αi and associated to each constraint. The dual functional obtained has to be minimized with respect to primal variables (w, ξi and ) and maximized with respect to dual variables (αi). The optimization of the obtained dual problem is usually solved through quadratic programming procedures [40,42], and the final solution provided by the SVR for a test example x can be expressed as
where only the non-zero Lagrange multipliers account in the solution. The corresponding input examples are called support vectors and represent the most critical samples in the distribution.
An important characteristic of the SVR training methodology is that one does not need to know explicitly the form of the mapping φ (x) but only a kernel function, which maps the samples into a high dimensional space. This kernel function appears in the form of dot products in (5), K (xi, xj) = φ(xi)·φ(xj) and can be viewed as a measure of similarity between samples. Therefore, in order to train the SVR model, one only has to select a kernel function, its free parameters, the parameter C, and the size of the ε-insensitivity zone. In this paper, we have only used the Gaussian (or Radial Basis Function, RBF) kernel, given by:
K (xi, xj) = exp (-γ||xi - xj||2). (6)
There are some reasons to select the RBF kernel a priori. The RBF kernel maps samples into a higher dimensional space so, unlike the linear kernel, it can handle efficiently cases in which the relation between the dependent and independent variables is non-linear. The RBF kernel has less numerical difficulties than sigmoid or linear kernels. In fact, sigmoid kernels behave like RBF for certain parameters [43,44] but unfortunately, they are non-positive definite kernels in all situations, which precludes their practical application [25]. Finally, for using the RBF kernel, only the Gaussian width has to be tuned. For tutorials, publications, and software resources on SVM and kernel-based methods, the reader can visit [45].
Recursive feature elimination (SVM-RFE)
The SVM-RFE algorithm has been recently proposed in [29] for selecting genes that are relevant for a cancer classification problem. The goal is to find a subset of size m among n features (m <n) that maximizes the performance of the predictor for a given measure of accuracy. This is a wrapper method and involves high computational cost. The method is based on a backward sequential selection. One starts with all the features and removes one feature at a time until m features are left. Basically, in each iteration, one focuses on the weight vector, which constitutes the solution provided by the SVR and therefore, its analysis is of fundamental relevance to understand the importance of each input feature. The removed feature is the one whose removal minimizes the variation of ||W||2. Hence, the ranking criterion Rc for a given feature i is:
where K(i) is the kernel matrix of training data when feature i is removed and are the Lagrange multipliers corresponding to sample k when the input feature i is removed. The idea underlying this procedure is basically to evaluate at each iteration which feature affects less the weight vector norm and, consequently, to remove it.
Results
In this section, we present and discuss the results obtained both regarding feature selection and prediction accuracy. Filter and wrapper feature selection methods will provide different subsets of representative features. SVMs are trained for each subset and their performance is evaluated in terms of robustness and accuracy.
Feature selection
The first approach to the FSP consisted of performing a correlation analysis in order to identify redundant variables. We adopted a similar strategy followed in [21], i.e. to remove features correlated to each other at >0.9 (p < 0.001), keeping the highest correlation to efficacy. This analysis discarded 12 redundant features out of the 68 original ones, and additionally provided a ranking of the most correlated features to efficacy. We finally selected the 14 top ranked features according to this criterion, ranging in correlation from -0.35 (ΔG) to -0.16 (# Adenine). We selected this number of features for the purpose of a fair comparison with the best subset in [21]. Table 1 shows selected features in both cases. Note that some di'erences are observed between the present work and [21] with regards the value of the correlation coefficient, (first and last columns, respectively). They are due to the facts that (1) we have included here very low efficacy oligos in the calculation, and that (2) because more features were added to the AO database, e.g. predicted secondary structure, oligos had to be discarded when the target RNA was unavailable.
A feature ranking according to the correlation coefficient can be useful to analyze input-output linear dependencies, but it is not good practice to rely only on this decision to build a model. As a second approach, we ran the MIFS method and selected a desired subset of best 14 features. We selected β = 0.75, which yielded a balanced estimation of both the MI with the output (AO efficacy), and the already-selected features. The more important features match the ones selected using the correlation function, but MIFS also included hairpin measurements. This is due to the fact that MIFS is not based on correlatedness but on mutual dependence criteria.
A third approach was the use of SVMs based on the RFE method. In this task, we trained an SVM to predict AO efficacy using all available features. It should be noted here that RFE is a wrapper method that involves a very high computational burden since the SVM must be retrained in each iteration with the selected features. The best model was selected by evaluating the RMSE (accuracy of the estimations) in the validation set through the 8-fold cross-validation method, which splits the data into eight parts, and uses seven parts for training and the eighth one for validation. The procedure is then repeated eight times. In our implementation, we included the possibility suggested in [29] by which it is possible to remove chunks of features at each iteration –a maximum value around 10 was a suitable option. In our application, only ten iterations were necessary to achieve the best 14 features (see Table 1). In [20,21], a surprising lack of correlation was observed between dimer energy and efficacy, which was attributed to some kind of bias in the databases. In the present work, nevertheless, SVM-RFE includes dimer energy as the 11th most relevant feature. In conclusion, SVM-RFE selects a combination of highly correlated but also mutually informative features.
We can also conclude that noticeable differences are observed between the obtained rankings. A possible explanation for discrepancies of this sort is the non-linear mapping that SVR methods perform. Explaining those input-output relationships is often difficult and biased conclusions are usually obtained. Different families of methods (SVM, neurofuzzy, decision trees, or neural networks) perform different mappings due to their specific guiding principles (structural risk minimization, membership optimization, entropy-based criteria, or empirical risk minimization, respectively) and thus, the interpretation of these methods is quite diffcult. In addition, different models (topologies, structures, kernels, membership functions) in a family would surely yield different results.
Model development
A greedy search was carried out for the free parameters (C, ε, γ) As regards the penalization parameter, it is a common practice trying exponentially increase sequences of C (C = 10-2, 10-1,..., 103). In our case study, we achieved good results in the range of C ∈ [1,1000]. The insensitivity zone was varied linearly in the range [0.001, 0.3]. The γ parameter was exponentially varied in the range γ = 10-7,...,10-1. For each free parameter combination, we evaluated the performance of the predictors through several measurements: the correlation coefficient between actual and predicted efficacies (r), the mean error (ME), and the root-mean-square-error (RMSE). Additionally, we computed the rate of observed efficacies above a defined predicted threshold of 0.75 (SR>0.75) and below 0.25 (SR<0.25). These prediction ranges are of particular interest, since they stand for high and low AO efficacies, respectively. In fact, it is not only important to identify high efficacy oligos but also factors causing AOs to be completely ineffective ([0,0.25]). However, care must be taken as more noise can be present in the low efficacy region.
Model comparison
At the first stage of the work, we trained SVMs using the 8-fold cross-validation method for RFE-based feature selection. However, this training methodology can lead to overoptimistic results because AOs on the same gene are not always independent data points. Hence, we also followed a different strategy, which entails removing all AOs targeting one gene for training, training the model, and then testing performance on predicting the efficacy of these oligos. This is a common method [22] and we refer to it as minus-one-RNA cross-validation (-RNA). It safely removes any overlap between training and test data, and thus ensures the generality of the model.
In AO prediction, we are most interested in predicting good oligos (high efficacy, > 0.75), and those that are bad (low efficacy, < 0.25). This previous knowledge about the problem can be introduced in the SVM formulation by tailoring specific confidence functions for the adaptation of the penalization factor C, and the ε-insensitive zone of each sample. The so-called Profiled SVR (P-SVR) [46] obviously implies making some changes in the original SVR formulation since now C and ε become sample-dependent. In [46,47], we designed profiles for the variation of C and ε in complex pharmacokinetic problems. In this paper, our intention relaxing or tightening ε and C depending on the observed AO efficacy value. A proposal for this variation is illustrated in figure 1. Note that we increase the penalization of errors committed in the high or low AO efficacy ranges since we are more interested in obtaining good results in these regions.
Additionally, the ε-insensitivity zone is reduced in these regions thus forcing a reduced error there. Some other profiles could be introduced in the training methodology without loss of generality.
Results for all approaches are shown in Table 2 for the validation set. We observe that RFE is the best method for selecting features. The choice of cross-validation method does not make much difference; the RMSE is the same while the goodness-of-fit (r) is almost unchanged. Using the P-SVR method (with features selected by the 8-fold crossvalidated RFE) we gained substantially in RMSE, and also obtained a better balance between the success rates of high and low predictions. This indicates that the P-SVR improves the performance of standard SVR even without a dedicated feature selection method, and suggests that even better results could be obtained if P-SVR were embedded in the RFE feature selection procedure.
These outcomes are worth analyzing because one could expect worse results when using -RNA cross-validation since this method removes the possibility of cross-talk in the training phase between overlapping oligos. However, we have to stress here that, by training the SVR with -RNA cross-validation, one only improves the r indicator, which is a biased estimator of the accuracy. In fact, accuracy (RMSE) remains basically the same, and bias (ME) becomes positive and higher, which could induce some distrust for the model. When analyzing results from the P-SVR, we can observe a general improvement in all indicators, which is basically due to the fact that by tightening the "tube" around the interesting ranges, a higher number of support vectors is selected there (but lower in the overall domain), which induces a richer solution in the interesting zones. In addition, the profiled C parameter penalizes higher the committed errors in these zones, which is particularly interesting to deal with outlying samples in the distribution and to provide a smoother solution in these particular zones. The designed profile, nevertheless, could lead to an overfitted solution in the interesting zone if εi and Ci were not well-controlled. However, by using the-RNA cross-validation method, this threat is avoided and better results are finally obtained. Therefore, the combined strategy of P-SVR and -RNA cross-validation results in a balanced and robust predictor. Additional consequences can be extracted: (1) the correlation coefficient is relatively low for all methods but superior to the ones obtained in [21]; (2) differences among the models are neither numerically (see Table 2) nor statistically significant as tested with One Way Analysis-Of-Variance (ANOVA) in bias (F = 0.01, p = 0.811) or accuracy (F = 0.06, p = 0.567); (3) prediction is more accurate, in general terms, for the higher efficacy levels (SR>0.75, > SR<0.25), as also noted in [22]; and (4) SVM-RFE can deal efficiently with high input spaces and produces robust results (compare results with those from the "All features" subset). Additionally, we can conclude that the P-SVR improved results in terms of accuracy of the predictions compared to the standard SVR.
Conclusions
In this paper, we have used standard and state-of-the-art methods for knowledge discovery in a relevant bioinformatics problem: the analysis and prediction of AO efficacy. We have engineered robust and accurate SVMs, and used filter and wrapper feature selection methods in order to build representative subsets of input features. Compared to [21], our results represent a significant improvement. In that work, SR>0.8 was reported to be 50%, and r = 0.30. The success of the P-SVR for the AO prediction problem suggests that it could be successfully applied to other prediction problems. A web server for AO prediction is available online at [48].
Our future work is concentrated to improving results with more careful design of profiles by the inclusion of fuzzy and rough sets. Additionally, we are exploring the possibility of providing confidence values for the predictions in the form of p-values from the Lagrange multipliers. This way, the user could get a set of best predictions back, then a second set that is more likely to be less accurate, and so on. This would allow the lab-user to choose the best ones first, but if they fail specificity controls they would have another set to work with.
Authors' contributions
GCV carried out the training of the feature selection and regression methods. AC participated in model development and testing process, and developed the web-server. AJSL collaborated in model development and assessment. JDMG engineered the profile function. ES conceived and coordinated the study. All authors contributed to the manuscript preparation, and approved the final manuscript.
Figures and Tables
Figure 1 Illustration of Gaussian-like profiles for the penalization factor and the ε-insensitive region in the P-SVR approach. In this case, we penalize harder the committed errors in the higher and lower efficacy regions. Additionally, the insensitive region becomes wider in medium AO efficacies, and thus few AOs will contribute to the cost function and, consequently, become support vectors. Only one additional parameter is introduced in the formulation, i.e. the width of the Gaussian profile, σP.
Table 1 Feature ranking using (a) the correlation coefficient between input features and efficacy (), (b) mutual information feature selection (MIFS) with β = 0.75, (c) SVM-based Recursive Feature Elimination (SVM-RFE), and (d) best selection in [21] using the correlation coefficient.
FEATURE FEATURE MI (β = 0.75) FEATURE SVM-RFE ||W||2 FEATURE in [21]
1 ΔG -0.35 ΔG 0.094 ΔH 0.680 GGGA 0.26
2 # Cytosine 0.31 # Cytosine 0.089 ΔS 0.671 # Cytosine 0.23
3 TCCC 0.28 %GC content 0.077 ΔG 0.193 ΔH -0.19
4 5pΔG -0.26 ΔG/length 0.075 # Cytosine 0.045 ΔG -0.18
5 ΔH -0.24 ΔH 0.064 Hairpin quality 0.035 CAGT -0.18
6 ΔH/length -0.22 ΔH/length 0.061 # Adenine 0.024 AGAG 0.18
7 %GC content 0.22 ΔS 0.060 # Thymine 0.018 GTGG 0.17
8 CCCT 0.21 # Adenine 0.043 Hairpin length 0.014 # Guanine -0.15
9 CCAC 0.21 # Guanine 0.042 5pΔG 0.009 3pΔG 0.14
10 CCCC 0.21 5pΔG 0.040 3pΔG 0.005 ΔS -0.14
11 CTCT 0.20 Hairpin quality 0.027 Dimer 0.004 CCCC -0.13
12 CCCA 0.20 Hairpin length 0.024 Hairpin energy (Mfold) 0.003 Hairpin quality -0.11
13 ACAC -0.16 Hairpin Energy 0.022 # Guanine 0.001 %GC content 0.11
14 # Adenine -0.16 # Thymine 0.016 Hairpin energy (vienna) 0.000 TGGC -0.10
Table 2 Mean error (ME), root-mean-squared error (RMSE) and correlation coefficient (r) of models in the validation set. Success rates (SR) for efficacy higher than 0.75 or below 0.25 are also given for each feature selection method.
Methods SVR P-SVR
Selection All feats. MI(β = 0.75) RFE RFE RFE
CV method - - - 8-fold -RNA -RNA
r 0.356 0.367 0.374 0.398 0.430 0.440
ME -0.0280 -0.0223 -0.0104 -0.0068 0.031 0.022
RMSE 0.312 0.300 0.301 0.299 0.299 0.278
SR>0.75 82.8 87.5 86.7 87.5 83.3 83.3
SR<0.25 71.4 73.9 71.4 73.9 76.2 82.9
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| 15383156 | PMC526382 | CC BY | 2021-01-04 16:02:46 | no | BMC Bioinformatics. 2004 Sep 22; 5:135 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-135 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-511538002110.1186/1477-7525-2-51ReviewMultimorbidity and quality of life in primary care: a systematic review Fortin Martin [email protected] Lise [email protected] Catherine [email protected] Alain [email protected] Antoine L [email protected] Danielle [email protected] Département de Médecine de famille, Université de Sherbrooke, 3001, 12e Avenue Nord, Sherbrooke (Québec), J1H 5N4 Canada2 Département des Sciences humaines, Université du Québec à Chicoutimi, 555, Boulevard de l'Université, Chicoutimi (Québec), G7H 2B1 Canada2004 20 9 2004 2 51 51 25 8 2004 20 9 2004 Copyright © 2004 Fortin et al; licensee BioMed Central Ltd.2004Fortin et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Many patients with several concurrent medical conditions (multimorbidity) are seen in the primary care setting. A thorough understanding of outcomes associated with multimorbidity would benefit primary care workers of all disciplines. The purpose of this systematic review was to clarify the relationship between the presence of multimorbidity and the quality of life (QOL) or health-related quality of life (HRQOL) of patients seen, or likely to be seen, in the primary care setting.
Methods
Medline and Embase electronic databases were screened using the following search terms for the reference period 1990 to 2003: multimorbidity, comorbidity, chronic disease, and their spelling variations, along with quality of life and health-related quality of life. Only descriptive studies relevant to primary care were selected.
Results
Of 753 articles screened, 108 were critically assessed for compliance with study inclusion and exclusion criteria. Thirty of these studies were ultimately selected for this review, including 7 in which the relationship between multimorbidity or comorbidity and QOL or HRQOL was the main outcome measure. Major limitations of these studies include the lack of a uniform definition for multimorbidity or comorbidity and the absence of assessment of disease severity. The use of self-reported diagnoses may also be a weakness. The frequent exclusion of psychiatric diagnoses and presence of potential confounding variables are other limitations. Nonetheless, we did find an inverse relationship between the number of medical conditions and QOL related to physical domains. For social and psychological dimensions of QOL, some studies reveal a similar inverse relationship in patients with 4 or more diagnoses.
Conclusions
Our findings confirm the existence of an inverse relationship between multimorbidity or comorbidy and QOL. However, additional studies are needed to clarify this relationship, including the various dimensions of QOL affected. Those studies must employ a clear definition of multimorbidity or comorbidity and valid ways to measure these concepts in a primary care setting. Pursuit of this research will help to better understand the impact of chronic diseases on patients.
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Background
With technological advances and improvements in medical care and public health policy, an increasingly large number of patients survive medical conditions that used to be fatal. As a result of this phenomenon, and parallel to the aging of the population, a growing proportion of primary care patients presents with multiple coexisting medical conditions. From available data, it was estimated that 57 million Americans had multiple chronic conditions in 2000 and that this number will rise to 81 million by 2020 [1]. Epidemiological data from several countries support this estimate [2-8]. On average, patients aged 65 years and older present with 2.34 chronic medical conditions [7]. In fact, 50% of patients with a chronic disease have more than one condition [9].
The terms "comorbidity" and "multimorbidity" have been used to describe this phenomenon. Feinstein [10] originally described comorbidity as "any distinct additional entity that has existed or may occur during the clinical course of a patient who has the index disease under study." Kraemer [11] later referred to comorbidity in studying specific pairs of diseases. Van den Akker and colleagues [12] further refined both concepts, reserving the term "multimorbidity" to describe the co-occurrence of two or more chronic conditions; they also proposed some qualifiers to better classify the type of multimorbidity (simple, associative and causal). Unfortunately, much confusion still exists in the literature, where the 2 terms often seem to be used interchangeably. For the purpose of this paper, the term "multimorbidity" will be used according to Van den Akker and colleagues' definition and the focus will be solely on chronic diseases.
Previous reports on multimorbidity or comorbidity have documented that this phenomenon influences outcomes in many areas of health care [13-19]. Outcome measures that have been related to multimorbidity include mortality, length of hospital stay, and readmission. An association between disability and multimorbidity in elderly patients has also been described [14,20-22].
Quality of life (QOL) is an outcome measure that is increasingly being used to evaluate outcomes in clinical studies of patients with chronic diseases [23-26]. QOL represents a subjective concept, with a multidimensional perspective encompassing physical, emotional, and social functioning [27]. It is important to address QOL as it has been associated with health and social outcomes [28] which may contribute to the worsening of the course of the diseases. In research and the medical literature, there is little distinction between health-related quality of life (HRQOL) and overall QOL (the latter encompasses not only health-related factors but also many non medical phenomena such as employment, family relationships, and spirituality) [29]. In practice, the terms are often used interchangeably. Different evaluation scales have been proposed to measure QOL or HRQOL. Some focus on a specific disease [30,31], while others have wider applications (i.e., generic measurements) [32-34].
Little is known about the impact of multimorbidity on QOL of primary care patients [35], although this is where most patients receive their care. Thus, the purpose of this systematic review is to clarify the association between the presence of several concurrent medical conditions and the QOL or HRQOL of patients seen or likely to be seen in a primary care setting.
Methods
Data sources
For this review, we consulted Medline and Embase electronic databases for the reference period 1990 to 2003. Figure 1 illustrates the search strategy. Since the term "multimorbidity" does not have any equivalent in the thesaurus, databases were searched for the following terms: multimorbidity, comorbidity, and their spelling variations. The term "multimorbidity" was searched as a keyword, while "comorbidity" was searched as a Medical Subject Heading (MeSH). The term "chronic disease" was used to increase the sensitivity of the search. We also used the MeSH "quality of life" and the keyword "health-related quality of life" to help target pertinent literature.
Figure 1 Selection of articles: Medline (Embase), years 1990–2003
To identify studies pertinent to the primary care setting, the following search terms were used: general practice, family practice, family medicine, family physician, and primary health care. A parallel strategy was used to identify all descriptive studies, regardless of the context of care, and the results were then combined. For the initial screening, the search was restricted to studies on human subjects, published in French or English. To be complete, we directly searched the Quality of Life Research and Health and Quality of Life Outcomes journals. We also screened references from key articles retrieved (hand searching).
Study selection
One researcher (LL) performed the initial screening. Any ambiguous findings were discussed with the lead investigator (MF) and a consensus was reached.
Inclusion and exclusion criteria
For the purpose of this systematic review, we selected original, cross-sectional, and longitudinal descriptive studies that had evaluated the relationship between multimorbidity or comorbidity and QOL or HRQOL as the main outcome of interest. As stated earlier, we focused on the population of patients seen, or likely to be seen, in the primary care setting including members of the general population and residents of nursing homes and home healthcare facilities. We also selected original descriptive studies that had examined the relationship between multimorbidity or comorbidity and QOL or HRQOL as a secondary outcome.
Figure 1 shows our exclusion criteria. In keeping with our objectives, we did not include studies on specific diseases (e.g., acquired immunodeficiency disease) or populations unlikely to represent a large part of primary care practice. We also excluded any studies that did not address physical comorbidities, including those that exclusively examined mental disorders and associated mental comorbidities. Finally, we excluded studies in which the main outcome of interest was not QOL or HRQOL as well as those that used a nonstandard approach to measuring QOL or HRQOL.
Assessment of study quality
Before being included in the synthesis, the quality of each article selected was critically analyzed. For this assessment, we devised a scale in which points were assigned for study parameters indicative of good quality (e.g., well-defined populations, clear definitions, valid measures). Using this scale (Table 1), 2 researchers independently determined a global quality score for each article. The scores for each article were then compared and adjusted by consensus. To ensure adequate methodological quality, the cut-off score for an article to be included in the synthesis was 10 out of a maximum of 20 points.
Table 1 Evaluation criteria
Evaluation criteria for the studies identified in the literature search: 0, 1, or 2 points per criterion or subcriterion (maximum score = 20)
Criterion1: Originality
Original study (cross-sectional or longitudinal) with a clear objective
Criterion 2: Population studied
2a) Primary care or general population
2b) Well-defined control group or good variability of the independent variable in a regression model
2c) Characteristics of the groups are described, including those of nonrespondents, and do not lead to bias
Criterion 3: Definition
Clear definition of multimorbidity and valid measure
Criterion 4: Outcome
4a) Quality of life was the primary outcome measure
4b) Quality of life was evaluated with a validated scale
4c) Evaluation of quality of life was independent of the multimorbidity/comorbidity score (i.e., blind evaluation)
4d) Effects of the main confounding factors (e.g., age, gender) are presented and discussed
Criterion 5: Limitations
Authors comprehensively discussed the limitations of their study
Synthesis or results
Figure 1 shows the number of articles found at each stage of the selection process. Of the 753 articles screened, 108 were evaluated according to the study's inclusion and exclusion criteria. We also assessed the quality of each study before selecting 30 for inclusion in the synthesis: 7 that had evaluated the relationship between multimorbidity or comorbidity and QOL as the main outcome (Table 2) and 23, as a secondary outcome.
Quality of life as the main outcome measure
Of the 7 studies that featured QOL as a primary outcome [36-42], 5 had been conducted in European populations. We analyzed theses studies in detail. Quality scores for these studies ranged from 10 to 18 (out of a maximum of 20 points) and were highest in 2 studies from the Netherlands, one from the United States, and another study from Sweden. Table 2 presents a synthesis of the various studies.
All studies came to the same conclusion, namely that there is an inverse relationship between the number of medical conditions and QOL or HRQOL. This association may be affected by the patient's age or gender. Whereas multimorbidity mostly affects physical dimensions of QOL or HRQOL [36,37,41], data from one study suggest that social and psychological dimensions may be affected in patients with 4 or more diagnoses [40].
In each study, investigators relied on simple count of chronic diseases from a limited list to measure multimorbidity. The chronic conditions included in this list varied among the studies, and no attempt was ever made to assess or account for the severity of each condition. Furthermore, 5 of the 7 studies did not consider psychiatric comorbidity, either because the illnesses considered did not include psychiatric diagnoses or because patients presenting with psychiatric diagnoses were excluded from the QOL evaluation. In most cases, the diagnostic information was obtained by a questionnaire that was completed by a nurse or a doctor or sometimes self-administered. One study assessed comorbidity via chart review.
To measure QOL, a variety of scales were used. Most studies (5/7) used tools from the Medical Outcomes Study i.e., the Short-Form-36 Health Survey (SF-36) and Short-Form-20 Health Survey (SF-20). However, the Nottingham Health Profile (NHP) was used in one study and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) was used in another. Although the number of domains explored varied from one study to the next, the measuring instruments used have excellent psychometric properties and validity.
The 4 studies associated with the highest quality scores explored only a limited number of potential confounders, namely age [37,38], gender [36,41], and socio-demographic and economic factors [38]. Effects of these confounders are reported in Table 2. The other 3 studies did not investigate potential confounders.
Quality of life as a secondary outcome measure
Of the 23 studies that evaluated the relationship between multimorbidity or comorbidity and QOL as a secondary outcome measure [43-65], most were done in Europe (9 studies) and the United States (12 studies). As with the main outcome studies, each used a simple count of a limited and varying number of chronic medical conditions to evaluate multimorbidity. While there was generally no attempt to assess or account for the severity of individual conditions, one study used a comorbidity index, the Duke Severity of Illness (DUSOI), for this purpose [48]. Diagnostic information was obtained from chart reviews and clinical evaluations (9 studies), from self-report questionnaires (13 studies), or both sources (1 study). Psychiatric comorbidity was evaluated in 13 studies.
As with the results from the main outcome studies, we found an inverse relationship between the number of medical conditions and the QOL relating to physical domains in all studies. However, the relationship between multimorbidity and QOL relating to psychological or social domains was less clear. Some investigators reported an effect of multimorbidity on these domains in patients with 3 or more diagnoses [54], while others reported no effect [48,55].
As in the main outcome studies, tools from the Medical Outcomes Study, including the SF-36 (17 studies), SF-20 (3 studies), and Short-Form-12 Health Survey (SF-12) (1 study), were used to evaluate QOL in most of these studies. However, the NHP was used in one study and the Quality of Well-Being Scale (QWB), in another. In the majority of studies, all of the QOL domains were explored.
Table 2 Synthesis of studies on multimorbidity with quality of life as the main outcome measure
Author (Country) Design Score Population Multimorbidity QOL scale Limitations Conclusions
Cheng 2003 [36] (United States) Cross-sectional design 17 Ambulatory, family medicine.
n = 316 (55–64 years) 7 diagnoses of chronic conditions obtained by chart review. Medical Outcomes Study (SF-36).
Administered by interviewer. Definition of multimorbidity was based on simple count of diseases. No assessment of disease severity or use of a healthy group for comparison.
No mention of psychiatric comorbidity.
Limited to low-income population. Small sample.
Age of the sample was limited. For every SF-36 domain, scores obtained in pregeriatric patients are significantly lower than those obtained in the general population. Lower physical component summary scores (PCS) and mental component summary scores (MCS) are associated with a greater number of chronic diseases, but this association is much stronger for PCS than MCS.
Wensing 2001 [37] (Netherlands) Cross-sectional design 18 Ambulatory, family medicine.
n = 4,112 (18+ years) 25 diagnoses of chronic conditions, with the possibility of including other diagnoses reported spontaneously.
Self-administered questionnaire. Medical Outcomes Study (SF-36); 8 domains.
Self-administered. Definition of multimorbidity was based on simple count of diseases. Medical conditions were self-reported by patient, with no assessment of disease severity. Psychiatric comorbidity was not evaluated.
Prevalences of chronic conditions were abnormally low, consistent with a selection or information bias. The QOL in each of the domains declines with the number of diagnoses (0, 1, 2 and over) but less so for the mental health domain.
The QOL score declines with age, especially in physical domains.
Michelson 2001 [38] (Sweden) Cross-sectional design 16 General adult population, stratified according to age.
n = 3,069 (18–79 years) 13 diagnoses of chronic conditions, divided into 4 categories based on the number of problems: (0, 1–2, 3–4, 5+).
Self-administered questionnaire. European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ); 5 domains.
Self-administered. Too few diagnoses considered. Medical conditions were self-reported by patients, with no assessment of disease severity. Psychiatric comorbidity was not evaluated.
Although adequate for use as a generic measure, the QOL questionnaire was developed for cancer patients. The presence of multiple chronic problems is associated with a lower QOL score. This association is present for each age group and tends to reduce the relationship between age and QOL.
The impact of socio-demographic and economic factors varies with age.
Cuijpers 1999 [39] (Nether-lands) Cross-sectional design at the beginning of a cohort study 10 Residents of homes for the elderly. n = 211 (Mean = 84.3 years) 7 diagnoses of chronic conditions, with the possibility of including other diagnoses reported spontaneously.
Questionnaire administered by the nursing staff. Short-Form-20 Health Survey (SF-20); 5 domains.
Administered by interviewer. Too few diagnoses considered. No assessment of disease severity.
Psychiatric comorbidity was not evaluated.
Data collection procedure was not standardized.
Many refusals to participate (30%), including some for health reasons.
Small sample. Aged patients. A lower QOL score is associated with a high number of chronic conditions.
Grimby and Svanborg 1997 [40] (Sweden) Cross-sectional design in a cohort follow-up 14 General ambulatory. n = 565 (76 years) 16 diagnoses of chronic conditions present in > 5%.
Medical questionnaire. Modified Nottingham Health Profile (NHP); part I: 6 dimensions; part II: 5 questions.
Self-administered. Definition of multimorbidity was based on a simple count of diseases. No assessment of disease severity.
Health of nonrespondents was not comparable (more ill).
No age variation (76 years). The loss of QOL is proportional to the number of diagnoses for the dimensions of energy, pain, mobility, and sleep. For social and emotional dimensions, QOL is little influenced until health is significantly impaired (4 or more diagnoses).
Kempen 1997 [41] (Nether-lands) Cross-sectional design at the beginning of a cohort study 17 Ambulatory, family medicine. n = 5,279 (57+ years) 18 diagnoses of chronic conditions.
Questionnaire administered by interviewer. Short-Form-20 Health Survey (SF-20); 6 domains.
Administered by interviewer or self-administered. Definition of multimorbidity was based on simple count of diseases reported by the patient.
Use of a list of diagnoses in correlation and multiple regression analyses.
No assessment of disease severity or psychiatric comorbidity.
Age of the sample was limited. The presence of chronic medical conditions explains a high proportion of the variance (25%) in the QOL score in most domains, especially self-perceived health. Personality influences QOL scores, especially in the mental health domain.
The association between the number of chronic conditions and the QOL score is slightly stronger for women than men.
Fryback 1993 [42] (United States) Cross-sectional design 13 General ambulatory. n = 1,356 (45–89 years) 28 diagnoses of chronic conditions, with the possibility of including other diagnoses reported spontaneously.
Questionnaire administered by interviewer. Medical Outcomes Study (SF-36) reduced to 2 domains.
Quality of Well-Being scale (QWB).
Administered by interviewer. Definition of multimorbidity was based on a simple count of diseases reported by patient. No assessment of disease severity.
QOL questionnaire completed by the same interviewer immediately after the medical questionnaire.
Characteristics of the healthy group were not described.
Multimorbidity data were not adjusted for age.
Questionnaire did not include all domains traditionally included in QOL assessment. The QOL score, as estimated with all of the measuring instruments, decreases with the number of chronic medical conditions. However, only limited domains of QOL were evaluated.
QOL: Quality of life
Discussion
Although this systematic review confirms the inverse relationship between multimorbidity and QOL, it also raises some important questions. First, the relative lack of studies in primary care evaluating the association between multimorbidity and QOL or HRQOL is surprising given the number of patients who suffer from multiple concurrent chronic conditions. Although the existence of this association makes logical sense, it still has to be demonstrated and thoroughly studied to find ways of improving care for specially affected patients. Thus, the pressing question may not be whether there is an association but rather how strong is the association and what factors are responsible for it? Identifying these factors may contribute to better care for the affected patients. There is a clear need for further studies to address these issues.
Ultimately, multimorbidity has the potential to affect all domains of QOL. However, the influence of multimorbidity on the social and psychological dimensions of QOL is much less clear than its influence on the physical domains. It is noteworthy that several studies showed a significant decline in social and psychological dimensions of QOL in patients with 3, 4, or more concurrent diagnoses. What does this finding mean? Is there any bias that can explain this difference, or is it related to a certain capacity for adaptation? Are there other factors associated with this finding? All of these questions have yet to be answered.
All the studies examined were cross-sectional in nature. The effect of multimorbidity may vary over time. Some medical conditions may improve while others worsen resulting in various effects on QOL. Therefore, cross-sectional studies may not capture the real effect of multimobidity on QOL and predict the direction of change over time.
Defining and measuring multimorbidity
The absence of a uniform way of defining and measuring multimorbidity is of special concern and may explain some of the variability in our results. In most of the studies we evaluated, investigators had used only a simple list of diseases to identify concurrent medical conditions in patients, providing very incomplete information. Furthermore, the numbers and types of medical conditions in these lists varied among the studies, precluding comparisons.
Given the urgent need for conceptual clarity, Van den Akker and colleagues' definition of multimorbidity should be refined and advanced to achieve a common understanding. A distinction must be made between simple and complex chronic diseases. Treated hypothyroidism (simple) and ischemic heart disease (complex) obviously do not have the same impact on QOL. Moreover, the influence of single-organ versus multi-organ diseases needs to be appropriately weighed. Additional factors to be considered when defining multimorbidity include the severity of the conditions and the presence or absence of associated pain.
The use of self-reported diagnoses in many studies is another methodological limitation that may have introduced error. Patients may confuse symptoms and minor ailments with more significant disease states or may forget to report important diagnoses that are still active. Self-reporting may even be completely inaccurate in the presence of psychosomatic disorders. Conducting a chart review, clinical interview or using any specific standardized method may be a better way to obtain data related to diagnoses.
Another methodological limitation of most of the studies evaluated was their failure to consider the influence of psychiatric comorbidity. This was either because psychiatric diagnoses were not included in the lists of disease states or because patients presenting with psychiatric diagnoses were excluded from QOL assessment. Given the importance of psychiatric conditions in primary care practice with a prevalence of more than 20% [66], this limitation is simply unacceptable.
Confounders
QOL tends to decrease with age [67], whereas the number of diagnoses increases with age. Thus, it is appropriate to consider age as a potential confounding variable. The effect of age was explored in some of the studies that used QOL as a main outcome measure [37,38,41]. Reference to established norms would have facilitated interpretation of these results.
Only a few of the studies evaluated had explored the effect of gender. Furthermore, their results were contradictory, with gender being more detrimental to the QOL of women in some cases [41,58] and men, in others [51].
Little has been reported about the effects of other potential confounding variables (e.g., socio-demographic and economic data, health habits, social support, number of drugs prescribed), although these factors are recognized as having an impact on QOL [68-71]. A few of the studies that used QOL as a secondary outcome measure considered the influence of socio-economic variables; however, their results were ambiguous, showing an impact in only about half of the studies. Some studies also demonstrated that, although socio-economic variables and health habits were significant predictors of QOL, the number of comorbidities was the strongest independent predictor of QOL [41,56]. Only one study took into account social support, and this study revealed a relationship with the mental dimension of QOL [58]. Only one study took into account the number of drugs prescribed and found an impact on the physical domain of QOL [49]. This study looked specifically at comorbidities associated with arterial hypertension and their impact on QOL. Finally, other potential confounding variables such as marital status and living arrangements were considered in some studies, with demonstration of an impact on QOL in about half the studies.
Many other factors should be explored in this regard. For example, the presence of coexisting acute conditions, the time since the diagnosis of important chronic conditions, and the duration and prognosis of health problems are among factors that may explain some of the variability in QOL or HRQOL.
Research agenda
In light of the findings of this systematic review, further research is needed to clarify the relationship between multimorbidity and QOL. The early work will certainly be conceptual and theoretical. The resultant conceptual clarity would benefit both researchers and practitioners. How do we define and how should we measure multimorbidity are among the first questions to be addressed. More descriptive studies, which take into account the influence of multiple potential confounders, can then be conducted. Multivariate analyses will help control for the effects of these confounding variables. The effects of age and gender also need to be further explored, with reference to established norms. Although there is still a need for cross-sectional studies, longitudinal studies are also needed to identify changes in the relationship between multimorbidity and QOL over time.
Study limitations
The main limitation of a systematic review is its inability to include all of the relevant literature. We realize that some articles may have been missed during the search stage. However, our review of a huge number of abstracts generated by different strategies improved the sensitivity of the search. Obviously, the absence of a keyword for multimorbidity is a limitation. However, we found that in the majority of cases in which the term "multimorbidity" was used to search, the term "comorbidity" also appeared in the list of keywords. Adding the term "chronic disease" also helped to circumvent the problem. Restricting the search to articles published in French or English is another limitation.
Conclusion
This systematic review focused on the relationship between the presence of several chronic coexisting medical conditions and QOL or HRQOL in a primary care setting. However, the studies evaluated had important limitations due to the lack of a uniform definition for multimorbidity or comorbidity, the absence of assessment of disease severity, the use of self-reported diagnoses, and the frequent exclusion of psychiatric diagnoses. The potential impact of important confounding variables was also neglected. In light of these observations, it seems clear that further studies are needed to clarify the impact of multimorbidity on QOL or HRQOL and its various dimensions (i.e., physical, social and psychological). A clear understanding of this relationship will ultimately help both researchers and primary health care professionals to deliver more comprehensive care.
Author contributions
MF was responsible for the conception and design of this systematic review and was also involved in the literature review. In addition, he was responsible for critically assessing the evaluated articles and drafting this manuscript. He takes responsibility for the integrity of the work as a whole and provided final approval of this version of the manuscript.
LL provided a major contribution to the literature review and critical appraisal of the identified articles. She also participated in the drafting of this manuscript and gave final approval of this version.
CH participated in both the conception and design of this review. She also contributed by critically revising this manuscript and gave final approval of this version.
AV participated in the design of this review. He also made an important contribution in critically revising the manuscript and gave final approval of this version.
ALN participated in the drafting of the manuscript and made an important contribution by critically revising it. He also gave final approval of this version.
DM participated in the drafting of the manuscript and made an important contribution by critically revising it. She also gave final approval of this version.
Acknowledgements
This study was made possible by grants from Pfizer Canada and the Family Medicine Department at the University of Sherbrooke.
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| 15380021 | PMC526383 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Sep 20; 2:51 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-51 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-341538505010.1186/1475-2875-3-34ResearchInfectious reservoir of Plasmodium infection in Mae Hong Son Province, north-west Thailand Pethleart Aree [email protected] Somsak [email protected] Wannapa [email protected] Boontawee [email protected] Roger [email protected] Christopher [email protected] London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK2 Faculty of Medicine, Thammasat University, Pathumtani 12120, Thailand3 Vector Borne Disease Section, Office of Disease Prevention and Control No.10, Chiang Mai, 52000, Thailand4 Vector-borne Disease Control Unit No.8, Mae Hong Son Province, Thailand2004 22 9 2004 3 34 34 26 5 2004 22 9 2004 Copyright © 2004 Pethleart et al; licensee BioMed Central Ltd.2004Pethleart et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
It was unknown whether the main reservoir of Plasmodium falciparum and Plasmodium vivax, which infects mosquitoes in Thailand, was (a) in people feeling sufficiently ill with malaria to come to a clinic or (b) in people who had remained in their home villages with some fever symptoms or with none.
Methods
Mass surveys were carried out in Thai villages to identify people with Plasmodium infections and with fever. Malaria patients were also located at a clinic which served these villages. Adults from both sources whose blood slides registered positive for Plasmodium spp. were requested to allow laboratory-bred Anopheles minimus to feed on them. Seven to nine days after the blood feeds the mosquitoes were dissected and checked for presence of oocysts.
Results and Discussion
There were higher rates of Plasmodium infection among people in the villages with fever than without fever and much higher rates of infection among clinic patients than among people who had remained in the villages. People with malarial infections identified via the clinic and the village surveys could infect mosquitoes, especially, but not only, if their blood slides showed visible gametocytes. Because only a very small minority of the village populations were visiting the clinic on any one day, assessment indicated that the main reservoir of infection was not primarily among clinic patients but among those in the villages, especially those feeling feverish.
Conclusions
Efficient use of an anti-gametocyte drug to suppress the parasite reservoir in a population requires that it be given, not just to clinic patients, but to infected people located by mass surveys of the villages, especially those feeling feverish.
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Introduction
An estimate of the infectiousness of the Plasmodium reservoir to mosquitoes is of interest in understanding the epidemiology of malaria and its changes after application of certain types of control measures. Different approaches have been used to investigate this, including direct feeding of mosquitoes through the skin of human subjects [1-5] or feeding through an artificial membrane [6-13]. The present study was designed to determine whether the reservoir of infection of vectors was mainly in people ill enough to go to a clinic or whether it was mainly in those with slight or no malarial symptoms who have remained in their villages.
Patients and Methods
Subjects
This study was approved by the Thai Ethics Committee and the Ethics Committee of London School of Hygiene and Tropical Medicine. Subjects were recruited from mass blood surveys in villages and from a clinic in Muang District, Mae Hong Son Province, which is in north-western Thailand. Anti-malarial drug use is tightly controlled in Thailand and these drugs are generally available on prescription. Self-medication is, therefore, unusual in this area. Mass blood surveys were conducted twice a year. After slides were examined for parasites, the people whose slides were positive and were more than 14 years of age were invited to take part in this study. The Thai ethical committee did not allow direct feeding of mosquitoes on children aged less than 15 years. Adults positive for malaria parasites were informed about the purpose of the study and, if they agreed, a consent form was signed and patients were interviewed.
Human subjects for the study were also enrolled from the Vector-borne Disease Control Unit No. 8 (VBDU: described as "Clinic" throughout this paper). In this Clinic, blood smears were taken by the clinic staff and stained with Giemsa to check for malaria. Patients who had presented with clinical malaria and/or parasitaemia and were more than 14 years of age were informed about the purpose of the study and asked to participate in it. If they agreed and signed the consent form, then the mosquito feeding was performed.
Mosquito feeding and dissection
Before feeding, patients' arms were cleaned with 70% alcohol. Then, 50 laboratory-bred female Anopheles minimus species A (aged 4–6 days), which had been starved for 9–12 hr, were allowed to feed on their arms for 30 minutes. After feeding was completed, anti-malarial drugs were given to all the human subjects by a malaria worker. Two hours after feeding the unengorged mosquitoes were removed from the cups using a sucking tube and destroyed, leaving only fully engorged mosquitoes in the cup. The mosquitoes were brought back to the Chiang Mai insectarium and maintained at 25 to 27 C and 70–80% relative humidity with permanent access to sucrose solution and without any further blood meals. Mosquitoes at 7–9 days post-feed were anaesthetized and the wings and legs were removed. Midguts were dissected on glass slides in a drop of 0.85% NaCl and examined at a 40× magnification. The number of oocysts present on the midgut to each mosquito was counted and recorded individually.
Quality control of blood slide data
Asexual parasitaemia and gametocytaemia were quantified as the number of aseuxal forms/200 white blood cells on a thick film. Throughout the period of the study, 20% of the negative blood smears and all positive slides which had been examined by the microscopist of the Clinic were chosen randomly and re-examined by a team from the Office of Vector-borne Disease Control No.2, Chiang Mai. All cases of positive slides without visible gametocytes and which led to oocyst production were re-examined by an expert team from the London School of Hygiene and Tropical Medicine to ascertain whether a few gametocytes might have been present but were initially missed.
Analyses
The database from mosquito feeding included 1) densities of gametocytes and trophozoites, 2) number of infected mosquitoes, and 3) mean number of oocysts per infected mosquito. The chi-square test or Fisher's exact test was used to examine the significance of differences in the proportion of people in various categories who transmitted malaria to the mosquitoes (i.e. yielded at least 1 oocyst). Regressions on the natural log of gametocyte density were also computed. Stata statistical software (version 6) was used for analysis.
Results
The upper part of Table 1 shows the results associated with 5,227 blood smears from children and adults in village surveys. 10.7% (561/5,227) of subjects reported fever within the previous seven days but the corresponding rate for slide positives was much higher (53.8%, 28/52). In children (age <15 yrs), 60% (9/15) of slide positives reported fever. Among 37 slide positive adults (age >14 years), 19 (51.4%) reported fever. Among these 19 cases, nine were infected with Plasmodium falciparum and 10 with Plasmodium vivax. The prevalence of infection of both species from the village surveys was higher in children (15/623 = 2.4%) as compared to adults (37/4604 = 0.8%) (χ2 = 12.8, P < 0.001). The lower part of Table 1 shows data from the Clinic where 101 patients aged >14 years were interviewed; 85.2% (86/101) reported fever. Among those, 39 were infected with P. falciparum and 47 with P. vivax.
Table 1 Data from village surveys and the clinic on reported fever and malaria infection.
Slide reading Fever reported Fever not reported Fisher's exact tests of association of fever with infection
Village surveys, age <15 yrs
P. falciparum +ve 2 (3.2%) 3 (0.54%) P = 0.082
P. vivax +ve 7(11.1%) 3 (0.54%) P = 0.000007
Total slides 63 560
Village surveys, age >14 yrs
P. falciparum +ve 9 (1.8%) 10 (0.24%) P = 0.00006
P. vivax +ve 10 (2.0%) 8 (0.19%) P = 0.000003
Total slides 498 4106
Clinic (age >14 yrs)
P. falciparum +ve 39 (45.35%) 10 (66.67%) χ2 = 0.39, P = 0.53
P. vivax +ve 47 (54.65%) 5 (33.33%) χ2 = 0.14, P = 0.51
Total slides 86 15
As shown in Table 2, at least one mosquito became infected with oocysts in the batches fed on 10/28 people from the village infected with either species (35.7%). The proportion of individual patients from the Clinic who infected at least one mosquito was 43.4% (40/92). The difference between these two rates was not significant (χ2 = 0.26, p = 0.6). The probability of infection of mosquitoes from people infected with P. falciparum was significantly less than from people infected with P. vivax (24.7% versus 57.1%, χ2 = 11.6, P < 0.001). 77.1% (44/57) of P. falciparum cases reported fever. Among those, only 27.3% could infect mosquitoes. For P. vivax 84.1% (53/63) reported fever; among those 56.6% could infect mosquitoes. More than half of the infections from whose blood no mosquitoes developed oocysts had parasite density > 3,960/μl. There was no association of probability of mosquito infection with parasite density (χ2 = 1.3, P > 0.05).
Table 2 Results of feeding mosquitoes on human blood
No. people from whose blood some mosquitoes developed oocysts No. people from whose blood no mosquitoes developed oocysts Significance of differences
P. falciparum
Villages 3 (23.1%) 10 (76.9%) Fisher not sig.
Clinic 11 (25.0%) 33 (75.0%)
Observable gametocytes 6 (46.2%) 7 (53.8%) Fisher P = 0.06
No observable gametocytes 8 (18.2%) 36 (81.8%)
Fever reported 12 (27.3%) 32 (72.7%) Fisher P = 0.32
Fever not reported 2 (15.4%) 11 (84.6%)
P. vivax
Villages 7 (46.7%) 8 (53.3%) χ2 = 0.41, P = 0.52
Clinic 29 (60.4%) 19 (39.6%)
Observable gametocytes 31 (68.9%) 14 (31.1%) χ2 = 7.3, P = 0.007
No observable gametocytes 5 (27.8%) 13 (72.2%)
Fever reported 30 (56.6%) 23 (43.4%) Fisher P = 0.56
Fever not reported 6 (60%) 4 (40%)
Parasite density (P. falciparum and P. vivax)
<3961/μl 21 (35.9%) 38 (64.4%) χ2 = 1.30, P = 0.25
>3960/μl 29 (47.5%) 32 (52.5%)
Considering each species separately, the probability of infectiousness to mosquitoes was significantly related to being observably gametocyte positive in P. vivax (P= 0.007), but the relationship was only of borderline significance for P. falciparum (Fisher's exact test, P = 0.06). Approximately 50% (7/13) of the P. falciparum cases with observable gametocytes failed to infect mosquitoes and 30% (14/45) of the P. vivax cases with observable gametocytes failed to infect. However, the difference was not significant (Fisher's exact test, P= 0.19). Approximately 21% (13/62) with no observable gametocyte of either species could infect mosquitoes.
58 of the infections with either species had observable gametocytes (11 cases from the village survey and 47 from the Clinic). Feeds on 37 of these 58 gametocyte carriers led to oocyst production. The regression of percent of mosquitoes infected on the natural log of gametocyte density was not significant (t = 0.87, df= 36, P= 0.39). Similarly, there was not a significant association of mean oocyst load per infected mosquito on the natural log of gametocyte density (t = 0.87, df = 36, P= 0.38).
Discussion
The results from the experiments with mosquitoes showed the infectiousness of subjects from the village surveys as well as from the Clinic. These indicated that some symptomatic and asymptomatic infections of each species could infect mosquitoes. The differences in percentages of infection in different studies [2,4,14] might be explained by several possible factors. First, the method of feeding: a recent study comparing the infectivity of gametocyte carriers to mosquitoes, using membrane and direct feeding, found significantly higher proportions of mosquitoes infected and higher oocyst burdens in mosquitoes fed directly on the skin [15]. Conversely, Vanderberg [16] reported that infectivity in mosquitoes fed through a membrane usually equaled or exceeded infections by direct methods. However, most studies gave better results for direct feeding than membrane feeding. Thus studies such as the present one using direct feeding may provide a more reliable estimate of the infectious reservoir. Second, recruited subjects: in several studies mosquitoes were fed on individuals selected randomly and not on the basis of gametocytaemia [5,10,14]. But in other studies mosquitoes were fed on selected gametocyte carriers [4,17] or parasitaemic cases with or without gametocytes (as in the present study).
Another relevant factor is variability in mosquito populations [18,19], such as mosquito size [20-22]. The number of blood meals may also affect the infection rate [8,23]. A careful comparison of Anopheles dirus, An. minimus and Anopheles maculatus infectivity in relation to size and blood-feeding behaviour would be of interest. Moreover, variability in susceptibility between different mosquito colonies is possible [4,24].
The results in the present study showed that some cases with undetectable gametocytes could infect mosquitoes. This apparent anomaly is presumably at least partly due to larger volumes of blood in mosquito bloodmeals than are observed on slides, so that sufficient gametocytes to infect a mosquito may have been below the level of detection on blood slides. An attempt was made to see if gametocyte densities are higher in bloodmeals than finger pricks. However, it was found that gametocyte density was not significantly higher (and actually appeared to be lower) in the blood taken up by mosquitoes than in the blood from finger pricks. In some cases high densities of gametocytes were not infectious and similar results have been reported in most studies assessing human malarial infectivity to mosquitoes [3,4,11,24,25]. It has been suggested that the prevalence of gametocyte carriers is not a good indication of the infectiousness of a population to mosquitoes [10,25-27].
It is clear that, as the Ethical committee only authorized us to request people over 14 years of age to take part in our experiments, this excluded a sector of the reservoir population, the children, from the study. It is also clear that the occurrence of transmission-blocking immunity and prior histories of the subjects taking anti-malarial drugs might have influenced whether they infected mosquitoes. No data on these factors was collected and so no comment can be made on their possible influence on infectivity of people's blood to mosquitoes. However, the fact that availability of anti-malarial drugs is much more tightly controlled in Thailand than in most malarious countries should be emphasized.
From the results of the study, an attempt can be made to estimate the number of adults in the catchment area of the Clinic who were reservoirs of infection. On the basis of the number of patients visiting the Clinic per day and the catchment population from which the patients come (Figure 1), it was concluded that the main reservoir of infection for mosquitoes was not in adult patients feeling ill enough to be motivated to come to the Clinic. Among villagers, occurrence of fever is a strong indicator of likely malarial infection (Table 1). Thus, fever is an indicator of likelihood of being part of the infectious reservoir for mosquitoes. However, because there are far fewer people who are feverish than those who are not, the numbers of people in the infectious reservoir who are, or are not, feverish do not differ greatly (Fig. 1). The calculations in Figure 1 do not take into account the fact that the infected people found in the villages will remain infected for several days, whereas a new group of about nine people go to the Clinic every day. It would be useful to know for how many days people remain infectious to mosquitoes, but ethically one cannot leave detected infections untreated in order to test this. The data from Figure 1 indicate that directing an anti-gametocyte drug only to the clinic patients would be ineffective. Instead the drug would have to be targeted at the village populations after mass surveys for parasitaemia. Feverishness would assist to some extent in helping to identify people most likely to be infected. In view of the current interest in anti-gametocyte drugs [28-30], these data may be of use in deciding how such drugs would have to be targeted to have an impact on transmission.
Figure 1 Diagram to show location of the reservoirs of infection (* data from the surveys, ** data from the Vector-borne Disease Control Clinic No. 8, Mae Hong Son Province)
Authors' contributions
Aree Pethleart led the data collection team in the field and laboratory, analysed data and drafted the paper;
Somsak Prajakwong provided research facilities;
Wannapa Suwonkerd organized mosquito rearing and dissection;
Boontawee Corthong was in charge of Clinic data and provided facilities for village surveys;
Roger Webber was responsible for early planning of the work; and
Christopher Curtis supervised data analysis and edited the draft.
Acknowledgements
We would like to thank the staff from the Vector-borne Disease Control Unit No.8, Mae Hong Son Province for blood collection. We wish to acknowledge the help and co-operation of Mrs Suparp Chatchatreechan, Mr Pongnarin Dee-in, Mr Somkid Sumonmard and the staff in the Entomological Department of the Office of Vector-borne Disease Control No.2, Chiang Mai (VBDO) for their help in looking after and dissecting mosquitoes. We would also like to extend our thanks to staff in the Investigation Department of VBDO and to John Williams and his team from the London School of Hygiene and Tropical Medicine, UK, for re-checking the slides. We are also thankful to Dr Apinun Aramratana, Dr Walter RJ Taylor and Dr Ilona Carneiro for their suggestions on collection, analysis and presentation of data. This work was supported by the Royal Thai Government and World Health Organization (TDR).
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| 15385050 | PMC526384 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Sep 22; 3:34 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-34 | oa_comm |
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Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-3-241548557510.1186/1476-072X-3-24ReviewRoad-traffic pollution and asthma – using modelled exposure assessment for routine public health surveillance Ferguson Elspeth C [email protected] Ravi [email protected] Mark [email protected] Public Health GIS Unit, School of Health and Related Research, The University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, United Kingdom2 Environmental Protection Service, Sheffield City Council, Environment and Regulatory Services, 2-10 Carbrook Hall Road, Sheffield S9 2DB, United Kingdom2004 14 10 2004 3 24 24 13 9 2004 14 10 2004 Copyright © 2004 Ferguson et al; licensee BioMed Central Ltd.2004Ferguson et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Asthma is a common disease and appears to be increasing in prevalence. There is evidence linking air pollution, including that from road-traffic, with asthma. Road traffic is also on the increase. Routine surveillance of the impact of road-traffic pollution on asthma, and other diseases, would be useful in informing local and national government policy in terms of managing the environmental health risk.
Several methods for exposure assessment have been used in studies examining the association between asthma and road traffic pollution. These include comparing asthma prevalence in areas designated as high and low pollution areas, using distance from main roads as a proxy for exposure to road traffic pollution, using traffic counts to estimate exposure, using vehicular miles travelled and using modelling techniques. Although there are limitations to all these methods, the modelling approach has the advantage of incorporating several variables and may be used for prospective health impact assessment.
The modelling approach is already in routine use in the United Kingdom in support of the government's strategy for air quality management. Combining information from such models with routinely collected health data would form the basis of a routine public health surveillance system. Such a system would facilitate prospective health impact assessment, enabling policy decisions concerned with road-traffic to be made with knowledge of the potential implications. It would also allow systematic monitoring of the health impacts when the policy decisions and plans have been implemented.
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Introduction
The prevalence of asthma is increasing and there is concern that the increase may in part be attributable to increasing road traffic related pollution. The concerns relate especially to childhood asthma. In this article, we set out the arguments for using modelled exposure assessment to create a surveillance system that will facilitate routine public health work, such as monitoring and health impact assessment. We first discuss the increasing prevalence of asthma and the effects of air pollution on asthma. We then set out the benefits of monitoring the link between outdoor air pollution related to road traffic and asthma and discuss methods of exposure assessment in some detail. We describe modelling air quality in the UK as an example and then address the implementation of a surveillance system.
Increasing prevalence of asthma
Asthma is a common disease. Figures from the International Study of Asthma and Allergies in Childhood suggest up to 25.9% of children in Oceania have ever had asthma [1]. Although less evidence is available with regard to the prevalence amongst adults, the European Community Respiratory Health Study which studied asthma prevalence throughout Europe, Australasia and the United States suggested asthma affects up to 11.9% of adults in Australia and 8.4% of adults in the UK [2]. Over recent years the prevalence of asthma appears to have been steadily increasing [3]. It has also been suggested by some that the severity of asthma is on the increase [4], although other studies do not confirm this suggestion [5]. Initially thought to be a disease of the western world, in recent years the incidence of asthma has also been shown to be increasing dramatically in less developed countries [6]. Much research has taken place to find the cause of such an increase but the reasons are not fully understood. Socio-economic status [7], ethnicity [8], allergen exposure [9], smoking [10], nutrition [11] and infection exposure [12] have all been considered as possible factors. Links have also been made to living in an urban as opposed to a rural area [13], raising speculation as to a possible effect of air pollution on the prevalence of the disease.
Effects of air pollution on asthma
Air pollution has been linked to morbidity and mortality of several diseases, including diverse conditions such as coronary heart disease [14] and Hodgkin's disease [15]. In terms of the effects on respiratory disease, exposure to air pollution has been linked to the aggravation of chronic respiratory symptoms and increased mortality from chronic obstructive pulmonary disease [16]. Over the 20th century, air pollution increased greatly alongside the noted increase in the prevalence of asthma. Yet the last decades of the century have seen a considerable reduction in pollutants such as sulphur dioxide (SO2) following a cut back on industrial emissions [17].
Levels of pollutants such as nitrogen dioxide (NO2) however, still remain problematic due to the increasing number of vehicles on our roads. In fact almost 50% of NO2 is thought to be produced by vehicles and much particulate matter is produced by diesel exhaust fumes [17]. Experimental studies have shown NO2 exposure increases cell membrane permeability, decreases ciliary beat frequency [18] and increases the response of asthmatics to inhaled allergens [19], whilst exposure to diesel exhaust particles in mice has been shown to alter IgE antibody production [20]. Epidemiological investigations into the effects of these pollutants have suggested an association between pollutant levels and the exacerbation of asthma. Results from a study in Paris showed an increase of 100 μg/m3 of NO2 to be associated with a relative risk of 1.175 for asthma admission [21]. Emissions of nitrogen oxides (NOx) have also been shown to influence emergency room visits in Israel [22]. Both particulate matter less than 10 μm in diameter (PM10) and NO2 appeared to consistently increase attendances at accident and emergency units with asthma in London [23]. With traffic emissions accounting for such a high proportion of these pollutants and with the volume of traffic on the increase, such a link between traffic-related pollution and asthma would be of importance.
It has been suggested that pollutant exposure may induce asthma, aggravate asthma or increase the permeability of the airways to other allergens to which asthmatics are susceptible [17]. Any of these effects could potentially cause a significant increase in asthma morbidity if the level of traffic continues to rise. A number of studies have considered the association between asthma and road-traffic pollution specifically. Ciccone et al. [24] showed the odds ratios for asthma and a number of asthmatic symptoms to be increased in those exposed to heavy lorry traffic. Heavy traffic flow has also been shown to increase childhood asthma admissions [25]. Studies carried out within the UK and the United States have suggested those living within close proximity to a road are also at an increased risk of hospitalisation with asthma [25,26].
Monitoring the link between air pollution and asthma
The evidence suggests that there is a link between air pollution and asthma but it is not conclusive. The increasing prevalence of asthma and the continuing increase in road traffic are both of concern. Monitoring the association between asthma and road traffic pollution would be useful for public health purposes, both in terms of surveillance and in terms of influencing policy. Policy implications might include routing of traffic, construction of bypasses, congestion reduction schemes, utilisation of non-fossil fuel cars and possibly even the location of schools. A monitoring system would use estimates of air pollution from road traffic which would be linked to data on asthma obtained from routine systems, such as hospital admissions, attendance at accident and emergency departments or primary care consultations or to data from periodic surveys on health, including asthma prevalence.
Methods of exposure assessment
A number of methods for exposure assessment have been used in studies examining the association between asthma and road traffic pollution and these are described below. These include comparing asthma prevalence in areas designated as high and low pollution areas, using distance from main roads as a proxy for exposure to road traffic pollution, using traffic counts to estimate exposure, using vehicular miles travelled and using modelling approaches which can take into account a number of variables.
(i) High vs low pollution areas
One method frequently used is estimating exposure levels of individuals based on assessing whether a residence is in a high or low pollution area. Many have estimated exposure status of individuals on the basis of whether or not they live on a street with heavy traffic, for example in the study conducted by Jedrychowski and Flak [27]. Another approach was used by Nicolai and v. Mutius [28] in their study of former East and West Germany. In this study, West Germany was classified as an area with high traffic-related NO2 emissions and low SO2 emissions from industry, whilst the opposite was said to be true of East Germany. The problem, particularly with this method, is that although the countrywide generalisation may hold true, it may not be true for each individual. Even when the measure is made for each individual, it should be remembered it may be subject to bias. It has been postulated that asthmatic individuals and their families may be more aware of the speculation over such a link between road-traffic and asthma and therefore may be more likely to report or consider heavy traffic to be associated with their symptoms [24].
(ii) Distance to roads
Distance from roads has commonly been used as a proxy for road traffic exposure in a number of studies. Postcodes are georeferenced and may be used in a geographical information system (GIS) to calculate the distance from an individual's residence to a road, most often a main road, carrying over a certain volume of vehicles. In certain cases the distance from a child's school to a main road has been used instead. Examples of the use of this method are studies by Livingstone et al. [29] and Wilkinson et al. [30]. A number of authors have only considered individuals living within 1000 m of a main road as they felt traffic would be unlikely to influence pollution levels beyond this distance [31]. Indeed, with analyses using this method, effects of pollutants have often only shown an effect within a short distance from main roads. By using this method an assumption is made that all individuals living within a certain distance of a road are subjected to the same level of exposure, yet this is unlikely to be the case. Traffic on different roads varies, both in volume and in type, and meteorological conditions can alter dispersion of pollutants. It is well known that cold weather conditions trap air close to the ground, prolonging the duration of the time pollutants remain close to where they were produced [17].
(iii) Traffic counts
Another popular method is considering traffic flow along the street of residence or one in close proximity, as used by English et al. [32]. In a similar way Wjst et al. [33] have investigated traffic flow around a child's school. The traffic count method has the advantage of being likely to be a more valid measure than distance to roads. It is worth however considering the daily movements of an individual. Throughout the day an individual travels between home and work or school experiencing a number of different exposure levels on the way. Recreational activities may also subject a person to different levels of exposure. Indeed even within the residential area, exposure may vary dependent on the time one spends indoors or outdoors. Indoor exposure to NO2 may be high, with levels possibly higher than outdoors if a gas stove is used in the home [34]. Another point to consider is the type of traffic exposure. Emissions vary greatly between cars and trucks. Some have approached this by analysing data from different vehicles separately, suggesting truck pollution to be more detrimental to health than that from cars [31]. It could be suggested that as car and truck pollution varies, for example, trucks produce a lot more particulate matter consisting of diesel particles than cars, that perhaps one should consider the effects of particulates separately from those of NO2. This however, is not without difficulties, as if one is exposed to traffic there will be a combined effect from a cocktail of pollutants produced by both trucks and cars.
(iv) Vehicle miles travelled
Some authors (e.g. Lin et al. [26]) have attempted to combine both length of road and traffic counts as a measure referred to as vehicle miles travelled. It involves multiplying the length of a road in a specified area around the home by the traffic volume travelling along that section of road. Authors have varied in the selection of roads used in such analyses. This method may have an advantage over measuring traffic flow alone, as exposures may be more accurate. There is still however, no account taken of individuals moving between areas through the day or different topographical conditions. Some feel that buildings in the vicinity of one's home should be considered when looking at exposure to traffic pollution [35], due to their influence on pollution dispersion, as well as the presence of bus stops and distance to street crossings which may influence exposure [27].
(v) Modelling approach
A number of studies have used modelling to estimate pollution exposure [35,36]. A model is capable of taking into account a whole range of factors that may affect exposure. As illustrated by Pershagen et al. [36] exposures both at home and at day-care centres or for others at school or work can be considered, with these being adjusted for the time spent in each location. Factors considered in the models used in the studies above have included vehicle type and density, presence and type of buildings on a street, meteorological conditions, street width and distance from house to middle of the street amongst other factors.
Even within a model however, accounting for personal day-to-day exposures is still problematic. In order to take previous exposures into account a cohort study would be necessary [37]. Certainly if one is trying to account for the prevalence of a disease like asthma, knowing previous exposure levels prior to the onset of the disease is important. To do this one would need to look at the previous residences and day-to-day exposures of that person throughout their life. An alternative would be to use a personal monitoring system. Both these methods of assessing long-term exposure, however, would be very expensive. One could consider the use of monitoring stations already in place throughout cities. The problem with using such stations is that they are generally widely dispersed while pollution levels may vary substantially within short distances, e.g. exponential decline in the concentration of certain pollutants with increasing distance from busy roads [38]. Installing sufficient monitoring stations to adequately capture spatial variation in levels of pollution encountered over short distances, would be both impractical and expensive.
Despite limitations, a model would appear to be the most practical way of assessing traffic-related exposure where routine surveillance is concerned. Information such as vehicle density, type of vehicle, risk of traffic congestion, presence of bus stops and street crossings, distance of residences to roads, street width, type of street, building presence and type and meteorological conditions (e.g. wind speed and direction, absolute temperature and temperature differences, global and gamma radiation) could be collected routinely for use in a variety of models for predicting exposure to NO2 and PM10. The model could be used to estimate exposures on all the streets within a certain radius of the home or place of work as dispersion of pollutants from these streets may also be affecting the individual. In a sophisticated model it may be possible to make adjustment for the height of an individual's residency or place of work in high rise buildings to account for the vertical dispersion of pollutants. Such a system could also be used to estimate exposures at previous residences, work places or schools of an individual so that an assessment of lifelong exposure could be made as accurately and practically as possible. However, the latter might be too complicated for a routine monitoring system.
Modelling air quality in the UK
The UK Government's current policy on air quality within the UK is set out in the Air Quality Strategy for England, Scotland, Wales and Northern Ireland published in January 2000 pursuant to the requirements of Part IV of the Environment Act 1995. The Strategy sets out a framework for improving air quality and for ensuring that international commitments are met. It is designed to be an evolving process that is monitored and regularly reviewed. The Strategy sets standards and objectives for ten pollutants that have an adverse effect on human health, vegetation or ecosystems and target dates for achieving them. The standards generally set concentration limits above which sensitive members of the public (e.g. children, older people, people who are unwell) might experience adverse health effects. In early 2003 an Addendum to the Strategy was published introducing standards and objectives for a new pollutant and revising those for three others.
The pollutants currently specified in the Strategy now include benzene, 1,3 butadiene, carbon monoxide, lead, NO2, PM10, SO2, ozone (O3), NOx and polycyclic aromatic hydrocarbons. The predominant source of most of these pollutants is road traffic, but industrial and domestic sources are also contributors.
The air quality standards or guideline limits are long-term benchmarks for ambient pollutant concentrations which represent negligible or zero risk to human health, based on medical and scientific evidence reviewed by the Expert Panel on Air Quality Standards (EPAQS) and the World Health Organization (WHO). For some pollutants, (e.g. NO2), there is both an annual mean guideline limit and a short-term mean guideline limit. These reflect the varying impacts on human health of exposure to some pollutants over differing time periods, (e.g. temporary exposure on the pavement adjacent to a busy road compared with the exposure of residential properties adjacent to a road).
The air quality objectives are medium-term policy-based targets set by the Government which take into account economic efficiency, practicability, technical feasibility and timescale. Some objectives are equal to the EPAQS or WHO recommended air quality standards and guideline limits, whereas others involve a margin of tolerance, i.e. a limited number of permitted exceedances of the standard over a given period.
The Government has issued guidance to local authorities on how to conduct Reviews and Assessments required under the system of Local Air Quality Management (LAQM). The latest available guidance is Policy Guidance LAQM.PG(03) and Technical Guidance LAQM.TG(03). Air quality modelling is key to assessing the future potential for attainment, or not, of the objectives. Part IV of the Environment Act 1995 requires a local authority to designate an Air Quality Management Area (AQMA) covering any part of its administrative area where air quality objectives are not likely to be achieved by, or at any point beyond, the relevant objective's target date at locations where the general public might reasonably be exposed. These AQMAs have been determined by modelling future scenarios. For each AQMA the local authority has a duty to draw up an Air Quality Action Plan (AQAP) setting out the measures the authority intends to introduce to deliver improvements in local air quality in pursuit of the air quality objectives. Local authorities are not statutorily obliged to meet the objectives, but they must show that they are working towards them. As of June 2004, there were 120 designated AQMAs in the UK, with 80 AQAPs produced outlining how air quality would be tackled in these areas.
Implementation of a surveillance system
A routine surveillance system would be one which links modelled air quality data, such as those derived in the UK described above, with routinely collected health data. There are a number of issues regarding the technical aspects of linking spatial information on air quality with health information, typically carried out using GIS. These are discussed in detail elsewhere [39]. The information on air quality could be used in a statistical model and analysed alongside hospital admission, accident and emergency attendance and prevalence data for asthma, or any other conditions to which a link with air pollution has either been made or considered.
The majority of studies examining the link between asthma and road-traffic pollution have concentrated on the prevalence of asthma, for example studies by Ciccone et al. [24], Jedrychowski and Flak [27], Livingstone et al. [29] and Nicolai and v. Mutius [28]. Prevalence data is typically gathered from health surveys, as information regarding disease prevalence is not generally available through routine recording. It would however be possible through the use of periodic health surveys to collect the relevant information required to analyse disease prevalence along with modelled pollution exposure data. The ISAAC investigators for example have designed a standardised questionnaire now being used throughout the world which is concerned with the prevalence of asthma amongst children. It considers those children suffering from asthma to be those who answer, or whose parents answer, that they have suffered from wheezing or whistling from the chest in the last twelve months [40]. Using a standardised questionnaire such as the ISSAC questionnaire for children would allow comparable information to be collected and compared both within and between countries.
A few of the studies examining the link between asthma and road-traffic pollution have used information that is readily available. For example studies by English et al. [32] and Lin et al. [26] have looked at children being hospitalised with exacerbations of asthma. Such information is routinely recorded in hospitals, but tends to reflect disease severity. Relating such information to levels of air pollution is still important in determining the effects of pollution on asthma.
A routine surveillance system recording spatial variation in pollutant levels would allow improved understanding of the link between road-traffic pollution and asthma, or indeed other diseases and could be used to help predict future health impact, particularly in cities and towns. The results of such assessment would allow local policy decisions concerning the routing of traffic around residential areas or schools and plans to reduce congestion to be made with knowledge of the implications of the decision on the health of the local population. It would also allow systematic monitoring of the health impacts when the policy decisions and plans have been implemented.
We should point out here that to examine acute effects (i.e. on events such as admissions, general practice consultations, emergency room attendances) using daily time series analyses, monitoring data would be required as modelling would probably be too insensitive to detect daily variation in outdoor air pollution levels. However, time series analyses are complicated research methods that are not within the realm of routine public health practice. What we have argued for here is a surveillance system that looks at spatial variation in pollution and asthma which would highlight problem areas and could monitor the effects of interventions to reduce pollution in these areas.
Conclusions
We believe that a routine surveillance system which links modelled outdoor air pollution data to health data would provide a useful tool for facilitating routine environmental public health work. Such a system would be especially useful for monitoring the health effects of traffic related pollution and for aiding health impact assessment. Implementation of the system will require close collaboration between public health and environmental health departments, protocols for sharing data and investment in training to develop the necessary technical expertise to set up and maintain the surveillance system. Of particular importance will be the ability of high level management to interpret surveillance information within a wider policy context.
Authors' contributions
RM proposed the idea for the article. ECF wrote the first draft, supervised by RM. MD contributed the section on modelling air quality in the UK. RM edited subsequent drafts.
Acknowledgements
ECF received a studentship from the Association of Physicians of Great Britain and Ireland. The views expressed in this article are those of the authors and not necessarily those of the funding or employing organisations.
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| 15485575 | PMC526385 | CC BY | 2021-01-04 16:39:01 | no | Int J Health Geogr. 2004 Oct 14; 3:24 | utf-8 | Int J Health Geogr | 2,004 | 10.1186/1476-072X-3-24 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-91550713110.1186/1743-7075-1-9Brief CommunicationThe J-shape association of ethanol intake with total homocysteine concentrations: the ATTICA study Pitsavos Christos [email protected] Demosthenes B [email protected] Meropi D [email protected] Christina [email protected] Yannis [email protected] Antonis [email protected] Antonia [email protected] Christodoulos [email protected] First Cardiology Clinic, School of Medicine, University of Athens, Athens, Greece2 Department of Dietetics and Nutrition, Harokopio University, Athens, Greece3 Department of Hygiene and Epidemiology, School of Medicine, University of Athens, Athens, Greece2004 14 10 2004 1 9 9 16 9 2004 14 10 2004 Copyright © 2004 Pitsavos et al; licensee BioMed Central Ltd.2004Pitsavos et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Epidemiological studies suggest a non-monotonic effect of alcohol consumption on cardiovascular risk, while there is strong evidence concerning the involvement of homocysteine levels on thrombosis. The aim of this work was to evaluate the association between usual ethanol consumption and homocysteine levels, in cardiovascular disease free adults.
Methods
From May 2001 to December 2002 we randomly enrolled 1514 adult men and 1528 women, without any evidence of cardiovascular disease, stratified by age – gender (census 2001), from the greater area of Athens, Greece. Among the variables ascertained we measured the daily ethanol consumption and plasma homocysteine concentrations.
Results
Data analysis revealed a J-shape association between ethanol intake (none, <12 gr, 12 – 24 gr, 25 – 48 gr, >48 gr per day) and total homocysteine levels (mean ± standard deviation) among males (13 ± 3 vs. 11 ± 3 vs. 14 ± 4 vs. 18 ± 5 vs. 19 ± 3 μmol/L, respectively, p < 0.01) and females (10 ± 4 vs. 9 ± 3 vs. 11 ± 3 vs. 15 ± 4 vs. 17 ± 3 μmol/L, respectively, p < 0.01), after controlling for several potential confounders. The lowest homocysteine concentrations were observed with ethanol intake of < 12 gr/day (Bonferroni α* < 0.05). No differences were observed when we stratified our analysis by type of alcoholic beverage consumed.
Conclusion
We observed a J-shape relationship between homocysteine concentrations and the amount of ethanol usually consumed.
ethanolhomocysteineinflammation
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Introduction
Alcoholic beverages are widely consumed throughout the world and it has long been known that heavy alcohol consumption is hazardous to various body organs. In several countries alcohol is considered as one of the leading causes of preventable deaths, after smoking [1]. However, there is now also substantial evidence that the intake of light to moderate amounts of ethanol is associated with reduced morbidity and mortality from several cardiovascular conditions, particularly coronary heart disease (CHD) [2]. The interpretation of these beneficial effects has been extensively discussed and it has been suggested that the effects on cardiovascular disorders might not be due to ethanol per se but to other confounding factors [3]. Low to moderate ethanol consumption has been associated with reduced mortality, primarily due to a reduction in coronary heart disease (CHD). Conversely, heavy drinking increases mortality, mainly due to haemorrhagic stroke and non-cardiovascular diseases [4,5].
Some investigators consider increased homocysteine levels as an independent risk factor of cardiovascular disease, and its involvement in mechanisms of thrombosis has well been documented [6,7]. Moreover, other studies suggest that an elevated plasma total homocysteine concentration increases the risk associated with some of the conventional cardiovascular risk factors [8,9]. However, there are findings that do not confirm or recognize homocysteine importance in actually causing coronary artery disease, while recent studies have considered homocysteine more as a result than a cause of arteriosclerosis, especially due to the confounding effect of various nutrients and other lifestyle-related factors, including alcohol drinking [10-13].
We therefore studied the relation between amount of ethanol consumption and homocysteine levels, in 3042 adults enrolled in the ATTICA Study.
Subjects and Methods
Study population
The "ATTICA" study [14] is a health and nutrition survey, which is being carried out in the province of Attica (including 78% urban and 22% rural areas), where Athens is the metropolis. The sampling was random, multistage and it was based on the age – sex distribution of the province of Attica, provided by the National Statistical Service (census of 2001). Also, all people living in institutions were excluded from the sampling, and we enrolled only one participant per household. From May 2001 to December 2002, 4056 inhabitants from the above area, who had no clinical symptoms or signs of cardiovascular or any other atherosclerotic disease (as assessed by the physical examination and reported medical history), nor evidence of chronic viral infections, were randomly selected to enter into the study. None of the participants was under current or chronic use of certain drugs that influence homocysteine levels, like methotrexate, trimethoprin, cholestyramine and cyclosporine. Moreover, subjects did not have cold or flu, acute respiratory infection, dental problems or any type of surgery in the preceding week. Of the 4056 inhabitants, 1518 men (46 ± 13 years old) and 1524 (45 ± 13 years old) women agreed to participate (75% participation rate). Participants were interviewed by trained personnel (cardiologists, general practitioners, dieticians and nurses) who used a standard questionnaire.
The selected sample was population-based and reflecting the underlying population with respect to sex, age and residence. The number of the participants was determined by power analysis and chosen to evaluate greater than 0.5 standardised differences between ethanol groups and homocysteine levels, with statistical power > 0.80 at < 0.05 probability level (P-value).
Measurements
The questionnaire included demographic characteristics (age, sex, mean annual income and years of school), detailed medical history and lifestyle habits, such as food items consumed, smoking habits and physical activity status.
Dietary intake during the year before enrolment was assessed through a semi-quantitative food frequency questionnaire provided by the EPIC-Greece Study. The questionnaire was administrated in person by specially trained dieticians and has been validated [15]. The daily ethanol intake was assessed in a 7-day food record. All alcoholic beverages consumed, i.e. wine, beer, whisky, traditional alcoholic drinks, like "retsina" or "tsipouro", and other spirits were recorded and daily ethanol intake (in grams) was calculated. For the presentation of our findings we categorized ethanol intake into five groups: (a) no ethanol intake, (b) low (< 12 gr), (c) moderate (12 – 24 gr), (d) high (25 – 48 gr) and (e) very high (>48 gr). Moreover, the frequency of consumption of several food groups was quantified approximately in terms of the number of times per month the food was consumed.
Regarding the rest of the investigated parameters the educational level of the participants (as an index of social status) was measured in years of school. Information about smoking habits was collected using a standardized questionnaire developed for the Study. Current smokers were defined as those who smoked at least one cigarette per day. Former smokers were defined as those who had stopped smoking more than one year previously. The rest of the participants were defined as non smokers. For the multivariate statistical analyses cigarette smoking was quantified in pack-years (cigarette packs per day × years of smoking), adjusted for a nicotine content of 0.8 mg / cigarette. All participants were classified at entry according to their habitual physical activity. Class 1 were sedentary, engaging in little exercise; class 2 were moderately active during a substantial part of the day; and class 3 performed hard physical work much of the time. Classification was based on the responses to questions about the occupation and usual activities, including part-time jobs and notable non-occupational exercise [14]. Body mass index was measured as weight (in kilograms) divided by standing height (in meters squared). Obesity was defined as body mass index > 29.9 Kg / m2.
Blood samples were collected from the antecubital vein between 8 to 10 a.m., in a sitting position after 12 hours of fasting and avoiding of ethanol. For the determination of plasma fibrinogen blood was anticoagulated with 3.8% trisodium citrate (9:1 vol/vol) and cooled on ice until centrifugation. For determination of homocysteine, blood was collected in a cool vacutainer containing EDTA, which was stored on ice for a maximum of 2 hours till the centrifugation at 3000 g for 5 minutes at 4°C. Plasma homocysteine levels were measured with an automatic Abott Axsym analyzer, which is based on the technology of polarized immunofluorescence. The intra and inter-assay coefficients of variation of homocysteine did not exceed 5%.
Arterial blood pressure was automatically measured at the end of the physical examination with subject in sitting position. Hypertension was defined as a systolic blood pressure >/= 140 mmHg, a diastolic blood pressure >/= 90 mmHg, or the use of any antihypertensive medication; hypercholesterolemia was defined as total cholesterol levels greater than 220 mg/dl or the use of lipid lowering agents and diabetes mellitus as a fasting blood glucose > 125 mg/dl or the use of antidiabetic medication.
Statistical analysis
Continuous variables are presented as mean values ± standard deviation, while qualitative variables are presented as absolute and relative frequencies. Associations between categorical variables were tested by the use of contingency tables and the calculation of chi-squared test. Comparisons between normally distributed continuous variables and categorical variables were performed by the calculation of Student's t-test and multi way Analysis of co-Variance (multi-ANCOVA), after controlling for homoscedacity and various potential confounders. In the case of asymmetric continuous variables the tested hypotheses were based on the calculations of non-parametric tests, such as Mann – Whitney and Kruskal – Wallis. Kolmogorov-Smirnov criterion assessed normality of continuous variables. Finally, correlations between continuous variables were tested through multiple regression analysis after the adjustment for the potential confounders and interactions. The J- shape association between the exposure variable (ethanol intake) and homocysteine levels was illustrated by connecting the mean values of the investigated parameters using 3rd order interpolating polynomials.
All reported P-values are based on two-sided tests and compared to a significance level of 5%. However, due to multiple significance tests we used the Bonferroni correction (since the number of comparisons was less than ten) in order to account for the increase in Type I error. SPSS 11.0 software (SPSS Inc. 2002, Illinois, USA) was used for all the statistical calculations.
Results
Thirty four percent of males and 62% percent of females reported ethanol abstinence within the recorded 7-day period (p < 0.001). In addition, 37% of males and 33% of females consumed < 12 gr of ethanol per day, 16% of males and 4% of females consumed 12 – 24 gr of ethanol per day, and 13% of males and 1% of females consumed > 24 gr of ethanol per day (1.6% of males and 0.4% of females consumed > 48 gr/d), during the preceding week. Furthermore, middle-aged male participants (45 – 65 years old) consumed higher quantities of ethanol compared to younger (< 45 years) or older individuals (18 ± 16 vs. 12 ± 14 vs. 15 ± 16 gr of ethanol per day, respectively, p = 0.002), while no statistically significant differences were observed between ethanol consumption and age, in females (9 ± 13 vs. 11 ± 12 vs. 12 ± 14 gr of ethanol per day, respectively, p = 0.391). Ethanol intake comes from wine in 65% of men and 77% of women, from beer in 22% of men and 11% of women and from spirits or other drinks 13% of men and 12% of women. Further, descriptive characteristics of the studied population by ethanol consumption level are presented in Table 1. By the exception of years of school (p = 0.02) and prevalence of hypertension (p = 0.01) no other associations were observed between ethanol intake and smoking habits, prevalence of hypercholesterolemia, diabetes and obesity.
Table 1 Descriptive characteristics of study's participants by alcohol intake, and by gender
Daily ethanol intake
Males None < 12 gr/d 12 – 24 gr/d 25 – 48 gr/d > 48 gr/d
Current smoking 39% 54% 43% 49% 54%
Physical inactivity 62% 56% 67% 69% 64%
Years of school (SD) 14(4) 13(4) 11(6) 11(4)** 9(4)**
Hypertension 28% 37%** 39%** 45%** 44%**
Hypercholesterolemia 33% 34% 44% 39% 34%
Diabetes 11% 11% 9% 9% 7%
Obesity 22% 24% 23% 19% 24%
Females
Current smoking 38% 25% 32% 37% 30%
Physical inactivity 64% 75% 62% 57% 80%
Years of school (SD) 13(4) 12(4) 11(3)* 10(4)** 8(3)**
Hypertension 17% 28%** 22% 22% 26%**
Hypercholesterolemia 28% 35% 32% 37% 40%
Diabetes 8% 10% 12% 6% 6%
Obesity 18% 15% 12% 17% 20%
** Bonferroni α < 0.01 and * α < 0.05 for the comparisons between ethanol intake and no intake groups.
Homocysteine values were higher in males as compared to females (14.5 ± 6 vs. 10.8 ± 3.5 μmol/L, p < 0.001). The 10th percentile for men was 8.6 μmol/L and for women 6.8 μmol/L, while the 90th percentiles were 18 μmol/L and 14 μmol/L, for men and women, respectively. Due to the significant differences observed between genders in homocysteine levels, all the following analyses will be gender-specific.
Unadjusted analysis revealed a J-shape association between ethanol quantities consumed during the past week (none, < 12 gr, 12 – 24 gr, 25 – 48 gr, >48 gr of ethanol per day) and homocysteine levels in both males (13 ± 3 vs. 11 ± 3 vs. 14 ± 4 vs. 18 ± 5 vs. 19 ± 3 μmol/L, respectively, p < 0.01) and females (10 ± 4 vs. 9 ± 3 vs. 11 ± 3 vs. 15 ± 4 vs. 17 ± 3 μmol/L, respectively, p < 0.01). Post hoc analysis revealed that the lowest values of homocysteine levels were observed in people who reported moderate daily ethanol intake of <12 gr (Bonferonni α = 0.02 for males and α = 0.02 for females). No differences were observed when we stratified our analysis by alcoholic beverages primarily consumed. Figure 1 illustrates the observed J-shape association between ethanol intake and homocysteine levels in males and females.
Figure 1 Homocysteine levels by daily ethanol intake in males (upper figure) and females (power figure) (continuous line is a 3rd order interpolating polynomial)
However, since several potential confounders may influence the relationship between ethanol intake and homocysteine concentration we repeated our analysis after taking into account age, gender, pack-years of smoking, presence of hypertension, hypercholesterolemia, and diabetes, body mass index, fruits and vegetables consumption, especially leafy green vegetables, legumes, citrus fruits and juices that are reached in folic acid, as well as years of school. Multivariate regression analysis showed that <12 gr/d ethanol intake was inversely associated with homocysteine levels (b-coefficient = -0.5, p = 0.02) as compared to no consumption. On the other hand, increased ethanol intakes, i.e. 12 – 24 gr/d, 24 – 48 gr/d or > 48 gr/d were positively associated with homocysteine concentration (b-coefficient = 1.2, p = 0.03, b-coefficient = 1.8, p = 0.02 and b-coefficient = 1.9, p = 0.02, respectively). No differences were observed when we stratified our analysis by gender.
Discussion
The results of the present study revealed a J-shape association between ethanol consumption and homocysteine levels, of a large, random and population representative sample, free of cardiovascular disease. The lowest values of homocysteine were observed in daily ethanol intake of less than 12 gr, both in men and women and remained significant after adjustment for several potential confounders.
Our results are in line with that of some other studies. For example, De Bree et al. [16] observed lower homocysteine concentrations at higher levels of ethanol consumption, with non drinkers having a (geometric) mean homocysteine of 14.2 μmol/L, compared to 13.9 μmol/L in drinkers of ≤ 20 gr ethanol/ day, 12.5 μmol/L in drinkers of between 20 and 40 gr/day and 13.1 μmol/L in drinkers of ≥ 40 gr / day. In our study, the lowest homocysteine concentrations were observed with ethanol intakes <12 gr /day. This difference between our and the previous study may attribute to the type of alcoholic beverage consumed, since in the study of Bree et al. beer was the main alcoholic drink, while in our study it was wine. In another study the most positive association of ethanol (from beer consumption) on homocysteine levels was observed at ethanol intakes 4 to 14 gr/d [17]. Another study in severely obese patients revealed a U-shaped association between homocysteine concentrations and the amount of ethanol consumption [18]. In particular, the most beneficial effect was observed with consumption of < 100 gr ethanol/ week and especially in red wine consumers, compared to subjects who consumed white wine, beer or spirits. However, the lower homocysteine concentrations in those consuming less than 100 gr ethanol/ week were not significant after controlling for serum folate concentration. Finally a study in elderly subjects also found a J-shape relation, with nondrinkers and subjects consuming ≥ 60 drinks/ month, showing higher homocysteine concentrations, compared to those consuming ≤ 60 drinks/ month [19]. However, the interpretation of the results from the previous study is difficult because the total amount of ethanol ingested was not calculated.
On the contrary, there are several studies that have shown a linear relationship between ethanol intake and homocysteine levels. For example, Folsom et al. [20] in a study of middle-aged men and women showed a positive association of ethanol on homocysteine. However, he studied very low intakes of ethanol, ranging from 27 to 47 gr/ week, and this may be the reason why a J -shaped association was not observed. According to our findings, a significant positive association was observed at much higher intakes (i.e. 84–168 gr/week). Another study in young women (aged 15–44) [21] showed that those consuming >7 drinks/ week were 90% more likely to have elevated homocysteine levels (> 10 μmol/l), compared to those who did not consume ethanol. In the same study, subjects consuming 1–7 drinks/week had the same homocysteine levels with those that didn't consume, supporting, partially, two relations between ethanol intake and homocysteine. However, the association between ethanol and homocysteine levels failed to achieve statistical significance. Finally, homocysteine was positively associated with ethanol intake in the Framingham Offspring cohort [22] at daily intakes of more than 15 g. In this study liquor and red wine consumption was significantly and positively associated with homocysteine. This association was not observed with beer and white wine consumption.
Our data were analyzed according to total ethanol intake and did not distinguish between different types of ethanol. Rimm et al. [23]reviewed the literature with respect to beverage-specific effects on coronary heart disease and could not find any systematic effects. On the contrary, they showed that the U-shaped relation between ethanol intake and cardiovascular disease mortality persisted in populations with very different drinking patterns. Although there have been many publications on this topic since the aforementioned review, no systematic pattern or results have emerged until now. Perhaps most notably in this respect are the findings which suggest similar protective effects of ethanol not only in Bavaria (Germany) and the Czech Republic, where beer is mainly consumed, but also in Mediterranean countries, where wine is the most popular alcoholic beverage [24]. Additionally, Greece is a Mediterranean country, where wine is the most commonly used alcoholic beverage. According to our findings as well as the recent results from the EPIC-Greece study [25] 72% of women's total ethanol intake comes from wine, 26% from beer and 12% from spirits. For men wine contributes to 56% of total ethanol intake, beer 15% and aniseed drinks 20%. Therefore our data do not support the assumption of Mennen et al. [26] who suggested that the inverse association between ethanol and homocysteine is seen in populations which consume predominantly beer.
Chronic alcoholism has been found to be associated with hyper-homocysteinaemia, which could attribute to disturbed folate metabolism and to changes in circulating concentrations of vitamin B12 and pyridoxal phosphate, as well as to ethanol intake per se [27]. Finally, the dual effect of ethanol consumption on homocysteine has also been confirmed from data of animal studies, which clearly show effects of excessive ethanol intake on the methionine cycle [13]. Nevertheless, the finding that subjects who do not consume ethanol have higher homocysteine levels than light to moderate drinkers needs further investigation. Whether this fact can be attributed to ethanol per se or to other substances of alcoholic beverages (e.g. folate, B12, B6, betaine) remains unclear and more intervention and experimental studies are necessary.
Limitations
This study as a cross-sectional one cannot establish causal relations but only generate hypothesis for associations. The population studied in this work is homogeneous and may reflect lifestyle habits in similar cultures, like Western Europe, Mediterranean etc. However, our findings could not extrapolate into other populations without further investigation and consideration. Also, the numbers of participants in categories of high intake (>48 gr of ethanol /d) were rather small, and the impression of the effects in homocysteine levels in even higher ethanol consumption may be misleading. Although this analysis has been adjusted for several known confounders, we have indirectly investigated the impact of serum folate and vitamins B6 and B12 intake (through food groups consumed) on homocysteine concentrations. In addition, kidney function is a strong determinant of homocysteine; however, we have not measured serum creatinine. The later may be another limitation of our study. Additionally, misreporting of ethanol consumption, due to social class can be a potential confounder.
Conclusion
The present study supports the existence of a J-shape association between ethanol consumption and homocysteine levels in both males and females, of a large, random and population representative sample, free of cardiovascular disease. Therefore our results indicate that daily consumption of 1–2 units of ethanol is associated with lower homocysteine concentrations and provide further evidence for a variant association between ethanol intake and coronary heart disease risk in both genders.
Acknowledgement
The Hellenic Society of Cardiology funds the ATTICA Study.
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| 15507131 | PMC526386 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Oct 14; 1:9 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-9 | oa_comm |
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Nutr JNutr JNutrition Journal1475-2891BioMed Central 1475-2891-3-191549622410.1186/1475-2891-3-19ReviewNutrition and cancer: A review of the evidence for an anti-cancer diet Donaldson Michael S [email protected] Director of Research, Hallelujah Acres Foundation, 13553 Vantage Hwy, Ellensburg, WA 98926, USA2004 20 10 2004 3 19 19 28 9 2004 20 10 2004 Copyright ©2004 Donaldson; licensee BioMed Central Ltd.2004Donaldson; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.It has been estimated that 30–40 percent of all cancers can be prevented by lifestyle and dietary measures alone. Obesity, nutrient sparse foods such as concentrated sugars and refined flour products that contribute to impaired glucose metabolism (which leads to diabetes), low fiber intake, consumption of red meat, and imbalance of omega 3 and omega 6 fats all contribute to excess cancer risk. Intake of flax seed, especially its lignan fraction, and abundant portions of fruits and vegetables will lower cancer risk. Allium and cruciferous vegetables are especially beneficial, with broccoli sprouts being the densest source of sulforophane. Protective elements in a cancer prevention diet include selenium, folic acid, vitamin B-12, vitamin D, chlorophyll, and antioxidants such as the carotenoids (α-carotene, β-carotene, lycopene, lutein, cryptoxanthin). Ascorbic acid has limited benefits orally, but could be very beneficial intravenously. Supplementary use of oral digestive enzymes and probiotics also has merit as anticancer dietary measures. When a diet is compiled according to the guidelines here it is likely that there would be at least a 60–70 percent decrease in breast, colorectal, and prostate cancers, and even a 40–50 percent decrease in lung cancer, along with similar reductions in cancers at other sites. Such a diet would be conducive to preventing cancer and would favor recovery from cancer as well.
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Review
Background
The field of investigation of the role of nutrition in the cancer process is very broad. It is becoming clearer as research continues that nutrition plays a major role in cancer. It has been estimated by the American Institute for Cancer Research and the World Cancer Research Fund that 30–40 percent of all cancers can be prevented by appropriate diets, physical activity, and maintenance of appropriate body weight [1]. It is likely to be higher than this for some individual cancers.
Most of the research on nutrition and cancer has been reductionist; that is, a particular food or a nutrient has been studied in relation to its impact on tumor formation/regression or some other end point of cancer at a particular site in the body. These studies are very helpful in seeing the details of the mechanisms of disease. However, they do not help give an overall picture of how to prevent cancer on a dietary level. Even less, they tell little of how to eat when a person already has a cancer and would like to eat a diet that is favorable to their recovery.
This review will focus on those dietary factors which has been shown to be contribute to increased risk of cancer and then on those additional protective dietary factors which reduce cancer risk. Finally, some whole-diet studies will be mentioned which give a more complete picture of how these individual factors work together to reduce cancer risk.
Over Consumption of Energy (Calories)
Eating too much food is one of the main risk factors for cancer. This can be shown two ways: (1) by the additional risks of malignancies caused by obesity, and (2) by the protective effect of eating less food.
Obesity has reached epidemic proportions in the United States. Sixty-four percent of the adult population is overweight or obese [2]. About 1 in 50 are now severely obese (BMI > 40 kg/m2) [3]. Mokdad et al [4] found that poor diet and physical inactivity was the second leading cause of death (400,000 per year in the USA), and would likely overtake tobacco as the leading cause of death.
It was estimated in a recent study, from a prospective cancer prevention cohort, that overweight and obesity accounted for 14 percent of all cancer deaths in men and 20 percent of those in women [5]. Significant positive associations were found between obesity and higher death rates for the following cancers: esophagus, colon and rectum, liver, gallbladder, pancreas, kidney, stomach (in men), prostate, breast, uterus, cervix, and ovary [5]. The authors estimated that over 90,000 cancer deaths per year could be avoided if the adult population all maintained a normal weight (BMI < 25.0) [5]. Clearly, obesity is a major risk factor for cancer.
On the other side, careful menu planning brings about an approach entitled CRON-Calorie Restriction with Optimal Nutrition. The basic idea is to eat a reduced amount of food (about 70–80 percent of the amount required to maintain "normal" body weight) while still consuming all of the necessary amounts of vitamins, minerals, and other necessary nutrients. The only restriction is the total amount of energy (calories) that is consumed. While being difficult to practice, this approach has a lot of scientific merit for being able to extend average life spans of many species of animals including rats, mice, fish, and possibly primates (currently being tested). Along with this life span extension is a reduction in chronic diseases that are common to mankind, reviewed in Hursting et al [6]. A recent meta-analysis of 14 experimental studies found that energy restriction resulted in a 55% reduction in spontaneous tumors in laboratory mice [7]. Calorie restriction inhibited induced mammary tumors in mice [8] and suppressed implanted tumor growth and prolonged survival in energy restricted mice [9]. Among Swedish women who had been hospitalized for anorexia nervosa (definitely lower caloric intake, but not adequate nutrition) prior to age 40, there was a 23% lower incidence of breast cancer for nulliparous women and a 76% lower incidence for parous women [10]. So, too many calories is definitely counter-productive, and slightly less than normal is very advantageous.
Glucose Metabolism
Refined sugar is a high energy, low nutrient food – junk food. "Unrefined" sugar (honey, evaporated cane juice, etc) is also very concentrated and is likely to contribute to the same problems as refined sugar. Refined wheat flour products are lacking the wheat germ and bran, so they have 78 percent less fiber, an average of 74 percent less of the B vitamins and vitamin E, and 69 percent less of the minerals (USDA Food database, data not shown). Concentrated sugars and refined flour products make up a large portion of the carbohydrate intake in the average American diet. One way to measure the impact of these foods on the body is through the glycemic index.
The glycemic index is an indication of the blood sugar response of the body to a standardized amount of carbohydrate in a food. The glycemic load takes into account the amount of food eaten. An international table of the glycemic index and glycemic load of a wide variety of foods has been published [11].
Case-control studies and prospective population studies have tested the hypothesis that there is an association between a diet with a high glycemic load and cancer. The case control studies have found consistent increased risk of a high glycemic load with gastric [12], upper aero digestive tract [13], endometrial [14], ovarian [15], colon or colorectal cancers [16,17]. The prospective studies' results have been mixed. Some studies showed increased risk of cancer in the whole cohort with high glycemic load [18-20]; some studies found only increased risk among subgroups such as sedentary, overweight subjects [21-24]; other studies concluded that there was no increased risk for any of their cohort [25-28]. Even though there were no associations between glycemic load and colorectal, breast, or pancreatic cancer in the Nurses' Health Study there was still a strong link between diabetes and colorectal cancer [29].
Perhaps the dietary glycemic load is not consistently related to glucose disposal and insulin metabolism due to individual's different responses to the same glycemic load. Glycated hemoglobin (HbA1c) is a time-integrated measurement of glucose control, and indirectly, of insulin levels. Increased risk in colorectal cancer was seen in the EPIC-Norfolk study with increasing HbA1c; subjects with known diabetes had a three-fold increased risk of colorectal cancer [30]. In a study of a cohort in Washington county, Maryland, increased risk of colorectal cancer was seen in subjects with elevated HbA1c, BMI > 30 kg/m2, or who used medications to control diabetes [31]. However, glycated hemoglobin was not found to be associated with increased risk of colorectal cancer in a small nested case-control study within the Nurses' Health Study [32]. Elevated fasting glucose, fasting insulin, 2 hour levels of glucose and insulin after an oral glucose challenge, and larger waist circumference were associated with a higher risk of colorectal cancer [33]. In multiple studies diabetes has been linked with increased risk of colorectal cancer [34-37], endometrial cancer [38], and pancreatic cancer [35,39]. It is clear that severe dysregulation of glucose metabolism is a risk factor for cancer. Foods which contribute to hyperinsulinemia, such as refined sugar, foods containing refined sugar, and refined flour products should be avoided and eliminated from a cancer protective diet.
Low Fiber
Unrefined plant foods typically have an abundance of fiber. Dairy products, eggs, and meat all have this in common – they contain no fiber. Refined grain products also have most of the dietary fiber removed from them. So, a diet high in animal products and refined grains (a typical diet in the USA) is low in fiber. In prospective health studies low fiber was not found to be a risk for breast cancer [25]. It is possible that fiber measurements are just a surrogate measure for unrefined plant food intake. Slattery et al [40] found an inverse correlation between vegetable, fruit and whole grain intake plant food intake and rectal cancer, while refined grains were associated with increased risk of rectal cancer. A threshold of about 5 daily servings of vegetables was needed to reduce cancer risk and the effect was stronger among older subjects [40]. Many other nutrients are co-variants with fiber, including folic acid, which is covered in detail below.
Red Meat
Red meat has been implicated in colon and rectal cancer. A Medline search in February 2003 uncovered 26 reports of human studies investigating the link between diet and colon or colorectal cancer. Of the 26 reports, 21 of them reported a significant positive relationship between red meat and colon or colorectal cancer [17,41-64]. A recent meta-analysis also found red meat, and processed meat, to be significantly associated with colorectal cancer [65]. Meat, and the heterocyclic amines formed in cooking, have been correlated to breast cancer in a case-control study in Uruguay as well [66].
Omega 3:6 Ratio Imbalance
Omega 3 fats (alpha-linolenic acid, EPA, DHA) have been shown in animal studies to be protect from cancer, while omega 6 fats (linoleic acid, arachidonic acid) have been found to be cancer promoting fats. Now there have been several studies that have tested this hypothesis in relation to breast cancer, summarized in Table 1. Except for the study by London et al [67], all of these studies found an association between a higher ratio of N-3 to N-6 fats and reduced risk of breast cancer. Long chain N-3 and N-6 fats have a different effect on the breast tumor suppressor genes BRCA1 and BRCA2. Treatment of breast cell cultures with N-3 fats (EPA or DHA) results in increased expression of these genes while arachadonic acid had no effect [68]. Flax seed oil and DHA (from an algae source) both can be used to increase the intake of N-3 fat, with DHA being a more efficient, sure source.
Table 1 Breast Cancer and Omega 3:6 Ratio.
Reference # of cases w/ breast cancer # of controls Post / pre Menopausal Measure of n-3 / n-6 fat Outcome Odds ratio (95% Confidence Interval)
[183] 565 554 (population and hospital) Pre & post Diet FFQ ↑N3/N6 ratio in premenopausal women = Non-signif. ↓Breast cancer risk 0.59 (0.29–1.19)
In study site with population controls, find ↑N3/N6 ratio = Signif ↓Breast Cancer risk 0.50 (0.27, 0.95)
[184] EURAMIC study Nested case-control study in population study Post Adipose tissue 4 out of 5 centers showed ↑N3/N6 ratio = ↓Breast Cancer risk 0.65 (p for trend = 0.55)
[185] 241 88 w/ benign breast disease Both Adipose tissue ↑DHA = ↓Breast cancer 0.31 (0.13–0.75)
↑Ratio of long chain N-3:N-6 fat = ↓Breast cancer 0.33 (0.17–0.66)
[186] 73 74 w/ macromastia ? Adipose tissue N-6 fat content signif. higher in cases P = 0.02
For given level of N-6 fat, EPA and DHA had a protective effect P = 0.06
[187] 71 (within ORDET study) 142 (nested case control) Post RBC membranes ↑DHA = ↓Breast cancer 0.44 (0.21–0.92)
[67] 380 397 Post Adipose tissue No associations between N-3:N-6 ratio and breast cancer
[188] 314 (within Singapore Chinese Health study) Diet, FFQ ↑Intake of N-3 fat from fish / shellfish = ↓Breast cancer, for all 3 highest quartiles 0.74 (0.58–0.94)
Among women in lowest quartile of N-3 fat intake, ↑N-6 fat intake = ↓Breast cancer 1.87 (1.06–3.27)
Flax seed
Flax seed provides all of the nutrients from this small brown or golden hard-coated seed. It is an excellent source of dietary fiber, omega 3 fat (as alpha-linolenic acid), and lignans. The lignans in flax seed are metabolized in the digestive tract to enterodiol and enterolactone, which have estrogenic activity. In fact, flax seed is a more potent source of phytoestrogens than soy products, as flax seed intake caused a bigger change in the excretion of 2-hydroxyestrone compared to soy protein [69].
Ground flax seeds have been studied for its effect on cancer, including several excellent studies by Lilian Thompson's research group at the University of Toronto. In one study the flax seed, its lignan fraction, or the oil were added to the diet of mice who had previously been administered a chemical carcinogen to induce cancer. All three treatments reduced the established tumor load; the lignan fraction containing secoisolariciresinol diglycoside (SDG) and the flax seed also reduced metastasis [70]. In another study the flax lignan SDG was fed to mice starting 1 week after treatment with the carcinogen dimethylbenzanthracene. The number of tumors per rat was reduced by 46% compared to the control in this study [71]. Flax or its lignan (SDG) were tested to see if they would prevent melanoma metastasis. The flax or lignan fraction were fed to mice two weeks before and after injection of melanoma cells. The flax treatment (at 2.5, 5, or 10% of diet intake) resulted in a 32, 54, and 63 percent reduction in the number of tumors, compared to the control [72]. The SDG, fed at amounts equivalent to the amount in 2.5, 5, or 10% flax seed, also reduced the tumor number, from a median number of 62 in the control group to 38, 36, and 29 tumors per mouse in the SDG groups, respectively [73].
More recently Thompson's research group studied mice that were injected with human breast cancer cells. After the injection the mice were fed a basal diet (lab mouse chow) for 8 weeks while the tumors grew. Then one group continued the basal diet and another was fed a 10% flax seed diet. The flax seed reduced the tumor growth rate and reduced metastasis by 45% [74].
Flax seed has been shown to enhance mammary gland morphogenesis or differentiation in mice. Nursing dams were fed the 10% flax seed diet (or an equivalent amount of SDG). After weaning the offspring mice were fed a regular mouse chow diet. Researchers then examined the female offspring and found an increased number of terminal end buds and terminal ducts in their mammary glands with more epithelial cell proliferation, all demonstrating that mammary gland differentiation was enhanced [75]. When these female offspring were challenged with a carcinogen to induce mammary gland tumors there were significantly lower incidence of tumors (31% and 42% lower in the flax seed and SDG groups, respectively), significantly lower tumor load (51% and 62% lower in the flax seed and SDG groups, respectively), significantly lower mean tumor size (44% and 68% lower in the flax seed and SDG groups, respectively), and significantly lower tumor number (47% and 45% lower in the flax seed and SDG groups, respectively) [76]. So, flax seed and its lignan were able to reduce tumor growth (both in number and size of tumors), prevent metastasis, and even cause increased differentiation of mouse mammary tissue in suckling mice, making the offspring less susceptible to carcinogenesis even when not consuming any flax products.
Other researchers have tested flax seed and prostate cancer. In an animal model using mice, Lin et al [77] found that a diet supplemented with 5% flax inhibited the growth and development of prostate cancer in their experimental mouse model. A pilot study of 25 men who were scheduled for prostatectomy surgery were instructed to eat a low-fat diet (20% or less of energy intake) and to supplement with 30 g of ground flaxseed per day. During the follow-up of an average of 34 days there were significant changes in serum cholesterol, total testosterone, and the free androgen index [78]. The mean proliferation index of the experimental group was significantly lower and apoptotic indexes higher compared to historical matched controls. Ground flax seed may be a very beneficial food for men battling prostate cancer. However, a meta-analysis of nine cohort and case-control studies revealed an association between flax seed oil intake or high blood levels of alpha-linolenic acid and prostate cancer risk [79]. It is quite likely that the lignans in flax seed are a major component of flax's anti-cancer effects so that flax oil without the lignans is not very beneficial. Some brands of flax seed oil retain some of the seed particulate because of the beneficial properties of the lignans.
Fruits and Vegetables
One of the most important messages of modern nutrition research is that a diet rich in fruits and vegetables protects against cancer. (The greatest message is that this same diet protects against almost all other diseases, too, including cardiovascular disease and diabetes.) There are many mechanisms by which fruits and vegetables are protective, and an enormous body of research supports the recommendation for people to eat more fruits and vegetables.
Block et al [80] reviewed about 200 studies of cancer and fruit and vegetable intake. A statistically significant protective effect of fruits and vegetables was found in 128 of 156 studies that gave relative risks. For most cancers, people in the lower quartile (1/4 of the population) who ate the least amount of fruits and vegetables had about twice the risk of cancer compared to those who in the upper quartile who ate the most fruits and vegetables. Even in lung cancer, after accounting for smoking, increasing fruits and vegetables reduces lung cancer; an additional 20 to 33 percent reduction in lung cancers is estimated [1].
Steinmetz and Potter reviewed the relationship between fruits, vegetables, and cancer in 206 human epidemiologic studies and 22 animal studies [81]. They found "the evidence for a protective effect of greater vegetable and fruit consumption is consistent for cancers of the stomach, esophagus, lung, oral cavity and pharynx, endometrium, pancreas, and colon." Vegetables, and particularly raw vegetables, were found to be protective; 85% of the studies that queried raw vegetable consumption found a protective effect. Allium vegetables, carrots, green vegetables, cruciferous vegetables, and tomatoes also had a fairly consistent protective effect [81]. Allium vegetables (garlic, onion, leeks, and scallions) are particularly potent and have separately been found to be protective for stomach and colorectal cancers [82,83] and prostate cancer [84].
There are many substances that are protective in fruits and vegetables, so that the entire effect is not very likely to be due to any single nutrient or phytochemical. Steinmetz and Potter list possible protective elements: dithiolthiones, isothiocyanates, indole-32-carbinol, allium compounds, isoflavones, protease inhibitors, saponins, phytosterols, inositol hexaphosphate, vitamin C, D-limonene, lutein, folic acid, beta carotene (and other carotenoids), lycopene, selenium, vitamin E, flavonoids, and dietary fiber [81].
A joint report by the World Cancer Research Fund and the American Institute for Cancer Research found convincing evidence that a high fruit and vegetable diet would reduce cancers of the mouth and pharynx, esophagus, lung, stomach, and colon and rectum; evidence of probable risk reduction was found for cancers of the larynx, pancreas, breast, and bladder [1].
Many of the recent reports from prospective population-based studies of diet and cancer have not found the same protective effects of fruits and vegetables that were reported earlier in the epidemiological and case-control studies [reviewed in [85]]. One explanation is that people's memory of what they ate in a case-cohort study may have been tainted by their disease state. Another problem might be that the food frequency questionnaires (FFQ) used to measure food intake might not be accurate enough to detect differences. Such a problem was noted in the EPIC study at the Norfolk, UK site. Using a food diary the researchers found a significant correlation between saturated fat intake and breast cancer, but using a FFQ there was no significant correlation [86]. So, inaccurate measurement of fruit and vegetable intake might be part of the explanation as well.
It must be noted that upper intakes of fruits and vegetables in these studies are usually within the range of what people on an American omnivorous diet normally eat. In the Nurses Health Study the upper quintiles of fruit and vegetable intake were 4.5 and 6.2 servings/day, respectively [87]. Similarly, the upper quintiles of fruit and vegetable intake in the Health Professionals Follow-up Study were 4.3 and 5.4 serving/day for fruits and vegetables, respectively [87]. Intakes of fruits and vegetables on the Hallelujah Diet are much higher, with median reported intakes of six servings of fruits (646 g/day) and eleven servings of vegetables per day (971 g/day) [88] in addition to a green powder from the juice of barley leaves and alfalfa that is equivalent to approximately another 100 g/day of fresh dark greens. So, it is very possible that the range of intakes in the prospective population based studies do not have a wide enough intake on the upper end to detect the true possible impact of a very high intake of fruits and vegetables on cancer risk.
Cruciferous Vegetables
Cruciferous vegetables (broccoli, cauliflower, cabbage, Brussels sprouts) contain sulforophane, which has anti-cancer properties. A case-control study in China found that intake of cruciferous vegetables, measured by urinary secretion of isothiocyanates, was inversely related to the risk of breast cancer; the quartile with the highest intake only had 50% of the risk of the lowest intake group [89]. In the Nurses' Health Study a high intake of cruciferous vegetables (5 or more servings/week vs less than two servings/week) was associated with a 33% lower risk of non-Hodgkin's lymphoma [90]. In the Health Professionals Follow-up Study bladder cancer was only weakly associated with low intake of fruits and vegetables, but high intake (5 or more servings/week vs 1 or less servings/wk) of cruciferous vegetables was associated with a statistically significant 51% decrease in bladder cancer [91]. Also, prostate cancer risk was found to be reduced by cruciferous vegetable consumption in a population-based case-control study carried out in western Washington state. Three or more servings per week, compared to less than one serving of cruciferous vegetables per week resulted in a statistically significant 41% decrease in prostate cancer risk [92]. Similar protective effects of cruciferous vegetables were seen in a multi-ethnic case-control study [93]. A prospective study in Shanghai, China found that men with detectable amounts of isothiocyanates in their urine (metabolic products that come from cruciferous vegetables) had a 35% decreased risk of lung cancer. Among men that had one or two genetic polymorphisms that caused them to eliminate these isothiocyanates slower there was a 64% or 72% decreased risk of lung cancer, respectively [94].
Broccoli sprouts have a very high concentration of sulforophane since this compound originates in the seed and is not made in the plant as it grows [95,96]. One sprout contains all of the sulforophane that is present in a full-grown broccoli plant. So, if sulforophane is especially cancer-protective, it would seem reasonable to include some broccoli sprouts in an anti-cancer diet.
Selenium
Selenium is a mineral with anti-cancer properties. Many studies in the last several years have shown that selenium is a potent protective nutrient for some forms of cancer. The Arizona Cancer Center posted a selenium fact sheet listing the major functions of selenium in the body [97]. These functions are as follows:
1. Selenium is present in the active site of many enzymes, including thioredoxin reductase, which catalyze oxidation-reduction reactions. These reactions may encourage cancerous cells to under apoptosis.
2. Selenium is a component of the antioxidant enzyme glutathione peroxidase.
3. Selenium improved the immune systems' ability to respond to infections.
4. Selenium causes the formation of natural killer cells.
5. P450 enzymes in the liver may be induced by selenium, leading to detoxification of some carcinogenic molecules.
6. Selenium inhibits prostaglandins that cause inflammation.
7. Selenium enhances male fertility by increased sperm motility.
8. Selenium can decrease the rate of tumor growth.
A serendipitous randomized, double-blind, controlled trial of a 200 μg/day selenium supplement in the southeastern region of the USA (where soil selenium levels are low) found that the primary endpoints of skin cancer were not improved by the selenium supplement, but that other cancer incidence rates were decreased by selenium [98,99]. There was a significant reduction in total cancer incidence (105 vs 137 cases, P = 0.03), prostate cancer (22 vs 42 cases, P = 0.005), a marginally significant reduction in colorectal cancer incidence (9 vs 19 cases, P = 0.057), and a reduction in cancer mortality, all cancer sites (40 vs 66 deaths, P = 0.008) (selenium versus control group cases reported, respectively) [98]. The selenium supplement was most effective in ex-smokers and for those who began the study with the lowest levels of serum selenium. Several prospective studies have also examined the role of selenium in cancer prevention, particularly for prostate cancer, summarized in Table 2.
Table 2 Prospective Nested Case Control Studies of Selenium and Prostate Cancer.
Reference Study # Cases # Controls Outcomes Comment
[189] Physicians Health Study 586 577 ↑Se = ↓risk of advance prostate cancer (OR = 0.52, 95% CI = 0.28–0.98) Result only in men with PSA ≥ 4 ng/mL
[190] Netherlands Cohort Study 540 1,211 ↑Se = ↓risk prostate cancer (OR for quintiles of Se = 1.0, 1.05, 0.69, 0.75, 0.69; 95% CI = 0.48–0.99) Results greatest in ex-smokers
[191] Baltimore Longitudinal Study of Aging 52 96 ↑Se = ↓risk prostate cancer (OR for quartiles of Se = 1.0, 0.15, 0.21, 0.24
[192] Washington County, Maryland 117 233 Top 4/5 of Se had reduction in prostate cancer risk; statistically significant result for Se only when γtocopherol levels were high Men in top quintile of serum γtocopherol had 5-fold reduced risk of prostate cancer compared to lowest quintile
[193] Health Professional Follow-up Study 181 181 ↑Se = ↓risk of advanced prostate cancer Adjusted OR = 0.35 (95% CI = 0.16–0.78)
[194] Prospective study ↑Se = ↓risk of gastrointestinal and prostate cancer Results not statistically significant
Overall, it appears that poor selenium levels, especially for men, are a cancer risk. If a person has low selenium levels and other antioxidant defenses are also low the cancer risk is increased even further. Women do not appear to be as sensitive to selenium, as breast cancer has not been found to be influenced by selenium status in several studies [100-104], although both men and women were found to be protected by higher levels of selenium from colon cancer [100] and lung cancer [105,106]. Good vegetarian sources of selenium are whole grains and legumes grown in selenium-rich soil in the western United States, brazil nuts (by far the most dense source of selenium), nutritional yeast, brewers yeast, and sunflower seeds.
Chlorophyll
All green plants also contain chlorophyll, the light-collecting molecule. Chlorophyll and its derivatives are very effective at binding polycyclic aromatic hydrocarbons (carcinogens largely from incomplete combustion of fuels), heterocyclic amines (generated when grilling foods), aflatoxin (a toxin from molds in foods which causes liver cancer), and other hydrophobic molecules. The chlorophyll-carcinogen complex is much harder for the body to absorb, so most of it is swept out with the feces. The chemoprotective effect of chlorophyll and its derivatives has been tested in laboratory cell cultures and animals [107,108]. There is so much compelling evidence for anti-carcinogenic effects of chlorophyll that a prospective randomized controlled trial is being conducted in Qidong, China to see if chlorophyllin can reduce the amount of liver cancer cases, which arise from aflatoxin exposure in their foods (corn, peanuts, soy sauce, and fermented soy beans). A 55% reduction in aflatoxin-DNA adducts were found in the group that took 100 mg of chlorophyllin three times a day [109]. It was supposed that the chlorophyllin bound up aflatoxins, but there were chlorophyllin derivatives also detected in the sera (which had a green tint to it) of the volunteers who took the supplement, indicating a possible role in the body besides binding carcinogens in the gut [110].
Protective Vitamins
Vitamin B-12
Vitamin B-12 has not been proven to be an anti-cancer agent, but there is some evidence indicating that it could be beneficial. The form of administered vitamin B-12 may be important.
Some experimental cancer studies have been carried out with various forms of vitamin B-12. Methylcobalamin inhibited tumor growth of SC-3 injected into mice [111], and caused SC-3 mouse mammary tumor cells to undergo apoptosis, even when stimulated to grow by the presence of growth-inducing androgen [112]. Methylcobalamin, but not cyanocobalamin, increased the survival time of mice bearing implanted leukemia tumor cells [113]. 5'-deoxyadenosylcobalamin and methylcobalamin, but not cyanocobalamin, were shown to be effective cytotoxic agents [114]. Methylcobalamin also was able to increase survival time and reduce tumor growth in laboratory mice [115].
Laboratory mechanistic evidence for the effects of vitamin B12 were seen in a laboratory study with vitamin B-12 deficient rats. Choi et al [116] found that the colonic DNA of the B-12 deficient rats had a 35% decrease in genomic methylation and a 105% increase in uracil incorporation, both changes that could increase risk of carcinogenesis. In two prospective studies (one in Washington Country, Maryland and the Nurses' Health Study) a relation between lower vitamin B12 status (but not deficiency) and statistically significant higher risk of breast cancer was found [117,118]. So, there is evidence from laboratory studies, prospective cohort studies, and mechanistic studies showing that vitamin B-12 is an important nutrient for genetic stability, DNA repair, carcinogenesis, and cancer therapy.
Folic Acid
Folic acid is the dark green leafy vegetable vitamin. It has an integral role in DNA methylation and DNA synthesis. Folic acid works in conjunction with vitamin B-6 and vitamin B-12 in the single carbon methyl cycle. If insufficient folic acid is not available uracil is substituted for thymidine in DNA, which leads to DNA strand breakage. About 10% of the US population (and higher percentages among the poor) has low enough intakes of folic acid to make this a common problem [119]. As shown in Tables 3 and 4, many studies have found a significant reduction in colon, rectal, and breast cancer with higher intakes of folic acid and their related nutrients (vitamin B-6 and B-12). Alcohol is an antagonist of folate, so that drinking alcoholic beverages greatly magnifies the cancer risk of a low-folate diet. Genetic polymorphisms (common single DNA base mutations resulting in a different amino acid encoded into a protein) in the methylenetetrahydrofolate reductase and the methionine synthase genes which increase the relative amount of folate available for DNA synthesis and repair also reduces the risk of colon cancer [120-123]. Cravo et al [124] used 5 mg of folic acid a day (a supraphysiological dose) in a prospective, controlled, cross-over study of 20 patients with colonic adenoma polyps. They found that the folic acid could reverse DNA hypomethylation in 7 of 12 patients who had only one polyp.
Table 3 Folate and Colon / Rectal Cancer.
Reference Study # Cases # Controls Outcomes Comment
[195] Case / control USA 35 64 Folate supplementation = 62% lower incidence of neoplasia result not SS
[196] Case / control NY state 800 Matched neighbor-hood controls ↑Folate = ↓rectal cancer, OR = 0.5 men, OR = 0.31, women Folate no effect for colon cancer SS
[197] Case / control Majorca, Spain 286 498 Colon cancer related to total calories, cholesterol, animal protein, low fiber, low folic acid .
[198] Case / Control Wash. state 424 414 ↑Alcohol = ↑cancer risk; ↑fiber = ↓risk; no relation to folate intake 2.5X risk for 30 g/day alcohol
[199] Nurses' Health Study & Health Professionals Follow-up Study 564 women, 331 men ↑folate = ↓risk of colorectal adenoma: ORwomen = 0.66, ORmen = 0.63
[200] Case / Control, Italy 1,326 2,024 hospital controls Protective trends for β-carotene, ascorbic acid, vit E, and folate (OR = 0.32, 0.40, 0.60, 0.52, respectively) Similar for colon and rectal cancer
[201] US male health professional cohort 205 ↑Alcohol = ↑colon cancer (OR = 2.07 for ≥ 2 drinks/day; folate weakly protective; ↑Alcohol + ↓folate = ↑colon cancer risk (OR = 3.30)
[202] α-tocopherol, β-carotene study cohort of smokers 144 276 ↑dietary folate = ↓colon cancer (OR = 1.0, 0.40, 0.34, 0.51, P-trend = 0.15); alcohol intake increased risk
[203] Case control, population based Composite dietary profile (alcohol intake, methionine, folate, vit B12, B6) trend of increasing risk for high risk group Marginal SS
[204] Nurses' Health Study 442 ↑folate intake = ↓colon cancer (OR = 0.69); long-term use of multi-vitamins beneficial Folate intake includes multi-vitamins
[205] NYU Women's Health Study 105 523 ↑folate = ↓colorectal cancer risk (OR = 0.52, P-trend = 0.04 Alcohol increased risk
[206] NHANES I Epidemiologic Follow-up Study ↑folate = ↓colon cancer (ORmen = 0.40, P-trend = 0.03; ↑alcohol, ↓folate = ↑colon cancer (ORmen = 2.67 Results not stat. signif in women
[207] Nurses' Health Study 535 ↑folate intake = ↓colon cancer in women with family history (OR = 0.48) Folate effect greater in women with family history
[208] Canadian National Breast Screening Study 295 5,334 ↑folate = ↓colorectal cancer (OR = 0.6, P-trend = 0.25 Results not SS
[209] Prospective cohort in The Netherlands 1,171 Rectal: OR, men 0.66, women no trend Trends SS only in men
[210] Case / Control Italy 1,953 4,154 ↑folate = ↓colorectal cancer (OR = 0.72) Population drinks alcohol regularly
[211] Iowa Women's health Study 721 ↑folate + (↑B12 or ↑B6) = ↓colon cancer (OR = 0.59, 0.65, respectively Nutrients not independent, alcohol increases risk
[212] Case / Control NC state 613 996 ↑β-carotene, vit C, calcium = 40–60 % ↓risk colon cancer in whites; in African Americans ↑ vit C and E = 50–70% ↓risk colon cancer; no relation to folate to cancer risk Colon cancer rates higher in Aftrican Americans in NC; due to less UV light absorption with dark skin?
[213] Wheat Bran Fiber trial, test for recurrence of adenoma polyps 1,014 men and women ↑homocysteine = ↑risk (OR = 0.69); ↑plasma folate = ↓risk (OR = 0.66) ↑folate or B6 intake (diet + supplements) = ↓risk (OR = 0.61 SS; cut-off for highest quartile is 664 μg/day (way above RDA)
SS = statistically significant
Table 4 Prospective Studies of Folate and Breast Cancer.
Reference Study # Cases # Controls Outcomes Comment
[214] Nurses' Health Study 3,483 ↓folate intake + alcohol = ↑risk of breast cancer (OR = 0.55, P-trend = 0.001) Folate intake not associated with overall risk of breast cancer
[215] Canadian National Breast Screening Study 1,336 5,382 ↓folate intake + alcohol = ↑risk of breast cancer (OR = 0.34, P-trend = 0.004) Folate intake not associated with overall risk of breast cancer
[216] Prospective study in USA with postmenopausal women 1,586 Among drinkers, ↓folate intake = ↑breast cancer risk (OR = 1.59) No association in overall cohort
[125] Shanghai Breast Cancer Study, China 1,321 1,382 ↑folate intake = ↓ risk (OR = 0.71, P-trend = 0.05); ↑folate, ↑methionine, ↑B6, ↑B12 = ↓risk (OR = 0.47, P-trend = 0.01) No alcohol, no supplements, unprocessed, unfortified foods
[217] Nurses' Health Study II, study of premenopausal women 714 Vitamin A protective (OR = 0.28); Vitamins C, E, and folate not associated with risk.
[118] Nurses' Health Study 712 712 matched ↑plasma folate = ↓risk (OR = 0.73, P-trend = 0.06). For women who drank alcohol, ↑plasma folate even more protective, OR = 0.11. ↑plasma B6 and plasma B12 were also protective
[218] Prospective study in USA with postmenopausal women 1,823, 308 with family history (FH) FH- +Alcohol = ↑risk (OR = 1.40) FH- + Alcohol + ↑folate = normal risk; FH+ ↓folate = ↑risk for drinkers (OR = 2.21) and non-drinkers (OR = 2.39); FH+ +Alcohol + ↑folate = ↑risk (OR = 1.67); FH+ + ↑folate = normal risk Women with family history of breast cancer can reduce risk by increasing folate intake and not drinking.
FH = Family History
Folate may be more important for rapidly dividing tissue, like the colonic mucosa. Therefore, the cancer risk associated with low folate intake is probably higher for colon cancer than for breast cancer. Most of the breast cancer studies only found a protective effect of folate among women who consumed alcohol (see Table 4). However, among women residents of Shanghai who consumed no alcohol, no vitamin supplements and ate unprocessed, unfortified foods there was a 29% decreased risk of breast cancer among those with the highest intake of folate [125]. So, there may be a true protective effect that is masked in the western populations by so many other risk factors. Two studies showed that the risk of cancer due to family history can be modified by high folate intake, so a prudent anti-cancer diet would be high in dark green leafy vegetables. The mean intake of folic acid on the Hallelujah Diet was 594 μg/day for men and 487 μg/day for women [88].
Vitamin D
Vitamin D is produced primarily from the exposure of the skin to sunshine. Even casual exposure of the face, hands, and arms in the summer generates a large amount of vitamin D. In fact, simulated sunshine, equivalent to standing on a sunny beach until a slight pinkness of the skin was detected, was equivalent to a 20,000 IU oral dose of vitamin D2 [126]. (Note that the RDA is 400 IU for most adults.) It has been estimated that 1,000 IU per day is the minimal amount needed to maintain adequate levels of vitamin D in the absence of sunshine [126], and that up to 4,000 IU per day can be safely used with additional benefit [127].
The concentration of the active hormonal form of vitamin D is tightly regulated in the blood by the kidneys. This active hormonal form of vitamin D has the potent anti-cancer properties. It has been discovered that various types of normal and cancerous tissues, including prostate cells [128], colon tissue [129], breast, ovarian and cervical tissue [130], pancreatic tissue [131] and a lung cancer cell line [132] all have the ability to convert the major circulating form of vitamin D, 25(OH)D, into the active hormonal form, 1,25(OH)2D. So, there is a local mechanism in many tissues of the body for converting the form of vitamin D in the body that is elevated by sunshine exposure into a hormone that has anticancer activity.
Indeed, 25(OH)D has been shown to inhibit growth of colonic epithelial cells [133], primary prostatic epithelial cells [134], and pancreatic cells [131]. So, the laboratory work is confirming what had been seen some time ago in ecological studies of populations and sunshine exposure.
The mortality rates for colon, breast, and ovary cancer in the USA show a marked north-south gradient [135]. In ecological studies of populations and sunlight exposure (no individual data) sunlight has been found to have a protective effect for prostate cancer [136], ovarian cancer [137], and breast cancer [138]. Recently Grant found that sunlight was also protective for bladder, endometrial, renal cancer, multiple myeloma, and Non-Hodgkins lymphoma in Europe [139] and bladder, esophageal, kidney, lung, pancreatic, rectal, stomach, and corpus uteri cancer in the USA [140]. Several prospective studies of vitamin D and cancer have also shown a protective effect of vitamin D (see Table 5). It could be that sunshine and vitamin D are protective factors for cancers of many organs that can convert 25(OH)D into 1,25(OH)D2.
Table 5 Prospective Studies of Vitamin D and Cancer.
Reference Study Vit D measure # Cases # Controls Outcomes Comment
[219] 19-year cohort study of 1,954 men Diet history ↑vit D + calcium = ↓colorectal cancer (rates for lowest to highest intakes were 38.9, 24,5, 22,5 and 14.3/1000 population Significant effect even after adjustments for confounding factors; 2.7 fold reduction.
[220] Washington county, Maryland cohort Serum 25(OH)D 34 67 matched ↑serum vit D = ↓colon cancer. Relative risk was 0.25 for 3rd quintile and 0.20 for 4th quintile. 4–5 fold reduction
[221] Physicians' Health Study Serum 25(OH)D & 1,25(OH)D2 232 414 No relation between vitamin D metabolite levels and prostate cancer
[222] Nurses' Health Study Dietary and supplement intake Colon cancer RR = 0.42 (SS) for total vitamin D, comparing top and bottom quintiles Calcium not related to colon cancer risks; 2.4 fold reduction
[223] Finnish clinical cohort Serum 25(OH)D & 1,25(OH)D2 146 292 ↑serum 25(OH)D = ↓risk of rectal cancer, RR by quartile = 1.00, 0.93, 0.77, 0.37, P trend = 0.06. Serum 25(OH)D 12% lower in cases than in controls (12.2 vs 13.8 ng/l, P = 0.01; 2.7-fold reduction
[224] NHANES I Follow-up Study Sunlight and diet 190 women Cohort matched Risk reductions for breast cancer for women in regions with high solar radiation (RR 0.35 – 0.75).
[225] Helsinki Heart Study Serum 25(OH)D 149 596 ↑serum 25(OH)D = ↓prostate cancer. 1.7 fold greater risk for below median level compared to above median level. Young men (<52 years old) with low 25(OH)D had much higher risk of advanced prostate cancer (OR = 6.3)
[226] Randomized controlled trial for colon adenoma recurrence Serum 25(OH)D & 1,25(OH)D2, and supplementary calcium 803 subjects total Above medium 25(OH)D and supplemental calcium reduced adenoma recurrence (RR = 0.71) Calcium and vitamin D appeared to work together to reduce colon cancer risk.
[227] Norway, Finland, Sweden cohort of men Serum 25(OH)D 622 1,451 ≤ 19 nmol/l and ≥ 80 nmol/l of 25(OH)D at higher risk of prostate cancer. (40–60 nmol/l had lowest risk).
Antioxidants
α- and β-Carotene and other Carotenoids
Carotenoids have been studied vigorously to see if these colorful compounds can decrease cancer risk. In ecological studies and early case-control studies it appeared that β-carotene was a cancer-protective agent. Randomized controlled trials of β-carotene found that the isolated nutrient was either neutral [141] or actually increased risk of lung cancer in smokers [142,143]. Beta-carotene may be a marker for intake of fruits and vegetables, but it does not have a powerful protective effect in isolated pharmacological doses.
However, there is a large body of literature that indicates that dietary carotenoids are cancer preventative (See Table 6). Alpha-carotene has been found to be a stronger protective agent than its well-known isomer β-carotene. Studies tend to agree that overall intake of carotenoids is more protective than a high intake of a single carotenoid. So, a variety of fruits and vegetables is still a better anti-cancer strategy than just using a single vegetable high in a specific carotenoid.
Table 6 Studies of Carotenoids and Lung Cancer.
Reference Study # Cases # Controls Outcomes Comment
[228] Hawaiian cohort 332 865 Dose-dependent inverse associations for dietary β-carotene, α-carotene, lutein; Subjects with highest intake of all 3 had the lowest risk Previous study showed variety of vegetables more protective than just foods rich in a particular carotenoid
[229] Washington county, Maryland residents 258 515 ↑Serum/plasma levels of cryptoxanthin, β-carotene, lutein/zeaxanthin = ↓cancer (OR = 0.74, 0.83, 0.90, SS)
[230] Case control, Spain 103 206, hospital No association for intake of α-carotene, β-carotene, or lutein.
[231] Case control, Uruguay 541 540 ↑total carotenoids = ↓cancer (OR = 0.43, SS) Risk reduction for vit E and glutathione also seen.
[232] Finland cohort 138 ↑α-carotene = ↓cancer (OR = 0.61, SS); β-carotene inversely related but not SS. 90% of α-carotene from carrots
↑Fruits and ↑root vegetables = ↓cancer (OR = 0.58, 0.56, respectively, SS)
[233] Nurses' Health Study & Health Professionals Follow-Up Study 794 ↑α-carotene, lycopene, total carotenoids = ↓cancer (OR = 0.75, 0.80, 068 respectively, SS); Never smokers + ↑α-carotene = ↓cancer (OR = 0.37, SS) 4–8 year lag between diet assessment and date of diagnosis gave strongest correlations.
[234] Shanghai men's cohort 209 622 ↑serum β-cryptoxanthin = ↓cancer (OR quartiles = 1, 0.72, 0.42, 0.45, P-trend = 0.02); Smokers with above median level of total carotenoids had a SS 37% reduction in cancer risk Study population had ~50% lower mean levels of serum carotenoids compared to US whites.
[235] Canadian National Breast Screening Study 155 5,631 Non-significant inverse trend in risk for α-carotene and β-cryptoxanthin β-cryptoxanthin most from citrus, red peppers
[236] Japan Collaborative Cohort Study 147 311 ↑α-carotene, β-carotene, canthaxanthin, total carotenoids = ↓risk (OR = 0.35, 0.21, 0.37, 0.27 respectively, SS); lycopene and β-cryptoxanthin reduce lung cancer risk, but not significantly
[237] Singapore Chinese Health Study 482 ↑dietary β-cryptoxanthin = ↓cancer risk (OR = 0.73, 0.63 for smokers, SS) No significant associations of other carotenoids with lung cancer
[238] Pooled analysis of 7 cohorts in USA and Europe 3,155 ↑ dietary β-cryptoxanthin = ↓lung cancer (OR = 0.76, SS) Other dietary carotenoids not significantly related to lung cancer.
SS = statistically significant difference between comparison groups.
The richest source of α-carotene is carrots and carrot juice, with pumpkins and winter squash as a second most-dense source. There is approximately one μg of α-carotene for every two μg of β-carotene in carrots. The most common sources of β-cryptoxanthin are citrus fruits and red sweet peppers.
Lycopene
Of the various carotenoids lycopene has been found to be very protective, particularly for prostate cancer. The major dietary source of lycopene is tomatoes, with the lycopene in cooked tomatoes being more bioavailable than that in raw tomatoes. Several prospective cohort studies have found associations between high intake of lycopene and reduced incidence of prostate cancer, though not all studies have produced consistent results [144,145]. Some studies suffer from a lack of good correlation between lycopene intake assessed by questionnaire and actual serum levels, and other studies measured intakes among a population that consumed very few tomato products. The studies with positive results will be reviewed here.
In the Health Professionals Follow-up Study there was a 21% decrease in prostate cancer risk, comparing the highest quintile of lycopene intake with the lowest quintile. Combined intake of tomatoes, tomato sauce, tomato juice, and pizza (which accounted for 82% of the lycopene intake) were associated with a 35% lower risk of prostate cancer. Furthermore, lycopene was even more protective for advanced stages of prostate cancer, with a 53% decrease in risk [146]. A more recent follow-up report on this same cohort of men confirmed these original findings that lycopene or frequent tomato intake is associated with about a 30–40% decrease in risk of prostate cancer, especially advanced prostate cancer [147].
In addition to the two reports above a nested case control study from the Health Professional Follow-up Study with 450 cases and controls found an inverse relation between plasma lycopene and prostate cancer risk (OR 0.48) among older subjects (>65 years of age) without a family history of prostate cancer [148]. Among younger men high plasma β-carotene was associated with a statistically significant 64% decrease in prostate cancer risk. So, the results for lycopene have been found for dietary intakes as well as plasma levels.
In a nested case-control study from the Physicians' Health Study cohort, a placebo-controlled study of aspirin and β-carotene, there was a 60% reduction in advanced prostate cancer risk (P-trend = 0.006) for those subjects in the placebo group with the highest plasma lycopene levels, compared to the lowest quintile. The β-carotene also had a protective effect, especially for those men with low lycopene levels [149].
In addition to these observational studies, two clinical trials have been conducted to supplement lycopene for a short period before radical prostatectomy. In one study 30 mg/day of lycopene were given to 15 men in the intervention group while the 11 men were in the control group were instructed to follow the National Cancer Institute's recommendations to consume at least 5 servings of fruits and vegetables daily. Results showed that the lycopene slowed the growth of prostate cancer. Prostate tissue lycopene concentration was 47% higher in the intervention group. Subjects that took the lycopene for 3 weeks had smaller tumors, less involvement of the surgical margins, and less diffuse involvement of the prostate by pre-cancerous high-grade prostatic intraepithelial neoplasia [150]. In another study before radical prostatectomy surgery 32 men were given a tomato sauce-based pasta dish every day, which supplied 30 mg of lycopene per day. After 3 weeks serum and prostate lycopene levels increaed 2-fold and 2.9-fold, respectively. PSA levels decreased 17%, as seen also by Kucuk et al [150]. Oxidative DNA damage was 21% lower in subjects' leukocytes and 28% lower in prostate tissue, compared to non-study controls. The apoptotic index was 3-fold higher in the resected prostate tissue, compared to biopsy tissue [151]. These intervention studies raise the question of what could have been done in this intervention was longer and combined synergistically with other effective intervention methods, such as flax seed, increased selenium and possibly vitamin E, in the context of a diet high in fruits and vegetable?
Vitamin C
Vitamin C, or ascorbic acid, has been studied in relation to health and is the most common supplement taken in the USA. Low blood levels of ascorbic acid are detrimental to health (for a recent article see Fletcher et al [152]) and vitamin C is correlated with overall good health and cancer prevention [153]. Use of vitamin C for cancer therapy was popularized by Linus Pauling. At high concentrations ascorbate is preferentially toxic to cancer cells. There is some evidence that large doses of vitamin C, either in multiple divided oral doses or intravenously, have beneficial effects in cancer therapy [154-156]. Oral doses, even in multiple divided doses, are not as effective as intravenous administration. Vitamin C at a dose of 1.25 g administered orally produced mean peak plasma concentrations of 135 ± 21 μmol/L compared with 885 ± 201 μmol/L for intravenous administration [154].
While vitamin C is quite possibly an effective substance, the amounts required for these therapeutic effects are obviously beyond dietary intakes. However, intravenous ascorbate may be a very beneficial adjuvant therapy for cancer with no negative side effects when administered properly.
Other Antioxidants
There are many more substances that will have some benefit for cancer therapy. Most of these substances are found in foods, but their effective doses for therapy are much higher than the normal concentration in the food. For example, grape seed extract contains proanthocyanidin, which shows anticarcinogenic properties (reviewed by Cos et al \ [157]. Also, green tea contains a flavanol, epigallocatechin-3-gallate (EGCG), which can inhibit metalloproteinases, among several possible other mechanisms [158]. And there are claims for various other herbal substances and extracts that might be of benefit, which are beyond the scope of this review.
Probiotics
The bacteria that reside in the intestinal tract generally have a symbiotic relationship with their host. Beneficial bacteria produce natural antibiotics to keep pathogenic bugs in check (preventing diarrhea and infections) and produce some B vitamins in the small intestine where they can be utilized. Beneficial bacteria help with food digestion by providing extra enzymes, such as lactase, in the small intestine. Beneficial bacteria help strengthen the immune system right in the gut where much of the interaction between the outside world and the body goes on. Beneficial bacteria can help prevent food allergies. They can help prevent cancer at various stages of development. These good bacteria can improve mineral absorption, maximizing food utilization.
However, the balance of beneficial and potentially pathogenic bacteria in the gut is dependent on the diet. Vegetable fiber encourages the growth of beneficial bacteria. A group of Adventist vegetarians was found to have a higher amount of beneficial bacteria and lower amount of potentially pathogenic bacteria compared to non-vegetarians on a conventional American diet [159]. Differences in bacterial populations were seen between patients who recently had a colon polyp removed, Japanese-Hawaiians, North American Caucasians, native rural Japanese, and rural native Africans. Lactobacillus species and Eubacterium aerofaciens, both producers of lactic acid, were associated with the populations with the lower risk of colon cancer, while Bacteroides and Bifidobacterium species were associated with higher risk of colon cancer [160]
There is a solid theoretical basis for why probiotics should help prevent cancer, especially colon cancer, and even reverse cancer. Probiotics produce short chain fatty acids in the colon, which acidify the environment. Lower colon pH is associated with lower incidence of colon cancer. Probiotic bacteria reduce the level of procarcinogenic enzymes such as beta-glucuronidase, nitroreductase, and azoreductase [161].
L. casei was used in two trials of patients with superficial bladder cancer. In the first trial, the probiotic group had a 50% disease free time of 350 days, compared to 195 days for the control group [162]. The second trial also showed that the probiotics worked better than the placebo, except for multiple recurring tumors [163].
Except for the two studies noted above, most of the research of probiotics and cancer has been done in animals. Studies have looked at markers of tumor growth or at animals with chemically induced tumors.
Studies in rats have shown that probiotics can inhibit the formation of aberrant crypt foci, thought to be a pre-cancerous lesion in the colon. Some of the best results were obtained with a probiotic strain consumed with inulin, a type of fructooligosaccharide. Total aberrant crypt foci, chemically induced, were reduced 74% by the treatment of rats with inulin and B. longum, but only 29 and 21% by B. longum and inulin alone, respectively [164]. There was a synergistic effect in using both products together. Similar synergy was seen in rats with azoxymethane-induced colon cancer in another study. Rats fed Raftilose, a mixture of inulin and oligofructose, or Raftilose with Lactobacilli rhamnosus (LGG) and Bifidobacterium lactis (Bb12) had a significantly lower number of tumors compared to the control group [165]. A probiotic mixture, without any prebiotic, given to rats fed azoxymethane reduced colon tumors compared to the control (50% vs 90%), and also reduced the number of tumors per tumor-bearing rat [166].
In lab mice bred to be susceptible to colitis and colon cancer, a probiotic supplement, Lactobacillus salivarium ssp. Salivarius UCC118, reduced fecal coliform levels, the number of potentially pathogenic Clostridium perfringens, and reduced intestinal inflammation. In this small study two mice died of fulminant colitis and 5 mice developed adenocarcinoma in the control group of 10 mice, while there was no colitis and only 1 mouse with adenocarcinoma in the probiotic test group [167].
The research on probiotics and disease is still an emerging field. There is a high degree of variation of health benefits between different strains of bacteria. As new methods for selecting and screening probiotics become available, the field will be able to advance more rapidly.
Oral Enzymes
Many people diagnosed with cancer have digestion or intestinal tract disorders as well. Impaired digestion will greatly hinder a nutritional approach to treating cancer. If the nutrients cannot be released from the food and taken up by the body, then the excellent food provided by the Hallelujah Diet will go to waste. Digestive enzyme supplements are used to ensure proper and adequate digestion of food. Even raw foods, which contain many digestive enzymes to assist in their digestion, will be more thoroughly digested with less of the body's own resources with the use of digestive enzymes. So, the enzymes taken with meals do not have a direct effect upon a tumor, but assist the body in getting all of the nutrition out of the food for healing and restoring the body to normal function. Recently, an in vitro system was used to test the use of supplemental digestive enzymes. The digestive enzymes improved the digestibility and bioaccessibility of proteins and carbohydrates in the lumen of the small intestine, not only under impaired digestive conditions, but also in healthy human digestion [168].
There is evidence that indicates the presence of an enteropancreatic circulation of digestive enzymes [169]. Digestive enzymes appear to be preferentially absorbed into the bloodstream and then reaccumulated by the pancreas for use again. There appears to be a mechanism by which digestive enzymes can reach systemic circulation.
Enzymes, especially proteases, if they reach systemic circulation, can have direct anti-tumor activity. Wald et al [170] reported on the anti-metastatic effect of enzyme supplements. Mice inoculated with the Lewis lung carcinoma were treated with a proteolytic enzyme supplement, given rectally (to avoid digestion). The primary tumor was cut out, so that the metastatic spread of the cancer could be measured. After surgical removal of the primary tumor (day 0), 90% of the control mice died by day 18 due to metastasized tumors. In the first group, which received the rectal enzyme supplement from the time of the tumor-removal surgery, 30% of the mice had died from metastasized cancer by day 25. In the second group, which received the enzymes from 6 days prior to removal of the primary tumor, only 10% of the animals showed the metastatic process by day 15. In the third group, which received the enzyme treatment since the initial inoculation of the Lewis lung carcinoma, no metastatic spread of the tumor was discernible. One hundred day-survival rates for the control, first, second, and third groups were 0, 60%, 90%, and 100%.
In a similar experiment, an enzyme mixture of papain, trypsin, and chymotrypsin, as used in the preparation Wobe-Mugos E, was rectally given to mice that were inoculated with melanoma cells. Survival time was prolonged in the test group (38 days in the enzyme group compared to 24 days in the control mice) and 3 of the 10 enzyme-supplemented mice were cured. Again, a strong anti-metastatic effect of the proteolytic enzymes was seen [171].
Further evidence of the efficacy of oral enzyme supplementation is available from clinical trials in Europe. Two different studies have demonstrated that two different oral proteolytic enzyme supplements were able to reduce high levels of transforming growth factor-β, which may be a factor in some cancers [172,173]. In the Slovak Republic an oral enzyme supplement was tested in a placebo-controlled trial of multiple myeloma. For stage III multiple myeloma, control group survival was 47 months, compared to 83 months (a 3 year gain) for patients who took the oral enzymes for more than 6 months [174].
Enzyme supplements have also been shown to reduce side effects of cancer therapy. Enzyme supplementation resulted in fewer side effects for women undergoing radiation therapy for carcinomas of the uterine cervix [175], for patients undergoing radiation therapy for head and neck cancers [176], and for colorectal cancer patients undergoing conventional cancer treatments [177]. In a large multi-site study in Germany women undergoing conventional cancer therapy were put into a control group or a group that received an oral enzyme supplement. Disease and therapy related symptoms were all reduced, except tumor pain, by the enzyme supplement. Also, survival was longer with less recurrence and less metastases in the enzyme group [178]. In all of these studies the oral enzyme supplements were well tolerated, with only a small amount of mild to moderate gastrointestinal symptoms.
Even though these few studies don't give a lot of evidence of the effectiveness of oral enzyme supplementation, it is clear that there are some circumstances that will be helped by enzyme supplementation, with very little danger of negative side effects. At the least, enzymes will improve digestion and lessen the digestive burden on the body, leaving more reserves for disease eradication. However, as the research indicates, the effect may be much greater than that, with the potential for direct anti-tumor activity.
Whole Diet Studies
A diet-based cancer therapy, the Gerson Therapy, was used to treat melanoma cancer. The five-year survival rates from their therapy compared very favorably to conventional therapy reported in the medical literature, especially for more advanced stages of melanoma [179] (see Table 7).
Table 7 Gerson Therapy for Melanoma [179].
Stage of melanoma Gerson Historical controls
I – II 100% (N = 14) 79% (N = 15,798)
IIIA 82% (N = 17) 39% (N = 103)
IIIA + IIIB 70% (N = 33) 41% (N = 130)
IVA 39% (N = 18) 6% (N = 194)
An Italian cohort of 8,984 women was followed for an average of 9.5 years, with 207 incident cases of breast cancer during that time. Their diets were analyzed by patterns – salad vegetables (raw vegetables and olive oil), western (potatoes, red meat, eggs and butter), canteen (pasta and tomato sauce), and prudent (cooked vegetables, pulses, and fish). Only the salad vegetable diet pattern was associated with a significantly lower risk of breast cancer, about 35% lower. For women of normal weight (BMI <25) the salad vegetable pattern was even more protective, about a 61% decreased risk of breast cancer [180]. The overall dietary pattern does make a very significant difference.
In US-based studies the "prudent" diet has been shown to be protective for colon cancer, while the "western" diet has been shown to be detrimental. The "western" dietary pattern, with its higher intakes of red meat and processed meats, sweets and desserts, French fries, and refined grains, was associated with a 46% increase relative risk of colon cancer in the Nurses' Health Study [45]. Slattery et al [17] found a two-fold increase in relative risk of colon cancer associated with a "western" dietary pattern, and a 35–40% decrease in relative risk associated with the "prudent" pattern, especially among those diagnosed at an earlier age (<67 years old). The "salad vegetable" pattern is still more likely to be protective compared to the prudent dietary pattern, but this pattern did not exist in this study population.
In an analysis of the colon cancer data from the Health Professionals Follow-up Study, Platz et al [56] found that there was a 71% decrease in colon cancer risk when men with none of six established risk factors were compared to men with at least one of these risk factors (obesity, physical inactivity, alcohol consumption, early adulthood cigarette smoking, red meat consumption, and low intake of folic acid from supplements). So, if all men had the same health profile as these healthier 3% of the study population, colon cancer rates would have been only 29% of what they measured.
A plant-based dietary pattern in being currently tested in the Women's healthy Eating and Living (WHEL) Study. About 3,000 women who were treated for an early stage of breast cancer have been randomized into two groups. The dietary goals for the test group of the study are 5 servings of vegetables, 16 oz of vegetable juice, 3 servings of fruit, 30 g of fiber, and <20% of energy from fat. No guidelines were given for animal product intake, and initial results seem to confirm, since there were no changes in body weight, total cholesterol, or LDL cholesterol [181], which would be affected by animal protein intake. However, over the first year of follow-up vegetable intake did increase to seven servings/day, fruit intake increased to 3.9 servings/day, energy from fat decreased from 28% to 23%. Also, plasma carotenoid concentrations increased significantly in the intervention group, but not in the control group. α-Carotene increased 223%, β-carotene increased 87%, lutein increase 29%, and lycopene increased 17% [182], indicating that a substantial dietary change had been made by these women. It will be very interesting to follow the results of this study.
Conclusions
What is the result when all of these things are put together? What if all of these factors reviewed here were taken into account and put into practice? This anticancer diet would have:
• adequate, but not excessive calories,
• 10 or more servings of vegetables a day, including cruciferous and allium vegetables; vegetable juice could meet part of this goal,
• 4 or more servings of fruits a day,
• high in fiber,
• no refined sugar,
• no refined flour,
• low in total fat, but containing necessary essential fatty acids,
• no red meat,
• a balanced ratio of omega 3 and omega 6 fats and would include DHA,
• flax seed as a source of phytoestrogens,
• supplemented with ~200 μg/day selenium,
• supplemented with 1,000 μg/day methylcobalamin (B-12),
• very rich in folic acid (from dark green vegetables),
• adequate sunshine to get vitamin D, or use 1,000 IU/day supplement,
• very rich in antioxidants and phytochemicals from fruits and vegetables, including α-carotene, β-carotene, β-cryptoxanthin, vitamin C (from foods), vitamin E (from foods),
• very rich in chlorophyll,
• supplemented with beneficial probiotics,
• supplemented with oral enzymes
As reviewed above, reductions of 60 percent in breast cancer rates have already been seen in human diet studies, and a 71 percent reduction in colon cancer for men without the known modifiable risk factors. These reductions are without taking into account many of the other factors considered in this review, such as markedly increased fruit and vegetable intake, balanced omega 3 and 6 fats, vitamin D, reduced sugar intake, probiotics, and enzymes – factors which all are likely to have an impact on cancer. Certainly cancer prevention would be possible, and cancer reversal in some cases is quite likely.
Competing Interests
Michael Donaldson is a research scientist at the Hallelujah Acres Foundation, a foundation for investigations pertaining to the Hallelujah Diet. Funding for this review was provided by the Hallelujah Acres Foundation.
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| 15496224 | PMC526387 | CC BY | 2021-01-04 22:25:48 | no | Nutr J. 2004 Oct 20; 3:19 | utf-8 | Nutr J | 2,004 | 10.1186/1475-2891-3-19 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-281547655710.1186/1476-4598-3-28ResearchA CpG island hypermethylation profile of primary colorectal carcinomas and colon cancer cell lines Lind Guro E [email protected] Lin [email protected]øvig Tone [email protected] Gunn I [email protected] Richard [email protected] Torleiv O [email protected] Manel [email protected] Ragnhild A [email protected] Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, 0310 Oslo, Norway2 Institute of Forensic Medicine, The National Hospital, University of Oslo, Norway3 The University Hospital of Akershus, Akershus, Norway4 INSERM, Paris, France5 Cancer Epigenetics Laboratory, the Spanish National Cancer Centre (CNIO), Madrid, Spain2004 11 10 2004 3 28 28 9 6 2004 11 10 2004 Copyright © 2004 Lind et al; licensee BioMed Central Ltd.2004Lind et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Tumor cell lines are commonly used as experimental tools in cancer research, but their relevance for the in vivo situation is debated. In a series of 11 microsatellite stable (MSS) and 9 microsatellite unstable (MSI) colon cancer cell lines and primary colon carcinomas (25 MSS and 28 MSI) with known ploidy stem line and APC, KRAS, and TP53 mutation status, we analyzed the promoter methylation of the following genes: hMLH1, MGMT, p16INK4a (CDKN2A α-transcript), p14ARF (CDKN2A β-transcript), APC, and E-cadherin (CDH1). We compared the DNA methylation profiles of the cell lines with those of the primary tumors. Finally, we examined if the epigenetic changes were associated with known genetic markers and/or clinicopathological variables.
Results
The cell lines and primary tumors generally showed similar overall distribution and frequencies of gene methylation. Among the cell lines, 15%, 50%, 75%, 65%, 20% and 15% showed promoter methylation for hMLH1, MGMT, p16INK4a, p14ARF, APC, and E-cadherin, respectively, whereas 21%, 40%, 32%, 38%, 32%, and 40% of the primary tumors were methylated for the same genes. hMLH1 and p14ARF were significantly more often methylated in MSI than in MSS primary tumors, whereas the remaining four genes showed similar methylation frequencies in the two groups. Methylation of p14ARF, which indirectly inactivates TP53, was seen more frequently in tumors with normal TP53 than in mutated samples, but the difference was not statistically significant. Methylation of p14ARF and p16INK4a was often present in the same primary tumors, but association to diploidy, MSI, right-sided location and female gender was only significant for p14ARF. E-cadherin was methylated in 14/34 tumors with altered APC further stimulating WNT signaling.
Conclusions
The present study shows that colon cancer cell lines are in general relevant in vitro models, comparable with the in vivo situation, as the cell lines display many of the same molecular alterations as do the primary carcinomas. The combined pattern of epigenetic and genetic aberrations in the primary carcinomas reveals associations between them as well as to clinicopathological variables, and may aid in the future molecular assisted classification of clinically distinct stages.
APCcolon cancer cell linescolorectal carcinomasCpG methylationE-cadherinhMLH1KRASMGMTMSIMSSp14p16TP53
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Background
During the last decade, epigenetic changes have been reported in many cancers and they are now recognized to be at least as common as genetic changes [1]. Aberrant methylation of cytosine located within the dinucleotide CpG is by far the best-categorized epigenetic change. The genome of the cancer cell demonstrates global hypomethylation [2,3] as well as regional promoter hypermethylation of several tumor suppressor genes [4]. Hypermethylation of selected CpG sites within CpG islands in the promoter region of genes is associated with loss of gene expression and is observed in both physiological conditions, such as X chromosome inactivation [5], and neoplasia [6]. By inactivating various tumor suppressor genes, this epigenetic modification can affect many important cellular processes, such as the cell cycle (RB, p15INK4b, p16INK4a), the TP53 pathway (p14ARF), the WNT signaling pathway (APC, E-cadherin), DNA repair (MGMT, hMLH1, BRCA1), apoptosis (DAPK), and the metastasizing process (E-cadherin, TIMP3) (reviewed in [1,7,8]).
Development of colorectal cancer through various morphological stages has been linked to several genetic and epigenetic changes. The majority of carcinomas have several chromosomal aberrations, a phenotype often referred to as chromosomal instability. Approximately 15% of the tumors are near diploid but exhibit microsatellite instability (MSI), seen as genome-wide short nucleotide insertions and deletions [9]. This phenotype is caused by a defect DNA mismatch repair system [9]. Subgroups of both types of colorectal carcinomas reveal aberrant methylation of tumor suppressor genes leading to lack of expression [10,11].
Human cancer cell lines are important tools in cancer research. Their commercial availability and unrestrained growth make them well suited for in vitro studies. Although many of the known genetic aberrations in colon cancer cell lines have been comprehensively described [12], several of these cell lines have not been analyzed for methylation status of pathogenetically important target genes.
The frequencies of both methylation and gene mutation differ among various studies of cell lines and primary tumors. The genome characteristics, profiles of gene mutations, and methylation status are rarely reported in the same samples, let alone in large series. In the present report we address these potentially connected pathogenetic mechanisms by presenting methylation profiles of a set of genes in a series of MSI and microsatellite stable (MSS) colon cancer cell lines and primary colorectal carcinomas. The methylation profiles are compared with various known genetic and clinicopathological features of the same series.
Results
Methylation status of target genes in colon cancer cell lines
The colon cancer cell line methylation-specific PCR (MSP) results are summarized in Table 1 and Figure 1a. Among the MSI cell lines 3/9, 5/9, 7/9, 8/9, 2/9, and 2/9 showed promoter hypermethylation of hMLH1, MGMT, p16INK4a, p14ARF, APC, and E-cadherin, respectively, whereas 0/11, 5/11, 8/11, 5/11, 2/11, and 1/11 of the MSS cell lines were hypermethylated for the same genes (Table 2). Hence, the cell lines with MSI generally showed higher methylation frequencies than did the MSS cell lines (Figures 1a, 2a). In most cases, methylation of the target genes was biallelic, but in 10 of the 20 cell lines, monoallelic methylation (detection of both methylated and unmethylated MSP gel bands) was found for one or more of the genes (Table 1). The MSS V9P was the only cell line unmethylated for all six genes analyzed.
Table 1 Promoter methylation of colon cancer cell lines. MSI, microsatellite instable; MSS, microsatellite stable; U, unmethylated; M, methylated. The references give results in agreement with our own data except when the reference is underlined. Note that reference 15 does not use the category monoallelic methyaltion, but reports the promoters only as methylated or unmethylated.
Cell line hMLH1 MGMT p16INK4a p14ARF APC E-Cadherin
MSI
Co115 M12 M M12 M U/M U
HCT15 U12,13,14,15 U/M15,16 M12,14,15 M14,15,17 U U15
HCT116 U12,13,15,18,19,20,21,22 U/M15,20 U/M12,15,20,21,22,23 U/M15,17,21,24 U/M U15
LoVo U12,13,14,15,18,22,26 U15,31 M12,14,15,22 M14,15,24,25 U U15
LS174T U12,13,18,22 U/M U12,22 U/M U U
RKO M15,18,19,20,22,26 U15,20 M15,20,22,27 M15,24 U M15
SW48 M12,13,14,15,18,20,22,26,28,29 M15,20,31 M12,14,15,20,22,27,29 M14,15,24 U U15
TC7 U12 U U12 U/M U U
TC71 U12 U M12 U U U/M
MSS
ALA U12 U M12 U M U
Colo320 U12,14,18,30 M M12,14,27 U14 U30 M
EB U12 M M12 U U U
FRI U12 U/M U12 U/M U U
HT29 U12,13,14,15,18,21,22,26,30 U15,31,32,33 M12,14,15,21,22,27 U14,15,21,24 U30 U15
IS1 U12,21 U M12,21 M21 U U
IS2 U12 U U/M12 M U U
IS3 U12 U U12 M U U
LS1034 U12,13 U/M U/M12 M U/M U
SW480 U12,14,15,19,21,22,26,30 U/M15 M12,14,15,21,22,27 U14,15,21,24,25 U30 U15
V9P U12 U U12 U U U
Table 2 Methylation frequencies among MSS and MSI colon cancer cell lines and primary colorectal tumors. Abbreviations; MSS, microsatellite stable; MSI, microsatellite instable; CRC, colorectal cancer; U, unmethylated;M, methylated. Note that the calculated methylation frequencies of the MSS cell lines includes results from three cell lines derived from the same patient.
MSS MSI Total
Gene Cell lines CRCs Cell lines CRCs Cell lines CRCs
hMLH1 0/11 (0%) 0/25 (0%) 3/9 (33%) 11/28 (39%) 3/20 (15%) 11/53 (21%)
MGMT 5/11 (45%) 10/25 (40%) 5/9 (56%) 11/28 (39%) 10/20 (50%) 21/53 (40%)
p16INK4a 8/11 (73%) 7/25 (28%) 7/9 (78%) 10/28 (36%) 15/20 (75%) 17/53 (32%)
p14ARF 5/11 (45%) 3/24 (12%) 8/9 (89%) 17/28 (61%) 13/20 (65%) 20/52 (38%)
APC 2/11 (18%) 7/25 (28%) 2/9 (22%) 10/28 (36%) 4/20 (20%) 17/53 (32%)
E-cadherin 1/11 (9%) 10/24 (42%) 2/9 (22%) 11/28 (39%) 3/20 (15%) 21/52 (40%)
Figure 1 Distribution of simultaneously methylated promoters in MSS and MSI colon cancer cell lines and colorectal carcinomas. The two panels illustrate the percentage of MSS and MSI samples displaying methylation of zero to all of the promoters analyzed in the present study in a) cell lines and b) primary colorectal tumors. Abbreviations: MSS, microsattelite stable; MSI, microsattelite instable.
Methylation status of target genes in primary colorectal carcinomas. Comparison with colon cancer cell lines
Methylation status was assessable in more than 99% of the total number of analyses (53 tumors × 6 genes = 318 analyses).
The results of the methylation analyses of 53 primary colorectal carcinomas (25 MSS and 28 MSI) are shown in Table 2 and illustrated in Figures 1b and 2b. All the methylated primary tumors examined showed an unmethylated band in addition to the methylated one, probably due to the presence of normal cells. The methylation frequencies varied from 0% among MSS tumors at the hMLH1 promoter to 61% among the MSI tumors for the p14ARF gene (Table 2).
Figure 2 Promoter hypermethylation in colon cancer cell lines and colorectal primary tumors. Methylation was evaluated by methylation-specific PCR (MSP). A visible PCR product in Lanes U indicates the presence of unmethylated alleles whereas a PCR product in Lanes M indicates the presence of methylated alleles. The upper panel (a) illustrates the methylation status of all the loci analyzed in a MSI cell line (RKO) and a MSS cell line (HT29). The lower panel (b) shows the methylation status of representative primary colorectal tumors. Abbreviations: NB, normal blood (positive control for unmethylated samples); MP, methylated placenta (positive control for methylated samples); neg, negative control (containing water as template); U, lane for unmethylated MSP product; M, lane for methylated MSP product.
Several of the primary tumor samples displayed widespread CpG island methylation (Figure 1b). Eighteen of 52 tumors (35%) were methylated in 3 or more of the 6 genes analyzed. Only 5/52 (10%) of the tumor samples did not show hypermethylation in any of the genes analyzed. We saw no statistical difference in the number of methylated target genes in colon cancer cell lines versus colorectal primary tumors (Mean Rank 32 for primary tumors versus 38 for cell lines, P = 0.231, Mann-Whitney test).
Methylation profiles compared with genetic characteristics
The methylation status of the primary tumors was compared with genetic characteristics of the same tumors (Table 3). In general, higher frequencies of gene methylation were found among diploid than among aneuploid tumors, reflecting the MSI status, but the differences reached statistical significance only for p14ARF (P < 0.001) and hMLH1 (P = 0.015). Sixteen of 49 primary tumors harbored TP53 mutations, and all of the tumors with TP53 mutations also harbored unmethylated hMLH1 (P = 0.009). p14ARF hypermethylation was less common in tumors with mutated TP53 than in tumors with wild type TP53, although this was not statistically significant (P = 0.127). Four tumors displayed a G:C to A:T TP53 mutation and three of them simultaneously harbored a methylated MGMT gene. Four of 11 tumors with G:C to A:T KRAS (KRAS2) mutations were methylated at the MGMT promoter. Overall, the presence of KRAS mutations was not associated with the methylation status of the genes analyzed. Among the 20 tumors with p14ARF methylation, 10 were also methylated at the adjacent p16INK4a gene (P = 0.067). Finally, the APC promoter was methylated in 17/53 (32%) tumors, and 8/17 (47%) tumors displayed both APC mutation and methylation.
Table 3 CpG island methylation of selected genes compared with the patients clinicopathological features and tumor genetics. Abbreviations: Gen. Characteristics, Genetic Characteristics; MSI, microsatellite instability; MSS, microsatellite stable; NS, not significant; Clin. and Path. Features, Clinical and Pathological Features. Comparison of different groups were tested with Fisher exact test or Pearsons χ2 test, P values are two sided and are considered statistically significant when P ≤ 0.05. The table is based on primary tumors (53) and not patients (52) *Statistically significant Pearsons χ2 tests with expected count less than 5.
hMLH1 MGMT p16INK4a p14ARF APC E-cadherin
M U M U M U M U M U M U
Individuals
No 11/53 42/53 21/53 32/53 17/53 36/53 20/52 32/52 17/53 36/53 21/52 31/52
Gen. Characteristics
Ploidy
Diploid 10 20 10 20 10 20 18 12 11 19 13 17
Aneuploid 1 22 11 12 7 16 2 20 6 17 8 14
P value 0.02 NS NS < 0.001 NS NS
MSI-status
MSI 11 17 11 17 10 18 17 11 10 18 11 17
MSS 0 25 10 15 7 18 3 21 7 18 10 14
P value < 0.001 NS NS 0.001 NS NS
TP53
Wild type 11 22 12 21 11 22 16 16 8 25 13 19
Mutation 0 16 7 9 5 11 4 12 7 9 7 9
P value 0.01 NS NS NS NS NS
wt+non G-A mutation 11 33 15 29 14 30 18 25 13 31 17 26
G-A mutation 0 4 3 1 1 3 1 3 1 3 2 2
P value NS NS NS NS NS NS
K-Ras
Wild type 8 19 13 14 9 18 12 15 7 20 9 18
Mutation 1 14 6 9 3 12 2 12 6 9 6 8
P value NS NS NS 0.08 NS NS
wt+non G-A mutation 8 23 15 16 9 22 13 18 8 23 10 21
G-A mutation 1 10 4 7 3 8 1 9 5 6 5 5
P value NS NS NS NS NS NS
APC
Wild type 7 19 12 14 10 16 9 17 9 17 12 14
Mutation 3 23 8 18 7 19 10 15 8 18 9 16
P value NS NS NS NS NS NS
Clin. and Path. Features
Sex
Male 2 23 9 16 8 17 6 19 10 15 8 17
Female 9 19 12 16 9 19 14 13 7 21 13 14
P value 0.04 NS NS 0.05 NS NS
Age (years)
<68 2 21 10 13 4 19 7 16 8 15 9 14
≥68 9 21 11 19 13 17 13 16 9 21 12 17
P value 0.09 NS 0.07 NS NS NS
Location
Right 10 8 7 11 7 11 12 6 7 11 7 11
Left 1 19 8 12 9 11 5 14 6 14 8 11
Rectum 0 14 6 8 1 13 2 12 4 10 5 9
P value < 0.001* NS 0.05 0.01 NS NS
Histologic grade
Poorly differentiated 4 8 7 5 6 6 7 4 5 7 4 7
Moderately differentiated 7 30 13 24 11 26 12 25 11 26 14 23
Well differentiated 0 3 1 2 0 3 0 3 1 2 2 1
P value NS NS NS NS NS NS
Dukes' classification
A 2 2 3 1 1 3 2 2 0 4 1 3
B 5 22 10 17 8 19 9 17 13 14 12 14
C 2 13 4 11 4 11 4 11 3 12 5 10
D 2 5 4 3 4 3 5 2 1 6 3 4
P value NS NS NS NS 0.07 NS
Among the tumors with widespread methylation (3 or more methylated genes), 13/18 (72%) tumors demonstrated MSI, whereas 5/24 (21%) were MSS (P = 0.080). We found no statistically significant associations between tumors with widespread methylation and presence of TP53, KRAS, or APC mutations.
Methylation profiles and clinicopathological features
The clinicopathological features and methylation status of the primary tumors are summarized in Table 3. We saw more methylation among tumors from females than in those from males for both hMLH1 (P = 0.043) and p14ARF (P = 0.050). Tumors from patients younger than the mean age (68 years) had a lower methylation frequency for p16INK4a than did tumors from older patients, although this was not statistically significant (P = 0.074). There was a strong association between methylation and right-sided tumor location as 10/11 (91%) tumors methylated in hMLH1 and 12/19 (63%) of the tumors methylated in p14ARF were located in the right side of the colon (P < 0.001 and P = 0.005, respectively). There was no statistically significant association between methylation and histological grade. Most of the tumors with APC methylation (13/17, 76%) belonged to the Dukes' B group, but the differences were not statistically significant (P = 0.068).
Tumors with widespread methylation (≥ 3 loci) are associated with right-sided localization; 10/17 (59%), versus 5/17 (29%) left-sided (P = 0.035). We saw no statistically significant associations between presence of widespread methylation and the remaining clinicopathological variables included in the present study.
Discussion
Tumor cell lines are commonly used as experimental tools in cancer research, including studies designed to assess epigenetic changes. But whereas the genetic aberrations of colon cancer cell lines have been comprehensively described [12], the methylation profiles of potential target genes in the same or similar cell lines are often described only sparingly. A literature survey of the 20 colon cancer cell lines and their methylation status analyzed in this study showed that some cell lines and genes had been extensively studied, whereas others were left undescribed (Table 1). For half of the cell lines included in the present study, both methylated and unmethylated alleles have been found for one or more of the genes studied. As non-neoplastic cells are not found in cultured cancer cell lines, this can not be caused by the presence of normal cells, and although several biological and technical explanations may exist, allele specific methylation seems the most likely interpretation [23,34]. In contrast, admixture of normal cells, tumor heterogeneity and/or monoallelic methylation may explain the coexistence of unmethylated and methylated bands in primary tumors.
It has been debated for some time whether cell lines are more frequently methylated than primary tumors [35]. Regarding overall CpG island hypermethylation, cancer cell lines have in general demonstrated an increased frequency of hypermethylation compared with primary tumors [15]. However, only a limited number of the genes analyzed have shown a statistically significant difference in methylation frequency [15]. Among several cancer types examined, colon cancer cell lines have been shown to resemble the most their respective primary tumor in this respect [36]. For the cell lines and primary tumors included in this study, the fraction of MSI and MSS samples was about the same and we saw no statistical difference in the overall number of methylated target genes in colon cancer cell lines versus colorectal primary tumors. Seemingly, large methylation percentage differences for individual genes were seen (Table 2) but they were statistically significant only for p16INK4a methylation, independent of MSI stratification. Comparisons of in vitro tumor cells with primary tumors of each subtype (MSS and MSI) have also shown similar frequencies of TP53, KRAS and APC mutations [12] and ploidy stem line [37], which further supports the conclusion that the in vitro system is a suitable experimental tool that closely reflect the in vivo situation.
Previously reported variations in promoter hypermethylation frequencies of different tumor suppressor genes in colorectal cancer can be explained by various ratios of MSI versus MSS samples in the series analyzed, different methods for analyzing methylation, the inter-individual variation in scoring of methylated samples, incomplete bisulphite modification, tumor heterogeneity, and the fact that different parts of the gene promoter region in question have been analyzed. In the present study, we used primer sets known to only detect methylation in tumor cells, never in normal tissues from the same patients [24,31,38-42]. The promoter hypermethylation in these areas has also shown an impressive correlation with lack of protein expression, confirming that these are essential regions for gene expression [24,31,38-42]. The hMLH1 primers we designed amplify a region of the promoter, in which methylation invariably correlates with the lack of hMLH1 expression [18,43,44]. Methylation of this region has only been detected in tumor cells and not in normal mucosa [18,43,44].
As expected, the MSI primary tumors showed more methylation overall than did the MSS group. However, this was only significant for the hMLH1 and p14ARF genes, whereas the four additional genes analyzed revealed similar methylation frequencies in the MSS and MSI groups. Promoter methylation of the hMLH1 gene was, not surprisingly, found only in tumors and cell lines with MSI, not in the MSS samples. The MSS tumors and cell lines per definition contain functional hMLH1 protein, and transcriptional silencing of hMLH1 by hypermethylation is known to be the main cause of MSI in sporadic CRC [26,28,45]. Also p14ARF methylation may have a specific role in MSI tumors, since it seems to be most often inactivated in tumors with wild type TP53 (see below). However, the relatively high methylation frequencies of the remaining analyzed genes, and also their overall similar frequency in MSI and MSS samples, imply that they are important in colorectal carcinogenesis independently of tumor site and MSI status.
Inactivation of tumor suppressor genes by promoter hypermethylation has been recognized to be at least as common as gene disruption by mutation in tumorigenesis [1]. Indeed, most types of primary tumors harbor several genes inactivated in this way and some genes, like p16INK4a, have been reported to be methylated consistently in most tumor types analyzed [46]. In colorectal carcinomas, the reported p16INK4a methylation frequencies vary from 18% [47] to 50 % [48] with most of the observations centered around 36–40% [11,27,46,49-51], i.e., slightly higher than our result. Both p16INK4a and p14ARF are more commonly methylated in tumors with MSI than in MSS [10,11,51-53], although we found that the methylation frequency of p14ARF is higher than that for p16INK4a in MSI colorectal carcinomas.
The DNA repair protein MGMT is able to remove promutagenic alkyl groups from O6-guanine by an irreversible transfer to an internal cysteine residue [54]. Left unrepaired, the alkylated O6-guanine has a tendency to base pair with thymine during replication, thereby introducing a G:C to A:T transition mutation in the DNA [55]. Inactivating promoter hypermethylation of the MGMT gene has previously been reported to be associated with G:C to A:T mutations in the tumor suppressor gene TP53 [56] and the proto-oncogene KRAS [57,58]. Our data support this assumption for TP53 but seemingly not for KRAS, although no certain conclusions can be drawn from the limited number of samples with G:C to A:T mutations.
The p14ARF protein interacts in vivo with the MDM2 protein, neutralizing MDM2's inhibition of TP53 [59]. Less hypermethylation of p14ARF in tumors with mutated TP53 than in tumors with wild type TP53 has been reported previously [24]. Additionally, several reports have described an inverse relationship between MSI and TP53 mutation in colorectal carcinomas [60-62]. The frequent methylation we report for the p14ARF gene in MSI tumors with few TP53 mutations is in agreement with a recent study [53] and supports the existence of this alternative pathway for TP53 inactivation.
Inactivation of the APC gene is frequent in colorectal and other gastrointestinal carcinomas, usually by truncating mutations [63,64]. An alternative mechanism to inactivate the gene in colorectal tumors is by promoter methylation, and we report a frequency of APC methylation in the upper range of what has been seen in previous studies [51,65,66]. Somatic mutations in APC are common in colorectal cancer [67,68] and, similar to what has been seen by others [12,22,69], almost half of the tumors displaying APC mutations in our study were also methylated. We have not looked at allele-specific mutation, but methylation and mutation in the same tumor might reflect one mutated allele and methylation of the other, in accordance with Knudson's two hit hypothesis. This has previously been demonstrated for APC in colorectal cancer samples by Esteller et. al [65]. APC has a central role in the WNT signaling pathway, which is suggested to play a part in colorectal carcinogenesis by its constitutive activation. Activation of this pathway results in increased transcription levels of genes like MYC and CCND1 (cyclin D1) further stimulating cell proliferation [63]. Among the 52 successfully analyzed primary tumors in this study, 35 had altered APC caused by methylation (n = 17) and/or gene mutation (n = 26). The E-cadherin gene was also methylated in 14/34 tumors with altered APC, presumably further stimulating WNT signaling [63]. Interestingly, APC methylation seemed to be more common in Dukes B stage tumors.
The present study confirms that methylation of hMLH1 in sporadic carcinomas is associated with proximal tumor location in the large bowel [14,21,45,70], as above 90% of the primary tumors harboring a methylated hMLH1 promoter were taken from the right side of the colon. An association between sporadic proximal colon carcinomas and methylation has also been reported for p16INK4a and p14ARF [14,21,45]. Among our 53 primary tumors, we can only confirm this statistically for p14ARF. However, p16INK4a demonstrated the same tendency. Both hMLH1 and p14ARF are strongly associated with MSI and MSI is in turn strongly associated with proximal tumor location [71,72], hence, it is not unexpected that the methylation of both genes is associated with proximal location.
When it comes to gene methylation and its association with other clinicopathological features, contradictory results have been reported. Our observation that methylation of p14ARF does not exclude p16INK4a methylation, is in accordance with previous studies [21,24]. Correlation of p16INK4a or p14ARF methylation with female gender and increased age has been described in some studies [14,47] but not in others [11,21,24]. We found such an association between female gender and methylation of p14ARF and hMLH1, but not of p16INK4a. We also found a weak association between p16INK4a methylation and increasing age. This potential age-specific methylation was not confirmed for any of the other genes studied. The gender-associated methylation of hMLH1 has previously been described [73,74] and might explain the increased prevalence of colorectal tumors of the MSI type in the female patient group [74].
Like Toyota et. al [51], we found no statistically significant associations between tumors with widespread methylation and age, gender, or stage of the colorectal cancer.
Conclusions
The data presented here demonstrate that multiple genes are methylated in colorectal carcinomas. This underlines the important role epigenetic inactivation of tumor suppressor genes plays during the process of tumor development. Epigenetic changes in colon cancer cell lines are overall comparable with those of primary carcinomas of the large bowel, which make the cell lines relevant models for the in vivo situation. The methylation profile of specific genes, in particular hMLH1 and p14ARF, has strong associations with genetic and clinicopathological features and might be related to biologically distinct subsets of colorectal tumors.
Methods
Patients and cell lines
Fifty-three primary colorectal carcinomas from 52 patients, including 25 MSS tumors and 28 MSI tumors, were submitted to methylation analyses. One of the tumors was from a patient with hereditary non-polyposis colorectal cancer (HNPCC), whereas the rest of the cases were sporadic [71]. The tumors have known DNA ploidy pattern [75], MSI status [76], as well as mutation status for TP53, KRAS and APC [62,64,77]. The genetic and clinicopathological variables are found in Table 3. Twenty colon cancer cell lines, 11 MSS and 9 MSI, were also included in the study. These cell lines have previously been characterized for MSI status [61,78-80], 31 different genetic alterations [12], and total genome profiles by Kleivi et. al [37]. The primary tumors included in the present study are from a series of carcinomas evaluated to contain a mean number of 84% tumor cells [81]. The DNA was extracted by standard phenol -chloroform procedure.
Methylation-specific PCR (MSP)
Promoter methylation was studied in hMLH1, MGMT, p16INK4a, p14ARF, APC and E-cadherin by MSP, a method that distinguishes unmethylated from methylated alleles of a given gene [38]. After bisulphite treatment of DNA, which converts unmethylated but not methylated cytosines to uracil, DNA is amplified by PCR using primers specific to methylated and unmethylated sequences.
One or two μg DNA from each sample was modified as described [82]. Previously reported primer sets were used for amplification of the MGMT [31,82], p16INK4a [38,82], p14ARF [24], APC [39,40] and E-cadherin fragments [41] (island 3). The primers for amplifying unmethylated and methylated hMLH1 fragments were designed in accordance with hMLH1 promoter methylation and gene expression studies [18,44]. All primer sets (see Additional file 1) were purchased from Medprobe AS (Oslo, Norway).
All the PCRs were carried out in a total volume of 25 μl containing 1 × PCR Buffer (15mM MgCl2 or no MgCl2; QIAGEN Inc., Valencia, CA), 200 μM dNTP (Amersham Pharmacia Biotech Products Inc., Piscataway, NJ), and 0.625 U HotStarTaq DNA Polymerase (QIAGEN). PCR products were loaded onto 7.5% polyacrylamide gels, stained with ethidium bromide, and visualized by UV illumination. An independent second "methylated reaction" of the MSP was performed for all the samples included in the present study. In cases with diverging results from the two rounds of MSP, we did a third independent MSP round.
Human placental DNA (Sigma Chemical Co., St. Louis, MO) treated in vitro with SssI methyltransferase (New England Biolabs Inc., Beverly, MA) was used as a positive control for MSP of methylated alleles, whereas DNA from normal lymphocytes was used as a control for unmethylated alleles. Water was used as a negative PCR control in both reactions.
Statistics
All 2 × 2 contingency tables were analyzed using Fisher's exact test. Three × 2 tables were analyzed by the Pearson χ2 test. Two of the statistically significant cross-tables analyzed by the Pearson χ2 had cells with expected count less than 5, with a minimum count of 2.96 (Table 3). The Mann -Whitney test was in addition performed when appropriate. All P values are derived from two tailed statistical tests using the SPSS 11.5 software.
Authors' contributions
GEL cultured and isolated DNA from all cell lines and carried out the MSP analyses of these and of the patient samples. GEL interpreted the results, performed the statistics and drafted the manuscript. LT participated in the study design, scored the MSP results independently of author 1, and contributed to manuscript preparation. TL was responsible for the update of the APC mutation status in the cohort. GIM and TOR have collected the series of human primary tumors and provided all clinical and pathological information. RH provided all cell lines and information about them. ME contributed with scientific discussions of the results and participated in the writing of the manuscript. RAL conceived the study, was responsible for its design and coordination, and contributed in the evaluation of the results and in preparation of the manuscript. All authors have read and approved of the final manuscript.
Supplementary Material
Additional File 1
Additional file 1 lists the MSP primers used in the present study.
Click here for file
Acknowledgements
We thank Professor Sverre Heim for critically reading the manuscript. GEL is a Research Fellow and LT a Post-Doctoral Fellow of the Norwegian Cancer Society. TL is Post-Doctoral Fellow of the Norwegian Foundation for Health and Rehabilitation. The study was funded by grants from the Norwegian Cancer Society (A95068 and D97127 RAL).
==== Refs
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| 15476557 | PMC526388 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Oct 11; 3:28 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-28 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-581549622710.1186/1477-7525-2-58ResearchFactors influencing quality of life in patients with active tuberculosis Marra Carlo A [email protected] Fawziah [email protected] Victoria C [email protected] Anita [email protected] J Mark [email protected] Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada2 Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal HealthResearch Institute, Vancouver, BC, Canada3 Division of Pharmacy and Vaccines, British Columbia Centre for Disease Control, Vancouver, BC, Canada4 Division of Internal Medicine, Faculty of Medicine, University of BritishColumbia, Vancouver, BC, Canada5 Centre for Health Outcome and Evaluation Sciences, St. Paul's Hospital, Vancouver, BC, Canada6 Division of Respiratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada2004 20 10 2004 2 58 58 3 9 2004 20 10 2004 Copyright © 2004 Marra et al; licensee BioMed Central Ltd.2004Marra et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
With effective treatment strategies, the focus of tuberculosis (TB) management has shifted from the prevention of mortality to the avoidance of morbidity. As such, there should be an increased focus on quality of life (QoL) experienced by individuals being treated for TB. The objective of our study was to identify areas of QoL that are affected by active TB using focus groups and individual interviews.
Methods
English, Cantonese, and Punjabi-speaking subjects with active TB who were receiving treatment were eligible for recruitment into the study. Gender-based focus group sessions were conducted for the inner city participants but individual interviews were conducted for those who came to the main TB clinic or were hospitalized. Facilitators used open-ended questions and participants were asked to discuss their experiences of being diagnosed with tuberculosis, what impact it had on their lives, issues around adherence to anti-TB medications and information pertaining to their experience with side effects to these medications. All data were audio-recorded, transcribed verbatim, and analyzed using constant comparative analysis.
Results
39 patients with active TB participated. The mean age was 46.2 years (SD 18.4) and 62% were male. Most were Canadian-born being either Caucasian or Aboriginal. Four themes emerged from the focus groups and interviews. The first describes issues related to the diagnosis of tuberculosis and sub-themes were identified as 'symptoms', 'health care provision', and 'emotional impact'. The second theme discusses TB medication factors and the sub-themes identified were 'adverse effects', 'ease of administration', and 'adherence'. The third theme describes social support and functioning issues for the individuals with TB. The fourth theme describes health behavior issues for the individuals with TB and the identified sub-themes were "behavior modification" and "TB knowledge."
Conclusion
Despite the ability to cure TB, there remains a significant impact on QOL. Since much attention is spent on preventative or curative mechanisms, the impact of this condition on QoL is often not considered. Attention to the issues experienced by patients being treated for TB may optimize adherence and treatment success.
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Introduction
Globally, tuberculosis (TB) is a major public health problem [1]. In 1997, the World Health Organization (WHO) estimated that 32% of the world's population was infected with Mycobacterium tuberculosis [2]. Tuberculosis was a major cause of morbidity and mortality in Canada early in the 20th Century. However, with the introduction of anti-tuberculosis medications in the 1940's and 1950's, the incidence of TB disease declined significantly [3]. However, after decades of continuous decline in TB rates, it has reached a plateau of 6 per 100,000 population, corresponding to about 2000 cases per year [3,4]. Although this rate of TB disease within Canada in global terms is relatively low, within special high-risk groups, rates exceed those seen in many developing countries. In particular, high rates are seen among Aboriginal persons – both on and off reserve as well as among the foreign born and marginalized inner city populations, especially injection drug users [5-8].
With the development of effective treatment strategies, the focus of TB management has shifted from the prevention of mortality to the avoidance of morbidity. As such, there is increased interest in the quality of life (QoL) experienced by individuals being treated for TB [9]. There are numerous aspects of active TB that may lead to a reduction in QoL. Treatment of active TB requires prolonged therapy (at least 6 months) with multiple, potentially toxic drugs that can lead to adverse reactions in a significant number of patients [10,11]. Also, among foreign born patients, there is considerable social stigma associated with active TB leaving the individual feeling shunned and isolated from their friends and families [12-14]. Finally, among Aboriginal and marginalized inner city populations, there is a lack of knowledge regarding the disease process and its treatment which may contribute to feelings of helplessness and anxiety [15-17].
Few studies have examined quality of life in patients with active TB [18,19]. While these studies determined specific decrements in the QoL in these patients, none have included the mixture of patients (marginalized and foreign born) treated within Canada. Therefore, the objective of our study was to identify areas of QoL that are affected by active TB infection using focus groups and individual interviews [20,21].
Methods
Design and Setting
This was a multi-site study involving three TB Centres in Vancouver, British Columbia. Patients were recruited from the TB Clinic at the BC Centre for Disease Control, Willow Chest Pavilion at Vancouver General Hospital and the Downtown TB Clinic. All these clinics are part of the Vancouver Coastal Health Authority. Ethics approval was obtained from the University of British Columbia Behavioural Research Ethics Board and each subject provided signed, informed consent to participate in the study.
Subjects
Subjects with active TB who were receiving treatment were eligible for recruitment into the study. Subjects who were less than or equal to 16 years of age and who did not speak English, Cantonese or Punjabi were excluded (interpreters for non-English language participants who spoke these languages were available).
Procedures
The initial contact was be made by the study nurse at the individual clinics. For those individuals residing in the inner-city region of Vancouver, gender-specific focus groups were planned with 6–8 participants who had active TB. Each participant was reimbursed $25 for their time. Focus group discussions are a variation of interviews designed for the purpose of gathering data about a specific topic from a group of individuals. Each focus group was led by an experienced facilitator.
For participants who came to the TB Clinic at the BC Centre for Disease Control (and those who were hospitalized), individual interview sessions were conducted assessing similar information as obtained in the focus groups. The reason for using individual interviews rather than the focus group approach was two-fold. Firstly, as these individuals often had other commitments (such as work or family care), assessments needed to be done at the time of their appointments and could not be specially scheduled. Secondly, due to the cultural backgrounds, most of these participants did not wish to participate in a group setting in which details of their disease and their feelings were explored. Each interview was conducted by an experienced interviewer in combination, when necessary, with an interpreter fluent in Cantonese or Punjabi.
Specifically, in either the focus group or interview setting, the facilitator began the session with an open-ended, standard question that began all sessions (e.g. "how did you find out you had TB?"). Using open-ended, probing questions, participants were asked to discuss their experiences of being diagnosed with tuberculosis, what impact it had on their lives, issues around adherence to anti-TB medications and information pertaining to their experience with side effects to these medications. Each participant was invited to comment on each question and provide their perspective on the content area. At the end of each session, the facilitator summarized salient points that arose during the discussion and invited further comments and discussion around these points and confirmed agreement.
Data collection and analysis
For all participants, data obtained were audio-recorded, transcribed verbatim, and analyzed. For participants who spoke Cantonese or Punjabi, field notes were kept by the nurse facilitators who are fluent in those languages.
Constant comparative analysis was used as a method to explore and identify patterns and themes that emerged from the data [21]. Various strategies were used to systematically monitor the validity and reliability of the data. Data were analyzed by two individuals experienced with qualitative data and consensus validation was used to confirm categories and the matching of transcribed quotes with categories derived from the analysis. Categories and transcript matching were then reviewed by the focus group facilitator to further ensure that the categories made sense and represented the data they contain. The categories were then collapsed and analyzed for emergent themes.
Results
We conducted two focus group sessions, one with seven male participants and another six female participants; the rest of the participants for the study underwent individual interviews, including 4 hospitalized patients. In total, 39 persons with active TB participated in the study. The demographics of the study participants are described in Table 1. The mean age was 46.2 years (SD 18.4) and 62% were male. Most of the participants were Canadian-born, either white or Aboriginal, while 38% were foreign-born from South-East Asia, South Asia, Latin America and Africa. The majority of participants were interviewed in English (69%) and the rest required either a Cantonese (18%) or Punjabi (13%) translator. For the majority of patients, concurrent illnesses included HIV, Hepatitis B or C. Thirty-six percent of patients drank alcohol or used illicit drugs on a daily basis. The majority of patients was unemployed with an annual income of ≤$15,999 and a mean level of education of 9.2 years of school (SD 3.1).
Table 1 Patient Characteristics
Participants (N = 39)
Mean age, yrs (SD) 46.2 (18.4)
Males, N (%) 24 (62)
Foreign-born, N (%) 15 (38)
Region of origin, N (%)
Canadian – Caucasian 10 (26)
Canadian – Aboriginal 14 (36)
India/Pakistan 5 (13)
South East Asia 8 (21)
South America 1 (2)
Africa 1 (2)
Language used during interview, N (%)
English 27 (69)
Cantonese 7 (18)
Punjabi 5 (13)
Interview/focus group session conducted, N (%)
Outpatient clinic 35 (89)
Hospitalized 4 (11)
Concurrent illness, N (%)
HIV-positive 12 (31)
Hepatitis B or C 12 (31)
Diabetes mellitus 5 (13)
Cardiovascular disease 3 (8)
Cancer 3 (8)
Epilepsy 1 (2)
Alcohol or recreational drug use, N (%)
Alcohol 8 (21)
Drugs 6 (15)
Employment status, N (%)
Full-time 4 (11)
Part-time 5 (13)
Unemployed 20 (50)
Retired 10 (26)
Income
≤$15,999 35 (89)
$16,000 – $39,999 1 (2)
$40,000 – $49,999 1 (2)
≥$50,000 1 (2)
Years of education, mean (SD) 9.2 (3.1)
Marital status, N (%)
Single 14 (38)
Married 11 (28)
Common-law 5 (13)
Divorced 8 (21)
Analysis identified four main themes comprising medication related issues, diagnosis, social support and knowledge of TB. The following text provides a summary of the content of the themes with illustrative quotes in Table 2 and 3.
Table 2 Selected Illustrative Quotes for Theme 1: Diagnosis Issues for TB
Theme 1: Diagnosis Issues
Sub-Theme: Symptoms
Coughing
"I felt tired all the time and had a cough that just wouldn't go away". (Female)
"I had a really bad cough for 3 months and then I started coughing up blood. This made me scared so I went to the doctor". (Male)
Fatigue/weakness
"I just felt tired all the time. I did not have the energy to do anything". (Male)
"I had fatigue and a continuous cough for 6 months. I thought I had persistent flu but then after a while the symptoms got so bad that I went to see a doctor". (Male)
Fever/nightsweats
"I had a fever and chest pain for 1 month; I thought this was pneumonia so I went to see my family doctor". (Male)
"I had night sweats for several months and a fever so after a while I went to see my doctor". (Male)
Asymptomatic
"I did not know I had TB, I was really surprised because I felt really good". (Male)
"I had a general examination and that's when I found out I had TB, otherwise I had no symptoms" (Female)
Sub-Theme: Health Care Provision
Delayed Diagnosis
"I had a friend who was sick with TB in the hospital. I asked my GP to get tested but he did not feel I needed to. Anyway, I was negative but I knew something was wrong so I asked for a chest X-ray. He did not agree at the beginning but finally he did and that's when I found that I had TB". (Female)
Hospitalization
"I came out of a coma from meningitis and that's when they told me I had TB. They threw me in a TB ward at VGH which was worse then a prison. I didn't like the restrictiveness so I took off...the isolation was too much". (Male)
"The only thing to do at the hospital was to eat and sleep. There are no programs there and you are confined in one area". (Male)
"Everyone wears gloves and masks to come and see you, you feel like a leper". (Male)
Sub-Theme: Emotional Impact
Calm, Accepting, or Apathetic
"I was okay about it. I knew people who had this before and so I knew I would be in the hospital for a while but then after taking medicines I would be fine". (Male)
"I felt calm and confident in the medical profession". (Male)
Scared, or Afraid
"I was scared of dying. My Grandma had it and she was in the sanitorium before she died of it". (Female)
Shocked/Surprised, or Devastated
"I was shocked. It was such a surprise because I was working full-time as a nurse in India before immigrating here and I was healthy". (Female)
"I was devastated because I had another illness. I didn't feel that I deserved it". (Female)
Worried/Concerned or Depressed
"I was worried about passing it on to other people". (Male)
"I was depressed because I had a daughter whom I could not see while in hospital". (Female)
Table 3 Selected Illustrative Quotes for Theme 2: Medication Issues for TB
Theme 2: Medication Issues
Sub-Theme: Adverse Events
Gastrointestinal Disturbances
"I had lots of vomiting after I started taking the pills and didn't have any appetite". (Male)
"I have to eat before I take my pills, if I don't then I feel sick and my stomach hurts". (Male)
Itchiness
"I felt itchy all over and was told to take benedryl but that made me really sleepy". (Male)
"I had lots of itchiness when I first started taking the pills but it is better now and I put lotion on my skin". (Female)
"I was so itchy with one of the pills that I could not sleep all night long for days". (Female)
Sub-Theme: Ease of Administration
Size of Medication
"I felt physically sick because of the size of the pills; they are too big". (Female)
"The pills are so big, it is hard to swallow them". (Female)
"I feel nauseated when I take the pills because they are so large". (Male)
"I can't swallow those white pills; I need to crush then otherwise I vomit it back up". (Female)
Number of Medications
"I thought that many tablets a day ...it is not possible to take on an empty stomach". (Male)
" There were too many pills to take at once, especially at the beginning but now it is much better with just six to take in a day". (Female)
Sub-Theme: Compliance
Clinic-based patients
"I was taking other pills so it was easy to take the TB medications too". (Female)
"I did not forget to take my pills because I want to get better". (Female)
"I understand the importance of taking the tablets so I do not forget; I take them in the mornings, half-hour before my breakfast". (Male)
"I place it in my container the night before so that I remember to take it the next day". (Male)
Inner-city Patients
"I always take my pills since I get them with my methadone everyday". (Female)
"The [street] nurse always finds us and gives me the medications". (Male)
"If I've been drinking too much then sometimes I don't know what the time is". (Male)
"If I'm picking empty cans and bottles on the other side of town, it's hard to get to [street nurse name] to get my pills every day". (Male)
"I missed taking some pills because I was drunk or high on drugs". (Female)
Theme 1: Diagnosis issues
This theme describes issues related to the diagnosis of tuberculosis (Figure 1). Sub-themes were identified as 'symptoms', 'health care provision', and 'emotional impact'.
Figure 1 Main themes and sub-themes related to tuberculosis as identified through transcribed focus groups
Symptoms
Thirty-five quotes pertained to symptoms experienced by the participants at the time of diagnosis. Of these, 19 were related to specific symptoms whereas 16 participants expressed the view that they were asymptomatic at the time of being diagnosed. The most common symptoms experienced by the respondents were cough (n = 13), fatigue/weakness (n = 5), fever/night sweats (n = 5) and shortness of breath (n = 4). Most patients sought medical attention due to cough or "pneumonia-like" symptoms and feelings of general malaise. For example, one patient stated "I was coughing up harsh yellow stuff"; while another stated "I started to feel real tired and had a cold that just wouldn't go away".
Four patients expressed that they were "unsure" how they acquired TB. For example, a woman stated "I didn't even know I had it. I was surprised 'cause I felt real good". Other illustrative quotes are shown in Table 2.
Health care provision
Most comments, related to the provision of health care at or around the time of diagnosis, were related to community health care providers and their initial hospitalization. Many patients expressed frustration with the health care system at their time of diagnosis due to lack of provider knowledge with respect to tuberculosis. Many patients either felt that they had a delayed diagnosis or delayed treatment due to issues related to their health care provider. For example, on male patient stated, "Family physicians should know more about this disease...where to refer patients to. This is an old disease". Another patient said, "Although my GP gave me a diagnosis, he told me to wait for treatment. We were concerned and phoned the British Columbia Lung Association who referred us to the TB clinic". Another stated, "I contracted a flu-type infection with fever. My GP said go home and take Tylenol but my symptoms continued so I went to see the locum who told me to take Advil. Then I started non-stop coughing. I asked my GP to get an X-ray but he flatly refused."
Many participants reported negative experiences with their initial hospitalization after being diagnosed with TB. Specifically, they stated feelings of isolation, rejection and boredom (Table 2). No participant gave a positive report about the initial hospitalization experience however one 30 year old male participant stated "I had no negative feelings about my hospital stay but it hurt my financial situation...but I knew I had to be there. There are laws against TB."
Emotional impact
Thirty-five quotes pertained to emotions experienced by the participants at the time of diagnosis. Of these, patients expressed a wide range of emotions from being calm, accepting or apathetic (n = 11), scared or afraid (n = 7), shocked or surprised (n = 6), "devastated" (n = 4), worried or concerned (n = 4), and depressed (n = 3). Representative quotes for these emotions are presented in Table 2.
Of the individuals expressing apathy or calmness related to the diagnosis, many expressed that TB was just another disease to contend with on top of other chronic conditions. For example, one patient stated "Well, it is like HIV. It is in my system. What can you do?" Another person with terminal cancer stated "I had no reaction to the diagnosis. I am more concerned with the spread of my cancer and that I don't have long to live anyhow".
Of those expressing concern, there were two distinct reasons cited for this emotion: 1) concern for themselves as they knew relatives or friends who had previously been infected with TB and had experience prolonged hospitalization or death; and 2) concern for others in terms of passing the disease on to family and friends. For example, a male patient said "I was kind of scared because the only person I knew who had TB died of it. Also, I was worried about other people catching it from me". Another woman stated "I was scared. It is like an old disease and I know when you have it, it is not very nice to have it, especially because I have a seven month old baby."
The individuals who expressed shock and surprise at the diagnosis attributed these emotions to their lack of symptoms. As such, they had not expected a diagnosis such as TB when they have visited their health care provider despite having other diseases such as HIV (see Table 2).
Theme 2: Medication issues
This theme discusses the most important factors with respect to medications for the treatment of TB. (Figure 1). As such, the sub-themes identified were "adverse effects", "ease of administration", and "adherence".
Adverse events
There were thirty-nine comments related to adverse events experienced by taking the medications. Most of these were related to specific symptoms that were thought to be related to taking specific drug therapies. The most common complaints were related to gastrointestinal disturbances (nausea, vomiting and diarrhea) and itchiness due to isoniazid. Despite having adverse events, patients stated that they continued to take their medications. For example, one female patient said, "There is nothing you can do. You have to just continue". Representative quotes from these participants are included in Table 3.
Ease of administration
Most comments related to the dose and dosing schedule pertained to the size (n = 3) and number of tablets/capsules (n = 7). For example, patients felt that the large size of some of the dosage forms (such as ethambutol and rifampin) led to gagging and vomiting. In addition, many patients expressed consternation at the number of pills that they had to take with each dose. For example, one patient said, "When I looked at ten tablets, I thought, on an empty stomach, I cannot". Representative quotes from these participants are included in Table 3.
Compliance
Individuals living in the inner city of Vancouver, expressed little concern for compliance-related issues as they either picked up the anti-TB medications with their methadone or the Street-Nurses would find them daily to administer the medications. As an example, one patient stated "It comes with my methadone. When I get that, I get my TB pills". Another stated, "I never worry about it. I know [the Street Nurse's name] will bring it to me". However, despite high compliance in these patients, several identified alcohol (n = 13) or other illicit drug use (n = 8) as being the reason why they had missed doses.
Those who came to the TB treatment clinic expressed high compliance due to the perceived gravity of the diagnosis. For example, one woman patient stated, "It is easy to remember because it is at the fore-front of mind. I want to get rid of it". Another person attributed compliance to the law: "I never forget to take my pills because I don't want to go to jail". Representative quotes from these participants are included in Table 3.
Theme 3: Social support and functioning
This theme describes social support and functioning issues for the individuals with TB (Figure 1). Specifically, the impact on their relationships with family, friends and peers was affected by TB. In addition, social functioning was impacted through the ability to interact with friends and family as well as engaging in social and leisure pursuits.
Most participants expressed that their family and friends were aware of their TB diagnosis (n = 18), while 11 stated that only their friends knew and 10 stated that only their immediate family knew. Of those who stated that only their friends were aware of their diagnosis, most of these persons residing in the inner city noted that they did not have family with whom they communicated. Representative quotes from these participants are included in Table 4.
Table 4 Selected Illustrative Quotes for Theme 3 and 4: Social Support and Health Behavior Issues for TB
Theme 3: Social Support and Functioning
Sub-Theme: Social Support
Clinic-based patients
"My family knows and they comforted me so I felt much better". (Male)
"My wife was calm about it and this gave me support". (Male)
"Mom was concerned for me since her grandmother had died of TB". (Female)
Inner-city Patients
"My friends stayed away when they found out, they thought I was contagious. I tried to tell them but still I did not see them again". (Male)
"My friends do no want to hang around me. It's the fear of the unknown...they just know it's airborne and contagious". (Male)
"My partner is okay with it because she has TB too". (Male)
"I don't have any family except my aunt but she was scared to come and see me because she has two children". (Female)
Theme 4: Health Behavior
Sub-Theme: Behavior Modification
Clinic-based patients
"I run more. I was always a runner. The endorphins help". (Female)
"Vitamins might interact with my medications so I don't take them" (Male)
Inner-city Patients
"I eat better... although with my income, this is difficult". (Male)
"This diagnosis was a wake-up call to change my lifestyle. I now eat better and sleep lots" (Male)
"I have been drinking more booze to help manage the side effects of the medications". (Female)
"I drink bottled water and avoid tap water due to my depressed immune system" (Male)
Sub-Theme: TB Knowledge
Clinic-based patients
"It is important to be cured but you can't get it again" (Female)
"It is very important to get cured...if you aren't cured, you could die" (Male)
"Family doctors should know more about TB. They didn't know what to do with respect to breastfeeding and TB. There really needs to more public education" (Female)
"I don't think I have TB. My doctor told me I have it and now I have to take medications but I am not sure that I really have it" (Male).
Inner-city Patients
"Once you get TB, it's in your system" (Male)
"As long as TB can be arrested ...not necessarily cured, that would be OK (Female)"
"People should be more active in spreading the word on the street that TB is still out there...there has to be more outreach programs" (Male)
However, one individual stated that she was "secretive because other people will feel that I am contagious". Another male participant stated a reluctance to tell his friends because "I do not want go cause mass hysteria". A school-age boy did not tell his friends out of fear of being shunned. In addition, he missed eight weeks of school and had to retake several courses. In one instance, fear of being shunned in a Punjabi speaking participant was instilled by the treating physician ("I was told by Dr. [physician's name] that if my community knew, it would empty out the hall [referring to the religious prayer hall]". Another reported that his family "told me not to take my pills anymore because they make me sick" despite being very supportive and understanding regarding the diagnosis. A Cantonese-speaking male stated that "I will be happier once I am cured. Then, I can go out to restaurants and public places again."
There were 39 comments related to the reactions of friends and/or family members to the participants' diagnosis of TB. Of these, most could be categorized as supportive or concerned (n = 21), although others had negative feelings such as fear (n = 7), shock or disbelief (n = 8), and anger (n = 3). One participant stated "my mom was really concerned but my friends did not believe it...they encouraged me to get the right diagnosis". Another stated that her partner had increased his reading on TB and was receiving regular skin tests although her mother and brother would not talk about the TB diagnosis or the clinic visits. Another stated that since his partner was not understanding about modifying his lifestyle, he was forced to end the relationship with her and move out. Representative quotes from these participants are included in Table 4.
Theme 4: Health behavior
This theme describes health behavior issues for the individuals with TB (Figure 1). Sub-themes were identified as "behavior modification" and "TB knowledge."
Behavior modification
When asked if they had done anything else beyond medications to help manage their TB, about half of the participants stated that they had done nothing in particular (n = 16). For example, a Cantonese speaking female stated "I do nothing special as TB is very common". However, of the 12 participants who stated that they had changed their health behavior, seven said that they consumed a healthier diet, four stated that they exercised more, three took vitamin supplements specifically to help their TB, and two used less illicit drugs and alcohol. Representative quotes from these participants are included in Table 4.
TB knowledge
In response to the question "do you believe that you will be cured of TB?", most participants (n = 33) stated that they believed that they would be eventually cured. However, some individuals believed that their TB would never be cured ("I believe that I can keep it in remission but it can't be cured") while others were not sure if it could be cured. Two participants denied having TB despite being informed by health care providers and taking medication. There were comments regarding the participants' impressions of provider knowledge of which a representative sample have been included in Table 4.
Discussion
This qualitative study has revealed that TB has a large impact on affected individuals' QoL through issues related to its diagnosis, treatment, social support and functioning, and health behavior. Specifically, we found that the domains of QoL that were affected by TB included those that are typically affected by most illnesses such as physical functioning and emotional/mental well-being. However, TB patients' social functioning was also affected through isolation, variable social support by family and friends, and the ability to continue with social and leisure activities. Also, the process of getting treatment for TB from the initial hospitalization to the daily medication schedules adversely affected the lives of our participants, although, almost all recognized the need for appropriate treatment.
Although other studies [22,23] have explored patients' attitudes and knowledge regarding TB, we identified only one other study [24] where general health perceptions of patients with TB were investigated. Similar to ours, this study also involved the use of focus groups to elicit areas of QOL affected by TB and many of their results were in general agreement to ours. For example, as with our study, these investigators found that physical functioning, social functioning, and role functioning were all adversely affected by TB. In addition, the participants reported a wide range of psychological reactions including fear, depression and anger. Finally, both studies found a number of comments regarding the difficulties of treatment including those related to the size, number and frequency of dosing of the medications.
However, there were some important differences between our two studies. For example, these investigators included only 10 English speaking patients from the Baltimore city area and 13 health care providers whereas ours included non-English participants through the use of interpreters, a much large sample of patients (n = 39), but no health care providers. In addition, we included hospitalized and ambulatory patients from both inner city and public health clinic environments in order to assess the full spectrum of patients afflicted with this disease. Finally, in our study, all patients had active TB and were receiving treatment at the time of the interviews unlike the Baltimore study who recruited patients who were already cured and had completed treatment. We believe that our methodology of interviewing currently afflicted patients might have minimized recall bias although one potential advantage of the Baltimore approach was determining long-lasting influences of TB on patients lives (the investigators received 17 comments in this regard). Also, the use of health providers in the Baltimore study added an interesting perspective with the provision of comments that were, at times, in direct contrast to those stated by patients with respect to the effects of TB on health related quality of life. For example, most physicians underestimated the impact that TB had on the QoL of patients assuming that, because it was curable, its detrimental effects would be minimal.
These differences in design between the two studies might have accounted for some different findings. For example, the Baltimore study found that the financial well-being of some of the participants was adversely affected through loss of income and health care expenses whereas participants in our study did not report this issue (although this might be attributable to the different health care environments that exist between the two countries in which the studies were conducted or differences in employment status between the two samples). Also, some of the male participants in the Baltimore study reported sexual dysfunction whereas this concern was not reported during our interviews. Finally, patients in the Baltimore study reported spirituality as an important domain which we did not identify as an important theme, perhaps due to the different ethnic/religious make-up of our sample.
One surprising aspect of our results was the negative feelings associated with TB diagnosis and the initial hospitalization. Some participants expressed frustration with their primary care physicians for the lack of a prompt diagnosis or inappropriate management. There was a common perception among many of the participants that health care providers needed more extensive education regarding TB. We have recently commented on the need to consider TB as a diagnosis and in the appropriate setting, consider the initiation of empiric TB treatment [25]. In addition, participants complained of boredom, frustration and isolation with their initial hospitalization. These modifiable factors should be the focus on future improvements in the diagnosis and treatment of TB.
Despite several negative comments regarding the size, dosing schedule and adverse effects of the anti-TB medications, most patients specified that they understood the need for treatment. As such, self-reported compliance was very high and participants reported a variety of different strategies to help manage adverse events. Our inner-city participants expressed gratitude for the street nurses who delivered their medications to them on a regular basis and did not report the intrusiveness and imposition on lifestyle that has been associated with similar programs (such as directly observed therapy or DOT) in other studies [24,26].
Although most comments were related to adverse impacts of TB on QOL, some participants stated that acquiring TB had resulted in positive health behavior modification. Many participants took the development of TB to be a "wake-up" call to change their lifestyle and improve their health behavior by either eliminating or reducing drug and alcohol intake, increasing exercise, or eating better. These findings were also reported by the Baltimore study group suggesting that the positive health behavior impacts of this disease might be widespread throughout those afflicted with TB in North America. Because many of those afflicted with TB in North America engage in other high-risk behaviors such as use of illicit drugs, the overall effects of this health behavior modification might be significant. Future studies should attempt to quantify this impact on the downstream development of other conditions. Although hospitalization for management of TB has negative aspects we have noted that this interlude in subjects with a history of substance abuse allows access to chemical dependency treatment resources while away from their usual chaotic environments.
Although some studies in other countries have shown that TB can result in job loss, participants did not report that this had occurred in our sample [27,28]. One possible reason for this observation could be due to low rate of employment in our sample with only 26% being employed full or part-time.
Our study had some limitations. We examined a self-selected group of TB patients who may not be representative of the entire population in Canada affected by TB. For example, in British Columbia, foreign-born persons account for close to 70% of all TB cases in the province. Despite this, we feel that we were able to get a representative sample of foreign-born persons (almost 40% of our sample was foreign born) as well as a good cross section of marginalized inner city patients [7,29]. In fact, because we attempted to select individuals from different socioeconomic groups (inner city patients vs. those voluntarily attending a public health clinic) and from different ethnic backgrounds (foreign-born, aboriginal-Canadian and other Canadian), we believe that the responses that we received are likely indicative of the areas of QoL which are affected by TB.
Conclusion
Our study indicates that despite the ability to cure TB with medical therapy, there still remains a sizeable impact on the lives of afflicted patients. Since much of the current attention on TB is spent on preventative or curative mechanisms such as drug therapy, the impact of this condition on QoL is either underestimated or rarely considered. In order to fully evaluate the outcomes that are achieved through TB prevention and treatment, QoL of these patients must be considered. Further studies need to build upon these observations and instruments need to be developed to better characterize QoL in patients with this disease. This process will not only provide an added parameter to evaluate the effectiveness of a given program, but will also focus care providers to be attentive to the non-medication aspects of TB management.
Authors' contributions
CAM conceived of the study, participated in the design, analysis and co-wrote the initial version of the manuscript. FM conceived of the study, obtained funding, participated in the interviews and focus groups, participated in the analysis, coordinated research staff, and co-wrote the initial version of the manuscript. VC participated in the interviews and focus groups and participated in the analysis. AP participated in the design of the study, and the interviews and focus groups. JMF participated in the design and analysis of the study. All authors read and approved the final manuscript.
Acknowledgements
This study was funded by a competitive research grant from the Canadian Society of Hospital Pharmacists. Dr. Carlo Marra is a Canadian Arthritis Network Scholar. Dr. Palepu is a Michael Smith Foundation for Health Research Senior Scholar and a recipient of a Canadian Institute of Health Research (CIHR) Investigator Award. Dr. FitzGerald is a BC Lung CIHR Scientist and a Michael Smith Foundation for Health Research Distinguished Scholar.
We would like to thank Dr. Anita Hubley for assistance with the initial design and analytic methods for the study. In addition, we would like to acknowledge the TB program nurses at the clinics (Ms. Shelley Dean, Ms. Nashreen Dhalla and Mr. Greg Stark) for aid in patient recruitment and helping to set-up the focus group sessions. Finally, we would like to thank Ms. Surita Jassal and Mr. Ajay Puri for assistance with the interviews and the focus groups.
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| 15496227 | PMC526389 | CC BY | 2021-01-04 16:38:12 | no | Health Qual Life Outcomes. 2004 Oct 20; 2:58 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-58 | oa_comm |
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Epidemiol Perspect InnovEpidemiologic perspectives & innovations : EP+I1742-5573BioMed Central London 1742-5573-1-41550713010.1186/1742-5573-1-4MethodologyA further critique of the analytic strategy of adjusting for covariates to identify biologic mediation Kaufman Jay S [email protected] Richard F [email protected] Sol [email protected] Department of Epidemiology, University of North Carolina School of Public Health, Chapel Hill, NC 27599-7435 USA2 Department of Otolaryngology, University at Buffalo, 3435 Main Street, Buffalo NY 14214 USA2004 8 10 2004 1 4 4 28 6 2004 8 10 2004 Copyright © 2004 Kaufman et al; licensee BioMed Central Ltd.2004Kaufman et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Epidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition: the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect. This contrast is typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediates.
Methods
We apply a causal framework based on latent potential response types to describe the limitations inherent in effect decomposition analysis. For simplicity, we assume three measured binary variables with monotonic effects and randomized exposure, and use difference contrasts as measures of causal effect. Previous authors showed that confounding between intermediate and the outcome threatens the validity of the decomposition strategy, even if exposure is randomized. We define exchangeability conditions for absence of confounding of causal effects of exposure and intermediate, and generate two example populations in which the no-confounding conditions are satisfied. In one population we impose an additional prohibition against unit-level interaction (synergism). We evaluate the performance of the decomposition strategy against true values of the causal effects, as defined by the proportions of latent potential response types in the two populations.
Results
We demonstrate that even when there is no confounding, partition of the total effect into direct and indirect effects is not reliably valid. Decomposition is valid only with the additional restriction that the population contain no units in which exposure and intermediate interact to cause the outcome. This restriction implies homogeneity of causal effects across strata of the intermediate.
Conclusions
Reliable effect decomposition requires not only absence of confounding, but also absence of unit-level interaction and use of linear contrasts as measures of causal effect. Epidemiologists should be wary of etiologic inference based on adjusting for intermediates, especially when using ratio effect measures or when absence of interacting potential response types cannot be confidently asserted.
effect decompositioncausalityconfoundingcounterfactual modelsbias
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1. Introduction
A large portion of epidemiologic research is devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are sought by researchers and are applied frequently in their work. Unfortunately, some of these techniques have been found to provide far more ambiguous evidence on which to base mechanistic conclusions than was first believed. For example, analysis of patterns of joint effects has been proposed as a means of identifying causal structure [1], but simple counterexamples show that in general the underlying etiologic model cannot be readily identified[2]. Typically, some method is proposed under a sound theoretical argument in a specific analytic setting, but this method is subsequently applied in a more general context in which those specific theoretical conditions no longer hold. For example, Greenland and Poole[3] provide a rational justification for deviation from additive joint effects as the benchmark for identifying mechanistic interaction between two factors [[4], pp. 332–339]. But this argument is not generally valid as is often assumed; it doesn't hold for all causal structures and target populations[5].
The list of such untenable overgeneralizations in epidemiologic practice is surely large and varied, and has led to any number of false conclusions and misunderstandings. We describe here one particular epidemiologic technique that is applied frequently in practice, and yet is invalid in all but a surprisingly narrow range of circumstances. It is a remarkable example in that the analytic strategy is exceedingly common, and yet is described infrequently in epidemiologic texts or methodologic articles. The few textbook citations that do exist provide no formal justification, and therefore there is little guidance available from within the sources in our field to guide users and warn them of important limitations of this approach. This situation motivates the present article, in which we will show that although widely applied, this analytic approach is almost never justifiable on the basis of reasonable assumptions about the data.
The methodologic approach of interest in this article is the decomposition of effects purportedly accomplished by contrasting two adjusted effect estimates for the exposure of interest: an estimate adjusted for potential confounders, and an estimate adjusted for the same set of potential confounders plus one or more additional variables hypothesized to be causal intermediates, i.e., to lie on pathway(s) through which the exposure exerts its effect. This contrast is then typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediate variables. If control of hypothetical causal intermediates greatly attenuates an exposure's estimated effect, it is generally inferred that the exposure's effect is mediated primarily through pathways involving these quantities; a small degree of attenuation is interpreted as evidence that other pathways predominate. These mechanistic inferences then inform policy recommendations concerning the utility of potential interventions. Although this effect decomposition approach is quite common in the epidemiologic literature, its general validity has not been adequately investigated.
This analytic strategy for effect decomposition in epidemiologic research is recommended by Susser [[6], pp. 121–124], and more recently by Szklo & Nieto [[7], pp. 184–187]. The latter authors quantify the degree of mediation as follows:
" The degree to which a given mechanism...explains the relationship of interest is given by the comparison of adjusted (A) and unadjusted (U) measures of association (e.g., a relative risk, RR). This comparison can be made using the ratio of the unadjusted RRs, RRU/RRA, or the percent excess risk explained by the variables adjusted for: ".
Calculations similar to this "% Excess Risk Explained" are the most common framework for describing the effect decomposition analysis in epidemiologic research.
For example, a study by Heck and Pamuk investigated the relation between education and postmenopausal breast cancer incidence using data from the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study[8]. Proportional hazards modeling was used to estimate the relation between breast cancer incidence and education level. The authors then reported that reproductive factors including nulliparity were found to mediate this relation. This assertion was based on the observation that adjustment for these factors reduced the magnitude of the positive relation between education level and risk of postmenopausal breast cancer. Furthermore, because the association between exposure and outcome was no longer statistically significant after adjustment for the putative mediators, the authors concluded that "the association between higher education and increased risk of breast cancer appears to be largely explained by differences in the known risk factors for breast cancer" [[8], p. 366]. This methodology is commonly applied, and therefore there are many similar examples in the published literature. On the basis of this approach numerous authors have made many similar mechanistic claims about mediation, for example, that blood pressure mediates the causal relation between homocysteine and cardiovascular risk[9], that behavioral risk factors mediate the causal relation between hostility and incident myocardial infarction[10], and that the protective effect of gene CCR5 heterozygosity on clinical AIDS occurrence is completely mediated through an effect on CD4 cell count[11]. The results of decomposition analyses are also frequently used to anticipate the impact of a potential intervention or policy related to the intermediate variable(s). For example, Lantz and colleagues adjusted for several measured behavioral intermediates in assessing the relation between income and mortality[12]. They noted that even after adjustment for these measured intermediates "the risk of dying was still significantly elevated for the lowest-income group (hazard rate ratio, 2.77; 95% CI, 1.74–4.42)..." and on this basis they offered the conclusion that "socioeconomic differences in mortality ...would persist even with improved health behaviors among the disadvantaged." [[12], p. 1703]
In a seminal article on the topic, Robins & Greenland[13] employed a causal framework based on latent potential response types in order to describe the limitations inherent in the effect decomposition analysis. Subsequent authors have for the most part focused on the Robins & Greenland finding that direct effect estimates, decomposed from the total effect by adjusting for an intermediate, may be biased if there is unmeasured confounding between the intermediate and the outcome [14-16]. As Robins & Greenland showed and these later authors reiterated, the decomposition strategy may fail even when the total effect is unconfounded. While this consideration is important, this is not our concern in the present discussion. Rather, we will show that even when there is no confounding of any relevant causal effect, the decomposition strategy will still generally fail, in the sense that a contrast such as that described above as the "% Excess Risk Explained" will fail to provide an unbiased estimate of the proportion of the causal effect that is relayed through the intermediate.
Our critique would appear to contradict standard practice in the social sciences, in which decomposition analysis is also commonly applied[17,18]. We suggest two explanations for this state of affairs. The first is that the development of the decomposition methodology by Wright[19] and other pioneering social science statisticians did not make use of an explicitly casual framework, but rather was derived algebraically from linear regression theory. One consequence is that the causal assumptions necessary for the model to be substantively meaningful were not readily apparent until the advent of a notational system for potential outcomes[20]. Secondly, we suggest that this is another example in which unwitting users have extrapolated a technique beyond the strictly defined original set of assumptions without assessing the impact of this extrapolation on the validity of the estimation. In this case, assumptions imposed in the original development of the decomposition methodology involved additivity of effects and linear contrast measures, neither of which are typical of the analysis of discrete events, such as occurrence of disease. Epidemiologists as well as others have generally been remiss in failing to attend to these crucial assumptions when applying these techniques more broadly. However, as we will describe, the causal assumptions required for the validity of the decomposition method are not verifiable from observed data, and furthermore are unrelated to any typical substantive knowledge. It may therefore be essentially impossible to apply this methodology with any confidence in a real-world analysis of data.
2. Framework, Notation and Causal Structure
For clarity, we limit our exposition to the simplest possible decomposition problem, which is the structure that includes three measured binary variables and sample size sufficiently large to justify the assumption of zero sampling error (see endnote 1). The three variables are designated as X, Y and Z. The causal relationships between these nodes are described by the directed acyclic graph (DAG)[21] in Figure 1. X is a randomly assigned (i.e., exogenous) treatment and therefore there are no arrowheads terminating at this node in the graph. X takes the value of 1 if treated, 0 otherwise. Y equals 1 if the outcome occurs, and 0 otherwise. Z takes the value of 1 if the intermediate occurs, and 0 otherwise, and like X is manipulable (i.e., may be fixed through external intervention to take either level). The framework adopted here is a deterministic counterfactual model in which each individual unit in the population is assumed to have a fixed potential response to each possible input pattern at each endogenous node of the DAG. As such, the observed data reveal only a subset of these fixed potential responses. We also assume that the potential responses of each unit do not depend on the treatments assigned the other units, which is referred to by Rubin as the "stable-unit-treatment-value assumption" (SUTVA)[22].
Figure 1 Decomposition of Total Effect of X on Y into Direct and Indirect Effects. The total average causal effect (ACE) of X on Y is achieved through two pathways, one which is termed "indirect'' because it operates through measured intermediate variable Z, and another that is termed "direct'' because it operates through no measured intermediates.
The potential response variable for unit u at node Z is denoted by Zux where index u identifies the individual unit and index x specifies the X value factually or counterfactually experienced by that unit. Given the deterministic model at the individual unit level, there are four possible patterns of response Zux to input x that unit u can exhibit, and these have received various appellations in the literature, such as "doomed" for Zux= 1 regardless of x, "causal" for Zux= x, "preventive" for Zux= 1-x, and "immune" for Zux = 0 regardless of x[23]. These four patterns may be represented by potential response type index values of 1, 2, 3, and 4, respectively, such that each unit in the population is classified by one of these four index values.
At the endogenous node Y, the counterfactual or potential response variable for unit u is denoted by Yuxz, where u identifies the individual unit and indices x and z specify the X and Z inputs to that unit. Conditional on the individual unit and conditional on the z input, there are four possible patterns of response Yuxz to input x: Yuxz = 1 regardless of x, Yuxz = x, Yuxz= 1-x, and Yuxz = 0 regardless of x. Therefore, each unit can be fully characterized by one of the 4 × 4 × 4 = 64 possible values of three indices, {ijk}, where index i specifies the Zux response, index j specifies the Yux0 response, and index k specifies the Yux1 response. As an illustration, {123} refers to a unit in which Z will equal 1 regardless of the value taken by X. Under this naturally occurring outcome for Z, Y will equal 1-x. However, if Z were to be manipulated by external intervention to equal 0, then Y would equal x.
In this way, the 64 possible potential response types for individual units in the population are symbolized by {ijk}; i = 1,...,4; j = 1,...,4; k = 1,...,4. We define qijk to be the proportion of type {ijk} in the total population. Furthermore, because X is exogenous, the potential response types occur in these same proportions inboth X = 0 and X = 1 subpopulations. The set of all 64 qijk proportions determines the causal behavior of the population in the context of the three observed variables (X, Y, Z) and potential confounding of the causal effects between them. The values of the 64 qijk proportions, however, are not identified from the 8 observed proportions in the study population: Pr(Y = y, X = x, Z = z); x = 0,1; y = 0,1; z = 0,1.
We make a further simplifying assumption of (strong) monotonicity for the remainder of this paper (see endnote 2). This assumption states that there are no individuals who exhibit preventive effects at either endogenous node. That is, for all units u and for z = 0,1 and x = 0,1:
Zu0 ≤ Zu1
Yu0z ≤ Yu1z
Yux0 ≤ Yux1
Since the binary values can be arbitrarily coded, the monotonicity of effects can be in any direction (i.e., preventive or causative, since reversing the coding is equivalent to interchangebetween subscript values 2 and 3 and between subscript values 1 and 4). This assumption reduces the number of potential response types in the population from 64 to 18, and may be reasonable on substantive grounds. For example, consider X to be assignment to cholesterol lowering drug cholestyramine versus placebo in the Lipid Research Clinics (LRC) Primary Prevention Trial[24], Z = 1 to be absence of hypercholesterolemia one year after initiation of the cholestyramine, and Y = 1 the absence of coronary heart disease (CHD) at follow-up. In this example, there are no individuals for whom assignment to cholestyramine (X = 1) will cause hypercholesterolemia (Z = 0), nor individuals for whom assignment to cholestyramine or absence of hypercholesterolemia will cause CHD (Y = 0). Note that monotonicity eliminates not only types {3jk}, {i3k} and {ij3}, but also types {i12}, {i14} and {i24}. This is why the assumption reduces the potential outcome patterns not merely to 3 × 3 × 3 = 27, but rather to (3 × 3 × 3)- (3 × 3) = 18. Complete descriptions of the 18 potential outcome types that occur under monotonicity are provided in the first seven columns of Table 1.
Table 1 Potential Response Type Characteristics Under Monotonicity Assumption (18 Response Types)
a b c d
Response of Y to fixing X to value: Response of Y to fixing X and Z to values: Contributes to:
Potential Response
Type Representation† X = 1 X = 0 X = 1
Z = 0 X = 0
Z = 0 X = 1
Z = 1 X = 0
Z = 1 Total
Effect Direct Effect in
(Z-stratum) Indirect Effect in
(Z-stratum)
{111} 1 1 1 1 1 1
{141} 1 1 0 0 1 1
{211} 1 1 1 1 1 1
{122} 1 0 1 0 1 0 + + (0,1)
{241} 1 0 0 0 1 1 + + (0,1)
{222} 1 0 1 0 1 0 + + (0,1)
{411} 1 1 1 1 1 1
{422} 1 0 1 0 1 0 + + (0,1)
{144} 0 0 0 0 0 0
{244} 0 0 0 0 0 0
{441} 0 0 0 0 1 1
{444} 0 0 0 0 0 0
{121}* 1 1 1 0 1 1 + (0)
{221}* 1 0 1 0 1 1 + + (0) + (1)
{421}* 1 0 1 0 1 1 + + (0)
{142}* 1 0 0 0 1 0 + + (1)
{242}* 1 0 0 0 1 0 + + (1) + (0)
{442}* 0 0 0 0 1 0 + (1)
* Unit-level interaction (interdependence) present because (a-b) ≠ (c-d)
† Potential response type representation indices are: 1 = "doomed", 2 = "causal" and 4 = "immune"
Index i of the {ijk}representation specifies the Z[X = x] response, index j specifies the Y[X = x; Z = 0] response (columns a and b), and index k specifies the Y[X = x; Z = 1] response (columns c and d).
3. Definitions of Causal and Associational Parameters of Interest
The total average causal effect (ACE) of the treatment X in the population is the proportion of all individuals in the population who would experience outcome Y if they were treated, but not if they were untreated, without regard to Z. Given the monotonicity assumption, this effect is the sum of 8 of the 18 potential response type proportions in the population:
ACE[X→Y] = average causal effect
= Pr(Y = 1|SET[X = 1]) - Pr(Y = 1|SET[X = 0])
= (q122 + q241 + q222 + q421 + q422 + q221 + q142 + q242)
The average causal (controlled) direct effect (ACDE) of the treatment X in the population is the proportion of individuals who would experience outcome Y if they were treated, but not if they were untreated, if Z were forced (SET) to have a specific value z (thus blocking any indirect effects). In general, there is no reason for this effect to take the same value if Z were forced (SET) to 0 as it would take if Z were forced (SET) to 1, and so for binary Z in our DAG there are two distinct average causal direct effects. Given the monotonicity assumption, these effects are the sums of 6 of the 18 potential response type proportions in the population:
ACDE[X→Y] | SET[Z = 0] = average causal direct effect for Z forced (SET) to 0
= Pr(Y = 1|SET[X = 1,Z = 0]) - Pr(Y = 1|SET[X = 0,Z = 0])
= (q122 + q222 + q422 + q121 + q221 + q421)
ACDE[X→Y] | SET[Z = 1] = average causal direct effect for Z forced (SET) to 1
= Pr(Y = 1|SET[X = 1,Z = 1]) - Pr(Y = 1|SET[X = 0,Z = 1])
= (q122 + q222 + q422 + q142 + q242 + q442)
A manipulative definition of the total average causal indirect effect, ACIE[X→Y], is not straightforward, and some authors assert that no general definition exists [e.g., [21], p. 165]. The usual interpretations granted to applications of effect decomposition methodology imply that analysts take ACIE[X→Y] to mean the proportion of all individuals who would experience outcome Y if they were treated, but not if they were untreated, but only via the pathway in which X has an effect on Z and then Z has an effect on Y. In this causal mechanism, therefore, external intervention to hold Z fixed will prevent X from having any effect on Y. Of the 18 potential response types that exist under the monotonicity assumption, clearly {241} corresponds to this conceptual definition. In units of this type, Z = X. But were Z to be blocked from occurring (i.e., SET to Z = 0) by external intervention, then Y = Z = 0, regardless of X. Alternatively, if Z were to be forced to occur (i.e. SET to Z = 1) by external intervention, then Y = Z = 1. For potential response types {242} and {221}, however, the common-sense meaning of an indirect effect may also apply, depending on the specific intervention applied to Z. Specifically, if the external intervention on the intermediate is SET[Z = 0], then potential response type {242} is an indirect type, whereas if the external intervention on the intermediate is SET[Z = 1], then potential response type {221} is an indirect type (Table 1). This is the ambiguity that has made it difficult to provide a general manipulative definition of the ACIE[X→Y] without prohibiting these interacting types, as we do in Section 5.
We can also define the value of the total average causal effect (ACE) of the treatment X on the intermediate covariate Z, which is the proportion of individuals who would experience intermediate Z if they were treated, but not if they were untreated. Given the monotonicity assumption, this effect is the sum of 6 of the 18 potential response type proportions in the population:
ACE[X→Z] = average causal effect of X on Z
= Pr(Z = 1|SET[X = 1]) - Pr(Z = 1|SET[X = 0])
= (q211 + q241 + q222 + q244 + q221 + q242)
Because the value of Y is determined through the joint effects of X and Z, it is also possible to define the effect of Z on Y as the proportion of individuals who would experience outcome Y if Z were forced (SET) to 1, but not if Z were forced (SET) to 0, conditional on X = x. In general, there is no reason for this effect to take the same value in the X = 0 subpopulation as it does in the X = 1 subpopulation, and so for binary X in our DAG there may be two distinct effects of Z on Y given strata of X. Given the monotonicity assumption, these effects are the sums of 6 of the 18 potential response type proportions in the population:
ACE[Z→Y] | X = 0 = Average causal effect of Z on Y in the X = 0 stratum
= Pr(Y = 1|SET[Z = 1], X = 0) - Pr(Y = 1|SET[Z = 0], X = 0)
= (q141+ q241 + q441 + q121 + q221 + q421)
ACE[Z→Y] | X = 1 = Average causal effect of Z on Y in the X = 1 stratum
= Pr(Y = 1|SET[Z = 1], X = 1) - Pr(Y = 1|SET[Z = 0], X = 1)
= (q141 + q241 + q441 + q142 + q242 + q442)
Recall that because X is randomized, the potential response type distributionsare independent of X, meaning that the proportions over the total population are the same within the X = 1 and X = 0 subpopulations. We can define ACE[Z→Y], the effect of Z on Y unconditionally, as the proportion of individuals who would experience outcome Y if Z were forced (SET) to 1, but not if Z were forced (SET) to 0, over the entire population. As this is not a stratum-specific quantity, there is only a single value, although this depends on the marginal distribution of X in the population [[21], eq 3.19]. By definition:
ACE[Z→Y] = Pr(Y = 1|SET[Z = 1]) - Pr(Y = 1|SET[Z = 0])
= Pr(Y = 1, X = 1|SET[Z = 1]) - Pr(Y = 1, X = 1|SET[Z = 0]) + Pr(Y = 1, X = 0|SET[Z = 1]) - Pr(Y = 1, X = 0|SET[Z = 0])
Given that X is not affected by Z in the specified DAG, this can be re-written as:
(Pr(Y = 1|X = 1,SET[Z = 1]) - Pr(Y = 1|X = 1,SET[Z = 0]) )Pr(X = 1) +
(Pr(Y = 1|X = 0,SET[Z = 1]) - Pr(Y = 1|X = 0,SET[Z = 0]) )Pr(X = 0)
= Pr(X = 1) ACE[Z→Y] | X = 1 + Pr(X = 0) ACE[Z→Y] | X = 0
As shown above, the ACE[Z→Y] | X = x terms are each comprised of the sums of 6 of the 18 potential response type proportions in the population, 3 of which are common across the two strata of X and 3 of which are unique to one or the other stratum, so that ACE[Z→Y] involves a weighted sum of 9 of the 18 potential response type proportions, with weights dependent upon the marginal distribution of X.
The causal effects defined above are counterfactual, in that they involve hypothetical manipulation of the treatment or intermediate or both. The realized data are the risks that arise in the form of observed proportions in the population. We define Rxz as the risk (proportion) of Y = 1 among those with X = x and Z = z, i.e., Pr(Y = 1|X = x, Z = z). With binary variables, exogeneity of X and the monotonicity assumption, these observable quantities are related to the latent response type proportions as follows:
The observed risk values Rxz are used to compute the associational estimates of effect (see endnote 3), as follows:
The risk difference RD[X→Y] = R1• - R0• is the associational estimate of the total average causal effect of X on Y on the additive scale, where Rx• indicates the risk under X = x collapsed over levels of Z, i.e., Rx• = Pr(Z = 0|X = x)Rx0 + Pr(Z = 1|X = x)Rx1. Because X is assumed to be randomized and therefore Pr(Z = z|X = x) = Pr(Z = z), RD[X→Y] equals the causal RD Pr(Y = 1|SET[X = 1] - Pr(Y = 1|SET[X = 0].
The direct risk difference DRD[X→Y] | Z = z = R1z - R0z is the associational estimate on the additive scale of the average causal direct effect of X on Y within the Z = z stratum. DRD[X→Y] | Z = z may be a biased estimate of the analogous causal quantity, i.e., DRD[X→Y] | Z = z is not necessarily equal to Pr(Y = 1|SET[X = 1,Z = z]) - Pr(Y = 1|SET[X = 0,Z = z]).
RD[X→Z] = Pr(Z = 1|X = 1) - Pr(Z = 1|X = 0) is the associational estimate on the additive scale of the effect of X on Z. Because X is assumed to be randomized, however, this associational estimate equals the analogous causal quantity Pr(Z = 1|SET[X = 1]) - Pr(Z = 1|SET[X = 0]).
RD[Z→Y] | X = x = Rx1 - Rx0 is the associational estimate on the additive scale of the effect of Z on Y within stratum X = x. Because randomization of X does not imply that the effect of Z on Y is unconfounded, it may be a biased estimate of the analogous causal quantity, i.e., RD[Z→Y] | X = x is not necessarily equal to Pr(Y = 1|SET[Z = 1], X = x) - Pr(Y = 1|SET[Z = 0], X = x)
sRD[Z→Y] = Pr(X = 1)RD[Z→Y] | X = 1 + Pr(X = 0)RD[Z→Y] | X = 0 is the associational estimate on the additive scale of the effect of Z on Y standardized to the distribution of X.
The associational estimate for the indirect effect is generally computed by one of two methods[25]:
1) by subtracting DRD[X→Y] | Z = z from RD[X→Y], or
2) by multiplying RD[X→Z] by sRD[Z→Y]
The first of these methods is the one more commonly applied in epidemiologic research, as represented for example by the expression for "Excess Risk Explained" in Szklo & Nieto[7]. Subtraction of DRD[X→Y] | Z = z from RD[X→Y] is also recommended in the social sciences methodology literature. For example, Stolzenberg writes: "Once the total and direct effects are calculated, indirect effects may be computed merely by subtracting the direct effect of an antecedent variable from its total effect. This subtraction procedure is applicable both to linear additive models ... and to nonlinear / nonadditive models." [[26] p. 483]. The second method follows from the path analysis rules of Wright [19], and this method also appears in the epidemiologic literature [e.g., [27]].
There will generally be two distinct estimates by the first method above, depending the level chosen for Z. This is a necessary consequence of the manipulative definition of the controlled direct effect, since it involves deactivation of the indirect pathway by preventing variation in Z, and so if Z is to be fixed to a unique value, this value must be specified. In the second method shown above, however, only the two components of the indirect pathway are involved, and so no explicit fixing of Z is specified. This leads to a single estimate of the direct effect, and so the two methods can only be consistent with one another when there is homogeneity of the ACDE over strata of Z (i.e., DRD[X→Y] | Z = 0 = DRD[X→Y] | Z = 1). The usual regression-based approach for the second method involves the regression of Z on X, followed by the regression of Y on both X and Z, and finally the multiplication of the coefficient estimate for X in the first model by the coefficient estimate for Z in the second model. This conditional estimation of the Z→Y effect in the second model is analogous to taking a weighted average over stratum-specific values as we have done for sRD[Z→Y]. Any presumed equivalence of the two approaches shown above by virtue of a homogeneity assumption for the stratum-specific estimates would often be unwarranted, as even under monotonicity it would generally require the additional restrictions that q142 = q242 = q442 = q121 = q221 = q421 = 0 (see endnote 4).
4. Absence of Confounding
Since X is randomized in Figure 1, there is no confounding of ACE[X→Z] or ACE[X→Y]. Furthermore, in our examples we wish to examine the most optimistic scenario in which there is no confounding between Z and Y. We therefore need to formally define exchangeability conditions that imply the absence of confounding. These conditions are a generalization of those provided in Robins and Greenland [[13], eq E1 and E2, p. 149].
We define 4 counterfactual parameters. The first two are the risks of outcome Y among those with X = x and Z = 1 that would have been observed had Z been forced (SET) to take the value 0 rather than the actually occurring value 1.
The second set of counterfactual parameters are the risks of outcome Y among those with X = x and Z = 0 that would have been observed had Z been forced (SET) to take the value 1 rather than the actually occurring value 0.
It is now possible to specify exchangeability conditions that are sufficient to guarantee that there is no confounding, meaning that associational measures and causal effects are equivalent[28]. The four equality conditions that guarantee the absence of confounding between the Z and Y nodes of the DAG are:
R11|SET[Z = 0] = R10
R01|SET[Z = 0] = R00
R10|SET[Z = 1] = R11
R00|SET[Z = 1] = R01
These conditions assert that the risk that is observed among those with X = x and Z = 0 is the same risk that would have been observed among those with X = x and Z = 1 had Z been forced (SET) from 1 to 0, and that the risk that is observed among those with X = x and Z = 1 is the same risk that would have been observed among those with X = x and Z = 0 had Z been forced (SET) from 0 to 1. The first two sets of exchangeability conditions imply that DRD[X→Y] | Z = 0 is unconfounded, whereas the latter two sets of exchangeability conditions imply that DRD [X→Y] | Z = 1 is unconfounded. Under the general scenario in which stratum-specific direct effects may differ, all four conditions are needed to guarantee that there is no confounding in either stratum of Z for any arbitrary choice of effect contrast that may be constructed from the four component risks.
5. Example 1: A Restriction that Permits Valid Effect Decomposition
We now impose an additional restriction which, as we will see in Section 6, is necessary for the general validity of the decomposition strategy described above. This restriction is that for no individual in the population may there exist both a causal effect of X on Y and a causal effect of Z on Y. For this condition to hold in general, it must be the case that Z does not modify the effect of X for any unit. This restriction, which can be characterized as the absence of unit-level synergism or interaction, implies homogeneity of the stratum specific direct effects ACDE[X→Y] | SET[Z = 0] and ACDE[X→Y] | SET[Z = 1]. Under the monotonicity assumption, this requires that 6 of the 18 types, namely {142}, {242}, {442}, {121}, {221}, and {421} be absent from the population. For example, potential response type {242} refers to a unit in which Z will equal x. When X = Z = 1, outcome Y will occur (Y = 1), and when X = Z = 0, outcome Y will not occur (Y = 0), leading to a unit-level casual effect of X on Y equal to (1-0) = 1. However, if Z were to be manipulated by external intervention (SET) to equal 0, then the unit-level effect of X on Y becomes (0- 0) = 0. That is to say, there is no direct effect at this controlled level of Z. In contrast, if Z were to be manipulated by external intervention (SET) to equal 1, the unit-level effect of X on Y remains (1-0) = 1. The direct causal effects of X on Y are heterogeneous for these 6 omitted potential response types because there is unit-level interaction; the value obtained by Y depends not only on the value taken by X, but also on the level to which Z is held by external manipulation. Homogeneity of the stratum-specific direct effects ACDE[X→Y] | SET[Z = 0] and ACDE[X→Y] | SET[Z = 1] also corresponds to absence of effect measure modification on the additive scale.
When such unit-level synergism is prohibited, then it becomes possible to state an unambiguous definition of the average causal indirect effect (ACIE) of the treatment X in the population as the proportion of all individuals who would experience outcome Y if they were treated, but not if they were untreated, by virtue of the effect that X has on Z and then the effect that Z has on Y. In this mechanism, therefore, external intervention to hold Z fixed will prevent X from having the effect on Y, regardless of the specific value to which Z is SET. Given the restrictions, the average causal indirect effect is merely a single potential response type proportion in the population: ACIE[X→Y] = q241. The total effect is indeed decomposable into the sum of direct and indirect effects under this restriction, and in the absence of confounding may be estimated without bias. The decomposition is valid because the ACE[X→Y] reduces to the sum of only 4 proportions (i.e., q122, q222, q422, and q241), since 4 of the previous 8 are restricted to be 0 (i.e., q221,q421, q142 and q242). The average causal direct effects (ACDE) of X on Y are the sums of 3 potential response type proportions (rather than 6), which are identical in the two strata: ACDE[X→Y] | SET[Z = 0] = ACDE[X→Y] | SET[Z = 1] = (q122 + q222 + q422). Likewise, ACE[X→Z] is the sum of four proportions, ACE[Z→Y] = ACE[Z→Y] | SET[X = 0] = ACE[Z→Y] | SET[X = 1] is the sum of three proportions, and the observed risks RXZ are similarly restricted by deleting the prohibited interacting potential response types from the quotients shown above (Table 1).
Consider data arising from a population of unit-level potential response type proportions qijk as shown in Table 2. This population satisfies the restrictions of monotonicity and absence of unit-level synergism. Simple addition of the proportions yields observed risks RXZ of R11 = 0.9170, R01 = 0.5377, R10 = 0.6101 and R00 = 0.2307. These observed risk values then determine the various associational estimates of effect. The total effect RD[X→Y] = (0.8763- 0.3117) = 0.5646. The observed stratum-specific risk differences DRD[X→Y] | Z = 0 = DRD[X→Y] | Z = 1 = (R1z - R0z) = 0.3793. Likewise the observed risk difference and stratum-specific risk differences for the effect of Z on Y are also homogeneous, sRD[Z→Y] = RD[Z→Y] | X = 0 = RD[Z→Y] | X = 1 = 0.3070. The observed effect of X on Z, RD[X→Z] = 0.6036.
Table 2 Example with No Interaction Permitted
Potential Response Type Representation Prevalence in the Population
{111} 0.0609
{141} 0.0810
{211} 0.1100
{122} 0.0710
{241} 0.1853
{222} 0.2873
{411} 0.0599
{422} 0.0210
{144} 0.0510
{244} 0.0210
{441} 0.0407
{444} 0.0110
Furthermore, the data in this example are constructed such that there is no confounding of the relation between Z and Y. To verify this property, we use the proportions in Table 2 to calculate the values of the counterfactual risks that would be observed under interventions on the intermediate Z. These are: R11|SET[Z = 0] = 0.6101; R01|SET[Z = 0] = 0.2307; R10|SET[Z = 1] = 0.9170; and R00|SET[Z = 1] = 0.5377. The absence of confounding is implied by the following set of equalities, the first two of which imply an absence of confounding of DRD[X→Y] | Z = 0 and the latter two of which imply an absence of confounding of DRD[X→Y] | Z = 1:
R11|SET[Z = 0] = R10 = 0.6101
R01|SET[Z = 0] = R00 = 0.2307
R10|SET[Z = 1] = R11 = 0.9170
R00|SET[Z = 1] = R01 = 0.5377
In this example, which was constructed to have no confounding and in which potential response types corresponding to unit-level synergism have been eliminated, the associational estimates of the total and direct effects are unbiased. That is, the true total average causal effect equals the observed risk difference (0.5646) and the homogeneous average causal direct effects equal the stratum-specific risk differences (0.3793). It only remains to show that the indirect estimate is valid and that the total effect is decomposable. The true average causal indirect effect in the table is the single potential response type proportion, ACIE[X→Y] = (q241) = 0.1853. As described above, there are two common approaches for estimating the associational measure of the indirect effect. The first is to subtract DRD[X→Y] | Z = z from RD[X→Y], which in this case yields (0.5646- 0.3793) = 0.1853. The second is to multiply RD[X→Z] by sRD[Z→Y], which yields (0.6036 × 0.3070) = 0.1853. The estimation of direct and indirect effects and their decomposition from total effects is valid, as will always be the case with this set of assumptions. The justification for this assertion is trivial: this set of assumptions is sufficient to guarantee that the true total ACE is the sum of three ACDE type proportions and one ACIE type proportion, whereas in general, without these restrictions, the ACDE is not constrained to be a subset of the total ACE. We have demonstrated a valid and unbiased estimation of the portion of a total effect that is transmitted through a specified intermediate when there is both absence of confounding and absence of unit-level interaction. We next relax this second constraint in order to demonstrate that the decomposition analysis can then fail.
6. Example 2: Removing the Restriction of No Unit-Level Interaction
Now we relax one assumption, the prohibition of unit-level interaction, which was operationalized in Section 5 by requiring that q142 = q242 = q121 = q221 = q421 = q442 = 0. Therefore we have, under the monotonicity restriction alone, 18 potential response types in the population. Consider data arising from a population of unit-level potential response type proportions qijk as shown in Table 3, which conform to this assumption, and additionally are constructed such that there is no confounding. Simple addition of the proportions yields observed risks RXZ of R11 = 0.9580, R01 = 0.3910, R10 = 0.4180 and R00 = 0.3170. These observed risk values then determine the various associational estimates of effect. The associational estimate of the total ACE is RD[X→Y] = (0.8166- 0.3470) = 0.4696. The observed stratum-specific risk differences are no longer constrained to be homogeneous: DRD[X→Y] | Z = 0 = (R10 - R00) = 0.1010 and DRD[X→Y] | Z = 1 = (R11 - R01) = 0.5670. The observed stratum-specific risk differences for the effect of Z on Y similarly need not be homogeneous: RD[Z→Y] | X = 0 = 0.0740 and RD[Z→Y] | X = 1 = 0.5400. sRD[Z→Y] will now depend on the observed marginal distribution of X. If values were assigned with equal probability, then sRD[Z→Y] = (0.5 × 0.0740) + (0.5 × 0.5400) = 0.3070. The observed effect of X on Z, RD[X→Z], equals 0.3327.
Table 3 Example with Interaction Permitted
Potential Response Type Representation Prevalence in the Population
{111} 0.1285
{141} 0.0100
{211} 0.1100
{122} 0.0100
{241} 0.0100
{222} 0.0200
{411} 0.0785
{422} 0.0210
{144} 0.0100
{244} 0.0210
{441} 0.0040
{444} 0.0110
{121} 0.0200
{221} 0.0200
{421} 0.0100
{142} 0.2269
{242} 0.1517
{442} 0.1374
To verify that, as in the previous example, the data in this example are unconfounded, the proportions in Table 3 are used to determine the values of the counterfactual risks that would be observed under interventions on the intermediate Z. These are: R11|SET[Z = 0] = 0.4180; R01|SET[Z = 0] = 0.3170; R10|SET[Z = 1] = 0.9580; and R00|SET[Z = 1] = 0.3910. The absence of confounding is implied by the following set of equalities, the first two of which imply an absence of confounding of DRD[X→Y] | Z = 0and the latter two of which imply an absence of confounding of DRD[X→Y] | Z = 1 :
R11|SET[Z = 0] = R10 = 0.4180
R01|SET[Z = 0] = R00 = 0.3170
R10|SET[Z = 1] = R11 = 0.9580
R00|SET[Z = 1] = R01 = 0.3910
Because X is randomized, the average causal effect of X on Y is identified by the observed associational measure of effect: RD[X→Y] = ACE[X→Y] = 0.4696. Furthermore, because we have established that there is no confounding, the average causal direct effect of X on Y is identified by the observed associational measure of effect: ACDE[X→Y] | SET[Z = z] = DRD[X→Y] | Z = z. In this example, ACDE[X→Y] | SET[Z = 0] = DRD[X→Y] | Z = 0 = 0.1010 and ACDE[X→Y] | SET[Z = 1] = DRD[X→Y] | Z = 1 = 0.5670. However, in this scenario in which the only assumption we have relaxed is to allow the presence of unit-level synergism, the total average causal effect is no longer decomposable into direct and indirect effects. The average causal indirect effect no longer has single unambiguously true value. If the external manipulation contemplated is to SET[Z = 0], then ACIE[X→Y] = (q241 + q242) = (0.0100 + 0.1517) = 0.1617. On the other hand, if the external manipulation contemplated is to SET[Z = 1], then ACIE[X→Y] = (q241+ q221) = (0.0100 + 0.0200) = 0.0300.
As described above, there are two common approaches for estimating the associational measure of the indirect effect. The first is to subtract DRD[X→Y] | Z = z from RD[X→Y], which in this case yields either (0.4696- 0.5670) = -0.0974 or (0.4696-0.1010) = 0.3686, depending on the stratum of Z, neither of which equals either of the corresponding true values of 0.0300 or 0.1617. The second approach is to multiply RD[X→Z] by sRD[Z→Y], which yields (0.3327 × 0.3070) = 0.1021, a value that equals neither of the corresponding true values of 0.0300 or 0.1617, nor is it the weighted average formed from any meaningful set of weights. In this scenario, the estimation of direct and indirect effects and their decomposition from total effects is not valid. It is immediately apparent that once unit-level interaction is permitted, there are potential response types that contribute to the ACDE but which do not contribute to the total ACE, making it incorrect to view the ACDE as a partition of the total ACE. Likewise, there are potential response types that contribute to the ACDE in one stratum of the intermediate, but which contribute to the ACIE in the alternate stratum, making it incorrect to view ACDE and ACIE as adding together to sum to a total effect. Therefore, for the decomposition methodology to be reliable, there must be both absence of confounding and absence of unit-level interaction. Because the absence of unit-level interaction would be difficult to assert with any confidence in a real-world application, the practical utility of decomposition as an analytic strategy is doubtful.
7. Discussion
The demonstration above would appear to be somewhat gloomy as regards the potential for analytic epidemiology to identify biologic pathways through the contrast of variously specified statistical models. Indeed, the situation is even more grim than stated above, because even the optimistic scenario in Example 1 (Section 5) relies on the linear causal contrast estimator (i.e., the risk difference). Epidemiologic applications, such as those recommended by Szklo and Nieto [[7], pp. 184–187] nearly always use ratio measures of effects, such as risk ratios, odds ratios and hazard ratios. For ratio contrasts, the total effect is not generally decomposable under any set of causal assumptions. Therefore, the recommended "% Excess Risk Explained", defined as a function of ratio parameters, will never have a causal interpretation and the inference generated will always be ambiguous. In Example 1 (Section 5), for instance, the crude observed RR = 2.81, the Mantel-Haenszel adjusted RR = 2.40, and the Szklo and Nieto "% Excess Risk Explained" therefore equals 22.8%, which does not equal the true proportion of the effect that is relayed though the intermediate, i.e., (ACIE[X→Y] / ACE[X→Z]) = (q241 / q211 + q241+ q222 + q244 + q221 + q242) = (0.1853 / 0.5646) = 32.8%. We note that a valid contrast between total and direct effects for ratio measures of effect was described by Joffe & Colditz[29], but that this does not correspond to a decomposition of effects because the authors did not assume that the ACDE was necessarily a partition of the total ACE.
Even if one steadfastly utilized the risk difference as the causal contrast and justified the no-confounding assumption, in order to reliably decompose the effect, one would still have to believe that there are no units in the population for whom Z and X both affect Y. Under the sharp null hypothesis for the exposure effect, this might be plausible. That is, if X has no effect on Y for any unit, then it follows that there are no units in the population for whom both X and Z have an effect on Y. However an average causal effect equal to the null does not imply this condition. If one were able to assert the no-confounding assumption, then observing that the association parameter is equal to the null would imply that the average causal effect is null, but no observation would imply the absence of unit-level interaction. The observation of heterogeneity would be sufficient to reject the assumption, but the observation of homogeneity would have no implications for this assumption. Nevertheless, as a practical matter, the incidental balancing out of unit-level causal effects leading to homogeneity might be considered unlikely, and therefore as a feasible approximation, the observation of risk difference homogeneity under a substantively defensible assertion of no-confounding might be taken as a setting in which effect decomposition can be attempted with a modicum of credence.
Several previous authors working with latent potential response models have commented on the non-decomposibility of total effects into direct and indirect effects, most notably Robins[30], Robins & Greenland[13] and Pearl [[21], pp. 126–131, 165]. What is perhaps surprising is that although many quantitative sociologists also utilize this same latent outcomes framework [e.g., [20,31,32]], there are to our knowledge no instances of this critique published in the social sciences literature. We find this surprising because effect decomposition is formally embraced in the sociological methodology literature as an essential inferential strategy in the context of structural modeling[25,33]. Indeed, rather than critique this approach, it is strenuously upheld, even for non-linear models[26].
We note that our specification of average causal direct effects in this manuscript corresponds to the controlled direct effect, which is to say, the proportion of individuals who would experience outcome Y if they were treated, but not if they were untreated, if Z were to be fixed to have a specific value z (thus blocking any indirect effects). Rather than impose through external intervention a uniform value of Z = z for all units, it is possible to define the average causal direct effect of X on Y that results from fixing Z to the value that would naturally occur under a specific single value of X, for example the unexposed level X = 0. This is referred to by Robins as the "pure direct effect"[34] and by Pearl as the "natural direct effect"[35]. It is noteworthy that this alternate definition does allow for the effect decomposition to hold more generally, and gives rise to additional concepts such as the "total direct effect", which is the difference between the total ACE and the pure (natural) indirect effect. Furthermore, analogously to the controlled direct effects formulation, which leads to as many direct effects as there are levels of intermediate Z, the pure (natural) direct effects formulation leads to as many direct effects as there are levels of exposure X.
Although it allows for decomposition without the assumption of no unit-level interaction, the approach involving pure (natural) direct and indirect effects has a substantial deficiency. The exchangeability conditions shown above (Section 4) characterize confounding in relation to hypothetical but defined manipulations of the target population. That is, X and Z are controlled to specific values. Because the pure (natural) direct and indirect effects are defined based on intermediate Z being manipulated to an unobserved value that it would have taken under an exposure X value that may not have occurred, the exact nature of this intervention remains obscure. Because the hypothetical manipulation cannot be specified, the decomposed effects no longer have any possible relevance to a specific public health intervention or policy [[34], Section 3].
For example, if one were to estimate that the pure (natural) indirect effect through intermediate Z equals 50% of the total effect, one could not infer that 50% of the outcomes attributable to the exposure could be prevented by blocking Z from occurring. Controlled direct effects can be used to make statements such as "The effect that postmenopausal hormone therapy would have on breast cancer risk if we were to persuade every woman to receive a screening mammography is ...." No similarly practical statement could be made for a pure (natural) direct effect, however, which would correspond to something like "The effect that postmenopausal hormone therapy would have on breast cancer risk if every woman were to engage in the screening mammography behavior that she would have exhibited under the absence of treatment is ...." This latter statement obviously has no clear public health policy implications, since it requires a policy of fixing the intermediate to different values, some of which are unobserved (e.g., the screening behavior that a woman taking postmenopausal hormone therapy would have experienced had she not taken hormone therapy).
In summary, the ubiquitous strategy of adjusting for one or more putative causal intermediates in order to estimate the portion of the effect that it mediated by this pathway, in epidemiology and in other fields, lacks a reliable foundation. There are highly constrained sets of assumptions which allow this strategy to be valid, but it is often difficult to know when, if ever, these assumptions are approximately satisfied. Previous critiques have focused on confounding between the intermediate and the outcome, but we show that even when there is no confounding, the total causal effect of treatment is not generally decomposable into direct and indirect effects. Valid estimation of the direct or indirect effects, or of the proportion of the total effect that is due to an intermediate variable, requires not only the assumption of no confounding, but also the assumed absence of unit-level synergism, the latter of which may be particularly difficult to assert in a real-world analysis. Furthermore, even under these conditions, the decomposition is only valid for the difference contrast as the measure of causal effect, not for ratio measures of effect such as risk ratios, rate ratios, hazard ratios or odds ratios. In circumstances when it is possible to estimate (controlled) average causal direct effects, these should not generally be interpreted as portions of the total average causal effect, nor should they generally be used to make any statement about the proportion of the effect that is attributable to the measured intermediate variable.
List of Abbreviations Used
ACE average causal effect
ACDE average causal (controlled) direct effect
ACIE average causal (controlled) indirect effect
CHD coronary heart disease
DAG directed acyclic graph
DRD direct risk difference
LRC Lipid Research Clinics
RD risk difference
RR relative risk or risk ratio
sRD standardized risk difference
SUTVA stable-unit-treatment-value assumption
Competing Interests
The authors declare that they have no competing interests.
Endnotes
1. Because of the assumption of zero sampling error "proportions" (in the observed sample) and "probabilities" (in the source population)are interchangeable.
2. The characterization of this monotonicity assumption as strong is intended to distinguish it from a weaker stochastic monotonicity assumption that may be defined:
Pr(Z0 = 1) ≤ Pr(Z1 = 1)
Pr(Y0z = 1) ≤ Pr(Y1z = 1); z = 0,1
Pr(Yx0 = 1) ≤ Pr(Yx1 = 1); x = 0,1
where Zx is the random variable representing the potential response at Z to SET[X = x], and Yxz is the random variable representing the potential response at Y to SET[X = x, Z = z].
3. Associational estimates are obtained from contrasts in the observed data, rather than being estimates of what would pertain under the hypothetical manipulation that is indicated by a SET operation.
4. Strictly, equivalence of the two approaches for specifying the indirect effect requires merely that (q142 + q242 + q442) = (q121 + q221 + q421), but this incidental cancellation would be difficult to anticipate, whereas the absence of some potential outcomes types is a more plausible form of background knowledge that an investigator could bring to the analysis.
Authors' Contributions
JSK led the writing, but all three authors contributed heavily to editing and revision. RFM devised the notational system and the numeric examples.
Acknowledgments
Supported by contract R01-HD-39746 from the National Institute of Child Health and Human Development. Michael E. Sobel provided helpful comments on a draft version of the paper.
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| 15507130 | PMC526390 | CC BY | 2021-01-04 16:36:33 | no | Epidemiol Perspect Innov. 2004 Oct 8; 1:4 | utf-8 | Epidemiol Perspect Innov | 2,004 | 10.1186/1742-5573-1-4 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-81550712910.1186/1743-7075-1-8ResearchResistant starch consumption promotes lipid oxidation Higgins Janine A [email protected] Dana R [email protected] William T [email protected] Ian L [email protected] Melanie L [email protected] Daniel H [email protected] University of Colorado Health Sciences Center, Center for Human Nutrition, Denver, Colorado 80262. USA2 University of Vermont, Department of Medicine, Burlington, Vermont 05405. USA3 University of Wollongong, Wollongong, NSW, 2522. Australia4 Preventive & Social Medicine, University of Otago, Dunedin, New Zealand2004 6 10 2004 1 8 8 14 8 2004 6 10 2004 Copyright © 2004 Higgins et al; licensee BioMed Central Ltd.2004Higgins et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Although the effects of resistant starch (RS) on postprandial glycemia and insulinemia have been extensively studied, little is known about the impact of RS on fat metabolism. This study examines the relationship between the RS content of a meal and postprandial/post-absorbative fat oxidation.
Results
12 subjects consumed meals containing 0%, 2.7%, 5.4%, and 10.7% RS (as a percentage of total carbohydrate). Blood samples were taken and analyzed for glucose, insulin, triacylglycerol (TAG) and free fatty acid (FFA) concentrations. Respiratory quotient was measured hourly. The 0%, 5.4%, and 10.7% meals contained 50 μCi [1-14C]-triolein with breath samples collected hourly following the meal, and gluteal fat biopsies obtained at 0 and 24 h. RS, regardless of dose, had no effect on fasting or postprandial insulin, glucose, FFA or TAG concentration, nor on meal fat storage. However, data from indirect calorimetry and oxidation of [1-14C]-triolein to 14CO2 showed that addition of 5.4% RS to the diet significantly increased fat oxidation. In fact, postprandial oxidation of [1-14C]-triolein was 23% greater with the 5.4% RS meal than the 0% meal (p = 0.0062).
Conclusions
These data indicate that replacement of 5.4% of total dietary carbohydrate with RS significantly increased post-prandial lipid oxidation and therefore could decrease fat accumulation in the long-term.
resistant starchfat oxidationglucoseinsulinamylose
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Background
Resistant starch (RS) is any starch that is not digested in the small intestine but passes to the large bowel for fermentation [1]. Retrograded amylose (a linear polymer of glucose residues linked by α(1→4) bonds; RS1), such as cooked and cooled starchy foods like pasta salad, and native starch granules (RS2), such as those found in high-amylose maize starch and bananas, are the major components of dietary RS. Calories from RS that are undigested in the small intestine can be salvaged by fermentation to short-chain fatty acids (SCFA; acetate, butyrate, proprionate) by the microflora of the large bowel. Fermentation of RS in the large bowel gives rise to increased production of SCFA which is reflected in higher epithelial and portal concentrations. SCFA concentration in the periphery, however, is very low and therefore difficult to measure accurately so any increase in production of SCFA in response to RS consumption may not be detectable in the peripheral circulation.
Acute human studies describe variable postprandial glycemic and/or insulinemic responses to RS ingestion. In general, it is accepted that RS consumption lowers postprandial glucose concentrations marginally and postprandial insulin concentrations markedly. Many groups report a decrease in postprandial glycemic or insulinemic responses to RS ingestion relative to digestible starch (DS) consumption [2-7], whereas some report no change [8-11]. It is important to note that the fat content of the diet has a significant impact on the glycemic response to a meal and some meal tests contained no fat or the fat content of the meal varied among the different RS diets making results from these studies difficult to interpret [2-4]. Also, there are many sources of RS, such as beans, high amylose corn starch, and potatoes, which possess different physicochemical properties. So, the source of RS can influence the glycemic/insulinemic response to RS ingestion.
Many studies have examined the relationship between RS ingestion and postprandial metabolite and hormone concentrations. Fewer studies have documented the effect of RS on lipid metabolism. In humans, five weeks of RS feeding lowered fasting cholesterol and triglyceride concentrations and postprandial plasma insulin concentrations relative to digestible starch (DS) feeding [12,13]. It has also been reported that chronic RS feeding in rats causes a decrease in adipocyte cell size relative to DS feeding [14,15]. In addition, expression of fatty acid synthase was lower in rats fed a RS-based diet than in those fed a DS-based diet [16]. Taken together, these studies provide evidence that RS intake has an effect upon the activity of key lipogenic enzymes and adipocyte morphology. Thus, it seems that the effects of this carbohydrate subtype on lipid metabolism should be carefully examined in human studies.
It is possible that strong physical association between RS and dietary lipid may slow the absorption, and thereby increase the oxidation, of dietary lipid. Currently, there is no evidence pertaining to the dose-response relationship for RS ingestion (as part of a mixed meal) and postprandial glycemia, insulinemia, fat oxidation, or meal fat storage. It is important that these parameters be defined before designing and conducting long-term, prospective RS feeding studies.
Results
No difference in fasting or postprandial insulin, glucose, FFA, or triglyceride concentration was observed between any of the RS doses examined (Figure 1).
Figure 1 Circulating glucose (a, b), insulin (c, d), free fatty acid (e), and triglyceride (f) concentrations in response to the RS content of a breakfast meal. Serum glucose and insulin measurements were conducted on 12 healthy adults. Data is presented as mean ± SEM.
Overall, the dose of RS in the meal had a significant influence on ΔRQ (respiratory quotient) values (F-test, 0.04; Figure 2). This overall effect was due to a significantly lower ΔRQ at the 5.4% RS dose than the 0% (p = 0.02) or 10.7% (p = 0.009) RS doses, indicating an increase in fat oxidation in response to the 5.4% RS meal relative to the 0% and 10.7% RS doses (Figure 2). ΔRQ was significantly lower for the 5.4% RS meal than 0% RS meal at 120, 240, 300 and 360 minutes (p = 0.05, 0.03, 0.02 and 0.04, respectively) whereas significant differences occurred at 120, 180, 240, 300 and 360 minutes (p = 0.01, 0.01, 0.005, 0.02, and 0.03, respectively) for the 5.4% RS versus 10.7% RS meals. These data are reflected in total macronutrient oxidation rates (Figure 3), which show a significant increase in the amount of fat oxidized at the 5.4% RS dose relative to the 0% RS meal, with a concomitant decrease in total carbohydrate oxidation.
Figure 2 Respiratory quotient (RQ; change from baseline) in response to RS content of a breakfast meal. Respiratory gas exchange measurements were conducted on 12 healthy adults using the ventilated hood method. Data is presented as mean ± SEM. * p < 0.05 for a difference from the 0% meal at the same time point. # p < 0.03 for a difference with the 10.7% meal at the same time point.
Figure 3 Total fat (a) and carbohydrate (b) oxidation in response to RS content of a breakfast meal. Macronutrient oxidation, assessed via indirect calorimetry and calculated from non-protein RQ, was measured in 12 healthy adults. Data is presented as mean ± SEM. * p ≤ 0.003 for a difference from the 0% and 10.7% RS meals.
Similarly, the oxidation of [14C]-triolein to 14CO2 was different between RS doses (F-test, 0.0005). Meal fat oxidation at the 5.4% RS dose was significantly higher than both the 0% (p = 0.0062) and 10.7% doses (p < 0.0001). Separate tests at 6 h or 24 h following the test meal gave comparable results (Figure 4a). Taken together, these independent measurements of fat oxidation (indirect calorimetry, oxidation of [14C]-triolein to 14CO2) suggest that the inclusion of 5.4% RS in the meal elevated postprandial fat oxidation. Unexpectedly, this effect was lost if the dose was increased to 10.7% RS.
Figure 4 Meal fat oxidation (a) and storage (b) in response to RS content of a breakfast meal. Meal fat oxidation, assessed via measurement of 14CO2 in expired air, and meal fat storage in gluteal adipose tissue was measured in 12 healthy adults. Data is presented as mean ± SEM. * p ≤ 0.0006 for a difference from the 0% and 10.7% RS meals at the same time point. FFM, fat free mass.
There was a trend for fat storage from the test meal, as assessed by incorporation of 14C into gluteal adipose tissue, to be lower for the 5.4% RS meal than all other meals, although this effect did not reach statistical significance (Figure 4b).
Discussion
This study demonstrated that the addition of RS to a mixed meal, balanced for total fat and fiber content, had no effect on postprandial glucose, insulin, FFA, or triglyceride excursions. However, meals containing a moderate amount of RS caused an increase in fat oxidation as measured by both indirect calorimetry and the production of 14CO2 from a 14C-triglyceride tracer. Unexpectedly, the dose-response relationship between RS content of the diet and fat oxidation was not linear. Although this result is difficult to explain in the current context, it emphasizes the need for careful selection of RS dose in prospective feeding studies.
There was no difference in postprandial glucose (Figure 1a), FFA (Figure 1e), triglyceride (Figure 1f), or insulin (Figure 1c) concentrations at any RS dose examined. This concurs with data from other acute human studies using complete, mixed meals which showed no difference in postprandial glycemia/insulinemia in response to RS content of the diet [8-11]. Although this seems contrary to the general perception that RS ingestion reduces postprandial insulinemia and glycemia, many of the studies indicating this did not balance test diets for total fat and/or fiber content [17]. However, in the current study all diets were carefully matched for total fat and fiber content. This an important distinction between this and other studies as fiber has extensively been shown to reduce postprandial glycemia/insulinemia and increasing the RS content of the diet intrinsically increases the total fiber content. Also, dietary fat can have potent effects on the accessibility of dietary carbohydrate to digestive enzymes and on the rate of gastric emptying/gut motility. Thus, the glucose- and insulin-lowering effects of RS that have been observed in other studies may be due to changes in fiber and/or fat between test meals which have been extensively shown to lower postprandial glycemic and insulinemic responses. So, the balanced conditions used in the meal tests for the study described herein, which included baked products and processed foods as part of a complete, mixed meal, balanced for total fat and fiber content, could account for the lack of difference in insulinemia and glycemia in response to increased RS content in the diet.
Both indirect calorimetry and 14C-tracer data indicate that there was an increase in fat oxidation between the 0% and 5.4% RS doses (Figures 2, 3, and 4a). This increase in total and meal fat oxidation in response to the 5.4% RS meal is not driven by disparate responses amongst subjects as 11 of the 12 subjects studied showed the greatest fat oxidation in response to the 5.4% RS meal, relative to the 0% and 10.7% RS meals (see Figure S1, Additional File 1, for individual responses). Tracer data showed that the addition of 5.4% of RS to a meal increased meal fat oxidation by more than 20% over the 6 h and 24 h post-meal ingestion period (Figure 4a). The increase in fat oxidation at 6 h accounted for approximately one-half of the total increase over 24 h, indicating that the increase in meal fat oxidation in response to a single meal containing 5.4% RS is a prolonged, sustained effect. In addition, comparison of total and meal fat oxidation (Figures 3a and 4a) indicates that endogenous fat stores were the predominant source of fat utilized for energy, contributing approximately 80% of the total fat oxidized, with a much lower contribution from ingested meal fat. Figure 3 shows that this increase in fat oxidation at the 5.4% RS dose is accompanied by a relative reduction in carbohydrate oxidation (does not reach statistical significance).
The increase in fat oxidation at the 5.4% RS dose relative to the 0% dose was not driven by any disparity in circulating glucose, insulin or FFA concentration (Figure 1; see Figures S2, S3, S4, Additional Files 2, 3, 4, respectively, for individual subject responses) nor by a difference in available carbohydrate between the 0% and 5.4% RS meals. If decreased carbohydrate availability was responsible for the observed increase in fat oxidation, the 10.7% RS meal, which has the least available carbohydrate, would show the greatest increase in fat oxidation. However, there was no difference in fat oxidation between the 0% and 10.7% RS meals. Thus, carbohydrate availability cannot be a contributing factor to the increase in fat oxidation observed at the 5.4% dose of RS. It is possible that this increase may be due to an increase in circulating SCFAs from the fermentation of RS reaching the large bowel. The observed increase in fat oxidation is not due to oxidation of these SCFAs per se as it was measured directly from conversion of 14C-labeled meal fat to 14CO2 (Figure 3a). Such a measurement would not detect any increase in SCFA oxidation. Rather, it may be that the metabolic effects of increased SCFA production cause an increase in fat oxidation.
RS consumption has been shown to alter the acetate:butyrate:propionate ratio compared to fermentation of non-starch polysaccharides [29]. In particular, the amount of butyrate is substantially elevated in response to RS fermentation [30,31]. In humans fed a low or high RS diet for three days, the concentration of excreted SCFA rose from 20 mmol/d to 33 mmol/d, respectively [19]. This increase in total SCFA concentration was caused by a doubling of the acetate and butyrate content changing the acetate:butyrate:propionate ratio from 12:3:3 to 21:6:4 in response to the low and high RS diets, respectively.
In vitro data from isolated animal tissues provide convincing evidence for the role of SCFAs in carbohydrate and lipid metabolism [26,32-34]. Acetate and/or butyrate have been shown to decrease glycogenolysis and glycolysis in isolated rat and sheep hepatocytes [35-37]. So, it is plausible that the fermentation of RS from the 5.4% RS diet increases the net production of SCFAs which inhibit glycolysis in the liver. In this scenario, the liver, deprived of carbohydrate-derived acetyl CoA would be more reliant on fat-derived acetyl CoA as a fuel source, thereby contributing to an overall increase in fat oxidation [17]. This possibility needs to be investigated in future studies.
No difference in fat oxidation was evident between the maximal 10.7% dose of RS and the 0% dose. This is an unexpected result that is difficult to explain. The loss of any effect on fat oxidation when the RS dose in the meal was increased to 10.7% may occur because this dose is at the threshold of the starch's properties as RS. That is, at the 10.7% dose of RS, the starch may not be completely fermented in the large bowel thereby causing a loss of energy from the diet via the feces. If this is the case, the strong physical association between RS and dietary lipid may cause excretion of lipid and therefore, less dietary fat to be available for oxidation at the 10.7% dose. Indeed, it has previously been shown that intake of high-amylose maize starch, such as that used in this study, caused an increased number of bowel actions per day [18]. RS has also been shown to decrease colonic transit time and, as more RS enters the large bowel, more starch is also excreted [19,20]. This indicates that, at higher levels of RS consumption, only a portion of the RS can be fermented and the remainder passes through the colon as an insoluble fiber. Furthermore, if indeed RS at the 10.7% dose is being excreted as insoluble fiber, less fermentation and SCFA production would be occurring. As SCFA are hypothesized to be the cause of the observed increase in fat oxidation in response to the 5.4% RS meal, this would have a large impact on the fat oxidation potential of the 10.7% RS diet.
The hypothesis that RS is acting like dietary fiber and being excreted can be tested by measuring the amount of fat excreted in the feces. As this outcome was not predicted, fecal samples were not collected from subjects during this study. It is important to consider that it is difficult to add 10.7% RS to a standard diet without the use of specially designed foods and/or without significantly increasing caloric intake. Therefore, this level would be difficult to attain in a free-living situation and the lower doses used in this study are more reflective of predicted levels if normal, starchy foods in the diet were to be replaced with commercially available RS products.
In addition, not all biological processes display linear dose-response curves. Dose-response curves can vary from sigmoidal to 'U'-shaped curves for processes as diverse as drug absorption/clearance [21], low dose radiation effects on cells [22], DNA repair following double-strand breaks [23], and metabolic parameters. Metabolic processes that are non-linear functions include the level of illuminance and plasma melatonin levels [24], caffeine intake versus plasma caffeine metabolite concentrations [25], allergen exposure (concentration) and histamine response [26], zinc-stimulated histamine release from mast cells [27], and fructose-1,6-diphosphate metabolism in cardiomyocytes [28]. Thus, it is possible that the lack of any effect on fat oxidation at the 10.7% RS dose may indicate that the relationship between RS intake and fat oxidation is indeed a 'U'-shaped curve. However, more RS doses between 5.4% and 12% must be tested to accurately define the shape of this dose response curve.
It must be noted that the calculation of oxidation of [14C]-triolein via measurement of 14CO2 did not take into account the dilution of tracer in vivo due to the incorporation of labeled carbons into intermediates of the TCA cycle and endogenous bicarbonate pools. Generally, an acetate correction factor is used to account for this effect. In this study, subjects consumed all four test meals under the same conditions and it was assumed that there was no difference in tracer recovery between tests. Also, these TCA intermediate and bicarbonate pools were not pre-labeled prior to the ingestion of the label in the meal which would cause a total underestimation of total fat oxidation. Therefore, the rate of fat oxidation calculated from 14CO2 recovery in the breath was probably underestimated in all subjects but remains valid to compare differences between test meals.
There was a trend towards a decrease in gluteal fat storage at the 5.4% RS dose relative to all other doses (Figure 4b). Again, the dose-response curve for this parameter was not linear, lending credence to the idea that the dose-response curve for fat oxidation is actually U-shaped. Although the decrease in fat storage at the 5.4% RS dose did not reach statistical significance, it is intuitive that, given the magnitude of the increase in fat oxidation observed at this dose, there would be a reciprocal decrease in fat storage. However, there was high variability associated with the measure of meal fat storage indicating that more subjects may be needed to decrease the standard deviation and, hence, detect any significant meal affect.
Conclusion
This study is the first to identify that addition of 5.4% RS to a single meal can cause a significant increase in total and meal fat oxidation in healthy individuals relative to a 0% RS diet over the postprandial/postabsorptive period (24 h). This discovery was verified using two different methods, indirect calorimetry and the oxidation of [14C]-triolein to 14CO2, to measure in vivo fat oxidation. This increase in fat oxidation was accompanied by a concomitant decrease in carbohydrate oxidation and fat storage, although these parameters did not reach statistical significance. Further, the magnitude of the increase in fat oxidation indicates that this effect is biologically relevant and could be important for preventing fat accumulation in the long term by effecting total fat balance under chronic feeding conditions. Finally, this study revealed that there may be a maximal effect of RS addition to the diet and that the addition of RS over this threshold confers no metabolic benefit or change from a 0% RS meal.
Methods
Subjects
12 healthy adults, 7 male and 5 female, participated in the present study. This study was approved by the Colorado Multiple Institution Review Board, in compliance with the Helsinki Declaration, and full written consent was obtained from all subjects. To participate, subjects were required to be between 28 and 45 years of age, have normal glucose tolerance (as judged via response to an oral glucose tolerance test; fasting glucose concentration < 6 mM, postprandial glucose concentration not higher than 9 mM), moderate level of physical activity (no more than 4 one-hour bouts of planned physical activity per week), and a BMI between 20 and 28. All female subjects were taking oral contraceptive pills or progesterone injections and were tested during the early follicular phase of the menstrual cycle. All subjects underwent dual energy X-ray absorptiometry (DEXA; Lunar Radiation Corp, Madison WI) for analysis of body composition. As a group, subjects were 33 ± 5 years of age, 1.7 ± 0.07 m tall, weighed 75 ± 11 kg, had a BMI of 24.7 ± 2.4, total fat mass of 18.3 ± 5.0 kg (mean ± SD), and a fasting RQ of 0.750 ± 0.023 (mean ± SEM).
Diet
Subjects received four meals differing only in resistant starch (RS) content in random order, approximately four weeks apart. Test meals contained either 0%, 2.7%, 5.4%, or 10.7% RS as a percentage of total dietary carbohydrate. All added RS was in the form of high-amylose maize starch, or RS2. High-amylose maize starch was chosen as it has the unique property of a very high gelatinisation temperature which allows it to maintain its granular structure during and after the processing conditions used to manufacture the foods being consumed in this study [38].
All meals were isocaloric, accounting for 30% of the subject's daily energy needs as measured by indirect calorimetry prior to study commencement (RMR × daily activity factor of 1.49). The composition of the test diet was 55% carbohydrate, 15% protein, and 30% fat as a percentage of total energy (Table 1). All meals were matched for total dietary fiber content and liquid volume (250 ml).
Table 1 Composition of test breakfasts. All values are based on a hypothetical subject who requires 8374 kJ (2000 kcal) per day.
RS content (% total carbohydrate) 0 2.7 5.4 10.7
RS content (g)1 0 g 2.5 g 5 g 10 g
Total energy (kJ) 2508 2506 2500 2506
Carbohydrate (g) 93.8 93.3 92.9 93.0
Protein (g) 22.7 22.6 23.0 23.0
Fat (g) 17.0 16.8 16.9 16.9
Total sugars (g) 45.6 45.2 45.7 45.1
Total Fiber (g) 9.4 9.3 9.5 9.5
Liquid volume (mL) 250 250 250 250
Foods consumed (g)
Canned spaghetti 197 58
*RS Canned spaghetti 147 218 216
Parmesan cheese 10 8 8 12
Margarine 4 3 2 2
Butter 2 1 1
Milk (2% fat) 250 250 210
*Up & Go breakfast drink 40 250
Bread 38 44 36
*Banana muffin 43
Strawberries 203 162 123
Grapes 80 93
*Fruit fingers 15 16
Sugar, white 10
* Denotes foods with added RS.
1 Absolute RS inclusion varied according to the energy needs of the subject so that RS content always remained the same fraction of total dietary carbohydrate, namely 0%, 2.7%, 5.4%, and 10.7% for the 0 g, 2.5 g, 5 g, and 10 g meals, respectively. For example, a subject who had a daily caloric need of 9421 kJ (2250 kcal) would receive meals containing 0 g, 2.7 g, 5.4 g, and 10.8 g RS.
2 Energy and macronutrient values were determined using the USDA Nutrient Database for standard foods and from information supplied by the manufacturer for foods with added RS. Note that energy values calculated from the carbohydrate, fat, and protein content of study foods using the 4-9-4 kcal/g factor method differ from reported energy values due to use of the Atwater system.
Three days prior to each test day, subjects received a standardized lead-in diet, equivalent to daily energy needs as judged by indirect calorimetry and of the same macronutrient composition as the test diet with no added RS, to ensure that they were in energy balance. All food for these three days was provided by the General Clinical Research Center (GCRC) on an outpatient basis. Subjects were instructed to eat all of the food/drink provided and not to consume any other foods. Non-caloric beverages could be consumed during the three day lead-in diet.
Protocol
Following an overnight fast (12 h), subjects were admitted to the GCRC and an intravenous catheter was placed for the purposes of drawing blood. The test meal began at 0 min (0800 h) with all food/drink fully consumed within 15 min. Blood samples were taken at 0, 30, 60, 90, 120, 180, 240, 300, and 360 min following meal ingestion and analyzed for glucose, insulin, triacylglycerol (TAG) and free fatty acid (FFA) concentrations. Respiratory quotient (RQ) was measured at hourly intervals after ingestion of the meal via gas collection under a ventilated plexiglass hood for 15 min (Sensormedics 2900 metabolic cart). All urine produced between 0 and 360 min was collected and analyzed for nitrogen content by the GCRC Core Laboratory to facilitate calculation of non-protein RQ.
In three of the test meals (0%, 5.4%, and 10.7% RS meals), the bread product in the test meal was spiked with 50 μCi [1-14C]-triolein (glycerol tri [1-14C]oleate; Amersham Pharmacia Biotech, Amersham, UK) suspended in olive oil and the tests were conducted as 24 h inpatient stays at the GCRC. The fat tracer was fed as a triglyceride (glycerol tri [1-14C]oleate) rather than a FFA (eg. [1-14C]oleate) in order to reflect any change in the absorption of triglyceride FFA which might be due to a strong physical association with RS thereby slowing absorption. At hourly intervals following the meal, then at 8, 10, 12, 14 and 24 hours, breath samples were collected via exhalation through a tube with a one-way valve into scintillation vials containing 2 mmol benzethonium hydroxide (to trap 2 mmol CO2), 1 ml methanol, and 1 mg phenolpthalene as a pH indicator. Gluteal fat biopsies were collected by aspiration through a 14 g stainless steel needle at baseline and 24 h after ingestion of the test meal. All breath and fat samples were assayed for the presence of 14C (as described below). For these 24 h tests, subjects received 30% of daily energy needs at each of breakfast, lunch, and dinner, with the remaining 10% of calories received in an evening snack. The timing of meals/snacks was kept constant over all tests. All food was provided by the GCRC on an inpatient basis and the macronutrient content of each meal was the same as that of the test meal. Only the test breakfast contained RS during these 24 h tests, all other meals were composed of standard, commercially available products.
Analyses
All glucose, FFA, and TAG assays were conducted by the GCRC Core Laboratory using an automated Cobas Mira Plus (Roche Diagnostics, Basel, Switzerland). Serum insulin measurements were also performed by the GCRC Core Laboratory using a human insulin RIA kit (Linco, St. Louis, USA).
Fat samples, frozen in liquid nitrogen and stored at -80°C until processing, were incubated in 450 μl Solvable (Packard Bioscience, Groningen, Netherlands) at 50°C for 12 h before the addition of 100 μl 30% (v/v) hydrogen peroxide (for sample bleaching). Fat samples were counted in Aquasol (Packard Bioscience, Groningen, Netherlands) whereas breath samples were counted in Scintisafe 30% (Fisher Chemical, New Jersey) using a Beckman LS6500 scintillation counter (Beckman Instuments, Fullerton, CA). After scintillant was added, all samples were kept in the dark at room temperature for 48 h before being counted to reduce chemiluminescence.
Calculations
Calculation of total fat and carbohydrate oxidation
Formulae used to calculate non protein RQ and subsequent estimations of carbohydrate and fat oxidation were based on the derivations described by Jéquier et al. ([39]).
Calculation of ΔRQ
ΔRQ = RQt - RQbaseline
where t is sample time (min).
Calculation of meal fat storage from biopsy data
μg fat stored/g fat tissue = (dpm24h/g tissue weight) - (dpmbaseline/g tissue weight) × 1/specific activity
μg fat stored/whole body = μg fat stored/g fat tissue × total fat mass (from DEXA)
Calculation of 14C-triolein oxidation
counts from sample (dpm/mol CO2)/vCO2 (min.ml) = (dpmt - dpmbackground) × 1/vCO2 =
dpm.mol CO2/ min.ml
dpm/min = dpm.mol CO2/ min.ml × 0.446 (as 1 ml CO2 = 0.446 mol)
g fat oxidized = AUC(dpm/min) × 1/specific activity
where vCO2 is the rate of CO2 production as assessed during indirect calorimetry. t is sample time (min). AUC is the incremental area under the curve.
Analysis
All statistical analyses were performed using the statistical analysis software SAS, version 8.1 (SAS OnlineDoc, 2000) with a significance level of p = 0.05 and p = 0.01 for interaction terms. All results are presented as mean ± SEM, except for subject characteristics which are described as mean ± SD. To investigate each of the outcomes (glucose, insulin, FFA, TAG, RQ, meal fat oxidation, and meal fat storage) we used a mixed model with fixed effect terms for RS DOSE, TIME and the interaction of the two, RS DOSE*TIME. Subjects were included as random effects. The interaction term was not significant for any of the outcomes tested so an additive model was used to test the overall effect of RS DOSE and the differences between doses. To test the effects of RS DOSE at different TIMES, a model that included RS DOSE, TIME and RS DOSE*TIME was used. The repeated measures nature of the study design was taken into account by using the covariance structures available in SAS PROC MIXED. For example, measurements within a subject are assumed to be more highly correlated than between subjects, and within a particular treatment, within a subject, the measurements are assumed to be more correlated. Measurements closer in time to one another were modeled with an autoregressive, or AR(1) covariance structure.
Abbreviation List
RS, resistant starch; DS, digestible starch; TAG, triacylglycerol; FFA, free fatty acid; FFM, fat free mass; SCFA, short-chain fatty acids; GCRC, General Clinical Research Center; RQ, respiratory quotient
Competing interests
Janine Higgins and Ian Brown are listed as inventors on RS patents filed by Penford Australia Limited. Both Drs. Higgins and Brown are listed as inventors on these patents as they have intellectual property ownership of some of data used in these but receive no financial benefit.
Authors' Contributions
JH conceived of the study design and was responsible for overall study coordination, conducting patient visits, data analysis, and manuscript preparation. DH was responsible for patient scheduling, day-to-day study coordination, conducting patient visits, and data entry. WD contributed to the study design and manuscript preparation, and conducted patient physical examinations and fat biopsies. IB contributed to the study design, selection of RS foods, and assisted with manuscript preparation. MB conducted all statistical analysis. DB contributed to the study design and manuscript preparation, and conducted patient visits, patient physical examinations and fat biopsies. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Individual meal (a) and total fat oxidation (b) in response to the RS content of a test breakfast. Meal fat oxidation, assessed via measurement of 14CO2 in expired air, and total fat oxidation, assessed via indirect calorimetry and calculated from non-protein RQ, and was measured in 12 healthy adults.
Click here for file
Additional File 2
Individual area under the glucose curve vs. meal (a) and total fat oxidation (b) in response to a test breakfast. Meal fat oxidation, assessed via measurement of 14CO2 in expired air, and total fat oxidation, assessed via indirect calorimetry and calculated from non-protein RQ, and was measured in 12 healthy adults. Data from all three test meals (0%, 5.4%, and 10.7% RS) is shown. The relationship between area under the glucose curve and fat oxidation remains the same (i.e. no relationship) when represented as individual doses or, as in this plot, for all doses (see Figure S3).
Click here for file
Additional File 3
Individual area under the glucose curve vs. meal fat oxidation in response to a 0% (a) or 5.4% (b) RS test breakfast. Meal fat oxidation, assessed via measurement of 14CO2 in expired air, and total fat oxidation, assessed via indirect calorimetry and calculated from non-protein RQ, and was measured in healthy adults. Data from individual test meals is shown.
Click here for file
Additional File 4
Individual area under the insulin curve vs. meal (a) and total fat oxidation (b) in response to a test breakfast. Meal fat oxidation, assessed via measurement of 14CO2 in expired air, and total fat oxidation, assessed via indirect calorimetry and calculated from non-protein RQ, and was measured in 12 healthy adults. Data from all three test meals (0%, 5.4%, and 10.7% RS) is shown. (Document type: Powerpoint, PPT)
Click here for file
Acknowledgements
The authors wish to thank Coni Francis, RD, PhD, and Therese Ida, MS, RD, for expert advice on all dietary issues and for designing all test meals. All funding for this work was provided by the NIH through a direct NIDDK grant (DK57492) and via GCRC support (M01 RR00051). All high RS foods were donated by Penford Foods, Australia.
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| 15507129 | PMC526391 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Oct 6; 1:8 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-8 | oa_comm |
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Curr Control Trials Cardiovasc MedCurrent Controlled Trials in Cardiovascular Medicine1468-67081468-6694BioMed Central 1468-6708-5-101548260210.1186/1468-6708-5-10ResearchElevated levels of matrix metalloprotein-3 in patients with coronary aneurysm: A case control study Tengiz Istemihan [email protected] Ertugrul [email protected] Emil [email protected] Cevad [email protected] Can [email protected] Imre [email protected] Central Hospital, Cardiology Department, Izmir, Turkey2 Kent Hospital, Cardiology Department, Izmir, Turkey3 Kocaeli University Medical School, Biochemistry Department, Kocaeli, Turkey4 Ege University Medical School, Microbiology Department, Izmir, Turkey2004 13 10 2004 5 1 10 10 22 5 2004 13 10 2004 Copyright © 2004 Tengiz et al; licensee BioMed Central Ltd.2004Tengiz et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Matrix metalloproteinases (MMPs) have been implicated in the pathogenesis of arterial aneurysms through increased proteolysis of extracellular matrix proteins. Increased proteolysis due to elevated matrix degrading enzyme activity in the arterial wall may act as a susceptibility factor for the development of coronary aneurysms. The aim of this study was to investigate the association between MMPs and presence of coronary aneurysms.
Methods
Thirty patients with aneurysmal coronary artery disease and stable angina were enrolled into study (Group 1). Fourteen coronary artery disease patients with stable angina were selected as control group (Group 2). MMP-1, MMP-3 and C-reactive protein (CRP) were measured in peripheral venous blood and matched between the groups.
Results
Serum MMP-3 level was higher in patients with aneurismal coronary artery disease compared to the control group (20.23 ± 14.68 vs 11.45 ± 6.55 ng/ml, p = 0.039). Serum MMP-1 (13.63 ± 7.73 vs 12.15 ± 6.27 ng/ml, p = 0.52) and CRP levels (4.78 ± 1.47 vs 4.05 ± 1.53 mg/l, p = 0.13) were not significantly different between the groups.
Conclusion
MMPs can cause arterial wall destruction. MMP-3 may play role in the pathogenesis of coronary aneurysm development through increased proteolysis of extracellular matrix proteins.
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Introduction
Coronary artery aneurysms are defined as dilated coronary artery segments that are greater than 1.5 times the diameter of adjacent normal segments [1,2]. The gold standard for diagnosing this type of aneurysm is coronary angiography, which provides information about the size, shape, location and number of aneurysms.
Coronary aneurysms may occur during the development of coronary atherosclerosis. Previous studies have shown that coronary aneurysms are observed in 1% to 5% of patients with angiographic evidence of coronary artery disease [3-6]. In some studies, coronary aneurysms have been associated with an increased risk of myocardial infarction [3,4]. Although the mechanisms responsible for coronary aneurysm formation during the atherosclerotic process are unclear, atherosclerosis-induced aneurysms derive primarily from thinning and/or destruction of the media [6-8].
Possible factors contributing to aneurysms are matrix-degrading enzymes such as collagenases, gelatinases, and stromelysins [9,10]. More specifically, matrix metalloproteinases (MMPs) are enzymes that can degrade the structural proteins of connective tissue. Degradation of extracellular matrix proteins may weaken the connective tissue, thereby leading to a weakened vascular wall.
We investigated the association between MMPs and coronary artery aneurysm by measuring the levels of MMP-1 and MMP-3 (both of which represent markers of proteolytic activity) in patients with coronary artery disease, some of whom had coronary aneurysms (cases) and others who did not (controls).
Methods
Patient population
We reviewed the medical records of patients who had undergone coronary angiography between January, 2002 and April, 2003. Among 4,456 cases reviewed, 55 patients (1.23%) diagnosed with aneurysmal coronary artery disease were selected. Sixteen patients with acute coronary syndromes and nine patients with balloon angioplasty history were excluded from the study. The remaining 30 patients with aneurysmal coronary artery disease patients were enrolled into the study. Transverse diameter of an aneurysm and reference vessel were measured using the post-processing software (Schimadzu Corporation, DIGITEX ALPHA Plus System, Kyoto, Japan, 2001). The ratio between dilated coronary artery segment and reference vessel diameter was calculated. The control patients (n = 14) had coronary artery disease, but were free of aneurysmal coronary dilatation. Both groups had positive exercise stress tests and had been diagnosed with stable angina. Blood biochemistry and echocardiography were performed in all patients. No patient had a history of coronary atherectomy or balloon angioplasty. All participants gave informed consent.
Autoimmune disease, inflammatory arteritis, chronic or, acute infectious disease, use of steroid or anti-inflammatory drugs within the last three months, renal failure and cancer were accepted as exclusion criteria.
Laboratory assays
Specimen collection
Fasting blood samples (8–10 hours fast) were obtained from the antecubital vein at approximately 9:00 a.m. These were centrifuged for 10 min at 3,000 × g at a temperature of about 4°C. Serum was stored at -70°C. Blood samples were analyzed at the Ege University Department of Microbiology, Section of Serology.
Assay protocol for MMP-1 and MMP-3
MMP levels were determined using enzyme-linked immunosorbent assay (ELISA) kits, according to the manufacturer's instructions (MMP-1, Biotrak Amersham Pharmacia Biotech, United Kingdom; RPN 2610; MMP-3, Biotrak Amersham Pharmacia Biotech, United Kingdom; RPN 2613). The ELISA kit measured total MMP-1 (pro MMP-1, free MMP-1, MMP1/tissue inhibitor MP-1 complex), total MMP-3 (pro MMP-3, free MMP-3, MMP3/tissue inhibitor MP-1 and MMP3/tissue inhibitor MP-2 complex) at >89% cross reactivity. Samples were incubated in microtitre wells pre-coated with anti-MMP-1 (lyophilized rabbit anti-MMP-1) and anti-MMP-3 (peroxidase labelled Fab antibody to MMP-3) antibodies. The assays use the pro form of a detection enzyme that can be activated (by captured active MMP) into an active detection enzyme. MMP-1 and MMP-3 can be measured in the range of 6.25–100 ng/ml and 3.75–120 ng/ml, respectively. The results received from the optic scanners at 450 nm were converted into ng/ml values from a standard curve. All samples were run in duplicate and were averaged. Within-assay precision values for duplicate determinations were 5.5%, 7.9% and 7.3% at MMP-1 concentrations of 16.89 ± 0.94 ng/ml, 35.53 ± 2.82 ng/ml and 54.08 ± 4.0 ng/ml, respectively. Between-assay precisions for repeated measurements of the same sample were 11.6%, 12.0% and 13.2% at MMP-1 concentrations of 23.19 ± 2.68 ng/ml, 55.27 ± 6.65 and 98.04 ± 12.93, respectively. The within-assay precisions for duplicate determinations were 4.8%, 2.4% and 2.1% at MMP-3 concentrations of 13.7 ± 0.66 ng/ml, 33.7 ± 0.83 ng/ml and 83.2 ± 1.76 ng/ml, respectively. Between-assay precisions for repeated measurement of the same sample were 13.3%, 11.7% and 8.8% at MMP-3 concentrations of 11.2 ± 1.49 ng/ml, 27.6 ± 3.24 and 75.4 ± 6.63, respectively.
Determination of C-reactive protein levels
Serums were obtained by centrifugation of vacutainer-clotted tubes at 3,000 rpm for 10 minutes. High sensitivity C-reactive protein (hs-CRP) samples were stored at -30°C and analyzed by latex particle-enhanced immunoturbidimetric assay. The total median inter-assay and intra-assay coefficients of variation for the assays were <6% for CRP. All results were recorded in the patients' files.
Statistical analyses
All values are reported as mean ± SD. Chi Square test was used in the comparison of categorical variables while student unpaired-t test or Mann-Whitney Rank Sum tests were used, where appropriate, in the univariate analysis. Statistical analyses were performed with SPSS statistical software. A value of p < 0.05 was considered to be statistically significant.
Results
There were no significant differences in baseline characteristics between cases and controls. High-density lipoprotein, low-density lipoprotein, total cholesterol and triglyceride levels were not statistically different between the groups. Clinical characteristics of and medication use by the groups are shown in Table 1.
Table 1 Clinical Characteristics and Medication Use of Study Participants
Group 1 (n = 30) Group 2 (n = 14) p
Mean age (yrs) 55.2 ± 10.0 51.8 ± 7.7 NS
Male sex % (n) 70%(21) 64%(9) NS
Diabetes Mellitus % (n) 13%(4) 14%(2) NS
Hypertension % (n) 30%(9) 21%(3) NS
Smoking % (n) 60%(18) 50%(7) NS
TC (mg/dl) 196.8 ± 31.7 195.1 ± 38.2 NS
TG (mg/dl) 148.7 ± 71.2 151.7 ± 64.0 NS
HDL-C (mg/dl) 46.7 ± 11.8 50.7 ± 13.0 NS
LDL-C (mg/dl) 125.1 ± 28.2 116.7 ± 34.2 NS
hs-CRP (mg/L) 4.78 ± 1.47 4.05 ± 1.53 NS
MMP-1 (ng/ml) 13.63 ± 7.73 12.15 ± 6.27 NS
MMP-3 (ng/ml) 20.23 ± 14.68 11.45 ± 6.55 0.039
Baseline therapy
Aspirin 73%(22) 64%(9) NS
Nitrate 57%(17) 64%(9) NS
Statin 17%(5) 21%(3) NS
Number of stenotic vessels
One vessel disease 37%(11) 36%(5) NS
Two vessel disease 50%(15) 43%(6) NS
Three vessel disease 13%(4) 21%(3) NS
Reference vessel diameter (mm) 2.95 ± 0.48 - -
Aneurysm vessel diameter (mm) 4.78 ± 0.93 - -
Aneurysm/reference vessel ratio 1.6 ± 0.1 - -
Aneurysm segment
Right coronary artery 53%(16) - -
Left anterior descending artery 27%(8) - -
Left Circumflex artery 30%(9) - -
Group 1:Patients with coronary aneurysm, Group 2:Patients without coronary aneurysm, TC:Total cholesterol, TG:Triglyceride, HDL-C:High-density lipoprotein cholesterol, LDL-C:Low-density lipoprotein cholesterol, hs-CRP:High sensitivity C- reactive protein, MMP-1:Matrix metalloproteinase-1, MMP-3:Matrix metalloproteinase-3, NS:Non-significant
Mean serum MMP-1 (13.63 ± 7.73 vs 12.15 ± 6.27 ng/ml, p = 0.52) and CRP levels (4.78 ± 1.47 vs 4.05 ± 1.53 mg/l, p = 0.13) were not significantly different between cases and controls. Mean serum MMP-3 values were significantly higher in the cases than in controls (20.23 ± 14.68 and 11.45 ± 6.55 ng/ml respectively, p = 0.039). MMP-1, MMP-3 and hs-CRP levels are shown in Figure 1.
Figure 1 text
Discussion
Essential factors contributing to the formation of coronary aneurysms include vessel media degradation and ulceration due to increased proteolytic activity. Connective tissue integrity, another factor contributing to aneurysm development, depends on the balance between degradation and repair of the extracellular matrix. Activation or inhibition of degrading enzymes affects extracellular matrix modeling [9,10], which, in turn, affects connective tissue and vascular wall integrity.
Matrix-degrading enzyme activity is a tightly controlled process that involves transcription, activation of latent pro-enzymes and inhibition of proteolytic activity [11-13]. A key step in the regulation of MMPs may occur at the level of transcription [14]. The mechanism by which gene transcription is mediated is thought to involve a prostaglandin E2(PGE2)-cAMP- dependent pathway. G-proteins have been implicated in this pathway [15]. Transcription activity can be stimulated by a variety of inflammatory cytokines, hormones, and growth factors [16-19]. Several factors are also known to inhibit MMP gene expression and these include indomethacin, corticosteroids, and interleukin-4 [17,20,21].
MMP activity is also regulated by tissue-specific inhibitors. There are four known tissue inhibitors of metalloproteinases (TIMP-1, -2, -3 and -4). The TIMPs are secreted by a variety of cell lines, including smooth muscle cells and macrophages. Their activity is increased by growth factors and either increased or decreased by different interleukins [22]. Increased levels of MMP-2, MMP-3, MMP-9 and MMP-12 have been identified in aneurysm vessel walls [23-27]. Gene disruption of MMP-9 suppresses the development of experimental abdominal aortic aneurysms [28]. Conversely, decreased levels of TIMPs have been found in the aneurysm wall [26]. Allaire et al. [29] reported that local expression of TIMP-1 may prevent aortic aneurysm degeneration and rupture in a rat model. Carrell et al. [30] examined differences in MMPs between patients with aortic aneurysm and patients with aortic atherosclerosis but without aneurysm. Among a wide range of MMPs tested, only MMP-3 was over-expressed in the aortic aneurysm samples. Reduced aneurysm formation has been observed in mice with MMP-3 gene inactivation [31]. Finally, the recent observation that high circulating levels of MMP-3 are associated with coronary lesions in Kawasaki disease [32] also supports an important role for MMP-3 in the pathogenesis of coronary aneurysms. These data suggest that proteolytic balance in the vascular wall plays a key role in aneurysm development.
MMP-1 (interstitial collagenase) and MMP-3 (stromelysin-1) are members of a family of proteinases that degrade one or more components of the extracellular matrix. In our study, it appears that elevated MMP-3 activity may represent a risk factor for coronary aneurysm formation. This finding is concordant with previously published studies. The mechanisms underlying this association are unclear. MMP-3 gene disruption may be responsible. Lamblin et al. [33] have reported similar findings, namely, that the MMP-3 5A allele is associated with the occurrence of coronary aneurysm.
Others have reported that MMP-3 is expressed in atherosclerotic plaque cells, but not by cells in normal arteries [34-37]. In addition, extensive inflammation and destruction of musculo-elastic vessel wall elements have been observed in dilated human coronary arteries [38,39]. Schoenhagen et al. [40] suggest that the degradation of extracellular matrix by MMP-3 may contribute to the expansion of the coronary vessel wall. This effect is characteristic of positive remodeling. Based on these and our own observations, we maintain that MMP-3 over-expression may occur in aneurysm segments. Histopathologic studies would be needed to clarify whether or not this is the case.
MMP levels are elevated in patients with acute myocardial infarction, unstable angina and coronary angioplasty [35,41,42]. All patients in our study had been diagnosed with stable angina before being enrolled into the study.
CRP reflects systemic inflammatory activity. In this study, we did not observe increased CRP levels in those patients with coronary aneurysms. One explanation for similar CRP expression between cases and controls might be that all study subjects had been diagnosed with stable angina pectoris.
Varying degrees of inflammation are reported among individuals with abdominal aortic aneurysms. This variation may relate to possible confounding due to clinical manifestations (asymptomatic or symptomatic) and aneurysm progression rates (cm/year). Other investigators have failed to observe increased CRP levels among asymptomatic patients with abdominal aortic aneurysm [43].
Because elevated MMP-3 levels likely contribute to the development of coronary aneurysms, this matrix-degrading enzyme may represent an important therapeutic target. Luan et al. [44] reported that a number of statins inhibit MMP-3 activity in rabbits. COX-2 inhibitors may also suppress MMP expression. Production of MMPs by macrophages occurs through a PGE2/cAMP-dependent pathway [45]. Theoretically, COX-2 inhibitors could attenuate this pathway. Another target of MMP inhibition has been demonstrated in animal models of adenovirus-mediated TIMP gene transfer [46].
In reporting our findings, we acknowledge that measurement of TIMP levels between cases and controls would have provided useful information about the possibility of proteolytic imbalance. Similarly, measurement of locally produced inflammatory cytokines, hormones and growth factors would be interesting to know about, since these regulate matrix-degrading enzyme expression [16-19]. This could provide relevant information, as systemic inflammatory activity may not reflect local inflammatory infiltration in aneurysm segments. Finally, the study would have benefited from having a larger sample size as well as genotype determination.
We conclude that MMP-3 overexpression due to a proteolytic imbalance may lead to coronary aneurysm development through degradation of matrix components, especially lamina elastica. New medical therapeutic options targeted specifically against MMP-3 may prove useful in the prevention of aneurysm formation.
Acknowledgements
This study was supported by GURVAK (Gürbüz Sağlık ve Eğitim Vakfı). Authors wish to thank Mrs.Fadime Önal for technical assistance.
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| 15482602 | PMC526392 | CC BY | 2021-01-04 16:47:31 | no | Curr Control Trials Cardiovasc Med. 2004 Oct 13; 5(1):10 | utf-8 | Curr Control Trials Cardiovasc Med | 2,004 | 10.1186/1468-6708-5-10 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-741545857510.1186/1471-2164-5-74Research ArticleGenome wide analysis of common and specific stress responses in adult drosophila melanogaster Girardot Fabrice [email protected] Véronique [email protected] Hervé [email protected] Institut Jacques Monod, 2 place Jussieu, 75251 Paris, France2 Present address: Equipe de Biologie Virtuelle, UMR 6543, Universilé de Nice, Parc Valrose, 06108 Nice Cedex 2, France2004 30 9 2004 5 74 74 4 5 2004 30 9 2004 Copyright © 2004 Girardot et al; licensee BioMed Central Ltd.2004Girardot et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
During their life, multicellular organisms are challenged with oxidative stress. It is generated by several reactive oxygen species (ROS), may limit lifespan and has been related to several human diseases. ROS can generate a wide variety of defects in many cellular components and thus the response of the organism challenged with oxidative stress may share some features with other stress responses. Conversely, in spite of recent progress, a complete functional analysis of the transcriptional responses to different oxidative stresses in model organisms is still missing. In addition, the functional significance of observed transcriptional changes is still elusive.
Results
We used oligonucleotide microarrays to address the specificities of transcriptional responses of adult Drosophila to different stresses induced by paraquat and H2O2, two oxidative stressors, and by tunicamycin which induces an endoplasmic reticulum (ER) stress. Both specific and common responses to the three stressors were observed and whole genome functional analysis identified several important classes of stress responsive genes. Within some functional classes, we observed that isozymes do not all behave similarly, which may reflect unsuspected functional specificities. Moreover, genetic experiments performed on a subset of lines bearing mutations in genes identified in microarray experiments showed that a significant number of these mutations may affect resistance of adult Drosophila to oxidative stress.
Conclusions
A long term common stress response to paraquat- or H2O2-induced oxidative stresses and ER stress is observed for a significant number of genes. Besides this common response, the unexpected complexity of the stress responses to oxidative and ER stresses in Drosophila, suggest significant specificities in protective properties between genes associated to the same functional classes. According to our functional analysis, a large part of the genome may play a role in protective mechanisms against oxidative stress in Drosophila.
DrosophilamicroarraysproteasomeP450GTstress.
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Background
Cells are frequently submitted to exogenous or endogenous stresses. In aerobic cells, reactive oxygen species (ROS), produced by respiration and other biological processes, are a major source of endogenous stress. These ROS include the superoxide radical (O2•-), hydrogen peroxide (H2O2) and the highly reactive hydroxyl radical (OH•). Increased endogenous production, exposition to exogenous sources of ROS or reduction in antioxidant defense capacity cause molecular damages such as alterations in proteins, lipids and DNA, and may lead to cell death. Oxidative stress is believed to limit the lifespan of multicellular organisms [1,2] and oxidative lesions have been implicated in several human cardiovascular and neurodegenerative diseases [3]. A better understanding of the in vivo responses to oxidative stress is thus of major fundamental and practical importance.
Much data describing the action of ROS and their derivatives in cultured cells are now available. For instance, ROS have been shown to activate signal transducing components, like p53 or members of the NF-κB pathway, resulting either in increased antioxidative protection or in activation of apoptotic pathways [2]. Nevertheless, a comprehensive integrated picture of the in vivo cellular responses of metazoans to oxidative stress is still not available. Genetic data suggest that different protection mechanisms are involved in vivo according to the type of ROS that induces the stress [4]. In addition, a wide diversity of macromolecules may undergo oxidative damage and induce secondary cellular stresses. These secondary effects of ROS could be similar to the alterations in macromolecules observed in other stress conditions, such as heat stress, endoplasmic reticulum (ER) stress (induced by accumulation of misfolded proteins), or UV-induced DNA damage. The relative importance of these common and specific responses to oxidative and other cellular stresses still has to be determined.
In the yeast Saccharomyces cerevisiae, microarray experiments have shown that similar transcriptional responses are observed in a large number of different environmental conditions, including oxidative stress induced by H2O2 or menadione [5,6]. According to the authors, this common environmental stress response (CER) may reflect the need for yeast to adapt quickly to rapidly changing external conditions. Similar transient variations of protein levels were also observed in proteomic experiments and highlighted the existence of an H2O2 stimulon [7]. It is not clear whether such common stress responses exist in long-living multicellular organisms since, unlike unicellular organisms, their cells are probably submitted to slower and smaller variations of the extracellular medium; furthermore, the survival of just one cell is not generally crucial for the survival of the organism as a whole.
Considering its powerful genetic and genomic tools, Drosophila melanogaster is a relevant model to address the question of the specificities of in vivo responses to various stresses in multicellular organisms and to identify novel genes that play a protective role. Nevertheless; there are some limitations to such studies on living flies. Firstly, adult flies are mainly composed of post mitotic cells; thus data obtained with flies may be relevant for comparison with the stress response of post mitotic tissues in other organisms (for instance mammals' neurons) but could be less useful to address the question of stress response in dividing cells. Secondly, limitations also arise from ROS-generating compounds delivery which in Drosophila is usually performed through food ingestion. This method severely limits kinetic studies of acute stress responses on flies, since, within a few hours, large fluctuations in the quantity of ingested food are observed in batches of flies transferred to a new medium. To overcome this problem some experiments were performed on starved flies. The major issue with such an approach is that the observed transcriptional changes could result from the starvation stimulus as well as from the effect of the studied compound. Therefore such experiments may in fact characterize the interference of two different stress responses with different induction times and kinetics rather than a bona fide oxidative stress response. A previous microarray study, performed with such a strategy on 4500 Drosophila genes, analyzed changes in transcription induced by paraquat, which produces superoxide radical (O2•-) intracellularly: 5.2% of the genes (n = 236) were found to be stress responsive. Kinetic analysis revealed that transcriptional modifications lead to the establishment of a more or less stable new expression profile 12 hours after stress induction, thus indicating the existence of a long term stress response (LTSR) [8]. This stability probably reflects the late response to paraquat-induced stress but variations before 12 h are more difficult to interpret since they may also arise from the stress effects of starvation. This analysis also did not address the question of the specific responses to different ROS and could not distinguish between specific responses to oxidative stress and responses common to other cellular stresses. Furthermore, since the arrays covered only 30% of the estimated total number of Drosophila genes, this study was also limited for functional statistical analysis and detailed analysis of functional classes involved in stress response.
From previous considerations, we chose to focus on comparisons of the long term transcriptional response (LTSR) of adult flies 24 h after ingestion of different stress-generating compounds. These responses may be representative of those of postmitotic tissue exposed to physiological chronic stress conditions (even if the level of stress is certainly higher in our experiments). Thus we investigated, at a full genome wide level, the transcriptional LTSR in adult Drosophila submitted during 24 h to three types of stresses: paraquat or H2O2-induced oxidative stresses and tunicamycin-induced ER stress. This latter drug inhibits N-linked glycosylation, thus leading to an accumulation of misfolded proteins in the ER (referred to as ER stress) which is known to elicit a specific response: the Unfolded Protein Response (UPR) [9]. We show in this paper that some of the transcriptional changes observed for these three stress conditions are similar, thus defining a class of multiple stress responsive genes. Nevertheless, in addition to this common long term stress response (CLTSR), many genes are transcriptionally regulated in a stress-specific manner. A statistical analysis identified classes of molecular functions or cellular processes over-represented inside clusters of genes undergoing transcriptional changes. Unexpectedly, both up and down regulations were observed for members of the same functional class. This may reveal novel functional specificities for these genes. In addition, we investigated whether genes that display significant transcriptional variations play a functional role in oxidative stress resistance. We present data suggesting that this could be the case for a large number of the stress-responsive genes identified in our study, which emphasizes the polygenicity of the stress responses, at both a molecular and a functional level.
Methods
Stocks
All the lines tested for paraquat stress resistance were collected from the Bloomington stock center. To minimize genetic background effects, when the mutation was linked to a w+ transposon insertion, the line was outcrossed for 4 generations against a w- isogenic strain of Canton S background before stress experiments. For homozygous lethal mutations we analyzed flies heterozygous for the mutation issued from a cross between the mutant line and the same w Canton S strain.
Stress resistance tests and collection of fly tissues
We used 50 ml vials containing 1 ml of a solid medium composed of 1.3% low melting agarose, 1% sucrose and either 1% H2O2, 5 or 15 mM paraquat, 12 μM tunicamycin (all from Sigma) or no toxic compound (control tubes). These compounds were incorporated at 45°C to avoid loss of activity.
3 day old males were placed in groups of 30 in these vials and maintained at 26°C with a 12:12 light-darkness alternation.
In survival tests, dead flies were counted twice a day until the end of the experiment. In each experiment at least 3 vials of 30 male flies were used. To test mutant lines we performed three independent experiments in order to minimize false positive detection. Survival data were submitted to a log-rank analysis to detect statistically significant survival differences between mutant and w Canton S flies. We considered that a mutation had a significant effect on survival under oxidative stress when the mean of log10 (p-log-rank) for the three experiments was lower than -3 and at least two experiments had p-log-rank < 0.001. For microarray experiments, for each condition, 200 Canton S males were kept 24 hours on the corresponding medium and then frozen in liquid nitrogen for subsequent RNA extraction. Independent batches of males from separate experiments were used for replicate experiments. All fly manipulations were performed at the same stages of the 12:12 light cycle to prevent any undesirable effects from circadian variations.
Sample preparation and data analysis
We analyzed 4 samples of control flies and 3 (paraquat 15 mM) or 2 (all other conditions) samples of stressed flies. Total RNAs were purified by three rounds of Trizol reagent (GIBCO/BRL) extraction before precipitation. cDNA were synthesized from 10 μg total RNA aliquots and biotin-labelled cRNA targets synthesized using the BioArray high yield RNA transcript-labelling kit (Enzo Biochem) according to the manufacturer's instructions. Hybridizations on Drosophila Genome Arrays (Affymetrix) and subsequent washing were performed on a GeneChip Fluidics Station according to the manufacturer's instructions before scanning on a GeneArray scanner.
Data extraction was performed first by the MAS5 Affymetrix program which provides absolute values (AV) and detection p-values (DP) for each probe set. These data were loaded into an Access database for subsequent analysis. We retained only the experimental points that presented a mean value greater than 0.1 for the DPs of all the different samples of at least 1 of our 4 experimental conditions. This reduced the number of probe sets further analyzed from 14028 to 8976. The AV data from each microarray were then normalized against the AV mean value of the 4 control samples by a quantile method which performs optimally [10]. Since a large number of flies (~300) were used for each RNA sample hybridized to a microarray, variations in signal arising from individual transcription differences are greatly reduced. This is reflected in the high values of correlation coefficients between microarrays corresponding to the same experimental condition (data not shown). The normalized values were used for further comparison of each of the stress condition samples with the 4 control samples and for statistical validations of the variations using the SAM program [11]. For this analysis, we used a fold change threshold value of 1.5 and a mean FDR (false detection rate) lower than 10%. 1368 independent probe sets that fulfilled these conditions for at least one type of stress were retained for further analysis.
A hierarchical divisive clustering of the data of these probe sets was performed using the SOTA [12] implementation available at . For each probe set, the ratios for all the combinations between stress conditions AVs and reference AVs were computed and Ln2 transformed. The SOTA algorithm used on this dataset with linear correlation distance with 0 offset, 1000 cycles and 1.01 variability threshold parameters, led to the detection of 19 clusters.
Functional analysis
Information from the Gene Ontology (GO) database (December 2003, [13]) was combined with the Affymetrix data through the THEA program to investigate which classes are over- or under-represented in the dataset of stress responsive genes. Briefly, according to the Gene Ontology hierarchical structure, each probe set was assigned, when possible, to its original annotation and to the associated parent annotations. The number of probe sets for the different GO terms was computed for groups of probe sets defined according to different criteria (such as whole microarray probe sets, detected probe sets or probe sets belonging to a given cluster).
For each GO term G, the distribution between the group D of all the detected probe sets (NGD probe sets issued from a total of ND, probability PG = NGD/ND) and a group C of particular interest, such as a cluster (NGC probe sets issued from a total of NC) are compared. The hypothesis of equal repartition between these two groups would predict that, inside the NC probe sets of group C, NC*PG probe sets should be associated to the GO term. We computed the p-value PN for the null hypothesis of no association between the two distributions, with a binomial distribution with NC tries, a probability PG and NGC successes. Threshold values for PN helped to define the GO terms over- or under-represented in the group C.
Quantitative real time RT-PCR analyses
Experiments were performed as described [14] with 2 μg of the total RNA samples used for microarrays (control, P15, P5, H1 and T12). Primers were designed to generate an amplicon of about 100 nucleotides and their sequences are described below (Forward/Reverse primers):
FBgn0015039: TATGCTCTTCAACCTACTGCTGC/TAGGCGTAAAATTGAATCCACTC
FBgn0010383: GACGCTGAACGGATATGGCAT/ATGTAGGTCATCCCGAACTGTC
FBgn0015035: CAACTCTGAATTTGGCTCTCATCC/AGCGGGTTTCTCCTCCTCAA
FBgn0034334: GAAGCCGGATATGTTACGCAAG/TTCACCAGATAGCCGATGATG
FBgn0038024: CCTCAACAAGTACCCGAATGTG/TACTCCCTTCAGTTCCACGGC
RP49: CCGCTTCAAGGGACAGTATCTG/CACGTTGTGCACCAGGAACTT
Annealing temperature was 62°C except for RP49 and FBgn0034334 transcript level quantifications, for which it was 60°C. We normalized samples by comparison with the levels of the RP49 housekeeping gene. Levels of transcripts under various stress conditions are compared with the transcript level observed in control flies.
Results
Transcriptome variations in adult Drosophila are strongly dependent on the type of stress to which they are submitted
We wished to compare the transcriptomes of flies submitted to continuous stresses induced by ingestion of paraquat, H2O2 or tunicamycin at concentrations leading to similar effects on viability. Survival curves were obtained for 3 day old male flies raised on media containing different concentrations of these drugs (Fig. 1). Concentrations of 1% H2O2, 5 mM paraquat and 12 μM tunicamycin had similar effects on the survival of flies and were chosen for further studies. A paraquat concentration of 15 mM was also used for comparison with previous studies [8].
Figure 1 Lifespan reduction in Drosophila submitted to paraquat-, H2O2- and tunicamycin-induced stress 3-5 day-old Canton S wild type males were placed at t = 0 by groups of 30 in vials containing 15mM paraquat (P15: ●), 5 mM paraquat (P5: ◆), 1% H2O2 (H1: △) or 12 μM tunicamycin (T12: □). Dead flies were counted twice a day to determine survival. 3 vials of 30 individuals were used for each condition. When no toxic compound had been incorporated in the medium, more than 90% survival was observed at t = 120 h (not shown). Similar average lifespan was observed for P5, H1 and T12 around t = 80 h while it was significantly reduced in P15. Arrow indicates the time (t = 24 h) at which flies were collected for RNA extraction. Note the 20% lethality observed at this time for P15 condition. Dead flies were discarded before RNA extraction.
RNA were obtained from separate experiments with 3 day old male flies reared at 26°C with a 12:12 hours light and dark (LD) alternation, on media containing no drug (4 reference samples), 15 mM paraquat (P15: 3 samples), 5 mM paraquat (P5: 2 samples), 1% H2O2 (H1: 2 samples) or 12 μM tunicamycin (T12: 2 samples). Thus a minimum of 8 pairwise comparisons were made for each condition which ensured good statistical significance, as confirmed by quantitative PCR experiments (see below). Stresses were induced 24 h before collection of flies, which occurred at the same time (9 h) of the 12:12 hours light/dark cycle to eliminate the effect of circadian variations. Hybridizations were performed on Affymetrix GeneChips and the data processed as described in the Material and Methods. The statistical significance of transcriptional variations was assessed using the SAM program with a threshold of 1.5 [11]. A good correlation was observed between our P15 results and previous studies with the same stress conditions [8]: among the 246 stress responsive ESTs of Zou et al., 201 were associated to a detectable probe set on our chip, 56% of which were selected by SAM analysis and 72% of which displayed a fold change greater than 1.3 (not shown). The remaining discrepancies may arise from differences in statistical selections, in analyzed tissues (thorax and abdomen in [8], whole flies in this study) or in genotype: compared to the w1118 flies used in [8], our wild type Canton S flies were more resistant to paraquat 15 mM (mortality of 20% vs 54% at 24 h) and presented an increased medium lifespan (48 vs 35 days at 26°C, on standard medium).
Among the 8976 probe sets significantly detected in adult flies (see Material and Methods), 1111 were up or downregulated with P15 treatment, this number being reduced to 608, 72 and 221 for P5, H1 and T12 treatments respectively. Thus, even with similar effects on flies survival, the fraction of the genome detected as stress responsive on microarrays was highly dependent on the nature of the stress, varying about ten times from 7% (P5) to 0.7% (H1). This first analysis defined a total of 1368 probe sets and 1343 genes which are induced or repressed at least in one stress condition. They were used for further analysis.
Common and specific responses to different stress
We plotted transcriptional variation correlations for the different oxidative stress conditions (Fig. 2). We observed a high degree of correlation between the two paraquat experiments (correlation coefficient c = 0.86, Fig. 2a). The slope of the linear regression curve, however, was 1.14 which indicates that variations in transcription induced by paraquat may be dose-dependent for most genes in D. melanogaster. Lower correlations were observed for the linear regressions between P5 ratios and either H1 ratios (c = 0.64, slope = 0.40, Fig. 2b), or T12 ratios (c = 0.43, not shown).
Figure 2 Correlations between P5 and P15 or H1 microarray measurements For each of the 1368 probe sets selected in the SAM analysis, the mean Ln2 ratios between the absolute values (AV) for stress and reference conditions were compared in two dimensional plots. Bold lines are the linear regression curves for the two comparisons, the thin lines correspond to a complete correlation for eye guidance. A good correlation is observed between P5 and P15 with a slope of 1.14, while it is much weaker between P5 and H1 (slope of 0.4).
Clustering analysis provided further information about the specificity of stress responses. We chose an unsupervised divisive clustering method (SOTA [12]) to analyze the data and we checked that other methods such as Self Organizing Maps [15] yielded similar results (not shown). The SOTA analysis predicted 19 clusters. The complete list of the 1368 probe sets with their cluster assignment is provided as Tab.S1 (Additional file1) in supplementary data. In Tab.1, (Additional file 8) we present the average log-ratios in each stress condition for the 19 identified clusters. These data confirm the high correlation between the results for P15 and P5 and the general tendency toward smaller variations for P5. However, the genes included in cluster 7 exhibit a more severe repression in the P5 condition than in the P15 condition which may reflect a differential transcriptional response as a function of oxidant concentration.
Notably, in clusters 5, 6, 7, 9, 10, 13, 16 and 18 which regroup 642 probe sets, significant variations for H1 were observed in the same direction than for P5 or P15. This suggests that, for a large number of genes, both oxidative compounds induce similar transcriptional responses. Therefore, the fact that the number of probe sets validated by the SAM procedure as being significatively affected in the H1 condition is smaller than in the paraquat conditions may be a consequence of a similar but weaker effect of H2O2 on the transcriptome rather than fundamental differences in the responses to the two oxidants.
What is the specificity of the oxidative stress responses induced by paraquat or H2O2 compared to the ER stress response induced by tunicamycin? The 19 clusters from Tab.1 (Additional file 8) can be regrouped into 7 large classes of genes: Classes A and B contain genes respectively downregulated and upregulated in both oxidative stress and ER stress conditions. Inside these two large groups, 237 genes included in clusters 9, 10 and 13 are regulated in a similar fashion in all four stress conditions. Genes from classes C and D (48% of stress responsive probes) are respectively downregulated and upregulated by oxidative but not ER stress. Conversely, class F genes are upregulated in ER stress but not in oxidative stresses. In the atypical classes E and G, opposite variations are observed for the two types of stress: genes of class G are upregulated by ER stress but downregulated in oxidative stress while genes of class E display an opposite behavior. Overall, our data emphasize both specificities and similarities in these stress responses: the classes A and B (238 and 276 probe sets, respectively) which include genes displaying similar responses to both oxidative and ER stresses, represent a sizeable fraction (38%) of the stress responsive probes. In contrast, genes that vary in opposite directions, included in the classes E (104 probe sets) and G (60 probe sets), represent a smaller part of those stress responsive probes (12%).
Classes of stress responsive genes
Using the Gene Ontology annotation [13] we identified the molecular functions that are over- or under-represented among all the 1368 stress-responsive probesets compared to the distribution of functions identified for the complete set of 8976 detectable probesets (see Material and Methods). The analysis was first performed independently for each set of genes validated by the SAM procedure for each stress Table 2a to c (Additional file 9). A similar analysis for biological processes is given in supplementary Table S2 (Additional file 2). The most prominent functional classes over-represented in the paraquat sets are the peptidases (including peptidases which are part of the proteasome complex), the peptidase inhibitors, the glutathione transferases (GT) and oxidoreductase enzymes or electron transporters, including the P450 cytochromes. These classes could all be involved in the detoxifying processes that follow oxidative stress and are discussed in more detail below. In addition, lipases and more prominently the triacylglycerol lipases, also over-represented, may contribute to the regeneration of membranes after oxidative damage. Most of these features seem to be part of a general stress response since triacylglycerol lipases, peptidases with chymotrypsin or trypsin activity and GTs are also over-represented in the H2O2 and tunicamycin specific sets of genes. The transaminases, the cyclohydrolases, the oxidoreductases and the hydroxymethyltransferases define the signature of functional classes over-represented in the two types of oxidative stresses. In contrast, proteins which bind to iron ions or monooxygenases are specifically over-represented in the paraquat set. As expected, the ER set presents features that are distinct from oxidative stress responses, that is the over-representation of hydrolases acting on glycosyl compounds, UDP-glucuronosyltransferases and tRNA ligases. This last class suggests that modifications of the translation rate may be an in vivo response to ER stress. Besides these ER stress-specific classes, peptidases with elastase activity and epoxide hydrolases are over-represented in both paraquat- and tunicamycin-induced stresses. Interestingly, this last class of proteins is involved in the metabolism of juvenile hormone which has been shown to be involved in heat stress response [16].
We then performed a similar analysis for the groups of genes identified in the clustering process. To increase the statistical significance of the analysis, we used the 7 groups A to G instead of the 19 initial clusters. This analysis, given as Tables S3 (Additional file 3) and S4 (Additional file 4) of supplementary data, allowed us to identify molecular function and process signatures in some clusters. For instance, for the genes repressed for oxidative and ER stress conditions (group A) specific over-representations are observed for alkaline phosphatases, diazepam binding proteins and proteins involved in acyl-CoA metabolism. Signatures of group B (genes upregulated for oxidative and ER stresses) include proteins involved in response to abiotic stimuli, including GTs and glutathione peroxydases, and tRNA ligases. This last feature may indicate that the organism reacts to sustained stress by an increase of protein synthesis. Nevertheless, genes involved in proteins biosynthesis are surprisingly under-represented among the stress responsive genes. Retinoid binding proteins and transporters are specifically over-represented in group C. Surprisingly, in this group of genes, a large number of peptidases are present along with a strong proportion of protease inhibitors. Signatures for group D (genes upregulated under oxidative but not ER stress) include chaperones associated with the heat shock response, glutamate synthases and proteins involved in ATP-dependent proteolysis. Glutamate synthases, together with the upregulated genes Ahcy13 and Eip55E, may be required to increase the pool of glutathione, a major actor in redox regulation and phase II detoxification [17]. Additional signatures for group D include two other processes, inosinate (IMP) biosynthesis and amino acid biosynthesis. Interestingly, we found under-representation of their parent processes (closer to the root of the ontology), namely nucleic acid metabolism and protein biosynthesis. Finally, in the ER stress specific groups F and G, the disulfide isomerase proteins and the glucuronosyltransferases, known to play an important role in the UPR following ER stress in yeast, are over-represented together with proteins involved in lipid metabolism.
Overall, our data suggest that oxidative and ER stress induce comparable transcriptional modifications of a significant number of genes known to be involved in a limited number of functional classes.
Gene-specific stress responses inside functional classes
In contrast to previous work limited to partial analysis of the genome, the use of whole genome Affymetrix chips allowed us to investigate the specificity of transcriptional responses for genes associated with a given functional class.
The thioredoxin system plays a major role in oxidative stress defense and needs to be better functionally characterized. In Drosophila, the peroxiredoxin proteins show thiol-dependent peroxidase activity and use thioredoxin, but not glutathione, as a source of reducing power. Indeed, Drosophila lacks glutathione reductase [18] and its function is apparently substituted by thioredoxin reductase. Interestingly, we observed significant differences in the transcriptional behavior of the members of the thioredoxin system when flies were submitted to paraquat stress. The thioredoxin class (GO:0030508) counts 7 members with either a sequence matching perfectly the consensus catalytic site WCGPCK (CG4193, CG3864, Txl/CG5495 and CG1141) or with one mismatch (CG8993, CG13473 and CG3719). Only the Txl gene is significantly overexpressed over the 1.5 fold threshold, the other genes presenting no change or a weaker overexpression (Trx-2). This strongly argues for a specificity of these thioredoxins in the defense process with an important role for the Txl gene. Similarly, among the five genes presenting a thioredoxin peroxydase activity (GO:0008379), only two (CG12013 and CG1633) are overexpressed, the others (CG12174, CG5826 and CG6888) being unaffected in the studied conditions. Among the related genes only the peroxyredoxin CG11765 is overexpressed, while the glutathione peroxydase-like CG15116, very similar to the thioredoxin peroxydase CG12013, is significantly repressed. These specificities strengthen the concept of a functional diversification of these proteins in spite of their common ability to confer resistance to oxidants in Drosophila cells [19].
When the organism is challenged to oxidative stress, in addition to performing direct enzymatic detoxification of toxic compounds, it must also limit the appearance of the most toxic species. Therefore, since free iron catalyses the production of the highly toxic hydroxyl radical (OH•) from H2O2 by the Fenton reaction, its concentration must be tightly controlled. Transferrin and ferritin proteins play a major role in this control [20]. Furthermore, variations in iron concentration may modify gene expression in the cell through the iron regulatory proteins Irp that bind to the iron responsive elements (IRE) located in their target genes UTRs. Under paraquat stress, we observed a coordinated and specific response of genes used in regulation of free iron concentration and iron-regulated response: the two ferritin subunits and the iron regulatory protein 1B (irp1B) are overexpressed, while the transferrin 1 (tsf1) gene is severely repressed. Nevertheless, neither the irp1A nor the tsf2 and tsf3 genes show any significant transcriptional change. This suggests that each isoform of these families plays a specific role in iron homeostasis in the organism.
More complex specificities can be observed in larger functional classes. The glutathione transferases (GTs; GO:0004364) play important roles the detoxification process after genotoxic stresses [21]. As expected, a large number of them (16/34) are overexpressed after paraquat-induced oxidative stress Table 3a (Additional file 10) but 4 are underexpressed under the same conditions. One of these GT repressed by paraquat (FBgn0034334) is also severely repressed by H2O2-induced stress. Moreover, among the 16 GTs overexpressed in paraquat-induced stresses, 7 are overexpressed and 3 underexpressed in ER-stressed flies, while 6 show no other significant transcriptional variation. Interestingly, all the GTs overexpressed in both paraquat and tunicamycin experiments are also slightly induced in H2O2-stressed flies. Overall, our data suggest that both "generalist" GTs that are able to protect the organism against various stresses and more specialized GTs, required only for protection against well defined stresses, coexist inside the cell.
A similar conclusion can be drawn for the P450 cytochromes (GO:0015034). Among 58 detectable P450 cytochromes, 12 are underexpressed and 12 overexpressed during paraquat stress, 4 of these latter being also upregulated in tunicamycin-stressed flies (Tab.3b, Additional file 10). We observed a general tendency of these paraquat-inducible P450 cytochromes to be also overexpressed in H2O2-stressed flies. One cytochrome gene (FBgn0015035) displays peculiar behavior since it is induced by paraquat but strongly repressed by H2O2. Another gene (FBgn0015039) is induced specifically by tunicamicyn. Quantitative RT-PCR experiments confirmed the specificities observed on microarrays (Fig. 3).
Figure 3 Comparison of transcript level variations detected with microarrays and with quantitative real-time PCR (Q-RT-PCR) Transcript levels were analyzed for genes encoding three P450 cytochromes (FBgn0015039, FBgn0010383 and FBgn0015035) and two glutathione transferases (FBgn0034334 and FBgn0010041). The Ln2 ratios between the transcript levels under stress conditions (P15, P5, H1 and T12) and the reference condition, obtained with Q-RT-PCR (white bars) and microarray analysis (black bars), are indicated for each gene. Error bars: standard errors.
The complete data for the peptidases class (GO:0008233) analysis – given as Tab.S5 (Additional file 5) of supplementary data- provides a striking feature: most of the 131 peptidases selected by the SAM analysis (among 361 that were detectable) are downregulated by either both paraquat-induced oxidative stress and ER stress (54 peptidases) or paraquat-induced stress only (41 peptidases); nevertheless, a small number (36) of them are upregulated by paraquat. Closer examination of these latter genes revealed that 22 are proteasome endopeptidases. Further analysis of the proteins belonging to the proteasome complex, (GO:0000502) (which also contains proteasome regulatory proteins) shows that 33 out of 45 detectable proteasome constituents (73%) are likely upregulated by paraquat treatment (Tab. 3c, Additional file 10), both 19S and 20S subunits being coordinately regulated. Interestingly, the induction level is clearly correlated to the dose of paraquat used. Moreover, this induction is very specific since it is not observed in H1 or T12 conditions for any of these genes. The functional significance of this observation needs to be addressed in Drosophila strains mutant for proteasome subunits, challenged with paraquat, H2O2 or tunicamycin stresses.
Many genes transcriptionally affected by oxidative stress modulate oxidative stress resistance
When a fly experiences an oxidative stress we can expect that the subsequent transcriptional modifications may arise from several mechanisms. Firstly, the organism can mount a protective response, for instance by inducing proteins which will reduce adverse consequences of the toxic compound. Only a few functional classes (such as GTs, electron transporters, chaperones) identified in our functional analysis of stress-regulated genes can be clearly associated to such known protective mechanisms from oxidative stress (Tab. 2, Additional file 9). Secondly the toxic drug itself may induce transcriptional changes which could play a role in its toxicity. The relative part of these protective or toxic responses to oxidative stress is unknown. We thus investigated whether genes detected in our microarray analysis could be involved in oxidative stress protection against paraquat or in its induced toxicity. We addressed this issue using a genetic approach, taking advantage of the availability of numerous strains bearing mutations in genes detected in the microarray paraquat set. Twenty nine such lines were recovered from public stock centers and adult flies were analyzed for their survival after transfer to a medium containing 10 mM paraquat. Most of the mutations used arise from P elements insertion in the 5' regulatory region of the genes which are expected to induce partial or complete loss of function mutations. Indeed, as shown in table 4 , most of them have been characterized as either lethal recessive mutations or hypomorphic loss of function mutations and, in some cases, do not complement a deficiency. Particular attention was paid to ensure that the genetic background was controlled in these experiments and stringent statistical conditions were used for the data analysis (see material and methods). Several conclusions can be drawn from these genetic experiments. a) First, as shown in Fig. 4a, under these conditions, a high proportion of the 29 tested strains present statistically significant survival differences from the w Canton S reference strain. Indeed, the results of our experiments show that 13 mutant lines out of 29 tested (45%) are either significantly more resistant (6 lines) or more sensitive (7 lines) to paraquat than their wild-type counterparts (Tab. 4, Additional file 11 and Fig. 4b). This ratio is at least 10 times higher that what is expected from previous genetic screens (see discussion) and suggest a strong relationship between transcriptional stress response and functional in vivo susceptibility to oxidative stress. b) For the genes studied there is no clear correlation between the observed induction or repression under paraquat treatment and the effect of the mutation on the paraquat resistance or sensitivity phenotypes (Tab.4, Additional file 11). This suggests that, in the steady stress conditions used, both deleterious and protective gene regulations are taking place. c) Our genetic data point out the large functional diversity of genes that are able to modulate the oxidative stress resistance in vivo: ion channel (Sh), thioredoxin reductase (Trxr-1), fatty acid elongase (Baldspot), phosphatase (aay) and phosphatase regulator (CG9238), transcription factor (Xbp1) and peptidase (Acer). Interestingly, among these 13 mutants, only 2 were previously known to be associated to oxidative stress resistance (Sh and Trxr-1) and most of them had no known function in adult flies. Coupling between microarray and genetic experiments is thus a powerful way to extend our knowledge on the biological function of Drosophila genes without biased hypothesis and to provide some clues on the function of mammalian homologues.
Figure 4 Resistance to paraquat-induced stress of flies mutant for genes identified in microarray experiments a) 29 Drosophila lines bearing mutations in genes identified in our microarray experiments as being stress-responsive were recovered from public stock centers. When the mutation was linked to a w+ transposon insertion these lines were outcrossed with a w+ Canton S reference line. 3–6 day old male flies were then tested for their resistance to oxidative stress 68 h after transfer to 10 mM paraquat medium. Tested flies were either homozygous (notation #i/#i in the X axis) for viable mutations or heterozygous (notation #i/w) for lethal mutations (in this case they are issued from a cross with w+ Canton S females). For simplicity, identification of lines (#i) refers to the Bloomington stock number and the genotype of the line is provided in Tab. 4. We present in this Figure the results of one of three independent experiments that we used for the complete statistical analysis presented in Table 4. Compared to male flies issued from a cross between w- males and Canton S females (noted w/+, dark bar), significant differences in resistance or sensitivity to paraquat can be observed for a large number of the lines tested. Error bars: standard error. b) Example of survival curves on 10 mM paraquat-containing medium of some mutant male flies. Flies heterozygous for a lethal mutation in the Angiotensin converting enzyme related (Acer) gene are sensitive to paraquat, while flies homozygous for an insertion in the gene CG9238 are clearly more resistant to paraquat than w/+ control flies. Neither of these genes was previously suspected to play a role in oxidative stress resistance.
For instance, we found that the Dgp-1 gene is induced in flies challenged with paraquat stress and that its disruption leads to stress resistance. The Dgp-1 protein is strongly similar to the mammalian GTPBP1 protein which presents a GTP binding domain and strong similarity with the elongation factor Ef-Tu [22]. Interestingly, expression of GTPBB1 is enhanced by gamma interferon in a monocytic cell line, suggesting that this protein in involved in host defense mechanisms. Nevertheless, no phenotype was observed in mice disrupted for this gene, maybe because of compensation by a gene of the same family [22]. Our data provide evidence that, in flies, Dgp-1, the GTPB1 homologue, is indeed involved in protective mechanisms against stress. The similarity with EF-Tu suggests that this protection might be linked to a downregulation of protein synthesis. In agreement to this hypothesis, it is noticeable that mutants for the translation negative regulator Thor present a significant sensitivity to paraquat stress (confidence index -2,4 in Tab. 4, Additional file 11 and Fig. 4a) and has been shown to be sensitive to bacterial infection [23].
Discussion
In this paper we present the characterization of the in vivo transcriptional responses of adult Drosophila males submitted to four different continuous stresses : paraquat (two conditions), H2O2 or tunicamycin. Experiments on yeast submitted to several types of stress including oxidative stress have shown that fast transient responses occurring during the first three hours are followed by stable long term (>12 hours) changes [5,6]. Similarly, previous experiments on paraquat-induced stress in Drosophila have shown sustained long term changes in transcript levels which are more or less stable 12 hours after stress induction [8]. Since, as discussed previously, there are clear technical limitations to short term kinetic studies on Drosophila submitted to ingestion driven stress, we focused our efforts on the observation of these long term stress responses (LTSR) and performed our transcriptome analysis 24 hours after stress induction. At this time point, more than 95% of flies were alive for P5, H1 and T12 treatments, while 19% of lethality was observed in the P15 experiments. In addition, during the next 24 hours, in all conditions, less than 30% of the animals died. We thus expect that any secondary effects linked to the level of lethality are minimal in our experiments. In agreement with this assumption we noticed that in the experiments of Zou et al. similar results were obtained when the transcriptome was analyzed 12 hours (when lethality was negligible) or 24 hours after ingestion of 15 mM paraquat. Furthermore, when functional analysis was performed, we were unable to detect significant differences in the signature of the genes detected in the P5 and P15 experiments, which should be the case if the level of lethality plays an important role for gene transcription. We thus conclude that the secondary effects linked to the levels of lethality in the Zou et al. experiments and in our work do not significantly affect the transcriptome and that the variations observed are primarily due to the stresses experienced by the flies.
Our data present clear evidence of a common long-term stress response (CLTSR) in transcription of Drosophila genes: at least 237 genes contained in clusters 9, 10 and 13 show similar changes in transcription for the three stressors studied. This number could be a minimum estimation of the extent of the CLTSR, since it is mainly limited by the weaker transcriptional variations observed in the H2O2-induced flies. We think that this may be due to a smaller number of cells experiencing stress when flies ingest H2O2. Additional data for comparison with various stress responses (immune stress [24], starvation [25] and, during the submission of this work, hyperoxia and aging [26]) are presented in Supplementary text T1 (Additional file 7) and Table S6 (Additional file 6) and confirm the existence of a core of similar transcriptional responses between these stresses.
The CLTSR shows certain similarities with the common environmental response (CER) described in yeast [5,6]: in both cases heat-shock genes, genes involved in the detoxification processes, or associated with fatty acid metabolism and DNA repair show similar changes in all the stress conditions studied. Nevertheless, there are also obvious differences between these two responses. For instance, in contrast to what occurs in CER, no large scale coordinated transcriptional changes for genes involved in translation inhibition or energy production were detected in CLTSR. This may reflect the fact that, in our experiments, the CLTSR corresponds to a long-term adaptation of the stressed Drosophila cells, while the variations observed in yeast are transient (of course we cannot exclude long term post-transcriptional modifications in the translation apparatus and the metabolic pathways activities of stressed flies). Alternatively, these data may reflect differences in the adaptation of dividing cells (yeast) and post-mitotic cells (Drosophila) to stress conditions. For instance, in the latter case, upregulation of the iron responsive protein 1b gene may lead to translational downregulation of the succinate dehydrogenase gene through an IRE [27] and hence modulate energy production as in the yeast, but in a different way. Additionally, in Drosophila, translation repression may also be involved in stress response but relying on a small subset of genes (which would then not have been detected with our functional analysis). Interestingly, in support to this hypothesis, we found that the translational repressor Thor is induced under stress conditions and that mutations in this gene confer a slight but significant sensitivity to paraquat-induced stress. However, our finding that tRNA ligases are upregulated in oxidative and ER stress may indicate a requirement for increased protein synthesis under sustained stress conditions. Kinetic studies using another oxidative stress paradigm are needed to clarify this point. In view of our results, it would be also interesting to investigate possible variations in stress response in mammalian tissues either mitoticaly active or quiescent.
Besides their similarities, the LTSRs also display marked differences. One of the most striking specific expressions is displayed by the genes encoding for the proteasome subunits. These proteins belong to the two large complexes 19S (regulatory complex) and 20S (proteolytically active complex) which, together, form the 26S proteasome [28]. Most of them (73%) are specifically induced by paraquat- but none by H2O2- or tunicamycin-induced stresses. It is also noticeable that, in contrast to proteasome constituents, ubiquitin protein ligases are under-represented among paraquat responsive genes. The 20S proteasome, inactive in its native form, is able to specifically degrade oxidized proteins in vitro and in vivo, and has been considered to be the main actor in this process [29]. Nevertheless, it has been recently proposed that, while the 20S proteasome is active during oxidative stress and limits the accumulation of oxidized proteins, the 26S, inactive in presence of ROS, "cleans" the cell in the following recovery process, eliminating thereby the accumulated altered proteins [30]. This seems to be a very important aspect of oxidative stress defense since oxidization of proteins can result in protein fragmentation and partial unfolding, and induce the formation of cytotoxic insoluble aggregates, a process that is known to be implicated in an increasing number of human pathologies [31,32]. The observed coordinated upregulation of genes encoding both 19S and 20S proteasome subunits when Drosophila cells are submitted to continuous paraquat stress strongly suggests that both complexes are indeed important in vivo for oxidized proteins degradation.
We observed no such induction of proteasome components in H2O2-stressed Drosophila. This result is coherent with previous studies shoving that the proteasome subunit are not transcriptionally regulated in cultured mammalian cells treated with H2O2 [33]. However this is surprising since it has been shown, in mammalian cells, that the proteasome is in fact involved in the degradation of misfolded glycoproteins as well as oxidized proteins after H2O2 treatment [34]. Recent data in lens epithelial cells showed that H2O2 induces an increase in proteasome activity and E1 ubiquiting activation enzyme levels without any increase in E1 mRNA levels [35]. In view of our data, we propose that two different strategies are used in D. melanogaster to deal with oxidative challenge and increase proteasome activity: one response, induced by H2O2, would rely on post-transcriptional mechanisms as shown in mammalian cells; while the other response, induced by paraquat, would rely on coordinated increase of transcription of the proteasome genes of both 19S and 20S subunits.
A number of functional classes are clearly over-represented among the genes involved in the LTSRs. The analysis of these specific functional classes revealed an important heterogeneity of stress-specific responses among their members. For instance, we have shown that only a subset of genes potentially involved in the thioredoxin pathway are upregulated during paraquat stress. Whether the remaining genes are involved in an earlier phase of the stress response, in a subset of tissues or in other processes unrelated to stress protection needs to be addressed. Interestingly, in agreement with this last hypothesis, one of these genes, Jafrac2, which codes for a thioredoxin peroxidase, has been recently assigned an unexpected role in caspase-regulated cell death [36]. The P450 cytochromes and the glutathione transferases also display striking stress-specific responses. For the GTs, 3 genes are downregulated by tunicamycin and 4 by paraquat, while 6 are upregulated by paraquat and 7 by both drugs. When we tried to correlate this information with GT classifications [21] we found that the latter group contained almost exclusively δ-type GTs (Table 3a). This suggests that this insect-specific class, unlike other Drosophila GTs, may have acquired a broad-spectrum detoxifying function which is required to counteract both oxidative and tunicamycin-induced cellular damages and/or that these GTs molecular targets are altered in both types of stress.
One important issue is whether our findings are representative of long term transcriptional responses in Drosophila submitted to real physiological chronic stresses. Indeed, the stress levels experienced by flies in this work are probably much higher than those experienced in real life. Nevertheless, the tight correlation that we observe between P5 and P15 experiments demonstrates that most of the genes undergoing transcriptional changes at a high concentration of paraquat display similar changes (although at a reduced level) when the concentration is threefold lower. This suggests that many genes identified in this study may also be induced in low intensity chronic stress.
A striking feature of our results is the large number of genes not previously associated with stress response which show transcriptional changes under paraquat-induced oxidative stress conditions. We investigated the biological validity of these observations in a genetic study of mutations in some of these genes. Since our microarray data suggest that the stress responses may be highly polygenic (with at least 10% of the genome involved), we took a particular care to ensure that there was a controlled genetic background in these experiments. We found that 45% of the mutations tested were associated with either resistance or sensitivity to paraquat, which confirms this idea of a highly polygenic process. It should be stressed that, since many of the tests were performed on heterozygous flies, the proportion of genes functionally involved in oxidative stress resistance may be higher. Extrapolation of the results obtained with this small subset of 29 genes to the 1107 genes found to be regulated by paraquat, suggests that some 500 genes may modulate paraquat sensitivity in vivo. This contrasts with two previous genetic screens to detect paraquat hypersensitive mutants, which concluded that only a few genes are involved in paraquat hypersensitivity [37,38]. These studies however analyzed only EMS viable mutations on the X, 2nd and 3rd chromosome. They would thus have missed any lethal mutations that could confer a sensitivity phenotype to heterozygous flies by gene dosage reduction. In fact, when we performed a P{w+; UAS}- based screen we found that a large proportion of P-element insertions may confer H2O2 or paraquat resistance or sensitivity ([14] and Girardot et al. unpublished) in agreement with the results presented here.
If all the transcriptional responses to a stress were protective for the organism; we would expect a clear correlation between the direction of the transcriptional response of the genes studied and the effect of their mutations on stress resistance. A significant result of our experiments is that we could not find such a correlation.
It thus appears that the transcriptional responses to oxidative stress may be either protective or deleterious for the flies. The simplest explanation for this result is that, besides the protective responses mounted by the organism cells (for instance in inducing detoxifying proteins), the paraquat also induces transcriptional changes that play a role in its toxicity. In mammalian cells, several transcription factors may be regulated by oxidative stress, either by direct modification by the ROS or through signaling pathways, and have either pro- (Jun, p53) or anti-apoptotic effects (NF-κB, HSF1) [2]. In addition, the choice between survival and apoptosis may depend on the intensity of the stress and on the cell type, as it has been clearly demonstrated in the case of p53 [39]. Signaling pathways which activate these factors are strongly conserved between mammals and Drosophila and it is conceivable that, like in mammalian cells, their activation in flies by oxidative stress may induce complex transcriptional responses of both pro-survival and deleterious factors. In this case the integration of these complex responses at the level of the organism will determine the final outcome (protective or deleterious) and, eventually, in the case of a transient stress of limited intensity, the return to an unstressed equilibrium state. Thus the protective or deleterious role of a stress responsive gene cannot be predicted simply but should be uncovered systematically by genetic studies.
Interestingly, in our genetic experiments, halving the dosage of the Xbp1 gene resulted in increased sensitivity of flies to paraquat-induced stress. Xbp1 is known to be involved in ER stress response in mammals [40]. It has been shown that it is regulated by processing of its mRNA by the C-terminal endonuclease Ire1. Conversely, we observed no transcriptional change of Xbp1 in Drosophila challenged with tunicamycin but it is overexpressed in oxidative stress conditions. Our in vivo genetic study suggests that this regulation is functionally relevant to oxidative stress protection in Drosophila. Thus Xbp1 may protect against different stress conditions through different modes of regulation (transcriptional or post-transcriptional regulation). In agreement to the conservation of this mechanism between flies and mammals, it has been shown recently that, in a mammalian dopaminergic cell line, Xbp1 is induced by the parkinsonian mimetic 6-hydroxydopamine which is known to induce oxidative stress [41].
Another gene that affects the flies stress resistance in vivo is Acer. This gene encodes one of two Drosophila proteins homologous to the mammalian angiotensin converting enzyme (ACE) gene family. Controversial findings have linked Ace to stress resistance and aging ([42] and references therein). Acer is more similar to the mammalian gene Ace2. It has been recently shown that both Acer and Ace2 are essential regulators of heart function [43]. Interestingly, complete targeted disruption of Ace2 in mice results in increased angiotensin II levels and upregulation of hypoxia-induced genes. In Drosophila, the targets of Acer are not known and complete loss of function of the gene results in embryonic lethality. We found that halving the dosage of Acer in adult flies results in increased sensitivity to paraquat stress. Considering the mammalian data, one hypothesis to explain this result is that heart cells of Acer/+ flies may already experience a mild hypoxic stress which sensitizes them to the additional paraquat-induced oxidative stress. Targeted expression of Acer in Drosophila heart cells may help to test this hypothesis.
In this genetic study, based on a small subset from the genes found to be regulated by stress in our microarray experiments, we identified genes with no previously known function as in vivo modulators of oxidative stress resistance. Since genomic programs steadily increase the number of transposon targeted genes it will become easier to perform this kind of genetic analysis to increase our knowledge of integrated mechanisms of stress resistance in Drosophila.
In conclusion, our data confirm that full genome scanning by microarray experiments and analysis of multiple experimental conditions constitutes a powerful tool to uncover potentially significant biological features that can be subsequently confirmed by genetic experiments.
Supplementary Material
Additional file 1
For each of the 1368 probe sets identified as stress responsive in our data analysis, we
calculated and reported in this table, for each stress condition, the mean ratio <Stress
condition> ≤ (AVstressi / AVrefj)>i,j where AVstressi and AVrefj correspond to the average value
measured for the ith sample in the stress condition and the jth sample respectively in the
reference condition. To facilitate visual inspection, we used a color code (red corresponding
to upregulation, green to downregulation) with thresholds corresponding to fold changes of
1.8 (dark colors), 1.5 (medium) and 1.25 (light). The standard error for each measurement is
given in parenthesis. For each probe set, the mean detection p-value from MAS5 analysis of
reference samples is reported in column 4 and cluster assignment in column 9.
.
Click here for file
Additional file 2
For stresses induced by a) paraquat (5mM and 15mM experiments), b) H2O2 or c)
tunicamycin we analyzed the distribution in biological processes (as defined by the Gene
Ontology (GO) database) of the genes selected by the SAM analysis (responsive genes) and
compared it to the same distribution for all the genes significantly detected on our
microarrays (analysed genes). We report here the significantly over- or under-represented
(P < 0.005) biological process and the number of analysed and responsive genes found inside
these classes, for the different stress conditions. The p-value P associated to the null
hypothesis of no association with a binomial distribution hypothesis is given for each class,
(only classes with P < 0.005 were retained). For clarity of the figure some redundant branches
of the tree were removed. Color codes for the classes: dark blue: classes present in the 3 stress
responses; yellow: classes present in the two oxidative stress responses; green: classes present
in paraquat and tunicamycin stress responses; light cyan: classes present in H2O2 and
tunicamycin stress responses.
34
Color code for statistical analysis: orange: underrepresented class, blue: over-represented
class.
.
Click here for file
Additional file 3
For over or under-represented molecular functions classes we report here the number of
analyzed (column 3) and responsive genes found inside the 7 groups of clusters A to G
(columns 4 to 10, see text for details on the definition of these groups). A schematic response
to oxidative and ER stress of the genes included in these groups is given in the first two lines.
The number of genes inside each group is given in line 3.
A color code identifies cases when the number of genes differs statistically (p<0.005) from a
random distribution: orange: under-represented class, blue: over-represented class.
Click here for file
Additional file 4
For over or under-represented biological process classes we report here the number of
analyzed (column 3) and responsive genes found inside the 7 groups of clusters A to G
(columns 4 to 10, see text for details on the definition of these groups). A schematic response
to oxidative and ER stress of the genes included in these groups is given in the first two lines.
The number of genes inside each group is given in line 3.
A color code identifies cases when the number of genes differs statistically (p<0.005)from a
random distribution: orange: under-represented class, blue: over-represented class.
Click here for file
Additional file 5
Stress response for the peptidases
Click here for file
Additional file 6
List of stress responsive genes detected in aging and hyperoxia, immune or
starvation stress experiments were compared with our data. Lines 1 to 3 indicate the number
of genes found to be repressed (-), induced (+) or either (total) in the different experiments.
Lines 5 to 7 indicate the number of genes in each of these categories found among our 1397
35
stress responsive genes (classes A to F) and the corresponding percentage from the initial
number. In lines 8, 9 (respectively 10, 11) the same analysis is reported for genes included in
the A (respectively B) classes defined as common stress responsive classes in our analysis.
O2, old, infection, starvation: expression data from the different experiments compared to
our data.
All: List of 26 genes which are responsive to at least 4 stresses in these independent
experiments
Click here for file
Additional file 7
Relationships to other stresses
Click here for file
Additional file 8
Table 1: Stress response characteristics of clusterized genes.The 1368 probe sets retained after statistical analysis were submitted to a divisive clustering algorithm (SOTA) which predicted 19 clusters. For each probe set k inside a cluster we calculated, for each stress condition, the mean ratio Rk = <Ln2 (AVstressi / AVref j )>i,j where AVstressi and AVref j denote the average value measured for the ith sample in the stress condition and the jth sample respectively in the reference condition. The mean of the Rk values provides a measurement of the mean intensity of variation for the genes inside a cluster, which is reported in this table. To facilitate visual inspection, we used a color code (red colors corresponding to upregulation, green colors to downregulation) with thresholds corresponding to fold changes of 1.8 (dark colors), 1.5 (medium) and 1.25 (light). The number N of probe sets in each cluster is also reported. From these values we identified groups of clusters (named from A to G) which present close behavior and were used for statistical functional analysis. Clusters corresponding to the common long term stress response (CLTSR) are outlined in red.
Click here for file
Additional file 9
Table 2: Functional analysis of stress responsive genes.For stresses induced by a) paraquat (5mM and 15mM experiments), b) H2O2 or c) tunicamycin we analyzed the distribution in functional classes (as defined by the Gene Ontology (GO) database) of the genes selected by the SAM analysis (responsive genes) and compared it to the same distribution for all the genes significantly detected on our microarrays (analysed genes). We report here the significantly over- or under-represented (P<0.005) molecular functions and the number of analysed and responsive genes found inside these classes, for the different stress conditions. The p-value P associated to the null hypothesis of no association with a binomial distribution hypothesis is given for each class, (only classes with P<0.005 were retained). For clarity of the figure some redundant branches of the tree were removed. Color codes for the classes: dark blue: classes present in the 3 stress responses; yellow: classes present in the two oxidative stress responses; green: classes present in paraquat and tunicamycin stress responses; light cyan: classes present in H2O2 and tunicamycin stress responses.
Color code for statistical analysis: orange: under-represented class, blue: over-represented class.
Click here for file
Additional file 10
Table 3: Analysis of stress responses for members of some functional classes.From the 1368 stress responsive probe sets we extracted the subsets associated with genes annotated in the GO database as a) glutathione transferases (GO:0004364), b) P450 cytochromes (from list at http://p450.antibes.inra.fr/) and c) proteasome component (GO:0004299). For each probe set k within one of these subsets, we calculated and reported in this table, for each stress condition, the mean ratio <Stress condition >k =<(AVstressi / AVref j)>i,j where AVstressi and AVrefj correspond to the average value measured for the ith sample in the stress condition and the jth sample respectively in the reference condition. To facilitate visual inspection, we used a color code (red corresponding to upregulation, green to downregulation) with thresholds corresponding to fold changes of 1.8 (dark colors), 1.5 (medium) and 1.25 (light). The standard error for each measurement is given in parenthesis. For each probe set, the mean detection p-value from MAS5 analysis of reference samples is reported in column 3 and cluster assignment in column 8. In column 9 additional information is reported for each class: in a) we indicate the GT class deduced from sequence comparison with human and mouse GTs and from [44] (D: delta, O: omega, T: theta, T2: distantly related to theta, Z: zeta); in b) the name of the genes are reported; in c) we indicate the proteasome subunit to which the genes defined in column 2 belong. Note that in c) a large number of genes not retained by SAM analysis (without cluster number) seem to be upregulated in P15 condition.
Genes used for comparison between microarray and quantitative RT-PCR (Fig. 3) are outlined in bold character.
Click here for file
Additional file 11
Table 4: Analysis of mutant flies' resistance to paraquat-induced oxidative stress. Paraquat resistance of 29 mutant lines was assayed in three independent experiments as described in Fig. 4. The survival data were submitted to a log-rank statistical analysis by comparison with w/+ reference flies. The results are presented in this table. Column 1 contains the tested genotype (same conventions as in Fig. 4a : Bloomington line numbers). The corresponding genotypes are described in column 6. The symbol for the gene affected is reported in column 2. Information from FlyBase about the allele used in this study is given in column 7 with a one character code: A: amorph; H: hypomorph; N: non complementation of deficiency; L: letal; R: recessive mutation; 5: insertion in the 5' regulatory region, 5'UTR or intron; C: insertion in the coding region. Column 8 indicates whether the tested line was outcrossed or not before the test. Column 4 is the result of a log-rank analysis of the second survival experiments shown in figure 4. A confidence index which refers to the mean of log10 (p-log-rank) for the three experiments is given in column 5. We considered that a strain had a significant effect on survival under oxidative stress conditions when this confidence index was lower than -3 and at least two experiments presented p-log-rank < 0.001. Under these stringent conditions 13 genotypes are shown to confer resistance (R) or sensitivity (S) to paraquat as indicated in column 3.
Click here for file
Acknowledgments
We would like to thank K. Wanherdrick and D. Gentien from the Institut Curie, Paris, for their efficient technical assistance for sample processing and microarray treatments and D. Busson, J. M. Camadro, T. Preat and F. Rouyer for their useful comments on the manuscript. This work was supported by the CNRS "Puces à ADN" program. F. Girardot was doctoral fellow supported by an "Allocation Couplée" grant from the Rectorat de Paris and the Pierre et Marie Curie University.
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| 15458575 | PMC526417 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Sep 30; 5:74 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-74 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047910.1371/journal.pbio.0030007Research ArticleDevelopmentEvolutionGenetics/Genomics/Gene TherapyVertebratesDanio (Zebrafish)MammalsHighly Conserved Non-Coding Sequences Are Associated with Vertebrate Development CNEs and Vertebrate DevelopmentWoolfe Adam
1
Goodson Martin
1
Goode Debbie K
1
Snell Phil
1
McEwen Gayle K
1
Vavouri Tanya
1
Smith Sarah F
1
North Phil
1
Callaway Heather
1
Kelly Krys
1
Walter Klaudia
2
Abnizova Irina
2
Gilks Walter
2
Edwards Yvonne J. K
1
Cooke Julie E
1
Elgar Greg [email protected]
1
1Medical Research Council Rosalind Franklin Centre for Genomics ResearchHinxton, CambridgeUnited Kingdom2Medical Research Council Biostatistics Unit, Institute of Public Health, Addenbrookes HospitalCambridgeUnited Kingdom1 2005 11 11 2004 11 11 2004 3 1 e730 7 2004 21 10 2004 Copyright: © 2004 Woolfe et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Unexpressed but Indispensable-The DNA Sequences That Control Development
In addition to protein coding sequence, the human genome contains a significant amount of regulatory DNA, the identification of which is proving somewhat recalcitrant to both in silico and functional methods. An approach that has been used with some success is comparative sequence analysis, whereby equivalent genomic regions from different organisms are compared in order to identify both similarities and differences. In general, similarities in sequence between highly divergent organisms imply functional constraint. We have used a whole-genome comparison between humans and the pufferfish, Fugu rubripes, to identify nearly 1,400 highly conserved non-coding sequences. Given the evolutionary divergence between these species, it is likely that these sequences are found in, and furthermore are essential to, all vertebrates. Most, and possibly all, of these sequences are located in and around genes that act as developmental regulators. Some of these sequences are over 90% identical across more than 500 bases, being more highly conserved than coding sequence between these two species. Despite this, we cannot find any similar sequences in invertebrate genomes. In order to begin to functionally test this set of sequences, we have used a rapid in vivo assay system using zebrafish embryos that allows tissue-specific enhancer activity to be identified. Functional data is presented for highly conserved non-coding sequences associated with four unrelated developmental regulators (SOX21, PAX6, HLXB9, and SHH), in order to demonstrate the suitability of this screen to a wide range of genes and expression patterns. Of 25 sequence elements tested around these four genes, 23 show significant enhancer activity in one or more tissues. We have identified a set of non-coding sequences that are highly conserved throughout vertebrates. They are found in clusters across the human genome, principally around genes that are implicated in the regulation of development, including many transcription factors. These highly conserved non-coding sequences are likely to form part of the genomic circuitry that uniquely defines vertebrate development.
Highly conserved non-coding sequences in vertebrate genomes are frequently located around genes involved in development and can direct tissue-specific gene expression in functional assays.
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Introduction
Identification and characterisation of cis-regulatory regions within the non-coding DNA of vertebrate genomes remain a challenge for the post-genomic era. The idea that animal development is controlled by cis-regulatory DNA elements (such as enhancers and silencers) is well established and has been elegantly described in invertebrates such as Drosophila and the sea urchin [1,2,3,4]. These elements are thought to comprise clustered target sites for large numbers of transcription factors and collectively form the genomic instructions for developmental gene regulatory networks (GRNs). However, relatively little is known about GRNs in vertebrates. Any approach to elucidate such networks necessitates the discovery of all constituent cis-regulatory elements and their genomic locations. Unfortunately, initial in silico identification of such sequences is difficult, as current knowledge of their syntax or grammar is limited. By contrast, computational approaches for protein-coding exon prediction are well established, based on their characteristic sequence features, evolutionary conservation across distant species, and the availability of cDNAs and expressed sequence tags (ESTs), which greatly facilitate their annotation.
The completion of a number of vertebrate genome sequences [5,6,7,8,9], as well as the concurrent development of genomic alignment, visualisation, and analytical bioinformatics tools (for an overview see [10]), has made large genomic comparisons not only possible but an increasingly popular approach for the discovery of putative cis-regulatory elements. Comparing DNA sequences from different organisms provides a means of identifying common signatures that may have functional significance. Alignment algorithms optimise these comparisons so that slowly evolving regions can be anchored together and highlighted against a background of more rapidly evolving DNA that is free of any functional constraints.
One of the key decisions inherent in comparative genomics is the choice of organisms for which the comparison will be made. A number of successful pairwise and multiple-species sequence comparisons have already been carried out to identify novel enhancer elements in mammalian genomes [11,12,13,14,15,16,17,18,19,20]. Unfortunately, owing to differences in mutation rates across the genome, many slower-evolving regions show a significant degree of non-coding sequence conservation that reflects the short evolutionary distance between mammals and the slow rate of neutral divergence in vertebrates [20]. Consequently, although approximately 40% of the human and mouse genomes is alignable, only approximately 5% is estimated to be under selection, making it difficult to identify functionally relevant sequences [8]. One approach has recently been described [21] that identifies only those sequences that are identical over at least 200 bp between human and mouse genomes (termed ultra-conserved elements) and examines their distribution in the genome. Around half of the 481 elements identified showed no evidence of transcription and are therefore likely to be regulatory.
Another highly successful approach to increasing the resolving power of comparative analyses is to use multi-species alignments combining both closely related and highly divergent organisms [14,22,23,24]. By using large evolutionary distances, even the slowest-evolving neutral DNA has reached equilibrium, thereby significantly improving the signal to noise ratio in genomic alignments. Although non-coding sequences generally lack sequence conservation between highly divergent species [22], there are a number of striking examples where comparison between human and pufferfish (Fugu rubripes) gene regions has readily identified highly conserved non-coding sequences that have been shown to have some function in vivo [25,26,27,28,29,30,31,32,33,34]. Humans and Fugu last shared a common ancestor around 450 million years ago [35], predating the emergence of the majority of all extant vertebrates, implying that any non-coding sequences conserved between these two species are likely to be fundamental to vertebrate life. The Fugu genome has the added advantage of being highly compact, reducing intronic and intergenic distances almost 10-fold [7,36]. Without exception, all reported examples of non-coding conservation between these two species have been associated with genes that play critical roles in development. This suggests that some aspects of developmental regulation are common to all vertebrates and that whole-genome comparisons may be particularly powerful in identifying regulatory networks of this kind.
As a first step towards identifying such networks in humans, we have used comparative genomics to identify and begin to characterise non-coding sequences that are highly conserved between human and Fugu. A general strategy for testing whether non-coding regulatory sequences of this type are functionally relevant involves assaying their ability to up-regulate reporter gene expression in vivo. “Enhancer” assays using transgenic animals, in particular mouse, are both slow and laborious, but have provided some exciting data on the function of non-coding DNA around developmental genes (e.g., [31]). An alternative approach uses transient expression in zebrafish (Danio rerio) embryos [37,38,39], which are particularly suited to this form of analysis. Whilst transient expression is highly mosaic, the availability of large numbers of fertilised eggs, ease of micro-injection, and transparency of the developing embryos means that hundreds of individuals may be screened at a time. This provides a rare opportunity for increasing the throughput of this kind of functional assay.
We have adopted a medium-throughput strategy to test DNA sequences for enhancer activity in zebrafish embryos, whereby results may be cross-referenced and compared through a generalised scheme. We present functional data for 25 highly conserved non-coding sequences that are located around four unrelated developmental regulators, SOX21, PAX6, HLXB9, and SHH in order to demonstrate the suitability of this screen to a wide range of genes and expression patterns.
Results
Identification of Highly Conserved Non-Coding Sequences in Vertebrate Genomes
To locate conserved non-coding sequences, we masked the majority of the coding and tRNA content of the Fugu genome assembly [7] and compared the remaining regions using MegaBLAST [40] with the human genome sequence contained in Ensembl release v18.34.1 [41]. From this analysis we identified 19,744 sequences with similarity between the two genomes. By only including alignments of at least 100 bp in length, the number of sequences was reduced to 4,400. We then removed telomere-like sequences and transposons, and excluded any known protein-coding sequence or non-coding RNA species that may have been missed (see Materials and Methods). Sixty-five unique human sequences had matches to two independent locations in the Fugu genome. This is due to additional gene or genome duplications in the teleost lineage [42], where regulatory elements have been retained together with both copies of the fish gene [43]. To avoid redundancy in the human set, the longest matching sequence was retained and the duplicate removed. Finally, from the 1,373 sequences that remained, we determined whether any formed part of untranslated regions (UTRs) of mRNA molecules. Eighty sequences (approximately 6%) are situated in the 5′ or 3′ UTRs of known mRNA molecules. In addition, a similar number match one or more EST sequences, although most of these appear to be unspliced genomic contamination within EST libraries or incompletely spliced pre-mRNA. We did not remove these potentially transcribed sequences as, unlike vertebrate UTRs in general, they demonstrate a remarkable degree of conservation, and it is not clear whether they might be functional at the genomic or the transcript level. The remainder had no match against any expressed sequence in any database. This core set of 1,373 highly conserved non-coding elements (CNEs) forms the basis of this study.
The set of CNEs comprise a total of 273 kb of sequence, with a maximum length of 736 bp (average = 199 bp) and identity ranging from 74% to 98% (average = 84.3%). This is considerably higher than the level of identity seen between coding regions in these two organisms. Unsurprisingly, virtually all of the CNEs are conserved in rodent and chicken genomes, as well as a majority in the zebrafish genome. Of the 1,373 CNEs, 1,365 are conserved in the mouse, 1,316 in rat, and 1,310 in chicken, aligning to the human sequence with average identities of 97% for mouse and rat and 96% for chicken; 1,093 are also found to be conserved in the zebrafish genome, aligning with an average identity of 87.6% to the Fugu sequence. The zebrafish, chicken, mouse, and rat genomes are at different stages of completeness, and therefore missing sequence information may account for the missing CNEs (as well as the lower percent identity in zebrafish), although it may also reflect regulatory differences between the lineages.
Although CNEs are found throughout the human genome in all chromosomes except 21 and Y, their distribution is not uniform; in fact, they appear highly clustered. To examine their distribution in more detail, we plotted the position of each CNE on its respective chromosome in the human genome (Figure 1A). We then calculated the percentage of CNEs that were located in close proximity to another. We found that 90% of CNEs are less than 1 Mb apart, 85% of CNEs have a neighbouring CNE within 370 kb, and 75% are located within 158 kb of another CNE. The probability that over 85% of CNEs would be within 370 kb of another in a random distribution is less than 10−76 (Figure 1B). By carefully examining the distribution of CNEs across the genome, we generated a total of 165 clusters, including 19 singletons (Table S1). Over 85% of the CNEs (1,172/1,373) are located in clusters containing five or more CNEs. The 20 largest clusters each contain 20 or more CNEs, comprising 43% (594/1,373) of the total number of elements.
Figure 1 Distribution of CNEs along the Human Genome
(A) Each CNE is plotted relative to its position along each of human Chromosomes 1 to 9 (data for other chromosomes not shown). The y-axis represents length along the chromosome (in megabases).
(B) Distribution of the fraction of CNEs that are within certain distances of each other; e.g., 85% of the distances between CNEs are less than or equal to 370 kb. χ2 tests were carried out by comparing observed cluster sizes with those generated randomly for each chromosome (see Materials and Methods).
We then looked at the type of genes that are associated with CNEs in the human genome. For each CNE, we extracted the closest gene from Ensembl and submitted the resulting set of genes to GOstat [44] in order to identify the most statistically over-represented Gene Ontology (GO) terms [45]. Critically, 12 of the most over-represented 13 terms (p < 0.001) relate to transcriptional regulation and development (Table S2).
We examined each cluster in turn to see how many were situated close to genes implicated in transcriptional regulation or development (we have termed these trans-dev genes). Over 93% of the clusters (154/165) have a trans-dev gene located within 500 kb of one or more of its CNEs (Figure 2; Materials and Methods; Table S1). Of the remaining 11 clusters, five are closest to genes with zinc finger domains as identified by InterPro [46], one is in a gene desert, one maps to the AUTS2 gene region [47], and four are located adjacent to uncharacterised genes.
Figure 2 CNE Clusters Are Found Close to Trans-Dev Genes in the Human Genome
Chromosomal locations of trans-dev genes that are within 500 kb of CNE clusters in the human genome (each cluster is represented by a green arrowhead). Genes in bold script are located next to clusters of ten or more CNEs. Gene names are taken from Ensembl v23.34e.1. Graph inset shows distribution of CNE cluster sizes in the human genome.
Whilst most of the clusters can be associated with one trans-dev gene, there are 15 clusters in which CNEs are located close to two or more trans-dev genes. In nine of these cases, the CNEs associate with a group of paralogous genes, including the HOX, IRX, Nkx2–2/2–4, and DLX clusters, although there are three instances where a pair of unrelated trans-dev genes are located next to a CNE cluster (SHH and HLXB9, PBX3 and LMX1B, and PAX1 and FOXA2). Finally, there are three clusters that associate with two or more zinc finger genes.
Trans-dev genes associated with CNE clusters tend to be located in regions of low gene density. We counted the number of genes situated within 500 kb upstream and downstream of a trans-dev gene, and compared this with the average for all human genes. Whereas the average for all human genes is 17, it is just six for the trans-dev genes. This is similar to the “gene desert” phenomenon described around the DACH gene [31]. Interestingly, the CNEs themselves are generally located large distances from their nearest gene. The average distance between a CNE and the 5′ end of the closest human gene is 182 kb (median = 120 kb), with 93 CNEs more than 500 kb, and 12 CNEs more than 1Mb, from any known gene.
A number of the trans-dev genes that we identified have previously been shown to have highly conserved cis-regulatory elements associated with them, including the Hox clusters [24,33], PAX6 [48], PAX9 [32], SOX9 [28], OTX2 [34], SHH [30], DLX genes [29], and DACH [31]. Five CNEs do not appear to cluster with any known genes in either the human or Fugu genomes and are located in a large gene desert on human Chromosome 22. Given that gene annotation and genomic sequencing of parts of the human genome are not yet fully complete, the discovery of CNEs here may well point to the existence in this region of an important transcriptional or developmental regulation gene with which they are associated. Indeed we find the largest number of CNEs (48) clustered around a relatively uncharacterised gene with zinc finger domains, ZNF503 on human Chromosome 10, the rat orthologue of which was recently characterised as a probable transcriptional regulator in brain development [49].
All CNEs were compared with each other to look for local similarities. Forty-three elements show significant similarity to at least one other CNE, and in each case are situated close to genes with clear paralogous relationships, e.g., the HOX and IRX clusters. The remainder of the sequences appear to be unique in the human genome.
In order to identify additional conserved sequences around specific genes for further functional assay, localised multiple-alignment comparisons were performed using the multiple LAGAN (MLAGAN) alignment tool kit [50]. This tool kit provides the opportunity to introduce genomic sequence from additional species, in this case mouse and rat, which significantly enhances the signal to noise ratio. For a random subset of 25 of the trans-dev genes associated with CNE clusters, stringent whole-genome alignment located 408 CNEs, whereas MLAGAN identified over twice as many conserved regions (871) of at least 100 bp in length. The whole-genome analysis was more stringent in that we used a minimum exact word match of 20 bp, whereas MLAGAN uses short inexact words to create anchors from which a more sensitive (Needleman–Wunsch) alignment is carried out. It is important to note that similar alignments on genes that are not implicated in developmental regulation do not identify conserved non-coding sequence (e.g., [22,51]).
The alignment of a known transcription factor, SOX21, identifies a large number of conserved non-coding sequence elements in addition to the CNEs found in the whole-genome analysis. We have called these “regionally defined CNEs” (rCNEs) (Figure 3A). In mammalian genomes, the distance between the first and last element around SOX21 is over 450 kb. As is the case for a number of the larger CNEs throughout the genome, some of the CNEs around the SOX21 gene are more highly conserved than the gene's coding exon. For example, in multiple alignments of mouse, rat, human, and Fugu sequence, one CNE (SOX21_19) has 90% identity over 558 bp whilst another (SOX21_1) contains a 112-bp region of 100% identity (Figure 3B), demonstrating an extraordinary level of conservation for genomes separated by 900 million years of divergent evolution.
Figure 3 Comparative Sequence Analysis of the SOX21 Gene
SOX21 genomic regions for mouse, human, and rat were extracted from Ensembl to include all flanking DNA up to the nearest neighbouring genes (ABCC4 and NM_180989 in the human genome and their orthologues in the rodent genomes). The region covering Fugu SOX21 (138–178 kb of Fugu Scaffold_293 [M000293]) was extracted from the Fugu Genome Server at http://fugu.rfcgr.mrc.ac.uk/fugu-bin/clonesearch.
(A) MLAGAN alignment of the SOX21 gene using Fugu DNA as the base sequence compared with mouse, rat, and human genomic DNA. Coloured peaks represent regions of sequence conservation above 60% over at least 40 bp. The SOX21 coding region (SOX21 is a single exon gene) is annotated, and sequence identity is shaded in blue. Non-coding regions of sequence identity are shaded in pink. The eight elements that have been functionally assayed are labelled. Six of these are identified in the global analysis as seven CNEs (SOX21_8–10 covers two CNEs). SOX21_7 and SOX21_18 are rCNEs.
(B) Multiple DNA sequence alignments of CNE SOX21_1 and CNE SOX21_19 between mouse, rat, human, and Fugu.
Finally we searched invertebrate sequence databases, including the whole-genome sequences of Ciona intestinalis, Drosophila melanogaster, and Caenorhabitis elegans, to see whether we could identify any of these highly conserved vertebrate sequences within the invertebrate lineage. Although many of the genes identified in our analysis have clear homologues within these genomes, we found no significant matches to any CNEs. More sensitive alignment using MLAGAN also failed to identify any conserved non-coding sequence similarity between vertebrates and non-vertebrates (including C. elegans, D. melanogaster and A. gambiae), whilst in each case the coding sequences were identified. This is surprising, given that the degree of identity between CNEs in vertebrates is higher than that of the coding regions for these genes. Thus, it is unlikely that the same set of sequences that appear to regulate important vertebrate trans-dev genes are found in invertebrates.
Functional Assay
We have assayed the ability of conserved non-coding sequences identified both from the whole-genome MegaBLAST analysis (CNEs) and from regional MLAGAN alignments (rCNEs) to up-regulate green fluorescent protein (GFP) reporter expression in zebrafish embryos (see Materials and Methods). We chose four cluster regions that contain different types of developmental genes: SOX21, PAX6, HLXB9, and SHH. Elements are co-injected with a minimal promoter–GFP reporter construct into early zebrafish embryos. This co-injection strategy [37,38] is an efficient, yet simple and rapid method for identifying enhancer activity; indeed enhancer activity of elements is more striking when tested in a co-injection assay than when ligated directly to a promoter–reporter construct [37].
A total of 25 conserved non-coding regions were selected (Figures 3, 4, and S1), of which ten were CNEs and 15 were rCNEs (Table 1). GFP expression was analysed in live embryos on the second day of development and recorded both schematically and in tabular form. A mean of 188 embryos were screened for each element, compared with a mean of just over 200 embryos per control (Table 1).
Figure 4 MLAGAN Alignments of Regions Encompassing the PAX6, HLXB9, and SHH Genes
PAX6 (A), HLXB9 (B), and SHH (C). In each panel, human (top), mouse (middle), and rat (bottom) genomic DNA from Ensembl is aligned with Fugu genomic DNA from orthologous regions. Alignment parameters are the same as in Figure 2. Seventeen elements that have been functionally assayed from these regions have been labelled. The following were identified as CNEs: PAX6_6, PAX6_9–10, KIAA0010_1, and KIAA0010_3.
Table 1 Elements Used in Functional Assay
aIn some cases, two conserved regions that are very close together have been included in one PCR. In this case, the length of each element is given with the region they span in parenthesis
N/A, not applicable
Controls in which no element was injected (GFP reporter construct injected alone), in which non-conserved, non-coding genomic DNA from the PAX6 or SOX21 regions was co-injected with the GFP reporter, or in which conserved, coding DNA from PAX6, SOX21, or SHH exons was co-injected with the GFP reporter produce essentially no up-regulation of GFP expression (Table 1; Figure S1). When conserved non-coding sequences were injected, up-regulation of GFP expression was observed with all but two of the elements tested, with between 4% and 44% of embryos screened being positive (Table 1). Furthermore, GFP expression was generally observed in consistent patterns, specific to the element injected (Figure 5).
Figure 5 Composite Overviews of GFP Expression Patterns Induced by Different Elements Tested in the Functional Assay
Cumulative GFP expression data, from SOX21-associated elements (A), PAX6-associated elements (B), HLXB9-associated elements (C), and SHH-associated elements (D). Cumulative data pooled from multiple embryos per element on day 2 of development (approximately 26–33 hpf) are displayed schematically overlayed on camera lucida drawings of a 31-hpf zebrafish embryo. Categories of cell type are colour-coded: key is at bottom of figure. Bar graphs encompass the same dataset as the schematics and use the same colour code for tissue types. Bar graphs display the percentage of GFP-expressing embryos that show expression in each tissue category for a given element. The total number of expressing embryos analysed per element is displayed in the top left corner of each graph. Legend for the bar graph columns accompanies the bottom graph in each panel; “blood+” refers to circulating blood cells plus blood island region, “heart+” refers to heart and pericardial region (Please note: Some cells categorised as heart/pericardial region may be circulating blood cells), and “skin” refers to cells of the epidermis or EVL. s. cord, spinal cord.
In order to build up a comprehensive picture of the GFP expression pattern induced by each of the elements, the expression profiles from multiple embryos positive for a given element were overlaid onto a schematic diagram, so providing a composite overview for each element (Figure 5). This also provided a convenient format for data storage and comparison between elements.
SOX21-associated elements
Of the eight SOX21-associated elements tested in our functional assay, seven enhance GFP expression (Table 1). Three of these enhancing elements direct reporter gene expression most prominently to the central nervous system (CNS) (SOX21_4 and SOX21_19 [Figures 5A, 6A, and 6B] and SOX21_7). SOX21_19 strongly directs remarkably widespread GFP expression throughout the brain and rostral spinal cord (88% of expressing embryos show GFP-positive cells in the CNS; Figures 5A and 6B). SOX21, a member of the SRY-related HMG-box (SOX) gene family of DNA-binding proteins, acts as a transcriptional repressor during early development [52], and is expressed in a complex, dynamic pattern in the developing vertebrate CNS [53,54,55].
Figure 6 Different Elements Enhance GFP Expression in Specific Tissue and Cell Types
GFP expression is shown in fixed tissue following wholemount anti-GFP immunostaining, bright-field views (A–D, F, J, K, and N), or in live embryos as GFP fluorescence, merged bright-field and fluorescent views (E, G–I, L, M, and O). Lateral views, anterior to the left, dorsal to the top (A, B, and D–O) or dorsal view, anterior to the top (C). Embryos approximately 28–33 hpf (A, D–I, L, and O), approximately 48 hpf (B, C, J, K, and N), or approximately 26 hpf (M). The identity of the element co-injected with the GFP reporter construct is shown at the bottom of each panel. Black arrows indicate the approximate position of the midbrain–hindbrain boundary; black and white arrowheads indicate GFP-expressing cells.
Scale bars approximately 100 μm (A–E, G–I, and L–O) and 50 μm (F, J, and K).
b, blood island; d, diencephalon; e, eye; f, fin fold; hb, hindbrain; l, lens; n, notochord; ov, otic vesicle; r, retina; s, somite; sc, spinal cord; t, telencephalon; te, tectum; y, yolk.
(A) SOX21_4. Head region (eyes removed): neurons in the telencephalon and diencephalon are GFP-positive (arrowheads).
(B) SOX21_19. Head region: numerous GFP-expressing neurons are visible in the forebrain, midbrain, and hindbrain. Retinal expression is also apparent.
(C) SOX21_5–6. Hindbrain region: white arrowheads indicate GFP expression by several cells in the epithelium of the right developing ear (ov). GFP-expressing cells in left deveoping ear are in slightly different focal plane.
(D) SOX21_1. Trunk region: two individual notochord cells express GFP (arrowheads).
(E) PAX6_6. Head region of live embryo: GFP is expressed in several retinal cells.
(F) PAX6_9–10. Anterior trunk region (at the level of somites 1–3): three spinal cord neurons with ventrally projecting axons express GFP (arrowheads).
(G) PAX6_1. Tail region of live embryo: arrowhead indicates GFP expression in the developing median fin fold.
(H) KIAA0010_1. Trunk region, three notochord cells express GFP (arrowheads).
(I) KIAA0010_2. Anterior end of embryo: arrowheads point to circulating blood cells expressing GFP.
(J) HLXB9_3. Trunk region: GFP-expressing muscle fibres in somite 5 (arrowheads) lie immediately dorsal and ventral to the horizontal myoseptum.
(K) HLXB9_3. Trunk region (at the level of somites 13–15): arrowheads mark GFP expression in six cells forming the epidermis or EVL.
(L) SHH_6. Whole live embryo: numerous GFP-expressing muscle fibres can be seen in the trunk.
(M) SHH_1. Tail region of live embryo: GFP is expressed in a single bipolar neuron near the caudal end of the spinal cord (arrowhead marks cell body).
(N) SHH_4. Head region (dorsolateral view): cells labelled with anti-GFP include midbrain and hindbrain neurons and cells in the retina (slightly out of focal plane). Arrowheads indicate cell bodies of hindbrain neurons, from which axons can be seen projecting ventrally.
(O) SHH_2. Trunk region of live embryo: GFP-positive cells in the region of the blood islands (caudal to the urogenital opening; arrowheads) show a slightly elongated morphology, suggesting they may be blood vessel precursors rather than blood cells.
Three elements strongly enhance GFP expression in the sense organs: SOX21_4 and SOX21_19 direct GFP expression to the developing eye (in 52% and 27% of expressing embryos, respectively; Figures 5A and 6B), and SOX21_5–6 strongly enhances reporter expression in the developing ear (75% of expressing embryos; Figures 5A and 6C). These observations draw parallels with prominent regions of endogenous SOX21 expression in the sense organs: i.e., the nasal epithelium, the lens and retina of the eye, and the sensory epithelia of the developing inner ear [55]. SOX21_1 strongly enhances expression in the notochord (62% of expressing embryos; Figures 5A and 6D), a domain not normally associated with SOX21 expression.
PAX6-associated elements
Six out of seven PAX6-associated elements tested in our functional assay enhance GFP expression (Table 1). Four of these six functional elements direct GFP expression most frequently to the developing eye (PAX6_6, 90% of expressing embryos; PAX6_19, 59% of expressing embryos [Figures 5B and 6E]; PAX6_2, 92% of expressing embryos; and PAX6_4, 100% of expressing embryos). A fifth element, PAX6_9–10, also directs reporter gene expression to the eye in a significant proportion (25%) of expressing embryos (Figure 5B) as well as to neurons most frequently in the hindbrain and spinal cord (Figures 5B and 6F).
Significantly, PAX6 is a paired-box-containing transcription factor, expressed in and playing essential roles in the developing eye; it is also expressed in the forebrain, hindbrain, and spinal cord (data from the Zebrafish Information Network; http://zfin.org). PAX6 is associated with the loss-of-function disorder aniridia. Some aniridia cases show chromosomal rearrangements downstream of an intact PAX6 gene, indicating that cis-acting elements can influence PAX6 gene expression in the eye at a significant distance from the coding region [56]. Indeed, PAX6 expression is known to be influenced by cis-acting elements in upstream, intronic, and downstream positions. For example, 5′ elements drive expression in the lens, pancreas, and parts of the neural tube [27], intronic elements drive expression in the retina, forebrain, and hindbrain [27,57], and several 3′ regions direct expression to the developing pretectum, neural retina, and olfactory region [58].
In addition to the eye and CNS, other tissues to which GFP expression is directed by our PAX6-associated elements include the blood islands (PAX6_9–10, 36% of expressing embryos; PAX6_1, 16% of expressing embryos [Figure 5B]) and the median fin fold (PAX6_1, 55% of expressing embryos; Figures 5B and 6G); these tissues have not been associated with endogenous expression of PAX6.
HLXB9-associated elements
We assayed six elements associated with a genomic region containing the HLXB9 and KIAA0010 genes (Table 1). Each of these elements induces GFP expression in a variety of tissues (data from four elements are shown in Figure 5C). Most notably, KIAA0010_1 directs GFP expression to the notochord in more than 87% of expressing embryos (Figures 5C and 6H), KIAA0010_2 directs expression to the blood (38% of expressing embryos; Figures 5C and 6I) and the pericardial region (36% of expressing embryos; Figure 5C), HLXB9_1 directs expression to the skin/enveloping layer (EVL; 52% of expressing embryos) and skeletal muscle (40% of expressing embryos; Figure 5C), HLXB9_3 directs expression to skeletal muscle (48% of expressing embryos; Figures 5C and 6J) and to skin/EVL (33% of expressing embryos; Figures 5C and 6K), and HLXB9_2 directs expression to the spinal cord (87% of expressing embryos).
HLXB9 is a Mnx-class homeobox gene associated with autosomal dominant caudal defects [59]. The zebrafish orthologue, hb9, is expressed in the notochord, hypochord, tail mesoderm, and tailbud [60], paralleling some of the domains of GFP expression induced by HLXB9/KIAA0010-associated elements.
SHH-associated elements
Two of the four SHH-associated elements tested in this study (Table 1) direct GFP expression most frequently to muscle cells (SHH_1, 46% of expressing embryos; SHH_6, 83% of expressing embryos [Figures 5D and 6L]). All four elements also prominently direct GFP expression to the CNS (SHH_1, 64% of expressing embryos; SHH_2, 42%; SHH_4, 57%; and SHH_6, 48% [Figures 5D, 6M, and 6N]).
The SHH signalling molecule is crucial for a number of developmental processes, and is extensively implicated in disease (reviewed in [61]). In zebrafish, shh and its co-orthologue twhh are both expressed predominantly in midline structures, i.e., floorplate and notochord. Later expression domains include the branchial arches, pectoral fin buds, and the retina [62,63]. GFP expression directed by SHH-associated elements and shh/twhh expression overlap in the floorplate; however, most of the other domains of GFP expression (e.g., muscle and blood islands; Figure 6O) are not reflected by endogenous expression of hedgehog genes.
Discussion
Understanding the intricate and finely tuned process of gene regulation in vertebrate development remains a major challenge facing post-genomic research. In order to begin to understand how genomic information can coordinate regulatory processes, we have adopted an approach integrating comparative genomics and a medium-throughput functional assay. Nearly 1,400 non-coding DNA sequence elements were identified that exhibit extreme conservation throughout the vertebrate lineage. Despite a degree of overlap, less than half of the non-coding ultra-conserved regions (109 out of 256) identified from the mouse and human genomes [21] are present in this set. Most, if not all, of the CNE sequences appear to be associated with genes involved in the control of development, many of them transcription factors. A significant proportion of genes identified in this study are homologous to those identified in the sea urchin and other invertebrates as master regulators of early development, leading us to believe that they interact in GRNs. Consequently, it is extremely likely that the CNEs identified compose at least part of the genomic component of GRNs in vertebrates, acting as critical regions of regulatory control for their associated genes. Such regions would mediate up- or down-regulation of expression, effecting a cascade of downstream events.
In agreement with current GRN models, and given the function of many of the genes we have identified in our analysis, it is logical to speculate that CNEs consist of modules of binding sites for transcription factors. However, the model of CNEs as transcription factor binding sites, even for large numbers of transcription factors, does not fully explain their high sequence identity across vertebrates, given that transcription factor binding sites are generally rather short and exhibit a level of redundancy. Consequently, we have not ruled out the possibility that the CNEs may have a completely different mode of action or act in numerous different ways.
The relative positions and order of CNEs within a cluster is completely conserved in all vertebrate genomes we have analysed (generally mouse, rat, human, and Fugu) together with some degree of proportional compaction in the Fugu genome. This suggests that the CNEs might play a role in structuring the genomic architecture around trans-dev genes, which in turn may lead to an additional level of transcriptional control. Further evidence that genomic architecture may be important comes from the fact the trans-dev genes are generally located in regions of low gene density.
Alternatively, despite the lack of EST data, it is possible that CNEs are transcribed and work at the RNA level. A number of other ideas on the evolutionary mechanisms responsible for “ultra-conservation” have been suggested [21,64], involving decreased mutation rate, increased DNA repair, and multiply-overlapping transcription factor binding sites, but without more functional studies such hypotheses remain speculative. Whatever their mode of action, the striking degree of conservation displayed by this set of CNEs suggests they play critically important functional roles.
Having established a “map” of the major locations of CNEs in the genome, we were able to take a more sensitive alignment approach in a number of these regions in order to identify additional CNEs (rCNEs). The distinction between CNEs and rCNEs is purely a bioinformatics one, based on our search parameters, and we have no reason to believe that there is any functional distinction between the two sets of elements. We selected a number of elements (both CNEs and rCNEs) as candidates for functional analysis. Data from our functional assay of 25 elements from four different developmental genes demonstrate that a significant proportion can act as enhancers, inducing expression of a GFP reporter gene in a tissue-specific manner. The observed expression patterns differ among elements, but are reproducible for individual elements. Enhanced GFP expression domains frequently coincide with endogenous expression domains of the trans-dev gene most closely associated with a particular element, although in several instances, expression of GFP was induced in a tissue in which the most closely associated developmental gene is not normally expressed. This is not surprising because we are assaying elements out of context and individually. Thus, in our assay, we may have excluded another regulatory sequence in the region that under normal circumstances acts to silence the enhancer activity of an element in a specific tissue. Indeed GRN models would predict that a number of different regulatory regions must interact in order to precisely effect a particular spatiotemporal pattern of expression. One of our future directions will therefore be to assay the combinatorial effects of injecting a number of elements together. Alternatively, we may have associated a CNE with the wrong gene, particularly where there are two or more trans-dev genes in the same region (see below).
Whilst it is straightforward to assign CNEs unequivocally to the SOX21 and PAX6 genes based on their location in the genome, the situation is more complex for elements in the vicinity of the SHH and HLXB9 genes, which are situated in close proximity to each other in the human, rodent, and Fugu genomes. This is exacerbated by the fact that some CNEs may also be found within or around neighbouring genes. This phenomenon has been described for both the PAX6 [65] and PAX9 [32] genes, as well as for the SHH gene [30], where a long-range enhancer in the intron of a neighbouring, unrelated gene regulates SHH expression in developing limb buds and demonstrates the large genomic distances over which regulatory regions may act. This enhancer is identified as a CNE in our dataset and, despite its established mode of action, is located much closer to the HLXB9 gene (200 kb in human and 12 kb in Fugu) than to SHH (1,000 kb in human and 60 kb in Fugu). Furthermore, a number of elements are located directly 5′ of the HLXB9 gene, whilst others are found located further upstream, in introns of the next gene, KIAA0010. Although we strongly suspect that all these elements are associated functionally with the HLXB9 gene (e.g., KIAA0010_1 directs expression prominently to the notochord, an expression domain of the zebrafish HLXB9 orthologue), we cannot rule out the possibility that they may associate with the SHH gene (also expressed in the notochord), which lies a few genes downstream. There are a number of cases where a CNE cluster is located close to more than one trans-dev gene, illustrating the value of correlating endogenous expression pattern with CNE enhancer activity. However, it should be noted that in order to build GRN maps for the elements, it is desirable but not essential to know which element associates functionally with which gene.
Our confidence in the correctness of our gene assignment for the elements tested in this study is borne out by the results of our functional analysis. For the elements that we have associated with PAX6 and SOX21, there is a good correlation between tissues that express the gene endogenously and tissues induced by the associated co-injected elements to express GFP, i.e., the major sites of endogenous gene expression are highly represented in our mosaically expressing embryos (e.g., eye, hindbrain, and spinal cord for PAX6; forebrain, midbrain, hindbrain, and spinal cord for SOX21; see Figure 5). However, for elements in the vicinity of the HLXB9, KIAA0010, and SHH genes, GFP expression overlaps less often with expression domains of the associated gene to which the element has been assigned. As mentioned above, this reduced correlation with endogenous expression of their “associated” genes is probably due to the difficulty of assigning genes to elements in this region of relatively high trans-dev gene density.
It is likely that we have missed some developmental regulators in our whole-genome analysis owing to the stringency of our search parameters. Both the RUNX2 [66] and WNT1 [26] genes, for instance, share conserved non-coding sequences in humans and fish but were excluded because they failed to satisfy our stringent whole-genome search parameters. We may also have missed some elements because they were inadvertently hidden during the process used to mask coding sequence. Nevertheless, this is the first comprehensive attempt to identify the most highly conserved non-coding sequences common to all vertebrates. The use of the compact Fugu genome sequence, with its large evolutionary divergence from mammals, was critical in providing an exceptionally low degree of background noise in comparisons at the level of whole-genome and genomic regions.
As with any high-throughput approach, our functional screen has limitations. Since there is a negligible background level of GFP expression from our reporter construct alone, as well as from our other negative controls (see Table 1), the expression we see is most likely to be directly attributable to the enhancer properties of the CNEs. However, since GFP is a relatively stable protein [67], down-regulation of expression will not be detected during the time course of this screen; instead, expression of GFP by a particular cell indicates that expression was stimulated at some previous point in that cell's lineage. False negatives are a further limitation of the assay, e.g., tissues that develop from few cells will be under-represented and late-developing tissues or cell types (after 24 h) will be missed completely in this screen, since there is a delay between the time of onset of GFP transcription and the time when GFP fluorescence is detectable.
The proportion of screened embryos that showed GFP expression varied from around 4% (SOX21_21) to around 44% (SHH_6); this is probably due to many factors, e.g., variations in the embryonic stage at the time of injection and stochastic variations from embryo to embryo with regard to which cells the injected DNA is segregated into during cleavage. However, by combining expression data from a number of expressing embryos (an average of 30 embryos per positive element), we can gain insight into the overall pattern of reporter gene expression prescribed by each element.
In addition to seeing GFP expression in “expected” domains (with respect to the associated gene), GFP expression was also often detected in tissues in which the associated gene is not normally expressed (e.g., muscle cells for SHH_6 and notochord for SOX21_1; see Figure 5). This might be due to incorrect association of gene to element (see above); alternatively, it might reflect the importance of genomic context for function of CNEs and rCNEs. It is possible that certain regions of the genome function as silencers or suppressors, repressing the transcription-stimulating activity of enhancer elements. In our assay we are testing the autonomous enhancing function of our CNEs independent of their normal genomic context. Whilst this enables us to screen rapidly for function in an unconstrained context, it might also result in a loss of the endogenous negative constraints. It will be interesting to determine the combinatorial language of CNEs, and to uncover the importance of genomic context for their function.
Conserved non-coding sequences are likely to function as negative as well as positive regulatory elements. Indeed, it is possible for a conserved non-coding element to act as either an enhancer or repressor of transcription depending on what factors are bound to it [68]. Whether any of our CNEs can function as negative regulatory elements is an interesting question that is beyond the scope of the present study.
Zebrafish are the ideal model vertebrate for this screen. These sequences are, by definition, highly similar between mammals and fish, and the data generated are therefore relevant to any vertebrate. Given that CNE DNA can easily be generated from any vertebrate species (given its high degree of sequence identity), subtle differences between CNE sequences may be tested functionally in this system. Zebrafish embryos are both readily produced and easily visualised, allowing convenient live screening throughout development. Their transparency makes the embryos ideally suited to GFP analysis and the problems associated with mosaicism in this screen are relatively easily overcome by injecting large numbers of embryos. Technical advances, such as the use of meganuclease injection, may facilitate this further.
The combination of a comparative genomics approach together with functional screening of conserved elements produces a large and complex dataset. Efficient access, integration, and interrogation of this bioinformatics and functional data is crucial, and of increasing interest to the scientific community, to begin to characterise GRNs in vertebrates. To this end, we have submitted all CNE DNA sequences from Fugu to the EMBL nucleotide database and are developing a publicly available relational database in order to store, curate, and analyse data from this study as well as data generated from ongoing identification and characterisation of rCNEs surrounding trans-dev genes.
We have identified an important set of highly conserved non-coding vertebrate sequences that associate with developmental regulators and have provided evidence that at least some of them demonstrate regulatory function. They are likely to be implicated in genetic disease, as has already been shown for the SHH gene [30]. Their distal location from coding sequence, often megabases away, makes them strong candidates as causative agents in position effect and breakpoint disorders [69,33]. They are amongst the most highly conserved of all sequences in vertebrate genomes yet they are completely unrecognisable in invertebrates. Given their strong association with genes involved in developmental regulation, they are most likely to contain the essential heritable information for the coordination of vertebrate development.
Materials and Methods
Similarity searching of non-coding sequence between Fugu and human genomes
GENSCAN [70] (using a suboptimal exon probability cutoff of 0.1) and tRNA-scan-SE (release 1.1) [71] were used to predict coding exons and tRNA genes within the Fugu draft genome assembly (release 3.0; Rosalind Franklin Centre for Genomics Research Comparative Genomics Group; http://fugu.rfcgr.mrc.ac.uk/). These predicted sequences were then masked in the Fugu sequence by supplying them as a “repeat library” to Repeatmasker35. The masked sequence was similarity searched against human genomic sequence from the Ensembl [41] database v18.34.1 in 1-Mb sections using MegaBLAST [40] version 2.2.6 (word size 20 and mismatch penalty –2). Human and Fugu sequences with alignments of 100 bp or over were selected to form the initial CNE sequence dataset.
All CNEs with a significant similarity to an expressed transcript in the EMBL database or protein sequence in Swiss-Prot/TrEMBL were removed from the dataset unless located within a UTR. CNEs with significant similarity to non-coding RNAs were also removed. These were located by comparing the CNEs to the microRNA Registry [72] and the Rfam database (version 5.0) [73] using BLASTn [74]. CNEs were also searched against Rfam using the INFERNAL software. This resulted in the detection of 1 microRNA, four U1 snoRNAs, six U2 snoRNAs, three U5 snoRNAs, one U6atac RNA, three 7S RNAs, one 7Sk RNA, and one 5S RNA. The CNEs were also searched against the UTRdb (http://www.ba.itb.cnr.it/BIG/UTRScan/, which is a collection of functional sequence patterns located in 5′ or 3′ UTR sequences, but no significant hits were found. We used the program QRNA [75] to see whether any of the BLAST matches had a pattern of mutation consistent with RNA secondary structure. However, the known RNAs detected above had the most significant hits from this analysis. QRNA uses the mutational pattern in a pairwise alignment to detect non-coding RNAs, but in general the sequence identity of the CNEs is too high for this to be of use.
Analysis of the distribution of CNEs in the human genome
In order to test whether CNEs were randomly distributed, a new random location was allocated uniformly for each CNE within its chromosome. This process was repeated 1,000 times for each chromosome, and the average cluster sizes were calculated for the different distances given in Figure 1B. These cluster sizes were then compared to the cluster sizes of the CNEs. χ2 tests were carried out comparing the number of clusters containing five or fewer CNEs with the number of clusters containing six or more CNEs. The p-values obtained from the χ2 test statistics on one degree of freedom are also shown in Figure 1B. They give very strong evidence against the CNEs being randomly distributed.
Identification of genes associated with CNEs
The closest gene (using the transcription start site as defined in Ensembl) to the start of each CNE was determined from a list of all human genes supported by external evidence (“known” genes) downloaded using EnsMart, available from the Ensembl Web site (release 24.34e.1; http://www.ensembl.org/). The GOstat program was used to find statistically over-represented GOs in this group of genes [44], using the “goa_human” GO gene association database as a comparator. The minimum length of a considered GO path was five. The false discovery rate option was used to adjust for multiple comparisons.
MLAGAN alignments
More sensitive global alignment of the CNE regions surrounding 25 orthologous genes in human, Fugu, and other vertebrate species was carried out using the MLAGAN alignment tool kit [50]. To locate the orthologous regions in mouse and rat, local similarity searches with BLASTn were carried out using the most outlying CNE associated with each gene. The relevant genomic regions were extracted from Ensembl for human, mouse, and rat. For Fugu the genomic regions were extracted from the Medical Research Council Rosalind Franklin Centre for Genomics Research Fugu Genomics Project Web site (http://fugu.rfcgr.mrc.ac.uk/) (where there is additional mapping information for scaffolds. All sequences were orientated prior to alignment so that the coding sequence of the gene was in positive orientation in all sequences. The MLAGAN alignment was visualised using the VISTA program [76], enabling the identification of conserved sequences. Because of the larger evolutionary distance between fish and mammals, conservation was measured using a 40-bp window and a cutoff score of 60% identity. Fugu was always used as the baseline sequence.
Similarity searching of human CNEs against other vertebrate and invertebrate genomes
To look for the presence of CNEs in other available vertebrate genomes, CNEs were similarity searched against Ensembl mouse (v19.32.2), rat (v21.3.2), chicken (v22.1.1), and zebrafish (v21.3.2) genome sequences using BLASTn with default parameters. All invertebrate sequences in the EMBL database were searched in the same way using BLASTn with non-stringent parameters (mismatch penalty –1, gap open penalty 1, word size 9, and soft masking). More sensitive alignment of flanking orthologous sequence around the SOX21 gene (up to the coding sequence of the genes on either side) from Ensembl C. elegans (v21.25), D. melanogaster (v21.3.1), and Anopheles gambiae (v21.2.2) was carried out using MLAGAN as above.
Fish care
Zebrafish were raised and bred and embryos staged following standard protocols [77,78]; stages are described as the approximate number of hours post-fertilisation (hpf) when embryos are raised at 28.5 °C. To prevent pigment formation, some embryos were raised in 0.003% phenylthiocarbamide in embryo medium from tailbud stage.
Functional Assay
We assayed for enhancer activity in embryos co-injected with candidate enhancer elements or control DNA and a minimal promoter–reporter construct in a method adapted from Muller and colleagues [37] as described below:
For the preparation of DNA and micro-injection, CNEs, rCNEs, and negative controls were PCR-amplified from Fugu genomic DNA (see Figure S1 for PCR primer sequences; primers are represented by the first and last 20 bp of each sequence). The reporter construct consisting of EGFP (Clontech, Palo Alto, California, United States) under the control of a minimal promoter from the mouse β-globin gene, was PCR-amplified from a plasmid vector (available upon request). Amplified DNA was purified using the GFX PCR purification kit (#27–9602-01; Amersham Biosciences, Amersham, United Kingdom) or the QIAquick PCR purification kit (#28106; Qiagen, Valencia, California, United States). Element DNA or control DNA (at 150–300 ng/μl), reporter construct DNA (at 25 ng/μl), and phenol red (at 0.1%, used as a tracer) were combined and co-injected into embryos produced from natural matings between the one-cell stage and early cleavage stages, using an Eppendorf (Hamburg, Germany) FemtoJet pressure injection system. Any embryos developing abnormally were discarded before screening.
For screening of embryos and data collection, on the second day of development (approximately 26–33 hpf), injected embryos were anaesthetised in Tricaine [77] and analysed for GFP expression by observation under fluorescence illumination using an Olympus (Tokyo, Japan) IX81 motorised inverted microscope. Images were captured using an FVII CCD monochrome digital camera and analySIS image-processing software.
GFP-expressing cells were classified according to the following tissue categories: forebrain, midbrain, hindbrain, spinal cord, eye, ear, notochord, muscle, blood (circulating)/blood islands, heart/pericardial region (Please note: Some cells classified in this category may be circulating blood cells), epidermis/EVL, or fins. Cells that did not fall into one of these major expression categories (or that were not possible to unequivocally identify from morphology or localisation) were categorised as “other”. The location and tissue category of each GFP-expressing cell for each embryo was recorded schematically using Adobe Photoshop software (Adobe Systems, San Jose, California, United States), by manually drawing colour-coded schematised cells in appropriate positions onto an overlay of a camera lucida drawing of a 31-hpf embryo (from staging series by C. Kimmel, downloaded from “Zebrafish: The Living Laboratory”, courtesy of the Zebrafish CD Exchange Project; contact Mark Cooper at E-mail: [email protected];data relating to tissue category was also recorded on a spreadsheet.
GFP expression data were collected from between 25 and 55 expressing embryos per element injected. Cumulative overlaid schematised expression data for each element were compressed into a single JPEG file (displayed in Figure 5). Thus, the JPEG image for each element is designed to give an overall impression of the spatial pattern to which the element directs expression. Coupled with the accompanying graphs, the data present an overview of the spatial localisation of GFP expression as well as an idea of the number of cells per tissue in which GFP expression was detected, indicating the strength of the element's enhancing properties or the size of the cell population to which expression is directed.
Anti-GFP immunostaining.
Embryos were fixed in 4% paraformaldehyde and stained with rabbit polyclonal anti-GFP (#TP401 at 1/1,000 dilution; AMS Biotechnology, Abingdon Oxon, United Kingdom) using standard protocols [79] and the ABC amplification system (Vectastain; Vector Laboratories, Burlingame, California, United States). Stained embryos were cleared in glycerol, flatmounted, and observed/imaged as above.
Supporting Information
Figure S1 DNA Sequence Data for Functionally Assayed Regions
Each sequence represents the PCR product used in the functional assay. Sequence in bold type represents the position of the conserved element or elements within the PCR product. All PCR products were generated from Fugu DNA.
(61 KB DOC).
Click here for additional data file.
Table S1 Chromosomal Locations of Genes Associated with CNE Clusters in the Human Genome (from Ensembl)
(40 KB XLS).
Click here for additional data file.
Table S2 Statistically Over-Represented GO Terms for Genes Located Closest to the CNEs
(67 KB DOC).
Click here for additional data file.
Accession Numbers
All 1,373 CNEs (CR846105 to CR847477) and 16 rCNEs (CR847478 to CR847493) have been submitted to the EMBL database.
This work was supported by the Medical Research Council and by private funds from Sydney Brenner.
Competing interests: The authors have declared that no competing interests exist.
Author contributions: MG and GE designed the study. AW, MG, DKG, PS, SFS, PN, and JEC performed the experiments. AW, MG, DKG, PS, GKM, TV, KK, JEC, and GE analysed the data. GKM, TV, HC, KW, IA, WG, and YJKE contributed reagents/materials/analysis tools. AW, GKM, JEC, and GE wrote the paper.
Academic Editor: Sean Eddy, Howard Hughes Medical Institute and Washington University, United States of America
Citation: Woolfe A, Goodson M, Goode DK, Snell P, McEwen GK, et al. (2004) Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biol 3(1): e7.
Abbreviations
CNEconserved non-coding element
CNScentral nervous system
ESTexpressed sequence tag
EVLenveloping layer
GFPgreen fluorescent protein
GOGene Ontology
GRNgene regulatory network
hpfhours post-fertilisation
MLAGANmultiple LAGAN
rCNEregionally defined conserved non-coding element
UTRuntranslated region
==== Refs
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| 15630479 | PMC526512 | CC BY | 2021-01-05 08:21:16 | no | PLoS Biol. 2005 Jan 11; 3(1):e7 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030007 | oa_comm |
==== Front
Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-331547947610.1186/1475-925X-3-33Book ReviewReview on ''Bioinformatics, Biocomputing and Perl'' by Michael Moorhouse and Paul Barry Cherkasov Artem [email protected] Division of Infectious Diseases, Faculty of Medicine, University of British Columbia. 2733, Heather street, Vancouver, British Columbia, V5Z 3J5, Canada2004 12 10 2004 3 33 33 Moorhouse M and Barry P .
Bioinformatics Biocomputing and Perl .
Wiley . 2004 . pp. ISBN 047085331X . 7 10 2004 12 10 2004 Copyright © 2004 Cherkasov; licensee BioMed Central Ltd.2004Cherkasov; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
==== Body
The book "Bioinformatics, Biocomputing and Perl" [1] attempts to encompass those numerous volumes which most bioinformaticians keep on their office bookshelves and which are often entitled as "Something in a Nutshell".
The book aims at both biology- and computation-oriented audiences and is designed as a number of 'crash-courses' quickly updating the reader on the basics of bioinformatics. It starts with a preface outlining main biological and technological concepts of the modern computational biology. The rest is organized into four sections consisting of 18 chapters elaborating on essential bioinformatics tools and skills.
The section 'Working with Perl' presents an extended tutorial with practical tips and useful references for Perl beginners. Following this is 'Working with Data', which familiarizes the reader with some public genomic and proteomic databases and discusses important subjects of database formats, non-redundancy, cross-referencing and programmable access, etc. By working through the section, the reader acquires basic skills for mySQL database use and DBI Perl programming.
Next, the authors offer Perl-based solutions for remote database access and for creation of WWW-based bioinformatics services using Perl functionalities in 'Working with the Web'.
The final topic of the book, 'Working with Applications', features basic tools for sequence alignment, protein homology modeling and data visualization, all commonly used in bioinformatics practice. The section also offers recent and relevant examples of BioPerl applications.
In general, the book reflects the state of bioinformatics field with its strengths and weaknesses. Many Perl chapters, such as Perl regular expressions, modular organization, DBI-programming, BioPerl and web-automation, are excellent. The presented material is rather comprehensive and yet easy to read – the authors spent appreciative efforts to make the book interesting and enjoyable. The authors also acknowledge the open-source nature of Perl and the bioinformatics community and offer on-line support and direct feedback to the readers.
There are also certain aspects, in which the book could be further improved. Several sections may be too advanced for the beginner level (such as Perl basics and database downloading), while others may contain too excessive details (the Protein Databank section). In addition, it may be of advantage to mention AcePerl [2], Perl-programmable access to the SRS as well as XML- [3] and distributed data processing by Perl. The book would greatly benefit from color illustrations. Several figures in the 'biological' sections are not very informative or readable (such as Figure 10.5), and one contains a critical error (Figure 1.1).
A very useful feature of the book is the use of maxims that highlight key points throughout the text. The authors also provide helpful technical comments where necessary and offer practical exercises at the end of each chapter. The book is concluded with six appendices covering the Linux basics, Perl installation, operators, on-line support and suggested reading materials which, in my mind, benefit the book tremendously.
Thus, the overall product, the "Bioinformatics, Biocomputing and Perl", serves well its purpose as an introductory textbook and a resource of reference materials for bioinformaticians.
List of Abbreviations used
AcePerl – is a Perl interface for the AceDB – a popular object-oriented bioinformatics database.
DBI Perl – the primary interface for database programming by Perl.
BioPerl – a collection of Perl modules specifically designed for several most common bioinformatics tasks.
XML – Extensible Markup Language – a popular standard for documents containing structured information.
SRS – the Sequence Retrieval System – a popular relational database for bioinformatics.
==== Refs
Moorhouse M Barry P Bioinformatics Biocomputing and Perl 2004 Wiley
AcePerl – for more info visit the home page of the AcePerl developer Dr. L. Stein at the Cold Spring Harbor Laboratory
Ray ET McIntosh J Perl & XML 2002 O'Reilly
| 0 | PMC526754 | CC BY | 2021-01-04 16:37:32 | no | Biomed Eng Online. 2004 Oct 12; 3:33 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-33 | oa_comm |
==== Front
Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-341548260110.1186/1475-925X-3-34ReviewReview and standardization of cell phone exposure calculations using the SAM phantom and anatomically correct head models Beard Brian B [email protected] Wolfgang [email protected] Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Rockville, Maryland, USA2004 13 10 2004 3 34 34 21 5 2004 13 10 2004 Copyright © 2004 Beard and Kainz; licensee BioMed Central Ltd.2004Beard and Kainz; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We reviewed articles using computational RF dosimetry to compare the Specific Anthropomorphic Mannequin (SAM) to anatomically correct models of the human head. Published conclusions based on such comparisons have varied widely. We looked for reasons that might cause apparently similar comparisons to produce dissimilar results. We also looked at the information needed to adequately compare the results of computational RF dosimetry studies. We concluded studies were not comparable because of differences in definitions, models, and methodology. Therefore we propose a protocol, developed by an IEEE standards group, as an initial step in alleviating this problem. The protocol calls for a benchmark validation study comparing the SAM phantom to two anatomically correct models of the human head. It also establishes common definitions and reporting requirements that will increase the comparability of all computational RF dosimetry studies of the human head.
==== Body
1. Background
Cell phone safety remains a topic of broad public concern that attracts frequent media attention. This attention is focused on two areas of scientific controversy concerning cell phone safety. The first area is that of non-thermal biological effects. The existence of these effects is an important open question, but it is not the topic of this paper. However, if these effects exist, their manifestation will certainly be related to the amount of RF energy deposited in the tissue – RF dosimetry [1]. The second area of controversy, and the topic of this paper, is that of RF dosimetry, specifically computational RF dosimetry. Simply put, this is a computer simulation that estimates the deposition of RF energy, the specific absorption rate (SAR), in the head of a user. Because live human heads can not be safely instrumented for these measurements, computational RF dosimetry provides the best estimate of SAR in actual human heads. For this same reason, compliance testing is done with phantom heads.
The phantom head that is now the world-wide standard for compliance testing is the Specific Anthropomorphic Mannequin (SAM). SAM was developed by members of IEEE Standards Coordinating Committee 34, SubCommittee 2, Working Group 1 (SCC34/SC2/WG1). This working group was created to develop recommended practices for determining SAR in the head via measurement techniques [2]. SAM has also been adopted by the European Committee for Electrical Standardization (CENELEC) [3], the International Electrotechnical Commission [4], Association of Radio Industries and Businesses [5], and Federal Communications Commission [6].
SAM is a lossless plastic shell and ear spacer. Because current technology does not allow reliable measurement of SAR in small complex structures, like a simulated pinna, SCC34/SC2 chose to use a lossless ear spacer on SAM to maximize the energy reaching the head and minimize measurement uncertainty. SAM's dimensions were taken from the 90th-percentile anthropometric data corresponding to the adult male head as tabulated by the US Army [7]. The SAM shell is filled with a homogeneous fluid having the average electrical properties of head tissue at the test frequency.
A primary design goal for SAM was that, "SAM shall produce a conservative SAR for a significant majority of persons during normal use of wireless handsets" [2]. To test whether this goal has been met, investigators have used computational RF dosimetry to compare the SAR in SAM to that in anatomically correct models of the human head. These anatomically correct head models are commonly derived from MRI scans. Each two-dimensional scan must be analyzed to identify individual tissue types. The two-dimensional scans must then be merged into a three-dimensional model that maintains smooth boundaries between tissue types [8,9]. Some investigators have found that SAM underestimates SAR in adults and children by a factor of two or more [10]. Other investigators have found that SAM overestimates SAR in both adults and children [11,12]. These contradictory findings produce confusion on the part of the public and regulatory agencies, and call the validity of computational RF dosimetry into question. While the published results of computational RF dosimetry comparing the SAM to anatomically correct models appear contradictory, a close examination of the work reveals that there are several procedural and reporting problems that may well account for the discrepancies in results.
The groups headed by Gandhi and Kuster are not the only ones pursuing computational RF dosimetry using anatomically correct models of the human head [13-25]. Not all of these studies included SAM but, to various extents, all evidenced the same procedural and reporting problems that make comparison of results difficult.
2. Problem areas
2.1 Treatment of the pinna
The first, and the most significant of these problems, is the treatment of the external ear (pinna). Specifically, the problem is determining whether the pinna may, or may not, be considered as part of the 1- or 10-gram SAR averaging volumes. When considering SAR averaging volumes the head and the pinna should be viewed as mutually exclusive, in-other-words the pinna is not part of the head but it is attached to the head. Some investigators have chosen to treat the pinna in accordance with IEEE Std C95.1-1999 [26] and the ICNIRP Guidelines [27]. These standards do not consider the pinna to be an extremity. This means the pinna is subject to the same exposure limit, for peak spatial SAR, as the head. Investigators that refer to these standards include pinna tissue in the 1- or 10-gram averaging volumes used to compute SAR in anatomically correct models. Because the pinna is usually the tissue closest to the feed-point of the cell phone antenna the highest point SAR values are usually found in the pinna; consequently, averaging volumes that include pinna tissue will produce higher SAR.
Other investigators have treated the pinna in accordance with draft revision IEEE Std C95.1-200X. This draft standard expands the definition of extremity to include the pinna, which makes the pinna subject to a higher spatial peak SAR, see Table 1. These investigators exclude pinna tissue from their head tissue SAR averaging.
Table 1 SAR limits SAR limits from three different standards for extremities and other tissues. These limits are for exposure of the general public in an uncontrolled environment.
ICNIRP 1998 IEEE C95.1-1999 IEEE C95.1-200X
Extremities 4 W/kg over 10 g 4 W/kg over 10 g 4 W/kg over 10 g
Other tissues 2 W/kg over 10 g 1.6 W/kg over 1 g 2 W/kg over 10 g
When comparing published results it is often difficult, or impossible, to determine whether head tissue SAR values are based on averaging volumes that include or exclude the pinna. In fact, some papers make no mention of how the pinna was treated. Although head tissue SAR is the major focus of attention, papers that consider the pinna as an extremity can not simply ignore its existence, the pinna must still meet the higher spatial peak SAR for extremities.
Another part of the problem dealing with the treatment of the pinna is simply determining what tissue constitutes the pinna. The IEEE defines the pinna as, the largely cartilaginous projecting portion of the outer ear consisting of the helix, lobule, and anti-helix [2]. Unfortunately these anatomical structures vary with each individual and their boundaries are subjective. Consequently, when excluding the pinna some investigators have excluded considerably more or less tissue than others. Because the pinna contains high SAR values, excluding or including tissue near the pinna from the averaging volume, markedly changes the peak spatial 1- or 10-gram average.
2.2 Models
The second problem area is the lack of common models. The only computer models that are common to all the computational RF dosimetry studies are the SAM and the Visible Human Male. The anatomic data for the Visible Human Male originated at the National Institutes of Health but many groups and individuals lent a hand in converting it into a computational model. While a few investigators have different models the only ones that can be compared across all the published results are SAM and the Visible Human. This also means that the only repeatable comparison that can be made is between the SAM and the Visible Human. It seems obvious that one can neither prove nor disprove that SAM produces a SAR greater than the maximum local SAR induced in humans for a significant majority of persons during normal use of wireless handsets, when there is only one anatomically correct model available for comparison.
Although not a major problem, it is still true that dielectric properties and names of tissue types in anatomically correct models have varied between investigators.
Of course the head model is only half of any computational RF dosimetry study, the model of the RF source is the other half. The only common source that has been used in several published studies is a dipole [15,28-30]. Simulated cell phones have varied in size, shape, antenna type, antenna length, and sophistication. Like the anatomical head models there are some very realistic models of cell phones in use but they are either proprietary or too expensive for widespread use.
2.3 Positioning
The third problem is that of inconsistent positioning of the model cell phone relative to the head model. Simulated SAR in near-field situations is mainly a function of the geometry of the RF current density distribution on the source model and its geometric separation from the lossy head tissue [2]. When the separation distance is small a one or two mm change can significantly alter the observed SAR [30,31]. The CAD files defining SAM show specific reference points and lines used to position cell phones for compliance testing. IEEE Std. 1528 defines two test positions for compliance testing, the touch and tilted position, see Figures 1 and 2 respectively. These positions are routinely used in computational RF dosimetry studies but the anatomical head models do not have defined reference points. These reference points are defined with respect to anatomical features but, as with the definition of the pinna, the interpretation of these anatomical features can vary from investigator to investigator. Consequently, even if two investigators are using the same cell phone and head model, there is no assurance that their positioning of the cell phone relative to the head model is the same.
Figure 1 Touch position. Specific Anthropomorphic Mannequin with cell phone in touch position on the left side. RE = Right Ear, LE = Left Ear, M = Mouth.
Figure 2 Tilt position. Specific Anthropomorphic Mannequin with cell phone in tilted position on the left side. RE = Right Ear, LE = Left Ear, M = Mouth.
2.4 Finite Difference Time Domain (FDTD) considerations
2.4.1 Rotation artifacts
Usual practice is to align a monopole cell phone antenna with the FDTD grid to avoid the stairstep effect. The head model is then rotated to the correct position relative to the cell phone. After rotation, the voxelized model must be remeshed to align the voxels with the FDTD grid. This is not a trivial task and algorithms to perform remeshing are constantly being improved.
The authors have noted some unintended artifacts in voxelized models after remeshing. The first of these is grooving. Figure 3 shows a planar slice through the ear spacer and cheek of the SAM. Note the grooves in what should be a smooth surface. The SAR is zero in the grooves but at the end of the grooves it is higher than in the surrounding voxels due to high E fields within the grooves. These artifacts can distort both the magnitude and location of the peak spatial SAR.
Figure 3 Artifacts in slice through ear and cheek of SAM. Slice through ear spacer and cheek of the Specific Anthropomorphic Mannequin (SAM). Two of the many groove artifacts caused by rotation and remeshing are annotated. The upper portion of the figure is the ear spacer which, because it is lossless, has no Specific Absorption Rate (SAR). The lower portion of the figure shows the SAR in the simulated tissue just inside the shell of SAM; red is the highest SAR, violet is the lowest SAR.
The jagged edges caused by grooving are not limited to surface features. Figure 4 shows unrotated and rotated slices through the same anatomic model. The smooth interface between tissue types has been distorted and isolated regions of different tissue types have been created in some locations.
Figure 4 Artifacts in slice through anatomically correct model. The image on the left is an XY slice through an unrotated anatomically correct model of a human head. Each color represents a different tissue type. Each tissue type comprises a contiguous region and the boundaries between types are smooth. The image on the right is another XY slice through the same model after rotation around all three axes and remeshing; this is not the same plane represented by the image on the left because that plane is no longer parallel to any of the coordinate axes. In the image on the right tissue types are no longer contiguous regions and the boundaries between types show an unrealistic sawtooth pattern.
Grooving has not been observed with all FDTD software and even when it has been seen it has not occurred with all models. Researchers should routinely examine their models after rotation to insure grooving is not a problem.
All FDTD programs must, of necessity, perform their calculations on voxelized models. However some programs use CAD models which are only converted to voxelized format after all rotation has been done. These programs avoid most coordinate transformation problem but they are not infallible. They must still convert smoothly undulating biological surfaces into rectilinear voxels. Figure 5 shows empty voxels (air) along a tissue interface where they should not exist.
Figure 5 Empty voxels along tissue boundary. This image is a close-up of empty voxels caused by rotation and remeshing along a tissue boundary. The white areas are empty voxels along the boundary between the two tissue types indicated by red and blue.
2.4.2 SAR Calculations
Because the FDTD method calculates the electric fields at the voxel edges, the X, Y and Z-directed power components associated with a voxel are defined in different spatial locations. These components must be combined to calculate SAR in the voxel. There are three possible approaches to calculate the SAR: the 3-, 6-, and 12-field components approaches. The 12-field components approach is the most complicated but it is also the most accurate and the most appropriate from the mathematical point of view [32]. The 12-field components approach correctly places all E field components in the center of the voxel using linear interpolation. Therefore, the power distribution is now defined at the same location as the tissue mass. For these reasons the 12-field components approach is preferred by IEEE 1529 [33]. However, the actual approach used to calculate SAR in the FDTD voxels is usually not reported.
After the SAR in every voxel is determined multiple voxels must be combined to compute the 1- or 10-gram SAR spatial averaging volumes. These normally cubic volumes become difficult to construct at the surface of a model or when the volume is constrained to a particular tissue type. The particular algorithm used to construct these volumes can influence the resultant 1- or 10-gram SAR values. However, the actual algorithm used to construct the spatial averaging volumes is usually not reported.
2.5 Reporting results
When the cell phone model is placed next to the SAM or anatomically correct model, it changes the cell phone's antenna feed-point impedance. The antenna feed-point impedance (Z), feed-point current (I) and net input power (Pnet) are related by
Because net power and feed-point current are usually not initial conditions in FDTD simulations, different feed-point impedances will produce different results for net power, feed-point current, and SAR. If different head models produce the same feed-point impedance this would not be a concern; however, several studies [16,17] have shown that the feed-point impedance depends on the head model, the size of the head next to the mobile phone and the mobile phone model itself.
To compare SAM with various anatomic models it is necessary to assume the same cell phone model at the same emission level for all simulations. Typically, for a given simulation, the SAR is normalized by feed-point current or net power. The normalized value is then multiplied by the feed-point current or net power level chosen for comparison. Commonly SAR is compared for net input power levels of 125 mW, 600 mW, or 1 W or for the corresponding feed-point current assuming a 50 ohm feed-point impedance.
Some investigators have chosen to scale their results to net power while others have used feed-point current. Unfortunately the choice of scaling is frequently omitted and the feed-point impedance is almost never reported making it impossible to compare differently scaled results.
3. A possible solution
To address the controversy and its underlying problems the Protocol for the Computational Comparison of the SAM Phantom to Anatomically Correct Models of the Human Head was developed by IEEE Standards Coordinating Committee 34, SubCommittee 2, Working Group 2 (SCC34/SC2/WG2). This working group was created to develop recommended practices for determining SAR in the head via computational techniques [33]. This standard is still in draft.
The protocol has two parts; a benchmark validation study; and a set of common definitions, models, and reporting requirements. The benchmark validation study is underway with fifteen participants. All participants should finish the study by mid-2004 and the results should be published by early 2005. Hopefully the common definitions, models, and reporting requirements will be used in future investigations making comparison of results easier.
3.1 Treatment of the pinna
The protocol asks all participants to report peak spatial SAR for averaging volumes that both include and exclude the pinna. The voxels comprising the pinna in the provided anatomic models are flagged so all participants will conform to one definition of the pinna. The pinna voxels are flagged by prefixing the standard tissue type with pinna-; such as pinna-skin, pinna-cartilage, and pinna-fat. The electrical properties of the flagged pinna voxels are unchanged. The IEEE Std 1528 definition for the pinna was followed and the choice of each flagged voxel was confirmed by an Ear-Nose-Throat surgeon.
3.2 Models
The Benchmark Validation Study calls for each participant to run twelve simulations: three head models, at two frequencies (835 and 1900 MHz), and in two cell phone positions (touch and tilted). The models are SAM, the Visible Human, and a seven year old Japanese male [16]. Each model is provided as a voxel file with an ASCII header file.
For the two anatomically correct models, the tissue names and properties in the header file were made consistent with the definitions found on the Italian National Research Council, Institute for Applied Physics web site [34].
Although they are not part of the benchmark validation study, SCC34/SC2/WG2 plans on releasing several new anatomically correct models in the next few months to expand the population of models available for study.
A generic cell phone is described for use in all benchmark validation studies, see Figure 6. The length of the antenna is 71 mm for 835 MHz and 36 mm for 1900 MHz. Because the cell phone and SAM are symmetric, and the anatomically correct models are approximately symmetric, SCC34/SC2/WG2 chose to do all simulations with the phone on the right hand side of the head.
Figure 6 Generic cell phone. The Generic cell phone designed for the intercomparison protocol. Blue = perfect electrical conductor, gray = plastic insulator, green = rubber insulator, red = antenna feed-point voltage source, yellow = acoustic output.
3.3 Positioning
The reference points, necessary for positioning the cell phone relative to the anatomically correct model, are also contained in the header file for each model.
To aid comparison of results from all the participants, a common coordinate system was defined with origin at the acoustic output of the cell phone, see Figure 7. The participants are asked to report the following positioning data for all simulations: the distance between the antenna feed-point and the nearest tissue voxel, the coordinates of the Ear Reference Point (ERP), and the direction cosines (as a rotation matrix) for the coordinate transformation of the head models for touch and tilted positions. As defined in IEEE Std 1528, the ERP is 15 mm posterior to the ear canal in the plane passing through the mouth and both ear canals.
Figure 7 Coordinate system. The left image shows the cell phone referenced coordinate system as seen from the right side of the Specific Anthropomorphic Mannequin (SAM). The right image shows the coordinate system as seen from the top of the SAM. The SAM Ear Reference Points, left and right, are where the Y axis intercepts the surface of the mannequin.
3.4 FDTD considerations
The FDTD technique is called for by the P1529 draft [33]. FDTD was chosen because it is stable and accurate, doesn't require enormous computational resources and can handle complex geometries.
The participants are asked to report the following FDTD data for all simulations: The boundary conditions used and the minimum distance between the model and the boundary of the computational space. The time step size and the number of time steps used. The grid (voxel) size and whether the grid was homogeneous or graded. To calculate the SAR in each voxel the protocol recommends the 12-field components approach. All 1- or 10-gram spatial averaging volumes are to be constructed in accordance with IEEE C95.3, Annex E.
3.5 Reporting
The participants are asked to report the following SAR data for all simulations: The peak spatial SAR, both 1 g and 10 g averages, for all tissue (head plus pinna), head only, and pinna only averaging volumes and the location of the averaging cubes. The peak point value SAR and its location. A color coded SAR distribution for both 1 g and 10 g averages, in the ZY plane.
3.5.1 Scaling reported results
For a realistic study it would be ideal to simulate the real world situation. The question that remains is: "Does a real-world cell phone keep the power or the feed-point current constant when placed next to different human beings with different head shapes and head sizes"? Unfortunately there is not a definitive answer to this question. The behavior of a mobile phone depends on the system design and the power amplifier circuits. A detailed discussion for real-world mobile telephones has to be addressed by future projects with more realistic mobile phones models for numerical simulations.
For now it is important to scale the calculated SAR values to net input power and feed-point current and to present both results. The behavior of a real-world mobile phone is within the SAR range of scaling to the net input power and scaling to the feed-point current. For human health and safety considerations a worst case approach is desirable. Until further knowledge on the behavior of a real-world cell phone is available, the scaling producing the worst case result (largest SAR value) must be taken into account.
4. Conclusion
The current version of IEEE Std C95.1 [26] does not classify the pinna as an extremity making it subject to the basic SAR exposure limitation of 1.6 W/kg over 1 g. However the much anticipated 200X revision of C95.1 will reclassify the pinna as an extremity raising its SAR exposure limit to 4 W/kg over 10 g. Confusion over the inclusion or exclusion of the pinna in the SAR averaging volume will continue until the IEEE officially releases C95.1-200X. The IEEE should release C95.1-200X as soon as practical, and if this can not be done in a reasonably short time, a supplement should be published clarifying the new status of the pinna as an extremity.
Investigators should inspect all models after rotation to be sure they are free of artifacts caused by meshing along the new coordinate axes. If necessary, artifacts should be manually corrected before running the simulation. Blindly accepting the output of meshing algorithms can lead to errors. All relevant data and assumptions for the computational RF dosimetry study, as discussed in section 2 "Problem areas", must be reported in such detail that the reader is able to compare the results to other studies. The names and electrical properties for all anatomically correct models should comply with those shown on the Italian National Research Council, Institute for Applied Physics web site.
To facilitate broad based comparisons, new anatomically correct models should be placed in the public domain or made available for a modest fee. The number of anatomically correct models suitable for electromagnetic modelling and widely available for comparison to the SAM is still low. Because the SAM is intended to represent a significant majority of persons during normal use of wireless handsets, comparison to a large variety of anatomically correct models is desirable. It is the hope of IEEE SCC34/SC2/WG2 that consistent results in the benchmark validation will show that, by adhering to some common definitions and procedures, FDTD studies from different investigators using different anatomically correct models are comparable.
Authors' contributions
BB, past chairman of IEEE SCC34/SC2/WG2, drafted the Protocol for the Computational Comparison of the SAM Phantom to Anatomically Correct Models of the Human Head and this manuscript. WK, present chairman of IEEE SCC34/SC2/WG2, wrote approximately 25% of the manuscript, developed and simulated the phone model and supplied several of the figures. Both authors read and approved the final manuscript.
Acknowledgements
Disclaimer: The opinions and conclusions stated in this article are those of the authors and do not represent the official position of the United States Food and Drug Administration.
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| 15482601 | PMC526755 | CC BY | 2021-01-04 16:37:31 | no | Biomed Eng Online. 2004 Oct 13; 3:34 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-34 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1491548557210.1186/1471-2105-5-149Methodology ArticleImproved hit criteria for DNA local alignment Noé Laurent [email protected] Gregory [email protected] LORIA/INRIA-Lorraine, 615, rue du Jardin Botanique, B.P. 101, 54602 Villers-lès-Nancy France2004 14 10 2004 5 149 149 26 7 2004 14 10 2004 Copyright © 2004 Noé and Kucherov; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The hit criterion is a key component of heuristic local alignment algorithms. It specifies a class of patterns assumed to witness a potential similarity, and this choice is decisive for the selectivity and sensitivity of the whole method.
Results
In this paper, we propose two ways to improve the hit criterion. First, we define the group criterion combining the advantages of the single-seed and double-seed approaches used in existing algorithms. Second, we introduce transition-constrained seeds that extend spaced seeds by the possibility of distinguishing transition and transversion mismatches. We provide analytical data as well as experimental results, obtained with the YASS software, supporting both improvements.
Conclusions
Proposed algorithmic ideas allow to obtain a significant gain in sensitivity of similarity search without increase in execution time. The method has been implemented in YASS software available at .
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Background
Sequence alignment is a fundamental problem in Bioinformatics. Despite of a big amount of efforts spent by researchers on designing efficient alignment methods, improving the alignment efficiency remains of primary importance. This is due to the continuously increasing amount of nucleotide sequence data, such as EST and newly sequenced genomic sequences, that need to be compared in order to detect similar regions occurring in them. Those comparisons are done routinely, and therefore need to be done very fast, preferably instantaneously on commonly used computers. On the other hand, they need to be precise, i.e. should report all, or at least a vast majority of interesting similarities that could be relevant in the underlying biological study. The latter requirement for the alignment method, called the sensitivity, counterweights the speed requirement, usually directly related to the selectivity (called also specificity) of the method. The central problem is therefore to improve the trade-off between those opposite requirements.
The Smith-Waterman algorithm [1] provides an exact algorithmic solution to the problem of computing optimal local alignments. However, its quadratic time complexity has motivated the creation of rapid heuristic local alignments tools. A basic idea behind all heuristic algorithms is to focus only on those regions which share some patterns, assumed to witness (or to hit) a potential similarity. Those patterns are formed by seeds which are small strings (usually up to 25 nucleotides) that appear in both sequences. FASTA [2] and BLAST [3,4] are well-known examples of such methods. BLAST is currently the most commonly used sequence alignment tool, and is a kernel of higher-level search tools, such as PSI-BLAST [4] for instance.
More recently, several new alignment methods have been proposed, such as BLAT [5], PatternHunter [6], LAGAN [7], or BLASTZ [8], to name a few. The improvement brought by all those tools results from new more efficient hit criteria that define which pattern shared by two sequences is assumed to witness a potential alignment. Two types of improvements can be distinguished. On the one hand, using two or more closely located smaller seeds instead of one larger seed has been shown to improve the sensitivity/selectivity trade-off [4-6], especially for detecting long similarities. On the other hand, new seed models have been proposed, such as spaced seeds [6], seeds with errors [5], or vector seeds [9].
In this paper, we propose further improvements in both those directions. In the first part (Section Group hit criterion), we propose a new flexible and efficiently computable hit criterion, called group criterion, combining the advantages of the single-seed ([3]) and multi-seed ([2,4-6]) criteria. In the second part (Section Generalized seed models), we propose a new more expressive seed model which extends the spaced seed model of PatternHunter [6] by the possibility of distinguishing transition and transversion mismatches. We show that this allows to obtain a further gain in sensitivity on real genomic sequences, usually rich in transition mutations. Both proposed improvements have been implemented in YASS software [10], used in the experimental part of this work.
Results
Group hit criterion
The first preparatory step of most heuristic alignment algorithms consists of constructing a hash table of all seeds occurring in the input sequences. In this section, we assume that a seed of weight k is a word consisting of k contiguous nucleotides (k-word), more general notions of seed will be considered in the next Section.
In the simplest case, implemented in the early version of BLAST [3], an individual seed occurring in both sequences acts as a hit of a potential alignment. It triggers the X-drop algorithm trying to extend the seed to a so-called High-scoring Segment Pair (HSP), used then to obtain a larger final alignment. Gapped BLAST [4] proposes a double-seed criterion that defines a hit as two non-overlapping seeds occurring at the same dotplot diagonal within a fixed-size window. This allows to considerably increase the selectivity with respect to the single-seed approach, and at the same time to preserve, and even to improve, the sensitivity on large similarities. On the other hand, Gapped BLAST is less sensitive on short and middle-size similarities of weak score. (We will show this more formally at the end of this Section.) Most of the existing alignment programs [5,6] use either a single-seed or a double-seed hit criterion.
Here we propose a new flexible hit criterion defining a hit as a group containing an arbitrary number of possibly overlapping seeds, with an additional constraint on the minimal number of matching nucleotides. The seeds of the same group are assumed to belong to the same similarity, and therefore should be proximate to each other. In contrast to other multi-seed hit criteria [4-6], we don't require seeds to occur at the same dotplot diagonal but at close diagonals, to account for possible indels. The possible placement of seeds is controlled by parameters computed according to statistical models that we describe now.
Group criterion
A hit criterion defines a pattern which is considered as an evidence of a potential similarity. Every time this pattern is found, its extension is triggered to compute a potential larger alignment. The extension is usually done via a dynamic programming algorithm and is a costly step. The hit criterion should be selective enough to avoid spurious extensions and, on the other hand, should be sensitive to detect as many relevant similarities as possible.
The hit criterion we propose is based on a group of seeds verifying conditions (1), (2) (see Section Methods). By the considered statistical analysis, this ensures a good sensitivity. However, many groups will contain a single seed or two strongly overlapping seeds, that either belong to a similarity with a low score, or do not belong to any similarity at all (i.e. don't belong to an alignment with a sufficiently high score). To cope with this problem, we introduce an additional criterion that selects groups that will be actually extended. The criterion, called group criterion, is based on the group size defined as the minimal number of matching individual nucleotides in all seeds of the group. The group size can be seen as a parameter specifying the maximal overlap of the seeds of a group. For example, if the group size is k + 1, then no constraint on the overlap is imposed, i.e. any group containing two distinct seeds forms a hit. If the group size is 2k, then the group must contain at least two non-overlapping seeds, etc.
Allowing overlapping seeds is an important point that brings a flexibility to our method. Note that other popular multi-seed methods [4,5] consider only non-overlapped seeds. Allowing overlapped seeds and controlling the overlap with the group size parameter offers a trade-off between a single-seed and a multi-seed strategies. This increases the sensitivity of the usual multi-seed approach without provoking a tangible increase in the number of useless extensions. In the next section, we will provide quantitative measures comparing the sensitivity of the YASS group criterion with BLAST and Gapped BLAST.
Some comparative and experimental data
In this section, we adopt the following experimental setup to estimate the sensitivity of the YASS group criterion compared to other methods. We first set a match/mismatch scoring system, here fixed to +1/-3 (default NCBI-BLAST values). The main assumption is that the sensitivity is estimated as the probability of hitting a random gapless alignments of a fixed score. Moreover, to make this model yet more close to reality, only homogeneous alignments are considered, i.e. alignments that don't contain proper sub-alignments of bigger score (see [11]). For a given alignment length, all homogeneous alignments are assumed to have an equal probability to occur.
In this setting, we computed the hit probability of a single-seed criterion with seed weight 11 (default for BLAST) and compared it with multi-seed criteria of Gapped BLAST and YASS for seed weight 9 (default for Gapped BLAST). For YASS, the group size was set to 13. Figure 1 shows the probability graphs for alignment score 25.
Comparing BLAST and Gapped BLAST, the former is more sensitive on short similarities (having higher identity rate), while the latter is more sensitive on longer similarities, in which two close non-overlapping runs of 9 matches are more likely to occur than one run of 11 matches. The YASS group criterion combines the advantages of both: it is more sensitive than the single-seed criterion even for short similarities, and than the non-overlapping double-seed criterion for large similarities (Figure 1).
Note, however, that for the chosen parameters, the YASS criterion is slightly less selective than that of Gapped BLAST as it includes any two non-overlapping seeds but also includes pairs of seeds overlapped by at most 5 bp. The selectivity can be estimated by the probability of a hit at a given position in a random uniform Bernoulli sequence (see [5]). For YASS, this probability is 2.1·10-8, which improves that of BLAST (2.4·10-7) by more than ten. For Gapped BLAST, this probability is 7.3·10-9. To make an accurate sensitivity comparaison of YASS and Gapped BLAST, parameters should be set so that both algorithms have the same selectivity level.
To compare the sensitivity of YASS and Gapped BLAST for an equal selectivity level, we chose a parameter configuration such that both algorithms have the same estimated selectivity (10-6). This is achieved with seed weight 8 for Gapped BLAST and group size 11 for YASS (while keeping seed weight 9). In this configuration, and for sequences of score 25, YASS turns out to be considerably more sensitive on sequences up to 80 bp and is practically as sensitive as Gapped BLAST on longer sequences (data not shown). At the same time, YASS is more time efficient in this case, as Gapped BLAST tends to compute more spurious individual seeds that are not followed by a second hit, which takes a considerable part of the execution time. This is because the YASS seed is larger by one nucleotide, and the number of spurious individual seeds computed at the first step is then divided by 4 on large sequences.
Compared to the single-seed criterion of BLAST, the YASS group criterion is both more selective (group size 13 vs single-seed size 11) and more sensitive for all alignment lengths, as soon as the alignment score is 25 or more. If the score becomes smaller, both criteria yield an unacceptably low sensitivity, and the seed weight has then to be decreased to detect those similarities.
Finally, we point out another experiment we made to bring more evidence that the group criterion captures a good sensitivity/selectivity trade-off. We monitored the partition of the execution time between the formation of groups and their extension by dynamic programming (data not shown). It appeared that YASS spends approximately equal time on each of the two stages, which gives a good indication that it provides an optimal distribution between the two complementary parts of the work.
Generalized seed models
So far, we defined seeds as k-words, i.e. short strings of contiguous nucleotides. Recently, it has been understood that using spaced seeds allows to considerably improve the sensitivity. A spaced seed is formed by two words, one from each input sequence, that match at positions specified by a fixed pattern – a word over symbols # and _ interpreted as a match and a don't care symbol respectively. For example, pattern ##_# specifies that the first, second and fourth positions must match and the third one may contain a mismatch.
PatternHunter [6] was the first method that used carefully designed spaced seeds to improve the sensitivity of DNA local alignment. In [12], spaced seeds have been shown to improve the efficiency of lossless filtration for approximate pattern matching, namely for the problem of detecting all matches of a string of length m with q possible substitution errors (an (m, q)-problem). The use of some specific spaced seeds for this problem was proposed earlier in [13]. Yet earlier, random spaced seeds were used in FLASH software [14] to cover sequence similarities, and the sensitivity of this approach was recently studied in [15].
The advent of spaced seeds raised new questions: How to choose a good seed for a local alignment algorithm? How to make a precise estimation of the seed goodness, or more generally, of a seed-based local alignment method? In [16], a dynamic programming algorithm was proposed to measure the hit probability of a seed on alignments modeled by a Bernoulli model. In the lossless case, an analogous algorithm that allows to test the seed completeness for an (m, q)-problem was proposed in [12]. The algorithm of [16] has been extended in [17] for hidden Markov models on order to design spaced seeds for comparing homologous coding regions. Another method based on finite automata was proposed in [18]. A complementary approach to estimate the seed sensitivity is proposed in [11]. Papers [19,20] present a probabilistic analysis of spaced seeds, as well as experimental results based on the Bernoulli alignment model.
Other extensions of the contiguous seed model have been proposed. BLAT [5] uses contiguous seeds but allows one error at any of its positions. This strategy is refined in BLASTZ [8] that uses spaced seeds and allows one transition substitution at any of match positions. An extension, proposed in [9], enriches the PatternHunter spaced seeds model with a scoring system to define a hit.
Here we propose a new transition-constrained seed model. Its idea is based on the well-known feature of genomic sequences that transition mutations (nucleotide substitutions between purins or between pyrimidins) occur relatively more often than transversions (other substitutions). While in the uniform Bernoulli sequence transitions are twice less frequent than transversions, in real genomic sequences this ratio is often inverted. For example, matrices computed in [21] on mouse and human sequences imply that the transition/transversion rate (hereafter ti/tv) is greater than one on average. Transitions are much more frequent than transversions in coding sequences, as most of silent mutations are transitions. ti/tv ratio is usually greater for related species, as well as for specific DNA (mitochondrial DNA for example).
Transition-constrained seeds are defined on the ternary alphabet {#, @, _}, where @ stands for a match or a transition mismatch (A ↔ G, C ↔ T), and # and _ have the same meaning as for spaced seeds. The weight of a transition-constrained seed is defined as the sum of the number of #'s plus half the number of @'s. This is because a transition carries one bit of information while a match carries two bits.
Note that using transition-constrained seeds is perfectly compatible with the group criterion described in Section Group criterion. The only non-trivial algorithmic issue raised by this combination is how to efficiently compute the group size during the formation of groups out of found seeds. In YASS, this is done via a special finite automaton resulting from the preprocessing of the input seed.
Transition-constrained seeds for Bernoulli alignment model
To estimate the detection capacity of transition-constrained seeds, we first use the Bernoulli alignment model, as done in [6,19,20]. We model a gapless alignment by a Bernoulli sequence over the ternary match/transition/transversion alphabet with the match probability 0.7 and the probabilities of transition/transversion varying according to the ti/tv ratio. The sequence length is set to 64, a typical length of a gapless fragment in DNA alignments. We are interested in seed weights between 9 and 11, as they represent a good sensitivity/selectivity compromise.
Table 1 compares the sensitivity of the best spaced seeds of weight 9, 10 and 11, reported in [20], with some transition-constrained seeds, assuming that transitions and transversions occur with equal probability 0.15. The transition-constrained seeds have been obtained using a stepwise Monte-Carlo search, and the probabilities have been computed with an algorithm equivalent to that of [16]. The table shows that transition-constrained seeds are more sensitive with respect to this model.
A natural question is the efficiency of transition-constrained seeds depending on the ti/tv ratio. This is shown in Figure 2. The left and right plots correspond to the seeds from Table 1 of weight 9 and 10 respectively. The plots show that the sensitivity of transition-constrained seeds greatly increases when the ti/tv ratio is over 1, which is typically the case for real genomic sequences.
Transition-constrained seeds for Markov alignment model
Homologous coding sequences, when aligned, usually show a regular distribution of errors due to protein coding constraints. In particular, transitions often occur at the third codon position, as these mutations are almost always silent for the resulting protein. Markov models provide a standard modeling tool to capture such local dependencies. In the context of seed design, papers [16-18] proposed methods to compute the hit probability of spaced seeds with respect to gapless alignments specified by (Hidden) Markov models. To test whether using transition-constrained seeds remains beneficial for alignments specified by Markov models, we constructed a Markov model of order 5 out of a large mixed sample of about 100 000 crossed alignments of genomic sequences of distantly related species (Neisseria Meningitidis, S. Cerevisiae, Human X chromosome, Drosophila). The alignments were computed with different seeds of small weight, to avoid a possible bias caused by a specific alignment method. We then designed optimal spaced and transition-constrained seeds of weight 9–11 with respect to this Markov model. Table 2 shows the results of this computation providing evidence that transition-constrained seeds increase the sensitivity with respect to this Markov model too.
Experiments
Seed experiments
In order to test the detection performance of transition-constrained seeds on real genomic data, we made experiments on full chromosomic sequences of S. Cerevisiae (chromosomes IV, V, IX, XVI) and Neisseria meningitidis (strains MC58 and Z2491). The experiments were made with our YASS software [10] that admits user-defined transition-constrained seeds and implements the group criterion described in Section Group criterion. The experiments used seeds of weight 9 and 11, obtained on Bernoulli and Markov models (reported in Tables 1 and 2). The search was done using group size 10 and 12 respectively for seed weight 9 and 11 (option -s of YASS). This means that at least two distinct seeds were required to trigger the extension, with no additional constraint on their overlap, which is equivalent to the double-seed criterion of PatternHunter. The scoring system used was +1/-1 for match/mismatch and -5/-1 for gap opening/extension. Both strands of input chromosomes has been processed in each experiment (-r 2 option of YASS).
For each comparison, we counted the number of computed alignments with E-value smaller than 10-3. Table 3 reports some typical results of this experiment. They confirm that using transition-constrained seeds does increase the search sensitivity. A side (non-surprising) observation is that, in all tests, the seed designed on the Markov model turns out to be more efficient than the one designed on the Bernoulli model. Note that the similarity search can be further improved by using transition-specific scoring matrices (for example, PAM Transition/Transversion matrices or matrices designed for specific comparisons [21]) rather than uniform matches/mismatch matrices, and transition-constrained spaced seeds are more likely to detect alignments highly scored by those matrices.
Another advantage of transition-constrained seeds comes from the fact that they are more robust with respect to the GC/AT composition bias of the genome. This is because purins and pyrimidins remain balanced in GC- or AT-rich genomes, and one match carries less information (is more likely to occur "by chance") than two match-or-transition pairs.
Program experiments
A series of comparative tests has been carried out to compare the sensitivity with traditional approaches. Several complete bacterial genomes ranging from 3 to 5 Mb have been processed against each other using both YASS and the b12seq programs (NCBI BLAST package 2.2.6.). The tests used the scoring system +1/-1 for match/mismatch and -5/-1 for gap opening/extension. The threshold E-value for the output was set to 10 (default BLAST value), and the sequence filtering was disabled. YASS was run with its default seed pattern #@#__##__#_##@# of weight 9 which provides a good compromise in detecting similarities of both coding and non-coding sequences.
For each test, the number of alignments with E-value less than 10-6 found by each program, and the number of exclusive alignments were reported. By "exclusive alignment", we mean every alignment of E-value less than 10-6 that does not share a common part (do not overlap on both sequences) with any alignment found by the other program. To take into account a possible bias caused by splitting alignments into smaller ones (X-drop effect), we also computed the total length of exclusive alignments, found by each program.
Experiments are summarized in Table 4 and show that within a generally smaller execution time, YASS detects more exclusive similarities that cover about twice the overall length of those found by b12seq. The gain in execution time increases when the sequence length gets larger.
Conclusions
In this paper, we introduced two independent improvements of hit criteria for DNA local alignment. The group criterion, based on statistical DNA sequence models, combines the advantages of both single-seed and double-seed criteria. Transition-constrained seeds account for specificities of real DNA sequences and allow to further increase the search sensitivity with respect to spaced seeds. Both options have been implemented in YASS software available at .
Transition-constrained seeds could be further extended using the idea of vector seeds [9], i.e. by assigning weights to each seed position, but also to each type of mutation. This would give a more general mechanism to account for the information brought by different mutations. But the model is also more flexible, an thus involves a larger search space to design seeds.
Another new direction for further improving the efficiency is a simultaneous use of several seed patterns [22-24], complementing the sensitivity of each other. However, designing such families is also hard problem, due to the involved search space.
Methods
Statistical analysis
We first introduce some notations used in this section. Let S1 and S2 be the input sequences of length m and n respectively. Each of them can be considered as a succession of m - k + 1 (respectively n - k + 1) substrings of length k, called k-words. If a k-word of S1 matches another k-word of S2, i.e. S1[i..i + k - 1] = S2[j..j + k - 1] for some i ≤ m and j ≤ n, then these two k-words form a seed denoted <i, j>. Two functions on seeds are considered: For a seed <i, j>, the seed diagonal d(<i, j>) is m + j - i. It can be seen as the distance between the k-words S1[i..i + k - 1] and S2[j..j + k - 1] if S2 is concatenated to S1, For two seeds <i1, j1> and <i2, j2>, where i1 <i2 and j1 <j2, the inter-seed distance D(<i1, j1>, <i2, j2>) is the maximum between |i2 - i1| and |j2 - j1|. The problem considered in this Section is to derive conditions under which two seeds are likely to be a part of the same alignment, and therefore should be grouped together. More precisely, we want to be able to compute parameters ρ and δ such that two seeds <i1, j1> and <i2, j2> have a probability (1 - ε) to belong to the same similarity iff
D(<i1, j1>, <i2, j2>) ≤ ρ, (1)
|d(<i1, j1>) - d(<i2, j2>)| ≤ δ. (2)
The first inter-seed condition insures that the seeds are close enough to each other. The second seed diagonal condition requires that in both seeds, the two k-words occur at close diagonals.
We now describe statistical models used to compute parameters ρ and δ.
Bounding the inter-seed distance
Consider two homologous DNA sequences that stem from a duplication of a common ancestor sequence, followed by independent individual substitution events. Under this assumption, the two sequences have an equal length and their alignment is a sequence of matched and mismatched pairs of nucleotides. We model this alignment by a Bernoulli sequence with the probability p for a match and (1 - p) for a mismatch. To estimate the inter-seed shift Dk, we have to estimate the distance between the starts of two successive runs of at least k matches in the Bernoulli sequence. It obeys the geometric distribution of order k called the Waiting time distribution [25,26]:
Using this formula, we compute ρ such that the probability is (1 - ε) for some small ε.
Note that the Waiting time distribution allows us to estimate another useful parameter: the number of runs of matches of length at least k inside a Bernoulli sequence of length x. In a Bernoulli sequence of length x, the probability of the event Ip,x,r of having exactly r non-overlapping runs of matches of length at least k is given by the following recursive formula:
This gives the probability of having exactly r non-overlapping seeds of length at least k inside a repeat of size x. The recurrence starts with r = 0, in which case and is computed through the Waiting time distribution.
The distribution allows us to infer a lower bound on the number of non-overlapping seeds expected to be found inside a similarity region. In particular, we will use this bound as a first estimate of the group criterion introduced later.
Bounding the seed diagonal variation
Indels (nucleotide insertions/deletions) are responsible for a diagonal shift of seeds viewed on a dotplot matrix. In other words, they introduce a possible difference between d(<i1, j1>) and d(<i2, j2>). To estimate a typical shift size, we use a method similar to the one proposed in [26] for the search of tandem repeats.
Assume that an indel of an individual nucleotide occurs with an equal probability q at each of l nucleotides separating two consecutive seeds. Under this assumption, estimating the diagonal shift produced by indels is done through a discrete one-dimensional random walk model, where the probability of moving left or right is equal to q, and the probability of staying in place is 1 - 2q. Our goal is to bound, with a given probability, the deviation from the starting point.
The probability of ending the random walk at position i after l steps is given by the following sum:
A direct computation of multi-monomial coefficients quickly leads to a memory overflow, and to circumvent this, we use a technique based on generating functions. Consider the function and consider the power Pl(x) = al.xl +…+ a-l.x-l. Then the coefficient ai computes precisely the above formula, and therefore gives the probability of ending the random walk at position i after l steps. We then have to sum up coefficients ai for i = 0,1, -1, 2, -2,..., l, -l until we reach a given threshold probability (1 - ε). The obtained value l is then taken as the parameter δ used to bound the maximal diagonal shift between two seeds.
Acknowledgments
We are grateful to Mikhail Roytberg for enlightening discussions, and to Marie-Pierre Etienne, Roman Kolpakov, Gilles Schaeffer and Pierre Valois for their helpful comments at early stages of this work.
Figures and Tables
Figure 1 Hit Probability. Hit probability as a function of length of fixed-score alignments
Figure 2 Seed Probability. Hit probability of seed models on Bernoulli sequences as a function on ti/tv ratio
Table 1 Bernouilli Model Hit probability of seeds on Bernoulli sequences of length 64 with match probability 0.7 and transition/transversion probabilities 0.15
weight spaced seed hit proba transition-constrained seed hit proba
9 B9 = ##_##_#_#___### 0.7291 B9tr = ##@_#@#__#_### 0.7366
10 B10 = ##_##___##_#_### 0.5957 B10tr = #@#_#_@#_@#__@### 0.6056
11 B11 = ###_#__#_#__##_### 0.4671 B11tr = #@#_#@__##_#_@@## 0.4784
Table 2 Markov Model Hit probability of seeds on a Markov model of order 5 trained on a large mixed sample of cross-species alignments
weight spaced seed hit proba transition-constrained seed hit proba
9 M9 = ##_##_##_### 0.822 M9tr = ##@##_##@## 0.845
10 M10 = ##_##_##___##_## 0.716 M10tr = #@@##_##_##@#@ 0.746
11 M11 = ##_##_##_##_### 0.603 M11tr = ##@##_##_##@## 0.632
Table 3 Seed experiments. Number of high-scoring similarities found with different seed patterns
sequences B9 B9
tr
B11 B11
tr
M9 M9
tr
M11 M11
tr
IX/V 323 336 275 279 312 325 274 293
IX/XVI 342 354 271 280 349 357 280 295
XVI/IV 1314 1361 1124 1172 1309 1348 1180 1235
MC58/Z2491 361896 380028 341113 364792 385444 392164 359348 366759
Table 4 Comparative Tests. Comparative tests of YASS vs b12seq (NCBI BLAST 2.2.6). Reported execution times have been obtained on a Pentium IV 2.4 GHz computer.
sequence 1 sequence 2 time (sec) # align. # ex. align. ex. align. length
name size name size Y. B. Y. B. Y. B. Y. B.
S.sp. 3.6 M.t. 4.4 122 148 494 310 130 27 29145 7970
S.sp. 3.6 C.g. 3.3 161 163 578 369 168 63 37310 30138
S.sp. 3.6 Y.p. 4.6 156 253 901 617 186 54 39354 19994
S.sp. 3.6 V.p. 3.3 164 167 940 465 349 60 65788 28883
M.t. 4.4 C.g. 3.3 211 542 1851 1265 397 160 102103 80012
M.t. 4.4 Y.p. 4.6 168 255 738 515 197 86 44348 23361
M.t. 4.4 V.p. 3.3 72 69 498 295 171 30 36474 12021
C.g. 3.3 Y.p. 4.6 130 161 962 640 186 45 34538 11277
C.g. 3.3 V.p. 3.3 95 93 1109 687 197 72 42009 21575
Y.p. 4.6 V.p. 3.3 149 217 2900 1953 622 264 186585 110352
C.g: Corynebacterium glutamicum ATCC 13032,
M.t: Mycobacterium tuberculosis (CDC1551),
S.sp.: Synechocystis sp. PCC 6803,
S.sp.: Vibrio parahaemolyticus RIMD 2210633 chr I,
Y.p.: Yersinia pestis KIM
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| 15485572 | PMC526756 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 14; 5:149 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-149 | oa_comm |
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J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-3-131546178310.1186/1477-3163-3-13ResearchOn the emergence of multifocal cancers Wodarz Dominik [email protected] Yoh [email protected] Natalia L [email protected] Department of Ecology and Evolution, 321 Steinhaus Hall, University of California, Irvine 92697, USA2 Department of Biology, Faculty of Science, Kyushu University, Fukuoka 812-8581, Japan3 Department of Mathematics, University of California, Irvine CA 92692, USA4 Department of Mathematics, Rutgers University, Piscataway NJ 08854, USA2004 1 10 2004 3 13 13 26 4 2004 1 10 2004 Copyright © 2004 Wodarz et al; licensee BioMed Central Ltd.2004Wodarz et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Several tumors can exist as multiple lesions within a tissue. The lesions may either arise independently, or they may be monoclonal. The importance of multiple lesions for tumor staging, progression, and treatment is subject to debate. Here we use mathematical models to analyze the emergence of multiple, clonally related lesions within a single tissue. We refer to them as multi-focal cancers. We find that multifocal cancers can arise through a dynamical interplay between tumor promoting and inhibiting factors. This requires that tumor promoters act locally, while tumor inhibitors act over a longer range. An example of such factors may be angiogenesis promoters and inhibitors. The model further suggests that multifocal cancers represent an intermediate stage in cancer progression as the tumor evolves away from inhibition and towards promotion. Different patterns of progression can be distinguished: (i) If tumor inhibition is strong, the initial growth occurs as a unifocal and self contained lesion; progression occurs through bifurcation of the lesion and this gives rise to multiple lesions. As the tumor continues to evolve and pushes the balance between inhibition and promotion further towards promotion, the multiple lesions eventually give rise to a single large mass which can invade the entire tissue. (ii) If tumor inhibition is weaker upon initiation, growth can occur as a single lesion without the occurrence of multiple lesions, until the entire tissue is invaded. The model suggests that the sum of the tumor sizes across all lesions is the best characteristic which correlates with the stage and metastatic potential of the tumor.
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1 Introduction
The occurrence of multiple lesions is observed in a variety of cancers. That is, not one, but several lesions are observed within a given tissue. Multiple lesions can occur by two basic mechanisms [1-5]. Either they originate independently by separate carcinogenic events, or they are generated by a single transformation event (monoclonal origin). Sometimes, the term "multicen-tric cancers" is used to describe the occurrence of clonally unrelated lesions, while the term "multifocal" refers to a mono-clonal origin [6]. Clinically, it is important to determine the nature of multiple lesions. The occurrence of multiple lesions can be indicative of a familial cancer, especially if they occur at a relatively young age. Examples are familial adenomatous poliposis (FAP) in the colon, and familial retinoblastoma [7]. The genetic predisposition of such individuals renders multiple independent carcinogenic events likely. Alternatively, multiple independent lesions can be the result of a large area of tissue which has been altered and is prone to the development of cancer, such as Barrett's esophagus [8], or by other mechanisms which are not yet under-stood. On the other hand, genetic analysis has indicated that multiple lesions in several cases have a monoclonal origin [8-18]. Examples are mammary carcinoma, gliomas, renal cell carcinoma, hepatocellular carcinoma, and esophageal adenocarcinoma.
In this paper we focus on multiple lesions with a monoclonal origin. We will refer to them as "multifocal" cancers. The mechanism by which such multifocal cancers are generated, and their relation to the stage and metastatic potential of the cancer, are not fully understood [19]. Yet, this understanding is important for decisions regarding treatment and surgery. Here, we report that multifocal cancers can be generated through the dynamical interplay between tumor promoting and inhibiting factors. Mathematical modeling indicates that somatic evolution away from tumor inhibition and towards tumor promotion results in the transition from a small contained tumor, to multi-focal tumors, and finally to a large tumor mass within a tissue. Multifocal tumors therefore represent an intermediate stage in tumor progression. Several studies have identified tumor promoting and inhibiting factors, produced either by the tumor cells themselves, or by surrounding tissue cells. An obvious example is angiogenesis inhibition and promotion, where simple mutations can change the balance away from inhibition and in favor of promotion [20,21]. Other inhibiting factors which are not related to angiogenesis have also been observed, although their exact identity and function remain unknown [22].
2 Results
We start with a simple model which describes tumor growth in relation to the production of promoters and inhibitors. We then extend this model to describe the local spread of cancer cells across space (tissue), and examine somatic evolution of cells away from tumor inhibition and towards promotion.
The basic model
We consider a basic mathematical model which describes the growth of a cancer cell population, assuming that the amount of blood supply influences the rate of cell division. The model includes three variables: the population of cancer cells, C; promoters, P; and inhibitors, I. It is assumed that both promoters and inhibitors can be produced by cancer cells. In addition, inhibitors may be produced by healthy tissue. The model is given by the following set of differential equations which describe cancer growth as a function of time,
The equations are based on a previous study [23]. The population of cancer cells grows with a rate r. Growth is assumed to be density dependent and saturates if the population of cancer cells becomes large (expressed in the parameter ε). In addition, the growth rate of the cancer cells depends on the balance between promoters and inhibitors, expressed as P/(I + 1). The higher the level of promoters relative to inhibitors, the faster the growth rate of the cancer cell population. If the level of promoters is zero, or the balance between promoters and inhibitors in heavily in favor of inhibitors, the cancer cells cannot grow and remain dormant [24-26]. Cancer cells are assumed to die at a rate δ. Promoters are produced by cancer cells at a rate ap and decay at a rate bp. Inhibitors are produced by cancer cells at a rate aI and decay at a rate bI. In addition, the model allows for production of inhibitors by normal tissue at a rate ξ.
Insights from the model
The analysis of the model above is presented in detail in the Materials and Methods section. It suggests the following patterns. There are two outcomes. (i) The cancer cells cannot grow and consequently go extinct.That is, C = 0, P = 0 and I = 0. The cancer goes extinct in the model because we only consider cells which require the presence of promoters for division. If the level of promoters is not sufficient, the rate of cell death is larger than the rate of cell division. In reality, however, it is possible that a small population of non-angiogenic tumor cells survives. Here, we omit this for simplicity. (ii) The population of cancer cells grows to significant levels, that is, C = .
How do the parameter values influence the outcome of cancer growth? The cancer extinction outcome is always stable. The reason is as follows. The cancer cells require promoters to grow. The promoters, however, are produced by the cancer cells themselves. If we start with a relatively low initial number of cancer cells, this small population cannot produce enough promoters to overcome the presence of inhibitors. Consequently, the cancer fails to grow and goes extinct. This outcome is always a possibility, regardless of the parameter values. Significant cancer growth can be observed if the intrinsic growth rate, r, lies above a threshold relative to the death rate of the cells, δ, and degree of tumor cell inhibition (ap and bp relative to aI and bI, i.e. the production and decay rates of promoters and inhibitors, respectively). The exact condition is given by (9). In this case, the outcome is either failure of cancer growth, or successful growth to large numbers. Which outcome is achieved depends on the initial conditions. Successful growth is only observed if the initial number of cancer cells lies above a threshold. Then, enough promoters are initially produced to overcome inhibition. This provides an important barrier to the successful growth of cancers. It could explain why it is difficult for cancers to escape angiogenesis inhibition, and why autopsies often reveal the existence of multiple small, non-pathogenic tumors which have failed to progress [27].
Modeling the spread of tumors across space
In this section, we introduce space into the above described model. We consider a one-dimensional space along which tumor cells can migrate. The model is formulated as a set of partial differential equations and is written as follows,
The model assumes that tumor cells can migrate, and this is described by the diffusion coefficient Dc. Inhibitors can also diffuse across space, and this is described by the diffusion coefficient DI. It is generally thought that inhibitors act over a longer range, while promoters act locally [21,24]. Therefore, we make the extreme assumption that promoters do not diffuse. Again, we ignore for simplicity the production of inhibitors by healthy tissue, ξ. As before, numerical simulations indicate that results are not changed qualitatively by this simplification. As mentioned above, the model considers tumor spread across space. It is important to point out that we do not consider long-range metastatic spread. Instead, we consider local spread of a tumor within a tissue, such as the breast, liver, brain, or esophagus.
Here we investigate the process of tumor growth and progression in relation to the degree of inhibition and promotion. A mathematical analysis is presented in the Materials and Methods section. Here we present biological insights and results of numerical simulations.
Insights from the spatial model
We start with a scenario where the degree of inhibition is much larger than the degree of promotion (aI/bI >>ap/bp). This corresponds to the early stages when the tumor is generated. We then investigate how tumor growth changes as the degree of inhibition is reduced relative to the level of promotion (i.e. the value of aI/bI is reduced). We consider the following parameter regions (Figure 1).
Figure 1 Outcome of the spatial model depending on the relative balance of promoters and inhibitors, captured in the variable ai. Parameters were chosen as follows: r = 1; δ = 0.1; aP = 5; bP = 0.1; bI = 0.01; DC = 0.00001; DI = 0.001; L = 2. For (a) aI = 3, (b), aI = 2, (c), aI = 1, (d) aI = 0.1.
1. If the degree of inhibition is strong and lies above a threshold, growth of the cancer cells to higher levels does not occur (not shown). Only a small number of cells which do not require promotion for survival would remain.
2. If the degree of inhibition is weaker, the cancer cells can grow. The spread across space is, however, self-limited (Figure 1a). The cancer cells migrate across space. The inhibitors produced by the cancer cells also spread across space, while the promoters do not. Therefore, as the cancer cells migrate, they enter regions of the tissue where the balance of inhibitors to promoters is heavily in favor of inhibitors. Consequently, these cells cannot grow within the space. They remain dormant and may eventually die. In biological terms, this corresponds to a single coherent but self-limited lesion (uni-focal). Note that this does not mean that it is in principle impossible to generate more lesions. It means that the space between lesions is bigger than the space provided for cancer growth within the tissue.
3. As the production of inhibitors is further reduced, we enter another parameter region. Now fewer inhibitors diffuse across space. We observe that multiple lesions or foci are formed (Figure 1b). They are separated by tissue space which does not contain any tumor cells. The separate lesions produce some inhibitors, and they diffuse across space. This explains the absence of tumor cells between lesions. Because the production of inhibitors is weakened, however, tumor growth is only inhibited in a certain area around the lesion, and not across the whole space. How many lesions are found within a tissue depends on the parameters in the model, in particular on the relative strength of inhibition and promotion (Figure 1b and 1c). The stronger the degree of inhibition, the larger the space between lesions, and the fewer lesions we expect. The weaker the degree of inhibition, the smaller the space between lesions, and the larger the expected number of lesions. In biological terms, this corresponds to the occurrence of multi-focal cancers.
4. If the degree of inhibition is further reduced and lies below a threshold, spread of inhibitors is sufficiently diminished such that the tumor cells can invade the entire space and tissue (Figure 1d). In biological terms, this corresponds to the most extensive tumor growth possible within a tissue.
In summary, as the relative degree of inhibition is reduced, the patterns of tumor growth change from absence of significant growth, to a single self-limited tumor, to the occurrence of multiple foci, and to the maximal invasion of the tissue by tumor cells. Multi-focal cancers may arise through the dynamical interplay between long range inhibition and local promotion. The following section will examine this in the light of somatic evolution.
3 Discussion
We have shown how the pattern of cancer growth can depend on the relative balance of promoters and inhibitors. Here we consider these results in the context of somatic evolution, and suggest some clinical implications.
Somatic cancer evolution and progression
At early stages of cancer progression, the balance between inhibitors and promoters is in favor of inhibition. Inhibitors are likely to be produced by healthy cells (e.g. in the context of angiogenesis), and they are more abundant than an initiating population of transformed cells. In the context of angiogenesis, specific mutations have been shown to result in the enhanced production of promoters or reduced production of inhibitors in cancer cells. Our model has shown that such mutants have to be produced at a relatively high frequency, so that a sufficient number of promoting cells are present in order to ensure that enough promoters are produced to overcome the effect of inhibition.
Once the promoting cells have succeeded to expand, cancer progression can occur in a variety of ways according to the model. How the cancer progresses depends on how much the balance between promotion and inhibition has been shifted in favor of promotion. We distinguish between three possibilities (Figures 2, 3 &4).
Figure 2 Tumor progression if the initial mutant cell line has only shifted the balance between promoters and inhibitors slightly in favor of promotion. This cell line can only give rise to self limited growth. Further tumor growth requires the generation of further mutants. The new mutant in the simulation is depicted by the dashed line. Parameters were chosen as follows: r = 1; δ = 0.1; aP = 5; bP = 0.1; aI = 3; bI = 0.01; Dc = 0.00001; DI = 0.001; L = 2. For mutant: aI = 0.5; aP = 20.
Figure 3 Tumor progression if the initial mutant cell line has shifted the balance between promoters and inhibitors more substantially towards promotion. Now, multiple foci can develop without the need for further mutations. The multiple foci develop, however, by first generating a single lesion which subsequently splits to give rise to two lesions during the natural growth process. Parameters were chosen as follows: r = 1; δ = 0.1; aP = 5; bP = 0.1; aI = 1; bI = 0.01; DC = 0.00001; DI = 0.001; L = 2.
Figure 4 Tumor progression if the initial cell line has largely escaped inhibition, and promotion is the dominant force. Now the tumor grows in space as a single lesion until the whole tissue is invaded. Parameters were chosen as follows; r = 1; δ = 0.1; aP = 5; bP = 0.1; aI = 0.1; bI = 0.01; DC = 0.00001; DI = 0.001; L = 2.
(i) The balance between inhibition and promotion has been shifted only slightly in favor of promotion, such that self-limited growth of the cancer is observed (Figure 2). That is, we observe a single lesion which can grow to a certain size but which is limited in the spread through the tissue. In order to progress further towards the occurrence of multiple lesions or towards more extensive invasion of the tissue, further mutants have to be generated which are characterized by enhanced production of promoters or by reduced production of inhibitors. This introduces a new problem: such a mutant will not have a selective advantage, but is selectively neutral relative to the other cells. This is because the promoters and inhibitors secreted from one cell affect the whole population of cells. If the mutant produces more promoters, not only the mutant, but the entire population of tumor cells benefits. This means that a mutant characterized by enhanced production of promoters will not invade the tumor cell population. Instead, we observe genetic drift which is stochastic and not described by the equations considered here. The model does, however, suggest the following (Figure 2): if the population of mutant cells remains below a given threshold relative to the rest of the tumor cells, it will not alter the growth pattern. If the population of mutant cells grows beyond a threshold relative to the rest of the tumor cells, it can change the pattern of cancer growth, even if the mutants do not become fixed in the population (Figure 2). The change can either be the generation of multiple lesions, or invasion of the whole tissue, depending on the amount by which the level of promotion has been enhanced by the mutant cell population. The chances that the mutant cell population drifts to levels high enough to cause such a change in tumor growth depend on the population size of the lesion. The larger the number of tumor cells, the lower the chance that the relative population size of the mutants can cross this threshold. If this cannot occur, further cancer progression not only requires the generation of a mutation which enhances the level of promotion, but an additional mutation which gives the promoter mutant a selective advantage over the rest of the cell population. That is, in addition to the mutation which shifts the balance in favor of promotion, a mutation is required either in an oncogene or a tumor suppressor gene so that the mutant can grow to sufficiently high numbers or fixation.
(ii) The first mutation shifts the balance between promoters and inhibitors to a lager extent which is sufficient to result in the generation of multiple lesions (Figure 3). The multiple lesions do not, however, occur immediately. First, the tumor grows as a single and self limited lesion (Figure 3). Over time, this lesion bifurcates to give rise to two lesions, or further lesions if the degree of promotion is large enough relative to the degree of inhibition (Figure 3). The temporal sequence from a single and self-controlled lesion to the occurrence of multiple lesions is the same as in the previous case. But in contrast to the previous case, no further mutations are required. This is because multiple foci arise from the split and migration of a single lesion. The number of foci that form depends on the exact degree of promotion which was achieved by the initial mutation. The higher the degree of promotion, the larger the number of lesions. Growth beyond this number of lesions (which will eventually result in maximal invasion) then requires higher levels of promotion. This is in turn achieved by further mutational events according to the same principles as described in the previous section.
(iii) Finally, assume that the initial mutation shifts the balance so much in favor of promotion that maximal invasion of the tissue is possible (Figure 4). Now we observe cancer progression without the generation of multiple foci. Instead, a relatively small single lesion expands in space until all the tissue has been invaded.
In summary, the model predicts different modes of cancer progression in relation to the evolution away from tumor inhibition and towards promotion. A single cancer lesion may spread across the tissue without the occurrence of multiple lesions. Alternatively, the cancer can first grow as a single, self-contained lesion. This can then bifurcate to give rise to multiple foci, either as a result of additional mutations, or as a result of the natural pathway by which multiple foci are generated, depending on the degree of tumor promotion conferred by the initial mutation. Further evolutionary events can then induce the multiple foci to become a single, maximally invasive mass. The occurrence of multiple foci therefore represents an intermediate stage in tumor progression towards malignancy.
Clinical implications
The models discussed here show that multiple foci with a monoclonal origin can develop through a dynamical interplay between tumor promoters and inhibitors. The cancer can only grow to high loads as a single mass if it has largely escaped all inhibitory effects. Otherwise, the cancer is likely to grow via the generation of a relatively small and self limited tumor which then bifurcates into multiple foci until it finally invades the entire tissue. The occurrence of multiple foci is therefore an intermediate stage in cancer progression. The higher the number of foci, the further advanced the stage of cancer progression.
A clinically important step in carcinogenesis is the process of metastasis. That is, the spread of tumor cells to the lymph node, entry into the blood supply, and the spread to other tissues. Various studies have investigated the metastatic potential of multi-focal compared to uni-focal cancers [19,28,29]. In uni-focal cancers, tumor size has been found to be a predictor of metastatic potential. For staging multi-focal breast carcinomas, it has been suggested to use the diameter of the largest tumor only [19]. This, however, assumes that the other foci do not significantly contribute to tumor progression. According to our arguments, this would under-stage the cancer. According to the model, the number of foci correlates with the stage of the disease. This has also been concluded in clinical studies, and is supported by data which show reduced patient survival with multi-focal compared to uni-focal cancers [19]. Moreover, because our model suggests that multi-focality can occur as a result of reduced tumor cell inhibition, successful metastatic growth might be easier to achieve. Although under debate, some data suggest that inhibitors produced by the primary tumor can prevent metastatic cells from growing [24]. If multi-focality correlates with reduced inhibition, then it could also correlate with an increased chance that metastatic cells grow and do not remain dormant.
Further, it is important to note that studies which aim to assess the correlation between multi-focality and metastatic potential should not only concentrate on the number of foci, but also on the size of the foci. As we have shown with the model, cancer progression might start with a small single lesion which can be considered uni-focal. It can then bifurcate to give rise to multiple foci, and finally spread through the entire tissue. When such spread occurs, the multiple foci turn into a big and single mass, and this would again be considered uni-focal. Hence, the cumulative size or volume of the tumor is likely to be the best predictor of malignant progression.
4 Conclusions
In conclusion, we suggest that the balance between tumor promoting and inhibiting factors might be an important driving force which determines the pattern on cancer progression, and can account for the occurrence of multi-focal cancers. The best worked out example of such promoter-inhibitor dynamics is angiogenesis. In this context, inhibitors are produced both by healthy tissue cells and by tumor cells. During the course of progression, tumor cells can mutate and evolve to produce less inhibitors and more promoters. The initial establishment of an angiogenic cell line is the most difficult step. Since the promoting factors are produced by angiogenic cells themselves, their initial abundance has to be sufficiently high, such that the balance can be shifted away from inhibition. This enables the population of cancer cells to expand beyond a very small size. This growth can then give rise to a self-limited uni-focal cancer which can bifurcate to give rise to multi-focal cancers. Further evolutionary events can finally lead to maximal tissue invasion. If the initial mutation allows the cells to sufficiently escape from inhibition, cancer progression can occur as a single expanding mass without the occurrence of multi-focality. These arguments not only apply to angiogenesis, but to any tumor promoting and inhibiting factors where inhibitors act over a long range while promoters act locally. Therefore, the therapeutic use of inhibitors should be further explored. This is an active area of research in the context of angiogenesis [30], and the identification of possible alternative inhibitors might open new avenues of investigation in this context.
5 Materials and Methods
Here we present mathematical methodology used to analyze the equations described in the text.
Linear stability analysis of the ODEs
Here we discuss a linear stability analysis of system (1–3). Let us first simplify the problem by using a quasistationary approach, that is, we will assume that the level of promoters adjusts instantaneously to its steady-state value (P = CaP/bP). It is convenient to denote
Now we have a two-dimensional system,
For simplicity we ignore the constant input term, ξ, which describes the production of inhibitors by healthy tissue. Numerical simulations have shown that results are not altered qualitatively by this simplification.
There can be up to three fixed points in this system,
where , and
It is obvious that if γ + ε - W < 0, and (γ + ε - W)2 - 4εγ > 0, then there are exactly three positive equilibria in the system. If either of these conditions is violated, the (0,0) solution is the only (biologically meaningful) stable point.
Stability analysis can be performed by the usual methods. For the (0,0) equilibrium, the Jacobian is
that is, this equilibrium is always stable. For the points (C±, I±), we get the following Jacobian,
where we denote for convenience, . It is easy to show that the eigenvalues of this matrix for the solution () are given by
and for the solution () we have eigenvalues
where Y ± ≡ 2bIW + δ(ε - γ - W ± Γ). We can see that solution () is always unstable and we will not consider it any longer. Solution (), which we call for simplicity () from now on, is stable as long as
Y+ > 0 (9)
Turing stability analysis
Here we present a linear analysis of system (4–6). As before, we are going to assume that promoters adjust instantaneously to their equilibrium level. By replacing P with C defined by , we can rewrite equation (4) as
This equation together with equation (6) gives a Turing model.
Let us go back to the system of ODEs, (7–8), and assume that solution () is a stable equilibrium. Of course, this solution also satisfies the system of PDEs, (10,6). Let us consider a wave-like deviation from this spatially uniform solution:
Here, the amplitudes of the perturbation, A and B, are small compared to the amplitude of the spatially uniform solution, and we assume an infinitely large space. The equation for the new eigenvalue, λ is
where we define
Equation (11) can be written as
λ2 + λ(bI - α + (DC + DI)ω2) + aIβ - (bI + DIω2)(α - DCω2) = 0. (12)
This is the dispersion relation which connects the growth-rate, λ, with the spatial frequency of the perturbation, ω. The stability conditions now are given by
bI - α + (DC + DI)ω2 > 0, (13)
aIβ - (bI + DIω2)(α - DCω2) > 0. (14)
Note that the stability conditions for solution () of the system of ODEs, (7–8), are obtained automatically from the conditions above by setting ω = 0:
bI - α > 0, (15)
aIβ - bI α > 0. (16)
Inequality (13) is always satisfied because of inequality (15). Let us derive conditions under which the spatially uniform solution is unstable. This requires that condition (14) is reversed. This can be expressed as follows:
F(ω) ≡ DIDCω4 - ω2γ1 + γ2 < 0. (17)
where we denoted for simplicity,
γ1 = αDI - bIDC, γ2 = αIβ - αbI > 0.
This is a fourth order polynomial, symmetrical with respect to the line ω = 0, with a positive leading term. The points, ±|ω|, satisfying
correspond to the two minima of the left hand side of inequality (17). Let us call these values of ω, ±ωc. The condition F(ωc) < 0 defines that the uniform solution () is unstable.
Let us plot the function F(ω) for different values of aI, see Figure 5. For small values of aI, F(ω) is strictly positive, and the spatially uniform solution is stable. As aI increases, the function F(ω) crosses the line F = 0. The critical value of aI, aI,c, for which F(ωc) = 0, is determined from
Figure 5 Emergence of Turing instability. As aI increases through its critical value, the function F(ω) (equation (17)) crosses zero. Negative regions of F(ω) correspond to unstable wave-numbers. The wave-number which becomes unstable first is denoted by ωc. The parameters are as follows: r = 1; δ = 0.1; aP = 5; bP = 0.1; bI = 0.01; DC = 0.00001; DI = 0.001.
(αDI - bIDC)2 = 4DIDC(aIβ - αbI),
where α and β both depend on aI. We solved this equation numerically to find the critical value of aI,c, see Figure 5.
The applicability of the above analysis depends on the parameters of the system. First of all, we need conditions (15–16) to be satisfied. They mean that without diffusion, a positive, spatially uniform solution is stable. Next, we need to be in a weakly nonlinear regime, where the function F(ω) has only very narrow regions of ω corresponding to negative values. More precisely, Δω ~ L-1, where L is the spatial dimension of the system. In terms of parameter aI, we require that it is sufficiently close to aI,c. Then, we can calculate the "most unstable" wavenumber, that is, ωc defined by equation (18), with aI,c. This value will determine the spatial period of the solution,
Stationary periodic solutions
In numerical simulations described in this paper, we used the following (Neumann) boundary conditions:
The simulation results are presented in the main body of the paper. Here we discuss the behavior of the system in the light of the analysis presented above. Let us assume that the value aI is below the critical, aI <aI,c. The system exhibits bistability. If we start in the vicinity of a (0,0) solution, then cancer will not grow and decay to zero. If we start from a point (C, I) in the domain of attraction of the solution (), then the system will develop towards this positive spatially homogeneous stationary solution.
Next, let us suppose we have aI >aI,c, but make sure that it is sufficiently close to aI,c (the exact meaning of "close" is specified in the analysis above). Again, if the initial conditions are close to the zero solution, then the zero state will be the state that the system will attain. However, if we start in the vicinity of the () state, we will observe interesting behavior. Solution () is now unstable, and we will see "ripples" developing on top of this solution. This is Turing instability. The spatial period of the ripple was calculated in the previous section. Long-time evolution of this state is of course not in the realm of linear stability analysis, but we can predict that the spatial scale of the resulting solution will be given by (19).
Finally, let us assume that aI is much higher than critical. Now, solution () is unstable even in the system of ODEs. However, a periodic solution will develop, unless the initial condition is in the domain of attraction of the zero solution. The spatial scale of the periodic solution is determined intrinsically by the parameters of the system, and it grows with aI. Intuitively this is easy to understand, because higher values of aI correspond to higher levels of inhibition, so the distance between regions of large C will become larger. Note that the exact period of the periodic solution is adjusted to fit the boundary conditions of the system. For instance, with the Neumann boundary conditions, the boundary points are forced to be troughs of the wave-like pattern. In other words, the period of the solution must be an integer fraction of L.
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Genet Vaccines TherGenetic Vaccines and Therapy1479-0556BioMed Central London 1479-0556-2-151548557710.1186/1479-0556-2-15ResearchEffects of recombinant adenovirus-mediated expression of IL-2 and IL-12 in human B lymphoma cells on co-cultured PBMC Ebert Oliver [email protected] Dorothee [email protected] Peter [email protected] Carsten [email protected] Dimitri [email protected] Ingo GH [email protected] Medizinische Klinik und Poliklinik I, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany2 Medizinische Klinik II, Klinikum Aschaffenburg, Germany3 Department of Gene and Cell Medicine, Mount Sinai School of Medicine, New York, New York, USA2004 14 10 2004 2 15 15 28 6 2004 14 10 2004 Copyright © 2004 Ebert et al; licensee BioMed Central Ltd.2004Ebert et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Modulation of the immune system by genetically modified lymphoma cell vaccines is of potential therapeutic value in the treatment of B cell lymphoma. However, the anti-tumor effect of any single immunogene transfer has so far been limited. Combination treatment of recombinant IL-2 and IL-12 has been reported to be synergistic for inducing anti-tumor responses in solid tumors but the potential of IL-2/IL-12 gene modified B cell lymphoma cells has not been explored yet.
Methods
Using three different human B cell lymphoma cell lines and primary samples from patients with B cell neoplasms, expression levels of the coxsackie B-adenovirus receptor (CAR) and alpha (v) integrins were analyzed by fluorescence-activated cell sorter (FACS). Adenoviral transduction efficiencies were determined by GFP expression analysis and IL-2 and IL-12 cytokine production was quantified by enzyme-linked immunosorbent (ELISA) assays. Proliferative activities of peripheral blood mononuclear cells (PBMC) stimulated with either cytokine derived from supernatants of transduced lymphoma cells were measured by cell proliferation (MTT) assays. An EuTDA cytotoxicity assay was used to compare cytotoxic activities of IL-2 and/or IL-12 stimulated PBMC against unmodified lymphoma cells.
Results
We found that B cell lymphoma cell lines could be transduced with much higher efficiency than primary tumor samples, which appeared to correlate with the expression of CAR. Adenoviral-expressed IL-2 and IL-12 similarly led to dose-dependent increases in proliferation rates of PBMC obtained from healthy donors. IL-2 and/or IL-12 transduced lymphoma cells were co-cultured with PBMC, which were assayed for their cytolytic activity against unmodified lymphoma cells. We found that IL-2 stimulated PBMC elicited a significant anti-tumor effect but not the combined effect of IL-2/IL-12 or IL-12 alone.
Conclusion
This study demonstrates that the generation of recombinant adenovirus modified lymphoma cell vaccines based on lymphoma cell lines expressing IL-2 and IL-12 cytokine genes is technically feasible, induces increases in proliferation rates and cytotoxic activity of co-cultured PBMC, and warrants further development for the treatment of lymphoma patients in the future.
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Background
Lymphoma cells are attractive targets for gene transfer, because these cells are potentially susceptible to immunotherapeutic strategies [1]. Among the various cancer gene therapies using a variety of genes with different gene transfer systems, immunogene therapy focuses on the use of genes for cytokines, chemokines, and co-stimulatory molecules [2]. Using an ex vivo approach of tumor cell transduction, it was shown that many cytokines could modulate tumorigenicity and protect the host from untreated tumor cells [3]. However, the effect of any single immunogene transfer has been limited, especially against low immunogenic tumors [4].
Interleukin-2 (IL-2) and interleukin-12 (IL-12) are cytokines that elicit strong antitumor effects by stimulating immune cells, including T cells and natural killer (NK) cells. Although either cytokine stimulates the proliferation of T cells, the production of interferon-γ (IFN-γ) by NK cells, and ultimately the cytolytic activity, the magnitude, and the spectrum of stimulatory effects by IL-2 and IL-12 are different. Although IL-2 is a stronger stimulator of proliferation and cytolytic activity, IL-12 is a stronger inducer of IFN-γ from NK cells and activated T cells. Although the combination of recombinant IL-2 and IL-12 treatment has been reported to be synergistic for inducing anti-tumor responses, systemic administration of these cytokines causes toxic side effects. Recent reports of intra-tumoral co-injection of adenoviral vectors expressing IL-2 and IL-12 demonstrated the regression of pre-established solid tumors with high frequency [5]. However, the significance of IL-2 and IL-12 immunogene therapy of hematopoietic neoplasms such as B cell lymphoma, has not been addressed yet.
Recently, we described an adenoviral protocol accomplishing highly efficient gene transfer to B-lymphoma cell lines [6]. The use of genes or genetically modified cells for therapeutic benefit may have a significant therapeutic role for patients with B cell lymphomas in the future. Adoptive immunotherapy using donor leukocyte infusion to treat aggressive B cell neoplasms in immunosuppressed patients has shown great promise clinically, and studies of idiotypic vaccination in patients with low grade B cell neoplasms are also underway. Results from in vitro and animal studies continue to suggest that it may become possible to use the immune system for therapeutic benefit, and many current basic research strategies in the gene therapy of B cell lymphoma are based on immune modulation of T cells or tumor cells themselves. Other major approaches to gene therapy for B cell malignancies are the introduction of directly toxic or suicide genes into B cells.
In the present study, we have evaluated the relationship between the amount of cytokine production by the combination IL-2 and IL-12 and the in vitro effective anti-tumor activity. Using three different human B cell lymphoma cell lines and primary samples from patients with B cell neoplasms, we transduced both IL-2 and IL-12 genes by adenoviral vectors, and monitored cytokine production and effects on proliferation and cytolytic activity of co-cultured human peripheral blood mononuclear cells (PBMC).
Methods
Cell culture and primary lymphoma cells
The following cell lines were analyzed: Raji (human Burkitt lymphoma cell line; obtained from "Deutsche Sammlung von Mikroorganismen und Zellkulturen" (DSMZ), Braunschweig, Germany), Daudi (human Burkitt lymphoma cell line; obtained from DSMZ), and OCI-Ly8-LAM53 (human follicular lymphoma cell line; obtained from R. Levy, Stanford University, CA). The cell lines were grown in RPMI 1640 with Glutamax (Life Technologies, Berlin, Germany) supplemented with 10% heat-inactivated fetal calf serum (FCS) (PAA, Martinsried, Germany), 50 μg/ml streptomycin, and 50 μg/ml penicillin (PAA), and were kept in a humified incubator with 5% CO2 at 37°C. Virus propagation was performed in the Ad5 E1-transformed human embryonic retina cell line 911 [7]. This cell line was grown in Dulbecco's modified Eagle's medium (DMEM) (Life Technologies) supplemented with 10% FCS, 50 μg/ml streptomycin, and 50 μg/ml penicillin.
Non-adherent Ficoll-Hypaque (Seromed, Berlin, Germany) separated human PBMC were obtained from whole blood from healthy donors and maintained in RPMI 1640 with Glutamax (Life Technologies) supplemented with 10% FCS (PAA), 50 μg/ml streptomycin, and 50 μg/ml penicillin. Cytokine-induced killer (CIK) cells were generated as described previously [8,9]. In brief, 100 U/ml recombinant interferon-gamma (Boehringer Mannheim, Germany) was added on day 0. After 24 h of incubation, 50 ng/ml of an antibody against CD3, 100 U/ml interleukin-1 (IL-1), and 300 U/ml interleukin-2 (IL-2) (PromoCell, Heidelberg, Germany) were added. Cells were incubated at 37°C in a humified atmosphere of 5% CO2 and subcultured every 3 days in fresh complete medium and IL-2.
Five patients diagnosed with lymphoma were included into this study; four patients with chronic B cell lymphocytic leukemia (B-CLL) and one patient with immunocytoma (IC). After informed consent, peripheral blood was obtained and lymphoma cells were isolated by Ficoll-Hypaque (Seromed) density centrifugation. Cell surface antigens were analyzed for the expression of CD19, integrin avβ3, integrin avβ5, and the coxsackie B-adenovirus receptor (CAR). Primary cultures were maintained in liquid culture in RPMI 1640 with Glutamax (Life Technologies) supplemented with 10% heat-inactivated FCS, 50 μg/ml streptomycin, and 50 μg/ml penicillin at 37°C, 5% CO2 and could be maintained in culture for 10–12 days.
Adenoviral transduction of lymphoma cells
Transduction of lymphoma cells with CsCl-purified adenovirus was carried out in 24-well plates with 5 × 105 cells in 50 μl of PBS plus 1 mM MgCl2/1% HS, at different multiplicities of infection (MOI). After 2 hours of incubation at 37°C, 5% CO2, 1 ml of complete culture medium was added to the cells. Because no visible toxic effect was observed in comparison with the controls (only PBS plus 1 mM MgCl2/1% HS), it was not necessary to remove the virus. Adenoviral transduction of primary lymphoma cells was considered successful if concurrent CD19 expression with green fluorescent protein (GFP) was observed.
Adenoviral vector preparation
The recombinant adenoviral Ad.GFP vector (pQB-AdBM5GFP), an E1- and E3-deleted replication-defective adenovirus type 5 under control of the cytomegalovirus (CMV) promoter, was purchased from Quantum Biotechnologies (Montreal, Canada). The adenovirus vector (Ad.IL-2) containing the human IL-2 sequence was kindly provided by Frank L. Graham, McMaster University, Hamilton, Ontario, Canada [10]. The E1/E3-deleted recombinant Ad5 vector expresses human IL-2 under control of the CMV immediate early promoter (HCMV IE) and the simian virus 40poly(A) signals (SV40 An). The Ad.Flexi-12 vector contains cDNA that encodes a single-chain protein, called Flexi-12, which retains all of the biological characteristics of recombinant IL-12 [11]. This E1/E3-deleted recombinant adenovirus type 5 was generated using the AdEasy system [12] and was kindly provided by Robert Anderson, Royal Free Hospital School of Medicine, London, UK. Infection of Ad.Flexi-12 can be tracked using GFP expression analysis which is present as an additional expression cassette in the viral genome.
Production of the adenovirus lots was performed as described previously [7]. Briefly, near confluent 911 cell monolayers in 175-cm2 flasks were infected with ~5 plaque-forming units (PFU)/cell in 2 ml of phosphate-buffered saline (PBS) containing 1% horse sera (HS). After 2 hours incubation at 37° in a humidified atmosphere of 5% CO2, the inoculum was replaced by fresh medium (DMEM/2% HS). After 48 h, nearly completely detached 911 cells were harvested and collected in 1 ml PBS/1% HS. Virus was isolated by three cycles of flash-freeze thawing. The lysates were cleared by centrifugation at 3000 rpm for 10 minutes. Viruses were then purified on double cesium chloride gradients and stored in PBS/10% glycerol at -80°C.
Plaque assays were essentially performed as described by Graham and Prevec [13]. Briefly, adenovirus stocks were serially diluted in 2 ml of DMEM (Life Technologies) containing 2% HS and added to nearly confluent 911 cells in 6-well plates. After 2 hours of incubation at 37°C, 5% CO2, the medium was replaced by F-15 minimal essential medium (Life Technologies) containing 1% agarose (Sigma, Deisenhofen, Germany), 20 mM N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid (pH 7.4), 0.0025% L-Glutamine, 5% yeast extract, 8.4% NaHCO3, 50 μg/mL streptomycin, 50 μg/mL penicillin, and 2% HS. The titers of the virus stocks were at least 1 × 1010 PFU/ml.
IL-2 and IL-12 enzyme-linked immunosorbent assays (ELISA)
IL-2 and IL-12 levels in conditioned medium were determined by an enzyme-linked immunosorbent assay (ELISA) method. The ELISA reagents were purchased from Endogen, (Cambridge, USA). Briefly, a microtiter plate was coated with a monoclonal antibody specific for IL-2 or IL-12. The IL-12 antibody recognizes only the p70 heterodimer and neither of the individual subunits, p35 or p40, or the homodimeric form of p40. The cytokines present in samples are bound by the immobilized antibody. After several washes to remove unbound proteins, an enzyme-linked (horseradish peroxidase) polyclonal antibody was added to the wells which binds IL-2 or IL-12. After washing, the substrate solution was added, and the color which developed was measured using a spectrophotometer at a wavelength of 450 nm. The optical density of the samples was then compared to a standard curve.
Cell proliferation assays
An MTT (3-(4,5-dimethylthiazol-2yl)-2,5-diphenyl tetrazolium bromide) based colorimetic assay [14] was performed to measure the proliferative activity of PBMC stimulated with cytokines either derived from supernatants of transduced Raji cells or recombinant with or without addition of neutralizing anti-IL-2 or anti-IL-12 antibodies. In brief, 2 × 105 PBMC were incubated in 96-well flat-bottom plates (Nunc, Denmark) in a final volume of 200 μl per well. After 3 days 20 μl of EZ4U reagent (Biozol, Eching, Germany) was added to each well and results were obtained on a multi-well scanning spectrophotometer at 450 nm.
Cytotoxicity assays
A EuTDA nonradioactive cytotoxicity assay (Wallac, Turku, Finland) was used to compare the cytotoxic activity of IL-2 and IL-12 stimulated PBMC against unmodified lymphoma cells [15]. This assay is a colorimetric alternative to the 51Cr release assay. The procedure is based on loading the target cells with a fluorescence enhancing ligand (BATDA, bis(acteoxymethyl)2,2:6,2-terpyridine-6,6-dicarboxylate). The hydrophobic ligand penetrates the membrane quickly and within the cell the esterbonds are hydrolysed to form a hydrophilic ligand (TDA, 2,2:6,2-terpyridine-6,6-dicarboxylic acid) which no longer passes the membrane. After cytolysis the ligand is released and introduced to the europium solution. The europium and the ligand form a highly fluorescent and stable chelate (EuTDA). The measured signal correlates directly with the amount of lysed cells.
Briefly, 2 × 106 lymphoma cells were washed and resuspended in 2 ml PBS. 4.5 μl BATDA solution was added and incubated at 37° for 30 min. Then, cells were washed 3 times, resuspended in 100 μl PBS, and incubated in 96-well flat-bottom plates (Nunc) at a density of 10,000 cells/well. 100 μl of effector PBL cells of varying cell concentations were added so that effector to target cell ratio ranged from 5:1 to 20:1. After incubation at 37° for 2 h cells were centrifuged for 5 min at 500 × g and 20 μl of the supernatant was transfered to a new flat-bottom plate. 180 μl of Europium solution was added, and after 15 min incubtion at room temperature the fluorescence was measured in a time-resolved fluorometer (Wallac). The percent specific release was calculated from
Statistical analysis
Wilcoxon matched-pairs test was used to analyze for statistical significance. A p value < 0.05 was considered significant. Data is presented as the mean ± standard error of the mean (SEM).
Results
Transduction efficiencies of lymphoma cells and CAR/integrin expression
Lymphoma cell lines, primary lymphoma cells, and CIK cells were transduced with Ad.Flexi-12 at various MOI (0, 50, 100, 200) and analyzed 72 h later. Transduction efficiencies were determined by GFP expression analysis using a fluorescence-activated cell sorter (FACS). Additionally, cell surface antigens were analyzed by FACS for the expression of CD19, integrin avβ3, integrin avβ5, and CAR. Adenoviral transduction of primary lymphoma cells was considered successful if concurrent CD19 expression with GFP was observed. It was demonstrated that most B cell lymphoma cell lines could be transduced with much higher efficiency than primary tumor samples or CIK cells. At an MOI of 200, up to 40% of Daudi cells and 70% of Raji cells could be transduced (Fig. 1A). In contrast, primary B-CLL cells were found to be relatively resistant with transduction efficiencies up to 6 %, whereas OCI-Ly8-Lam53 (LAM53) cells, primary IC cells, and CIK cells were completely refractory (Fig. 1B). Transduction efficiency could be correlated with the expression of CAR. High expression of CAR was evident in Raji and Daudi cells, averaging 72% and 86%, respectively. Primary B-CLL cells were found to have moderate CAR expression of 36%. In contrast, there was no CAR expression detectable in LAM53, IC, and CIK cells (Table 1). Expression of integrin receptors, however, was low or absent in all lymphoma cells examined.
Figure 1 Transduction efficiencies in various human lymphoma cell lines (A), primary human lymphoma cells, and CIK cells (B). All cell types were transduced with Ad.Flexi-12 at various MOI as indicated and analyzed for GFP expression 72 h later by FACS analysis (mean ± SEM; n = 3).
Table 1 Expression analysis of adenovirus binding (CAR) and internalization receptors (avβ3, avβ5) on various human lymphoma cell lines (Raji, Daudi, OCI-Ly3-LAM53), primary B lymphoma cells (B-CLL, IC), and CIK cells by FACS analysis (mean ± SEM; n = 3; n.d., not detectable).
CAR avβ3 avβ5
Cell lines:
Raji 72.5 ± 6.2 1.2 ± 0.1 1.0
Daudi 86.3 ± 1.8 1.1 ± 0.1 1.0
OCI-Ly8-LAM53 3.9 ± 1.5 1.1 ± 0.1 1.0
Primary cells:
B-CLL 36 ± 6.4 0.6 13.6
IC 3.2 n.d. n.d.
CIK 1.3 ± 0.2 15.0 2.0
Adenoviral-mediated expression of IL-2 and IL-12 in lymphoma cells in vitro
Cytokine gene expression was analyzed in lymphoma cell lines using an ELISA assay as described above. Daudi, Raji, and LAM53 cells were infected with Ad.IL-2 or Ad.Flexi-12 at various MOI (0, 50, 100, 200) with Ad.GFP as a control vector. Cytokine production was assayed 72 h post-infection. As shown in Fig. 2A, IL-2 produced by Ad.IL-2-transduced Raji and Daudi cells at an MOI of 200 averaged 10.6 ng/ml/106 cells and 2.7 ng/ml/106 cells, respectively. In contrast, there was no IL-2 detectable in Ad.IL-2-transduced LAM53 cells. Kinetic analysis of IL-2 production in Raji cells revealed peak secretions between day 2 and 3. IL-2 was detectable until day 8 post-infection (Fig 2B). Similarly, IL-12 gene expression of Ad.Flexi-12 transduced Raji and Daudi cells revealed 219 ng/ml/106 cells and 15.6 ng/ml/106 cells, respectively. No expression was detectable in Ad.Flexi-12-transduced LAM53 cells (Fig. 3A). Peak expression of IL-12 was evident between day 1 and 3, with IL-12 detectable by ELISA until day 10 post-infection (Fig. 3B).
Figure 2 IL-2 gene expression analysis in human lymphoma cell lines by using an ELISA assay. (A) Daudi, Raji and LAM53 cells were infected with Ad.IL-2 or Ad.GFP at various MOI (0, 5, 100, 200). 72 h post-infection, IL-2 produced by Ad.IL-2-transduced Raji and Daudi cells at an MOI of 200 averaged 10.6 ng/ml/106 cells and 2.7 ng/ml/106 cells, respectively (mean ± SEM; n = 3). (B) Kinetic analysis of IL-2 production in Raji cells transduced at an MOI of 200 revealed peak secretions between day 2 and 3 and IL-2 was detectable until day 8 post-infection (mean ± SEM; n = 3). All experiments were performed in triplicates.
Figure 3 (A) IL-12 gene expression of Ad.Flexi-12 transduced Raji and Daudi cells revealed 219 ng/ml/106 cells and 15.6 ng/ml/106 cells at 72 h post-infection, respectively (mean ± SEM; n = 3). No expression was detectable in Ad-Flexi-12 transduced LAM53 cells. (B) Peak expression of IL-12 in transduced Raji cells was evident between day 1 and 3, with IL-12 detectable until day 10 post-infection (mean ± SEM; n = 3). All experiments were performed in triplicates.
Increase in proliferation rates of PBMC stimulated with adenoviral-expressed cytokines
To determine if adenoviral-expressed cytokines from transduced lymphoma cells would have an impact on the proliferation rates of PBMC from healthy donors, the following experiment was performed. PBMC were freshly isolated and various concentrations of cytokines (1–1000 pg/ml) either derived from the supernatants of transduced lymphoma cells or recombinant were added. Then, an MTT assay to assess the proliferation rate was performed five days later. For blocking experiments, a neutralizing monoclonal antibody against IL-2 or IL-12 was used. Figure 4 shows that addition of adenoviral-expressed IL-2 (Fig. 4A) and IL-12 (Fig. 4B) led to dose-dependent increases in proliferation rates of PBMC. There was no significant difference between the effects of both cytokines. Furthermore, the proliferative effect could be blocked by addition of a neutralizing antibody against either cytokine. Finally, it was demonstrated that there was no significant difference between adenoviral-expressed and recombinant cytokines.
Figure 4 PBMC were incubated with cytokines (1–1000 pg/ml) either derived from supernatants of transduced Raji cells or recombinant with supernatants from Ad.GFP-transduced Raji cells as controls and assayed for their proliferative activity (mean ± SEM; n = 3). Adenoviral-expressed IL-2 (A) and IL-12 (B) led to dose-dependent increases in proliferation rates of PBMC. No significant difference between the effects of either cytokine was found. The proliferation effect could be blocked by addition of a neutralizing antibody against either cytokine. There was no significant difference between the effects of adenoviral-expressed or recombinant cytokines. MTT assays were performed in triplicates.
Cytolytic activity of co-cultured PBMC against unmodified lymphoma cells
Raji cells were transduced with Ad.IL-2 (MOI 200), Ad.Flexi-12 (MOI 200), or Ad.IL-2 and Ad.Flexi-12 together (MOI 100 each) and co-cultured with PBMC for 72 h. Non-transduced (control) and Ad.GFP transduced Raji cells were used as controls. Stimulated PBMC were harvested and assayed for their cytolytic activity against unmodified lymphoma cells using a EuTDA nonradioactive cytotoxicity assay. It could be shown that Ad.IL-2 transduced lymphoma cells produced a significant (p < 0.05) anti-tumor effect but not the combined effect of Ad.IL-2/Flexi-12 or Flexi-12 alone (Fig. 5).
Figure 5 Raji cells were transduced with Ad.IL-2 (MOI 200), Ad.Flexi-12 (MOI 200), or Ad.IL-2 and Ad.Flexi-12 (MOI 100 each) and co-cultured with PBMC for 72 h. Non-transduced (control) and Ad.GFP-transduced Raji cells were used as controls. Stimulated PBMC were harvested and assayed for their cytolytic activity against unmodified lymphoma cells by using an EuTDA non-radioactive cytotoxicity assay. Ad.IL-2-transduced lymphoma cells elicited a significant anti-tumor effect but not the combined effect of IL-2/IL-12 or IL-12 alone (mean ± SEM; n = 5; * p < 0.05).
Discussion
The rationale for genetically modified lymphoma cell vaccines is to augment the immunogenicity of poorly immunogenic lymphoma cells, thereby eliciting a systemic immune reponse that is capable of controlling the disseminated disease. Transgene candidates to potentially achieve that goal include genes encoding for cytokines, lymphotactic chemokines, allogeneic MHC molecules, or co-stimulatory molecules [4]. The co-stimulatory molecule CD40 ligand expressed from a recombinant adenoviral vector in autologous chronic lymphocytic leukemia cells has been tested in a recent clinical trial with encouraging results. [16]. Immunotherapy that combines two or more of these immunostimulatory molecules will likely prove more effective than single agents [17]. In this regard, adenoviral-mediated expression of both the IL-2 and IL-12 cytokine genes in several solid tumor models has been found to induce strong and specific anti-tumor responses [5,18]. Interestingly, Wang et al. demonstrated that IL-2 enhances the reponse of NK cells to IL-12 through up-regulation of the IL-12 receptor, signal transducer, and transcription protein STAT4 [19]. Therefore, we were interested in evaluating the potential of IL-2 and IL-12 transduced lymphoma cells for their ability to stimulate and activate immunologic effector cells.
Lymphoma cells are relatively resistant to transduction with most currently available vector systems [20,21]. This problem may be overcome ex vivo by using Epstein-Barr virus vectors [22], adeno-associated virus vectors [23], or modified adenoviral vectors [24,25]. Recently, we described a transduction method accomplishing highly efficient adenoviral-mediated gene transfer in lymphoma cells [6]. Using this protocol, expression of the wild-type p53 tumor-suppressing gene in lymphoma cell lines with mutant p53 showed increased sensitivity to cytotoxic drug and immuno-mediated toxicity [26]. In the current study, we observed low expression levels of cell surface integrins avβ3 and avβ5 on all lymphoma cells studied, which suggests that the adenoviral entry into these cells may be mediated by CAR, expressed at high levels on Raji and Daudi cells. As a consequence, Raji and Daudi lymphoma cell lines could be transduced with higher efficiency, whereas primary lymphoma cells and normal lymphocytes with low-level expression of CAR were refractory. Turturro et al. have also shown that anaplastic large cell lymphoma cells express high levels of CAR and integrins, which could be transduced by adenoviral vectors with high efficiency [27]. These results indicate the importance of determining the expression levels of CAR and integrins in tissues or cells derived from patients for the generation of adenoviral vector-modified lymhoma cell vaccines. Previously reported transduction efficiencies of adenoviral vector-transduced lymphoma cells were obtained with non-purified viruses [6]. Since this protocol is not feasible for clinical application, the present studies were performed with CsCl-purified viruses and lower transduction efficiencies were achieved. The exact reason for this difference is currently unknown and will be elucidated in the future.
In our hands, human Burkitt's lymphoma cell lines were most efficiently transduced with adenoviral vectors. Expression of IL-2 and IL-12 cytokines in Raji cells transduced at a relatively low MOI of 200 was transient, peaked between 1 and 3 days post-infection, and was detectable up to 10 days. The produced cytokines were assayed for their biological ability to stimulate PBMC from healthy donors in comparison with recombinant cytokines as controls. Our data indicates that adenoviral expressed cytokines were equally effective compared with recombinant cytokines in enhancing the proliferation rates of PBMC. This effect could be blocked by the addition of neutralizing antibodies against either cytokine. In a cytotoxicity assay, IL-2 stimulated PBMC were able to lyse unmodified Raji cells, while IL-12 or the combined IL-2 and IL-12 stimulated PBMC were clearly less effective.
Previously, we have shown that cytotoxic CD8+ NKT cells are readily expandable in vitro in large quantities suitable for adoptive immunotherapy. These activated effector cells have significant cytotoxic activity against human lymphoma xenografts with limited toxicity [8,28]. We have also demonstrated that CD8+ NKT cells can be generated in vitro using either IL-2 or IL-12 [29]. Interestingly, adoptive T cell therapy combined with intratumoral administration of adenoviral expressed IL-12 was shown to have strong synergistic effects against large transplanted tumors [30]. Therefore, expression of IL-2 and IL-12 in lymphoma cells may be used to further increase their sensitivity towards adoptively transferred CD8+ NKT cells in the future.
Conclusion
This study demonstrates that the generation of recombinant adenovirus modified lymphoma cell vaccines based on lymphoma cell lines expressing IL-2 and IL-12 cytokine genes is technically feasible, induces increases in proliferation rates and cytotoxic activity of co-cultured PBMC, and warrants further development for the treatment of lymphoma patients in the future.
Competing interests
The author(s) declare that they have no competing interests.
Authors contributions
OE and DW designed the experiments and performed the experimental studies presented in this paper. PB developed the experimental protocols and assisted in the analysis of the results. CZ, DF, and ISW participated in the design of the study and its coordination. All authors have read and approved this manuscript.
Acknowledgments
The authors thank Dr. H. Grant Prentice and Dr. Robert Anderson, Royal Free Hospital School of Medicine, London, UK, for their kind gift of Ad.Flexi-12. Ad.IL-2 was generously provided by Dr. Frank Graham, McMaster University, Hamilton, Ontario, Canada. This work was supported by a grant from the H. W. & J. Hector Stiftung, Germany.
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Gonzalez R Vereecque R Wickham TJ Facon T Hetuin D Kovesdi I Bauters F Fenaux P Quesnel B Transduction of bone marrow cells by the AdZ.F(pK7) modified adenovirus demonstrates preferential gene transfer in myeloma cells Hum Gene Ther 1999 10 2709 2717 10566899 10.1089/10430349950016753
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CytojournalCytoJournal1742-6413BioMed Central London 1742-6413-1-41550423110.1186/1742-6413-1-4EditorialSecond edition of 'The Bethesda System for reporting cervical cytology' – atlas, website, and Bethesda interobserver reproducibility project Nayar Ritu [email protected] Diane [email protected] Northwestern University Feinberg School of Medicine, Feinberg 7-210, 251 East Huron Street, Chicago IL 60611, USA2 Division of Cancer Prevention, NCI, NIH, DHHS, 9000 Rockville Pike, Bethesda, MD 20892, USA2004 21 10 2004 1 4 4 18 8 2004 21 10 2004 Copyright © 2004 Nayar and Solomon; licensee BioMed Central Ltd.2004Nayar and Solomon; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A joint task force of the American Society of Cytopathology (ASC) and the National Cancer Institute (NCI) recently completed a 2-year effort to revise the Bethesda System "blue book" atlas and develop a complementary web-based collection of cervical cytology images. The web-based collection of images is housed on the ASC website, which went live on November 5th, 2003; it can be directly accessed at .
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The second edition of the Bethesda 'blue book' atlas maintains an easy to read format with bulleted morphologic criteria and half-page color illustrations. The content has been divided into chapters based on the major 2001 Bethesda System interpretive categories. Highlights of the new edition include: (1) incorporation of liquid based cytomorphologic criteria and images; (2) new sections addressing ancillary testing, educational notes and recommendations, computer assisted interpretation and anal-rectal cytology; and (3) inclusion of sample reports, references, and detailed legends for images. Overall, the second edition has tripled in size from the original version, with a total of 186 images of which 90% are new images and 40% are from liquid based specimens. Some are classic examples of an entity while others have been selected to illustrate interpretive dilemmas or "borderline" cytomorphologic features that may not be interpreted in the same way by all cytologists.
In parallel with production of the Bethesda book atlas, the ASC-NCI task force has also developed a Bethesda web-based collection of images. Approximately 350 images, (40% of which are from liquid based specimens) along with linked explanatory notes, including all the images in the published atlas, can be viewed on this site. The website is user friendly and has several search modalities for viewing the images including searching by Bethesda terminology, atlas chapter headings, keyword(s), or preparation type. It also allows for individual self-assessment by participating in a "self test" in which viewers can compare their interpretation to other participants' responses.
The image selection process for the book atlas and website involved a multistage review: Step 1: individual Bethesda forum group members (32 participants) reviewed and selected images for their chapter from among those in the first edition of the atlas and new submissions; and Step 2: the images selected from Step 1 were reviewed individually ("validated") by 13 task force members and scored on a scale of 1–5 for agreement with interpretation and quality of image. In all over 1000 images were reviewed of which 186 images were selected for the atlas and an additional 163 for the website.
A subset (n = 77) of the book atlas images were posted as "unknowns" on the Internet from mid July to mid September 2003 as part of a study – the Bethesda Interobserver Reproducibility Project (BIRP). The site was open to the cytopathology community to view the images and provide their interpretations. Immediately after submitting their response, participants were able to view a histogram of the distribution of results submitted by all prior participants for that image. Over 600 cytologists from around the world participated in BIRP. Summary histograms for each of the 77 images can be viewed on the Bethesda atlas website (select BIRP images from the left menu). Preliminary BIRP results presented at the ASC annual meeting in Orlando in November 2003 showed that Negative for Intraepithelial Lesion or Malignancy (NILM) and Low Grade Squamous Intraepithelial Lesion (LSIL) reference images attained the highest concordance scores, while glandular abnormalities demonstrated the most splay in distribution of interpretations. BIRP analyses are ongoing and further results should be available in 2004.
ASC-NCI Working Group for the Second Edition Bethesda Atlas and Website
ASC Bethesda 2001 Task Force:Ritu Nayar (Chair), Diane Solomon (Co-Chair, NCI) George Birdsong (Adequacy), Jamie Covell (Glandular Lesions), Ann Moriarty (Endometrial cells), Dennis O'Connor (Educational notes and recommendations), Marianne Prey (Computer assisted interpretation), Steve Raab (Ancillary testing), Mark Sherman (Atypical squamous cells), Sana Tabbara (Other malignant neoplasms), Tom Wright (Squamous lesions), Nancy Young (Non-neoplastic findings).
ASC Consultants: ASC 2002/2003 Presidents: Diane Davey and Dave Wilbur.
Information Technology representatives: Mike Montgomery (NCI) and Brandon Winbush (Northwestern University), Aquilent (Laurel, MD).
The second edition of the 'blue book' atlas can be ordered through Springer-Verlag (1-800-SPRINGE) for $34.95.
| 15504231 | PMC526759 | CC BY | 2021-01-04 16:36:21 | no | Cytojournal. 2004 Oct 21; 1:4 | utf-8 | Cytojournal | 2,004 | 10.1186/1742-6413-1-4 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-371550068310.1186/1475-2875-3-37CommentaryImproving epidemic malaria planning, preparedness and response in Southern Africa DaSilva Joaquim [email protected] Brad [email protected] Vonai [email protected] Sabine M [email protected] Simon J [email protected] Stephen J [email protected] WHO-ICP-MAL-SAMC; Parirenyatwa Hospital Annex; P.O. Box CY 348 Causeway, Harare, Zimbabwe2 SADC Drought Monitoring Centre; P.O. Box 150, Belvedere, Harare, Zimbabwe3 Ministry of Health and Child Welfare, P.O. Box CY 1122, Causeway, Harare, Zimbabwe4 International Research Institute for Climate Prediction (IRI); The Earth Institute at Columbia University; Monell, Lamont Campus, 61 Route 9W, Palisades, New York 10964-8000, USA2004 22 10 2004 3 37 37 18 10 2004 22 10 2004 Copyright © 2004 DaSilva et al; licensee BioMed Central Ltd.2004DaSilva et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Malaria is a major public health problem for countries in the Southern Africa Development Community (SADC). While the endemicity of malaria varies enormously across this region, many of the countries have districts that are prone to periodic epidemics, which can be regional in their extent, and to resurgent outbreaks that are much more localized. These epidemics are frequently triggered by climate anomalies and often follow periods of drought. Many parts of Southern Africa have suffered rainfall deficit over the past three years and countries expect to see increased levels of malaria when the rains return to more 'normal' levels. Problems with drug and insecticide resistance are documented widely and the region contains countries with the highest rates of HIV prevalence to be found anywhere in the world. Consequently, many communities are vulnerable to severe disease outcomes should epidemics occur.
The SADC countries have adopted the Abuja targets for Roll Back Malaria in Africa, which include improved epidemic detection and response, i.e., that 60% of epidemics will be detected within two weeks of onset, and 60% of epidemics will be responded to within two weeks of detection. The SADC countries recognize that to achieve these targets they need improved information on where and when to look for epidemics. The WHO integrated framework for improved early warning and early detection of malaria epidemics has been recognized as a potentially useful tool for epidemic preparedness and response planning. Following evidence of successful adoption and implementation of this approach in Botswana, the SADC countries, the WHO Southern Africa Inter-Country Programme on Malaria Control, and the SADC Drought Monitoring Centre decided to organize a regional meeting where countries could gather to assess their current control status and community vulnerability, consider changes in epidemic risk, and develop a detailed plan of action for the forthcoming 2004–2005 season. The following is a report on the 1st Southern African Regional Epidemic Outlook Forum, which was held in Harare, Zimbabwe, 26th–29th September, 2004.
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Introduction
The Southern African region has a long and varied history of malaria control with periodic epidemics occurring [1,2]. These epidemics can be regional in scale, as in 1996 and 1997, or much more focal, affecting specific districts or sub-districts. The countries of the Southern African Development Community are committed to the Abuja Targets for Roll Back Malaria in Africa, and this includes improved detection and response to epidemics [1]. To meet these targets countries are expected to detect 60% of malaria epidemics within two weeks of onset, and respond to 60% of epidemics within two weeks of their detection. The countries recognize that to achieve these targets they need improved information on where epidemics are most likely to occur, and ideally some indication of when they are likely to occur. The WHO guidelines on the development of Malaria Early Warning Systems (MEWS) for Africa are seen as offering a useful framework for an integrated approach to epidemic preparedness and response planning [3-5]. Experience and evidence of the successful application of this approach within the National Malaria Control Programme in Botswana over the past few years was demonstrated by the national malaria programme manager at the Southern Africa Regional Malaria Planning and Consultation Meeting in Gaborone in July 2004. Other countries in the SADC region considered this approach to provide a useful framework for planning epidemic preparedness and response strategies and, in view of the perceived vulnerability of communities throughout much of the region, called for a regional meeting that could launch the scaling-up of this process to include other epidemic prone countries beyond Botswana. The WHO Southern Africa Inter-Country Programme for Malaria Control (SAMC) responded to this demand and together with SADC's Drought Monitoring Centre (DMC) organized the 1st Southern African Regional Epidemic Outlook Forum, which was held in Harare, Zimbabwe, 26th–29th September, 2004 and hosted by Zimbabwe's Ministry of Health and Child Welfare. Representatives from malaria control services in nine Southern African countries participated in the meeting: Angola, Botswana, Madagascar, Mozambique, Namibia, Swaziland, Tanzania, Zambia, and Zimbabwe.
The purpose of the meeting was: to enable malaria control services from epidemic prone countries to gather and review their control programme status and epidemiological trends for the past 3–5 years and identify and map districts they consider to be vulnerable to epidemics; to learn about advances in the science of seasonal climate forecasting and review the implications of the forecast for the forthcoming season; to learn about environmental variables pertinent to epidemic risk and readily available sources of monitoring information; to review methods of early detection using case surveillance data; and, using the WHO framework for MEWS, to develop plans of action for epidemic preparedness and response for the forthcoming season.
Discussion
The MEWS framework as set out by WHO consists of four components: 1) vulnerability monitoring; 2) seasonal climate forecasting; 3) environmental monitoring; and 4) sentinel case surveillance. This framework is illustrated in Figure 1.
Figure 1 MEWS gathering cumulative evidence for early and focused response (WHO 2004)
Vulnerability monitoring
There are many factors that increase the vulnerability of a population to malaria epidemics [6,7] and increase the severity of disease outcome should a malaria epidemic occur. Co-infection with other diseases such as HIV-AIDS is a major consideration for Southern African countries. Resistance to therapeutic drugs and insecticides has also been a recent problem throughout much of the region. Drought, food insecurity and associated population movements between areas of differing endemicity combine to make certain populations more vulnerable to epidemics. These factors and consideration of the where and how to get appropriate information were discussed and countries were encouraged to identify measurable indicators and key informants.
Seasonal climate forecasting
In recent years there have been significant scientific advances in our ability to predict climate on the seasonal timescale [8]. The skill associated with these predictions varies from region to region, but is generally higher within the tropics. Scientists from the SADC Drought Monitoring Centre and the International Research Institute for Climate Prediction (IRI) joined with meteorologists from Democratic Republic of Congo, Malawi, Namibia, Zimbabwe and the World Meteorological Organization (WMO) to deliver the climate forecast for the forthcoming 2004–2005 season. An overview of climate variability in the SADC region was presented. The inherent issues of probability and uncertainty in climate forecasting were discussed with participants from the malaria control services. A number of myths were exploded and the variables that could or could not be skilfully forecast were reviewed. The malaria control participants gave their views on how communication of the forecast should be improved and made more understandable to the non-climate-specialist. Following a subsequent working session by the climate and meteorological specialists, an outline of additional or alternative forecast indicators was provided.
Environmental monitoring
The availability of environmental variables pertinent to malaria transmission, such as rainfall, temperatures, humidity, and flooding, were discussed and information on where they could be obtained was provided. The two basic sources of such information are periodic summaries (usually satellite-derived and interpolated estimates) available through the internet from the SADC DMC, the Famine Early Warning Systems Network (FEWS-NET) or the International Research Institute for Climate Prediction, Columbia University (IRI) websites; or directly from national meteorological services' ground-based weather observations. Generally, summary products are available free of charge, whereas the meteorological services may need to charge for raw data. Countries were encouraged to begin dialogue with their national meteorological services and discuss the more specific information requirements and support they may need.
Sentinel case surveillance
The paramount importance of developing good health information and sentinel surveillance systems was acknowledged. The process of MEWS development is seen as offering opportunities for strengthening integrated health systems surveillance. It is in itself dependent on good epidemiological data for testing and validating the relationships between the component parts. Methods of using indicators for epidemic early detection were discussed. Various indicators such as the mean × 2 standard deviations, the 'normal channel', cumulative-sum and weekly case thresholds have been tried, tested and used in a number of Southern Africa countries, and countries are encouraged to develop and use what is most appropriate and effective for their purposes. However, a number of the countries acknowledged having a poor statistical basis on which to develop and test early warning and detection indicators.
Following the formal presentations setting out the MEWS components and epidemiological trends, the discussions centred around the countries' perceived control needs over the coming season and the information requirements for developing appropriate plans of action for epidemic preparedness and response. The countries represented varied markedly in their current levels of endemicity/epidemicity, surveillance and control coverage. Tanzania is for the most part a highly endemic country with an estimated 16–19 million cases per year. Botswana and Swaziland, by contrast, are currently recording cases in the low thousands and hundreds respectively. Zimbabwe's economic situation has recently compromised its control programme, and two of the countries, Mozambique and Angola, are in process of reconstructing their control programmes after recently emerging from major disruption due to long-term conflict situations. However, all of the countries did acknowledge the integrated MEWS approach as offering a useful framework for improving their epidemic planning, preparedness and response capabilities.
Based on the the climate forecast for October, November, December, and the extended forecast for January, February, March, which are posted on the DMC website . The participants discussed the difficulties in access to and interpretation of meteorological data. The representatives from the meteorological services expressed a willingness to engage in closer collaboration to address these issues. The participants voiced a clear need to improve the availability of the seasonal climate forecast to the epidemic prone districts. They also highlighted the need for better communication of the forecast to non-climate users. Requests were specifically made for forecasts that are more 'meaningful' to the health sector. In response, the meteorological sector pointed at the necessity to know more specifically what information the health sector requires in order to then meet this need. Forecasts could, for example, be expressed simply as the probability of the coming season being wetter or drier than the previous season, or two, or three, or n seasons; or compared to that of the last epidemic season; or as probabilities of exceeding a given threshold for the season. However, it was stressed that forecasts will always be probabilistic and not deterministic. Moreover, countries were encouraged to refer to forecast updates as the season progresses. The issues of how to communicate better the probabilities and uncertainties associated with seasonal climate forecasts were addressed more closely. While many activities in malaria control are based on probabilistic, uncertain premises (clinical diagnosis and presumptive treatment, for instance), public health professionals are well aware of the limitations of their own indicators. While recognizing the potential value of advance lead-times for planning, they are understandably cautious in basing critical decisions on uncertain information from others, and the health and meteorological sectors probably need to work this through in more local collaborative settings.
One additional issue that came out strongly during the meeting was the need for broader cross-border collaboration on epidemic prevention and control as 'true epidemic' prone areas are often based on particular environmental zones rather than administrative boundaries. For example, high rainfall anomalies in Angola may ultimately find their way as increased stream-flow into Botswana and Namibia, and create extensive breeding sites for vectors. Drought, food security, or a range of other factors, may lead to migrations of people across borders from one level of endemicity to another and pose a significant increase in epidemic risk. Development of national epidemic risk maps therefore ought to reflect the situation in neighbouring countries. There are a few examples of cross-border initiatives in the region: Republic of South Africa, Swaziland and Mozambique; Republic of South Africa and Zimbabwe. Both are showing promising results.
Conclusions
The meeting ended with the presentation of the recommendations, to be followed up within the next twelve months. The majority of the recommendations highlighted the need for stronger collaboration a) within the health sector itself; b) among the health sector and the climate-meteorological sector, and other relevant sectors; and c) among the various countries in the region. The participants committed themselves, with their partners, to developing integrated early warning systems as a decision support tool for improving epidemic preparedness and response planning. They recognized that this will be best achieved by drawing on appropriate scientific and technological advancements (and challenging these where necessary), by conducting operational research, and with the help of technical development and support, strengthen the capacity for improved epidemic preparedness and response in the districts at risk.
The successful implementation of MEWS will depend on close cooperation among several partners: National Malaria Control Programmes must work closely with National Meteorological Services, supported by the regional Drought Monitoring Center, WMO and WHO. Opening these channels of communication will allow public health professionals and climate-environmental scientists and practitioners to incorporate more meaningful variables into the seasonal forecasts. In addition, it is necessary to exchange information with institutions dealing with vulnerable populations such as food security and refugee agencies, to develop mutually beneficial mechanisms that ensure easy access and utilization of relevant information for planning or decision-making. It was recognized that by adopting the MEWS approach for malaria control planning the overall health information and surveillance system would be strengthened as other diseases have strong climate and environmental components to their distribution, and further dialogue with Integrated Disease Surveillance and Response services would be useful. Training and capacity building requirements were discussed with WHO-AFRO regarding implementation within the national IDSR activities in the Southern Africa sub-region, and sub-regions elsewhere in Africa. In the final evaluation of the meeting participants, from both health and climate communities, considered that the meeting had provided a very useful overview of the issues and offered a good starting point for them to develop more flexible Plans of Action for Epidemic Preparedness and Response in their countries. It was recommended that a similar meeting be held each year prior to the onset of the rainy season.
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Musawenkosi L Mabaso H Sharp B Lengeler C Historical review of malarial control in southern Africa with emphasis on the use of indoor residual house-spraying Trop Med Int Health 2004 9 846 856 15303988 10.1111/j.1365-3156.2004.01263.x
WHO Malaria Early Warning Systems: concepts, indicators and partners: A framework for field research in Africa 2001 World Health Organization; Geneva
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Thomson MC Connor SJ The development of malaria early warning systems for Africa Trends Parasitol 2001 17 438 445 11530356 10.1016/S1471-4922(01)02077-3
Bates I Fenton C Gruber J Lalloo J Lara AM Squire SB Theobald S Thomson R Tolhurst R Vulnerability to malaria, tuberculosis, and HIV/AIDS infection and disease: Part II: determinants operating at environmental and institutional level Lancet Inf Dis 2004 4 368 375 10.1016/S1473-3099(04)01047-3
Bates I Fenton C Gruber J Lalloo J Lara AM Squire SB Theobald S Thomson R Tolhurst R Vulnerability to malaria, tuberculosis, and HIV/AIDS infection and disease: Part 1: determinants operating at individual and household level Lancet Inf Dis 2004 4 267 277 10.1016/S1473-3099(04)01002-3
Goddard L Mason S Zebiak S Ropelewski R Basher R Cane M Current approaches to seasonal to interannual climate predictions Int J Climatol 2001 21 1111 1152 10.1002/joc.636.abs
| 15500683 | PMC526760 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Oct 22; 3:37 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-37 | oa_comm |
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Clin Mol AllergyClinical and molecular allergy : CMA1476-7961BioMed Central London 1476-7961-2-111549406910.1186/1476-7961-2-11Case ReportFirst successful case of in vitro fertilization-embryo transfer with venom immunotherapy for hymenoptera sting allergy Sills Eric Scott [email protected] Susan C [email protected] Carolyn R [email protected] Mark [email protected] Michael J [email protected] Georgia Reproductive Specialists, Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Atlanta Medical Center; Atlanta, Georgia USA2004 19 10 2004 2 11 11 18 8 2004 19 10 2004 Copyright © 2004 Sills et al; licensee BioMed Central Ltd.2004Sills et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
To describe immune and endocrine responses in severe hymenoptera hypersensitivity requiring venom immunotherapy (VIT) during in vitro fertilization (IVF).
Case presentation
A 39-year old patient was referred for history of multiple miscarriage and a history of insect sting allergy. Four years earlier, she began subcutaneous injection of 100 mcg mixed vespid hymenoptera venom/venom protein every 5–6 weeks. The patient had one livebirth and three first trimester miscarriages. Allergy treatment was maintained for all pregnancies ending in miscarriage, although allergy therapy was discontinued for the pregnancy that resulted in delivery. At our institution ovulation induction incorporated venom immunotherapy (VIT) during IVF, with a reduced VIT dose when pregnancy was first identified. Serum IgE was monitored with estradiol during ovulation induction and early pregnancy. Response to controlled ovarian hyperstimulation was favorable while VIT was continued, with retrieval of 12 oocytes. Serum RAST (yellow jacket) IgE levels fluctuated in a nonlinear fashion (range 36–54%) during gonadotropin therapy and declined after hCG administration. A healthy female infant was delivered at 35 weeks gestation. The patient experienced no untoward effects from any medications during therapy.
Conclusion
Our case confirms the safety of VIT in pregnancy, and demonstrates RAST IgE can remain <60% during IVF. With proper monitoring, VIT during IVF can be safe and appropriate for selected patients and does not appear to adversely affect blastocyst implantation, early embryo development or perinatal outcome. Further studies will be needed to develop VIT guidelines specifically applicable to IVF.
allergyhymenopteraIgEimmunologyin vitro fertilization
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Introduction
Insect sting allergies affect approximately 3% of the general population, and patients with insect sting allergy during pregnancy are generally advised to continue venom immunotherapy (VIT). However, there have been no descriptions of VIT during infertility therapy despite increased utilization of the advanced reproductive technologies [1]. In this report, we present endocrine and immunologic parameters observed during a successful in vitro fertilization cycle where a standard insect sting allergy protocol was used.
Case report
A 39 year-old Caucasian G4P1031 was referred for evaluation and management of recurrent pregnancy loss. Medical history was significant for known carrier state for β-thalassemia. Mild hypothyroidism had been diagnosed in 2002 with immediate initiation of replacement therapy. The patient was a non-smoker, in good general health and had no gynecologic complaint. BMI was 21.7 kg/m2. In 1997, she experienced a severe hypotensive anaphylactic reaction following a yellow jacket sting (Vespula spp.) resulting in a full allergy work-up. The patient began subcutaneous injection of 100 mcg mixed vespid hymenoptera venom/venom protein (Pharmalgen®; ALK Abello, Hørsholm, Denmark) every 5–6 weeks, which was well tolerated.
All four conceptions were established without medical assistance, involved the same partner, and were achieved after the hymenoptera hypersensitivity diagnosis. The initial pregnancy occurred three years before presentation and resulted in a first trimester spontaneous abortion. No adjustment was made to the allergy injection regimen during that pregnancy. Fetal cardiac activity was initially present, but was lost at 10 weeks' gestation for unknown reasons. No curettage was performed.
One year later, a second pregnancy was established but for this pregnancy hymenoptera venom therapy was discontinued when pregnancy was first recognized (~6 weeks). A 3170 g female infant was delivered vaginally at 40 1/2 weeks' gestation. In 2001 and 2002, the patient established two additional pregnancies and hymenoptera therapy was maintained at 5–6 week intervals for both; both resulted in first trimester spontaneous abortions. For these miscarriages, dilation and curettage was undertaken but no karyotype was performed and no cause for the losses was identified.
At our institution, euthyroid status was verified, the thalassemia carrier state was confirmed, and we identified a new homozygous A223V mutation at the methyltetrahydrofolate reductase (MTHFR) locus. Folic acid intake was immediately increased to 800 mcg/d, although a baseline serum homocysteine level was not measured. Factor V Leiden, protein S, protein C, and other coagulation tests were normal, as were karyotypes obtained from both partners. Anticardiolipin, antiphospholipid and antiovarian antibody titres were all negative. However, transvaginal saline uterine sonography revealed a uniform 5 mm echodense lesion consistent with an endometrial polyp. Outpatient hysteroscopic polypectomy was performed without complication. After discussing various infertility therapies and associated success rates given her age, the patient elected to undergo IVF.
In March 2003, the patient began programmed ovarian hyperstimulation using a combined recombinant-FSH+hMG protocol (300 IU/d Humegon®, Ferring Pharmaceuticals Inc.; Tarrytown, NY USA and 300 IU/d Gonal-F®, Serono Labs; Norwell, MA USA). Pre-treatment pituitary downregulation was achieved via 5 u/d leuprolide acetate and was continued × 3 d after gonadotropin therapy commenced. No alteration was made in the patient's allergy injection sequence during ovulation induction (i.e., 100 mcg every 5–6 weeks), and serum yellow jacket RAST IgE measurements were obtained via commercial fluoroimmunoassay including positive and negative controls (UniCAP® IgE kit, Pharmacia Diagnostics, Uppsala, Sweden). While absolute IgE levels remained <0.35 kU/l throughout therapy, percentage IgE results were variable and these data are summarized in Figure 1.
Figure 1 Relationships among serum luteinizing hormone (LH-blue), estradiol (E2-black), and yellow jacket RAST %IgE (IgE-red) observed during in vitro fertilization and concomitant venom immunotherapy.
On cycle day 10, subcutaneous hCG (10,000 IU) was given [2] with serum estradiol at 1090 pg/ml. Twelve oocytes were retrieved and 7 advanced to the 2pn stage following conventional insemination. A four-day course of methylprednisolone (16 mg/d) was started on the day of oocyte retrieval. On post-fertilization day three, the ultrasound-guided transfer of four embryos was performed. Immediately following embryo transfer, the patient was placed on oral aspirin (81 mg/d) and subcutaneous heparin (5,000 IU b.i.d). Luteal phase support was administered as daily 50 mg IM progsterone in oil injections.
Two weeks after embryo transfer, serum hCG was 72 mIU/ml. On May 5, 2003, transvaginal ultrasound confirmed a single intrauterine pregnancy with fetal cardiac rate at 126/min. Progesterone was discontinued at the 10th gestational week. Immunology and perinatology consultants agreed with reduced dose allergy protocol through the third trimester, and hymenoptera venom protein extract (75 mcg) treatment was maintained to 32 weeks gestation. The patient experienced no untoward reaction or hypersensitivity to gonadotropins, VIT, or supplementary progesterone during therapy.
At 32 weeks, obstetrical sonogram suggested reduced amniotic fluid levels and the patient was given intramuscular betamethasone (12 mg/d × 2 days) and placed on bedrest. At this point allergy injections were discontinued since the patient was not outdoors and risk for insect sting was regarded as low. Intravenous oxytocin was started at 35 weeks due to oligohydramnios and resulted in vaginal delivery of a 2495 g female infant. Mother and baby were discharged home after an uncomplicated two-day postpartum course. Allergy injections resumed (100 mcg every 5–6 weeks) when breastfeeding was completed three months later.
Conclusion
Overall, the incidence of allergy to insect stings is ~3% in adults [3], with allergy to hymenoptera species venom comprising an important subset of this population. Whether or not severe insect sting allergy contributes to poor reproductive outcome has been discussed in earlier reports [3-5], yet the immunology of pregnancy remains complex and poorly understood. While connections between infertility/spontaneous abortion and immune dysfunction have been explored by others [6,7], the reported conclusions have been highly variable [8,9]. In addition, difficulty with immunoassay standardization has made some findings difficult to reproduce [10].
Although continuation of allergy therapy during pregnancy is generally recommended [11], the interaction between ovulation induction agents and hymenoptera venom therapy has never been characterized. Our patient experienced no hypersensitivity or untoward effects during allergy therapy and gonadotropin use; both were well tolerated when administered together. We observed a variable IgE pattern, with a gradually increasing IgE response progressing with follicular growth during ovulation induction. Interestingly, a sharply diminished IgE level was registered immediately after hCG administration. The significance of the reduced terminal immunoglobulin titre is unkown but may reflect an immunomodulatory attenuation effect of hCG and/or progesterone [12].
Evaluation of this patient with a history of multiple spontaneous abortions identified additional factors which might contribute to a poor reproductive outcome. Specifically, endometrial polyps [13] and homozygous MTHFR mutation [14] are recognized independent risk factors for miscarriage. In addition to venom protein, our patient received other medications which modify immune response including aspirin [15], heparin [16], progesterone [17], and human chorionic gonadotropin [12]. Methylprednisolone [18] was administered at embryo transfer and betamethasone was given near delivery [19]. While for our patient VIT was a component of therapy culminating in a satisfactory reproductive outcome, the result should be recognized as the sum of all clinical interventions and insufficient data exists to ascribe specific roles for individual treatments. Levels of IgG4 blocking antibody were not quantified in this study, although measurement of this parameter in subsequent studies may offer additional insight into potential mechanisms of protection (i.e., reduction of miscarriage risk).
It has been hypothesized that early production of IL-10 associated with VIT may induce T-cell anergy, dampening T helper type 2 response and resulting in a T helper type 1 dominant cytokine response. As the role of T helper 1 type immune response in blastocyst implantation becomes more completely characterized, T helper type 1 function may prove to be important in early placental dysfunction or recurrent pregnancy loss [20]. These potential mechanisms notwithstanding, when anatomic and hematologic abnormalities were corrected and VIT for insect sting allergy therapy was continued, a healthy livebirth after IVF was achieved. Our report offers, for the first time, reassurance for women undergoing IVF who also suffer from severe insect sting allergy requiring VIT. Data derived from further studies will be helpful as VIT guidelines are developed specifically for patients undergoing IVF.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
ESS was the principal physician and coordinated the research. SCC, CRK and MP edited the manuscript. MJT was chief embryologist and edited the manuscript.
Acknowledgement
Written permission was obtained from the patient for publication of this research. These data presented in part at the XIIth World Congress on Human Reproduction, 10–13 March 2005 (International Academy of Human Reproduction-Venice, Italy)
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| 15494069 | PMC526761 | CC BY | 2021-01-04 16:36:22 | no | Clin Mol Allergy. 2004 Oct 19; 2:11 | utf-8 | Clin Mol Allergy | 2,004 | 10.1186/1476-7961-2-11 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-731548815310.1186/1477-7827-2-73ResearchTransmembrane carbonic anhydrase isozymes IX and XII in the female mouse reproductive organs Hynninen Piritta [email protected]ämäläinen Jonna M [email protected] Silvia [email protected] Jaromir [email protected] Abdul [email protected] William S [email protected] Eija [email protected] Pertti [email protected] Seppo [email protected] Department of Gynecology and Obstetrics, University of Tampere and Tampere University Hospital, Tampere, Finland2 Institute of Medical Technology, University of Tampere and Tampere University Hospital, Tampere, Finland3 Center of Molecular Medicine, Institute of Virology, Slovak Academy of Sciences, Bratislava, Slovak Republic4 Edward A. Doisy Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, Missouri, USA2004 17 10 2004 2 73 73 3 6 2004 17 10 2004 Copyright © 2004 Hynninen et al; licensee BioMed Central Ltd.2004Hynninen et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Carbonic anhydrase (CA) classically catalyses the reversible hydration of dissolved CO2 to form bicarbonate ions and protons. The twelve active CA isozymes are thought to regulate a variety of cellular functions including several processes in the reproductive systems.
Methods
The present study was designed to investigate the expression of transmembrane CAs, CA IX and XII, in the mouse uterus, ovary and placenta. The expression of CA IX and XII was examined by immunoperoxidase staining method and western blotting. CA II and XIII served as positive controls since they are known to be present in the mouse reproductive tract.
Results
The data of our study indicated that CA XII is expressed in the mouse endometrium. Only very faint signal was observed in the corpus luteum of the ovary and the placenta remained mainly negative. CA IX showed weak reaction in the endometrial epithelium, while it was completely absent in the ovary and placenta.
Conclusion
The conservation of CA XII expression in both mouse and human endometrium suggests a role for this isozyme in reproductive physiology.
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Background
Carbonic anhydrases (CAs) are zinc-containing metalloenzymes that are responsible for the reversible hydration of carbon dioxide in a reaction CO2 + H2O ↔ H+ + HCO3-. CAs are produced in a variety of tissues where they participate in several important biological processes such as acid-base balance, respiration, carbon dioxide and ion transport, bone resorption, ureagenesis, gluconeogenesis, lipogenesis and body fluid generation [1,2]. The mammalian α-CA gene family includes at least twelve enzymatically active isoforms with different structural and catalytic properties. CA I, II, III, VII and XIII are cytosolic enzymes [1,3,4]. CA VA and VB are mitochondrial proteins encoded by nuclear DNA [5,6]. CA VI is the only secretory form being present in saliva and milk [7]. The cluster of membrane-bound CAs includes four isozymes: CA IV, IX, XII, and XIV [8-11]. The other members of the CA gene family (CA VIII, X and XI) are inactive isoforms whose functions have not yet been described [3,12,13].
It has been previously suggested that CAs may play important roles in the uterine endometrium by maintaining the appropriate pH balance through the catalysis of the production of bicarbonate ions [14]. Indeed, the role of bicarbonate in fertilization has been demonstrated in a number of previous studies. It is functionally involved in some key processes such as sperm cell capacitation and regulation of sperm motility [15-17]. Similarly, CAs may have several functions also in the placenta. They can be active in intermediary metabolism and provide ions for exchange in transepithelial movement of ions and fluid [18].
CA activity has been studied in pig, horse, cow, mink, rat and human placentas, and the results show considerable heterogeneity among different species [18]. Previous immunochemical studies have shown evidence for expression of CA II but not CA I or III in the bovine placenta [19]. Both CA I and II are expressed in the human syncytiotrophoblasts [20-22] and, especially CA II, in the fetal villous endothelium of mature placenta [22]. CA IV-positive staining has been reported in the mouse placenta by Rosen and coauthors [23]. Their data showed strong CA IV immunoreactivity in the mouse trophoblasts and endodermal layer of the yolk sac. In the mouse genital tract, CA I, II and III have been reported by Ge and Spicer [24]. These isozymes were reported to be present in the theca interna cells in the mouse ovary, and CA I was found in the zona pellucida and cytoplasmic foci in follicular granulosa cells. In the mouse oviductal epithelium, CA II expression showed distinct variation. The reaction was absent in the infundibulum, whereas the ampulla and isthmus showed positive staining. CA XIII is the newest member of the CA enzyme family, which has been described in the mouse and human endometrium along with several other positive tissues [4]. As a cytosolic isozyme it may be one of the major proteins regulating the pH and bicarbonate homeostasis not only in the endometrial cells but also in the lumen of the uterus. These mechanisms are complex due to the presence of several isozymes, however, and may greatly differ between species. For example, the human endometrium contains CA II only in the capillaries, whereas this high activity isozyme is abundantly expressed in the epithelial cells of the mouse endometrium [4,24].
CA IX is expressed at the basolateral plasma membrane of the human, rat and mouse epithelial cells [25,26]. In a recent extensive study, Ivanov et al [27] analyzed a number of normal human tissues for the expression of CA IX. Among reproductive organs, they reported positive signal for CA IX mRNA and protein in the efferent ducts, rete testis, and rete ovarii.
Human CA XII is expressed in several organs including colon, kidney, and pancreas [28-30]. In the human female reproductive tract, CA XII has been shown both in the glandular and surface epithelium of the endometrium, while it was only occasionally present in the cervix [14]. Ivanov et al [27] further confirmed CA XII expression in the glandular epithelium during the proliferative phase.
In this report we studied the expression of CA II, IX, XII and XIII in mouse female genital organs including uterus, ovary and placenta. The studies were specially focused on CA IX and XII, which have been designated as tumor-associated isozymes [9,10]. In addition to some normal tissues, both isozymes are overexpressed in several carcinomas such as renal and colorectal cancers [9,27,31,32]. A previous study has also demonstrated CA IX and XII expression in a number of neoplasias derived from the female reproductive tract [27]. However, there have been no previous studies on these isozymes in the female murine reproductive organs. The conservation of CA XII expression in both mouse and human endometrium shown in the present paper suggests a role for this isozyme in reproductive physiology.
Materials and methods
Antibodies
In the present study, we used the following antibodies which have been produced and characterized earlier: rabbit anti-mouse CA II [4], rabbit anti-mouse CA IX [26], rabbit anti-mouse CA XII [33], and rabbit anti-mouse CA XIII [4].
Collection of tissue samples
Two adult Balb/c mice were sacrificed by CO2 asphyxiation followed by decapitation. Uterus, ovary and placenta samples were collected from both animals. The samples were immersion-fixed overnight in Carnoy's fluid (ethanol, chloroform and acetic acid (6:3:1)). Then the specimens were treated with absolute ethanol for 30 min, with 1:1 mixture of ethanol and chloroform for 15 min, and finally with chloroform for 30 min. Paraffin embedding was performed in a vacuum oven for 2 h at +58°C. Paraffin wax was purchased from Fluka Chemie GmbH (Buchs, Schwitzerland). To collect a placenta sample, a mouse was sacrificed at 9 days of pregnancy. The ninth day was chosen since it represents the middle gestational phase. It is also the time when the most critical steps of organogenesis occur in mouse. For western blotting, uterus, kidney and colon were removed and rapidly frozen in liquid nitrogen. The tissue samples for western blot were homogenized with HEPES buffer. Total protein concentration was determined after homogenization using BCA Protein Assay Kit (Pierce, Rockford, IL) according to manufacturer's instructions. The study protocols were approved by the Animal Care Committee of Tampere University.
Immunohistochemistry
In the mouse tissues, the localization of CA IX and XII was examined by immunoperoxidase method. Antibodies against CA II and XIII were used as positive controls for the immunostaining. All experiments were performed in duplicate and included control staining with non-immune normal rabbit serum (NRS). NRS was obtained from a rabbit that was later immunized against mouse CA XIII. The tissue samples fixed in Carnoy's fluid and embedded in paraffin were cut at 5 μm sections and placed on microscope slides. The peroxidase-anti-peroxidase complex method included the following steps: a) pretreatment of the sections with undiluted cow colostral whey (Biotop, Oulu, Finland) for 40 min and rinsing in phosphate-buffered saline (PBS); b) incubation for 1 h with the primary antiserum (anti-mouse CA II, CA IX, CA XII or CA XIII) or NRS diluted 1:100 in PBS containing 1% bovine serum albumin (BSA) (BSA-PBS solution); c) treatment with undiluted cow colostral whey (40 min); d) incubation for 1 h with swine anti-rabbit IgG (Dakopatts, Copenhagen, Denmark) diluted 1:100 in 1% BSA-PBS; e) incubation for 30 min with peroxidase-anti-peroxidase rabbit conjugate (Dakopatts) diluted 1:500 in PBS; f) incubation for 2 min with 3,3'diaminobenzidine tetrahydrochloride (DAB) solution (6 mg DAB in 10 ml PBS plus 3.3 μl H2O2) as chromogen. The sections were washed three times for 10 min in PBS after incubation steps b and d and four times for 5 min after incubation step e. All of the incubations and washings were carried out at room temperature. The sections were finally mounted in Neo-Mount (Merck, Darmstadt, Germany). The stained sections were examined and photographed with a Zeiss Axioskop 40 microscope (Carl Zeiss, Göttingen, Germany).
Western blot
The samples containing 50 μg of protein from mouse uterus, kidney and colon were analyzed by SDS-PAGE under reducing conditions [34]. All of the reagents and the protein standard (BenchMark™ Prestained Protein Ladder) for SDS-PAGE were purchased from Invitrogen (Carlsbad, CA) except Laemmli sample buffer that was obtained from Sigma (St. Louis, MO). Electrophoresis (200 V for 40 min) was performed in a Novex Xcell II mini cell electrophoresis unit (Invitrogen) with a 10% Bis-Tris gel (Invitrogen). The separated proteins were transferred electrophoretically from the gel to a polyvinylidene fluoride (PVDF) membrane (Invitrogen) in a Novex Xcell II blot module (Invitrogen). The transfer buffer (NuPAGE Transfer Buffer™) was purchased from Invitrogen. The blot module was filled with the transfer buffer until the gel/membrane assembly was covered. The outer buffer chamber was filled with 650 ml deionized water. The protein transfer was performed using a constant voltage of 36 V for 1 h 20 min. After the transblotting, the sample lines were detected by ECL western blotting detection reagents and analysis system (Amersham Biosciences, Buckinghamshire, UK) according to the manufacturer's instructions. First, the sample lines were incubated with TBST buffer (10 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0,3 % Tween 20) containing 10 % cow colostral whey for 25 min and then the first antibodies diluted 1:2000 (anti-CA II, anti-CA IX, anti-CA XII, NRS) or 1:1000 (anti-CA XIII) in TBST buffer for 1 h. The PVDF membranes were washed five times for 5 min with TBST buffer and incubated for 1 h with peroxidase-linked ECL Anti-Rabbit IgG (Amersham Biosciences) diluted 1:25 000 in TBST buffer. After washing four times 5 min in TBST buffer, the polypeptides were visualized by a chemiluminescence substrate (ECL detection reagents 1 + 2, Amersham Biosciences). Kodak™ Biomax™ MS-1 films (Amersham Biosciences) were exposed to the chemiluminescence for 5 min (CA IX and XII) or 1 min (CA II and CA XIII). All the steps were carried out at room temperature. The western blotting experiments were performed in triplicate to confirm the reproducibility of the results.
Results
Immunohistochemistry
All the studied CA isozymes showed positive immunostaining in the epithelial cells of the mouse endometrium (Fig. 1). CA II and XII showed a somewhat reciprocal distribution pattern in that CA II was confined to the surface epithelial cells (Fig. 1C), while CA XII was more intensely stained in the deeper endometrial glands (Fig. 1A). It is noteworthy, however, that CA XII was clearly expressed also in the surface epithelial cells, but the staining intensity was weaker compared to the glands. As expected, the strongest reaction for CA XII was associated with the basolateral plasma membrane, and unexpectedly, also CA II immunoreaction was most intense at the plasma membrane. CA IX and XIII showed weak reactions in both surface and glandular epithelia (Fig. 1B,1D). The control immunostaining with NRS was negative (Fig. 4A).
Figure 1 Immunohistochemical staining of CA XII (A), CA IX (B), CA II (C), and CA XIII (D) in the mouse endometrium. All the studied CA isozymes show positive immuostaining, although the staining intensity varies between different isozymes. CA XII shows stronger reaction in the deep endometrial glands compared to the surface epithelium. This pattern is inversed with CA II showing high reaction in the surface epithelium. Insert in panel A shows that the CA XII immunostaining is most abundant in the basolateral plasma membrane of the epithelial cells. Insert in panel C demonstrates that CA II immunoreactivity is also closely associated with the plasma membrane. CA IX and XIII show faint immunoreactions in both the surface and glandular epithelia. Arrows = endometrial glands, arrowheads = surface epithelium. Original magnifications: × 400.
Figure 4 Control immunostaining of mouse uterus, ovary and placenta with normal rabbit serum. No immunoreaction is seen. Oringinal magnifications: × 400.
In the ovary, immunoreactions for different CA isozymes were negligible (Fig. 2). In fact, only CA XII showed occasional positive cells in the corpus luteum (Fig. 2A). No staining for these isozymes was observed in the developing follicles. No immunoreaction was obtained with NRS (Fig. 4B)
Figure 2 Immunolocalization of CA XII (A,B), CA IX (C,D), CA II (E,F), and CA XIII (G,H) in the mouse corpus luteum (A,C,E,G) and follicle (B,D,F,H). Only faint positive reaction for CA XII can be seen in occasional cells of the corpus luteum that is indicated in the insert of the panel A (arrows). Original magnifications: × 200 (A,C,E,G), × 400 (B,D,F,H).
In the 9-days-old mouse placenta, the immunostaining reactions for CA isozymes remained quite weak or absent (Fig. 3). CA II was located to the endothelium of the placenta blood vessels and erythrocytes (Fig. 3E), and it was also present in the amnionic epithelium (Fig. 3F). The amnionic epithelium showed no or very weak staining for CA XII, whereas the decidual glands were strongly labeled (Fig. 3B). The control staining again showed no positive signal (Fig. 4C).
Figure 3 Immunohistochemical staining of CA XII (A,B), CA IX (C,D), CA II (E,F), and CA XIII (G,H) in the mouse placenta (A,C,E,G) and amnionic epithelium (B,D,F,H). CA II is located in the endothelium of the blood vessels and erythrocytes (arrows in the panel E). It is also expressed in the amnionic epithelium (arrows in the panel F). Insert of the panel B shows that CA XII is highly expressed in the decidual glands, while the amnionic epithelium is negative. DE = Decidua, P = placenta. Original magnifications: × 400.
Western blot
Western blotting was performed for the mouse uterine protein to evaluate the specificity of the immunoreactions. Mouse kidney and colon samples were used as positive control tissues, since they are known to express CA II, XII and XIII [4,33,35], and the colon contains CA IX [36]. CA II and XIII were positive in all tissue specimens (Fig. 5). Both CA IX and XII showed weak positive reactions in the mouse uterus. The molecular weights for these isozymes were 51 and 46 kDa, respectively. Based on the western blotting the expression of CA XII was weaker in the uterus than in the colon or kidney. On the other hand, CA IX showed the strongest signal in the colon. It is notable that anti-mouse CA XII serum cross-reacted with 30 kDa polypeptide in the western blotting. Previous immunostaining of gastric mucosa with the same anti-CA XII and anti-CA II antibodies has clearly indicated that anti-CA XII serum does not recognize CA II which has a molecular mass of 30 kDa in western blot [35]. Even though gastric epithelial cells contain high levels of CA II, no immunoreaction was obtained with anti-CA XII serum in those cells. Furthermore, no staining has been obtained by anti-CA XII antibody in the red cells which contain high levels of CA I and II, nor in the brain which expresses high levels of CA II and XIII (data not shown).
Figure 5 Western blotting of total homogenate from mouse uterus, kidney and colon for CA II, IX, XII and XIII. Normal non-immune rabbit serum (NRS) was used instead of the first antibodies as a negative control. Both CA IX and XII show weak positive reactions for the uterine proteins (arrowheads). The molecular weights for these isozymes are 51 and 46 kDa, respectively. The signal for CA XII is weaker in the uterus than in the colon or kidney. Note that anti-CA XII serum cross-reacts with a 30-kDa polypeptide. This cross-reaction is evident only in western blotting conditions as pointed out in the Results section. CA IX shows the strongest signal in the colon. CA II and XIII are positive in all tissue specimens.
Discussion
This study describes the expression of CA II, IX, XII and XIII in mouse female genital organs including uterus, ovary and placenta. CA II showed a very limited distribution pattern in the mouse placenta, being present only in the erythrocytes, endothelium of the blood vessels and amnionic epithelium. In previous studies, CA II has been detected by immunohistochemistry in the human villous syncytiotrophoblasts and in varying amounts in fetal villous endothelium [21,22]. Using a histochemical staining method, Ridderstråle et al [18] showed in several species that the highest CA activity located in the maternal capillaries, and the membrane-bound CA activity varied among different species. To date, CA IV is the only membrane-bound CA isozyme which has been detected in the mouse placenta [23]. In our study, CA IX and XII were not found in the mouse placental tissue except that CA XII showed a very weak reaction in the amnionic epithelium. Concluding from the results of the present and previous studies, CA I and II appear to represent the enzyme forms that are most relevant for the placental function [22], while CA IX and XII may play a role in other reproductive organs such as the male excurrent duct and female uterus [14,37].
It is known that CA activity facilitates transport of CO2 across biological membranes by converting it to bicarbonate and hydrogen ions. These ions are then translocated across the plasma membrane through specific carrier proteins in a coordinated manner. It is of considerable interest that CA isozymes II and IV have been recently described to form active metabolon systems with ion exchanger proteins such as anion exhanger isoform 1 (AE1) and Na+/H+-exhanger isoform 1 (NHE1) [38-40]. Even though these associations have not yet been described in the placental tissue, it is possible that such metabolons play a role in facilitating placental ion transport processes.
Previous studies have shown CA activity in the endometrium of several mammalian species [41,42]. Until now the only established isozymes in the human endometrium are CA XII [14] and CA XIII [4]. Interestingly, the high activity isoenzyme, CA II, is not expressed in the human endometrial epithelium [4]. In the present study, all the examined CA isozymes – including CA II – showed positive immunostaining in the epithelial cells of the mouse endometrium. To our knowledge, there are only a few examples of clear species-specific difference in CA expression. These include e.g. CA XII expression in the kidney (human principal cells versus mouse intercalated cells) [33,43] and CA XIII in the human and mouse testis [4]. What would be the physiological consequence of such variation between different species? Of course, there are marked differences in human and rodent reproductive physiology. Mouse is characterized by tremendous reproductive potential. Females generally have 5–10 litters per year if conditions are suitable. Gestation period is 19–21 days. Litters consist of 3–12 (generally 5 or 6) offspring, and the mice reach sexual maturity at 5–7 weeks. Even though our observations do not provide any clues whether CA expression could contribute to some of the described characteristics, these differences may have fundamental physiological effects that should be addressed in future investigations.
In the present study, CA XII and II showed more intense staining in the surface endometrial epithelia than CA IX and XIII. CA XII was more intensely stained in the deeper endometrial glands, while CA II was confined to the surface epithelial cells. Interestingly, CA II showed positive immunoreaction not only in the cytoplasmic compartment but also at the plasma membrane of the cells that is quite surprising for a cytosolic enzyme. The same phenomenon is detectable in some other tissues including the human gallbladder [25] and gut [44]. The cell membrane reactivity may reflect a possible physical association between CA II and ion transport proteins, which has been demonstrated in cell cultures [38-40]. When CA XII was first discovered in the normal human endometrium, it was suggested to play a role in reproductive functions [14]. In the endometrium, pH and ion balance has to be tightly regulated to ensure normal fertilization. For example, the bicarbonate concentration has been implicated in the regulation of sperm motility, capacitation, and acrosome reaction [15,17,45]. One major regulatory pathway includes a bicarbonate-sensitive adenylate cyclase that is present in the plasma membrane of the sperm cell [46]. In the female genital tract, the endometrial and oviductal epithelium may produce an alkaline – bicarbonate rich – environment for maintaining the sperm motility. This suggestion is in agreement with the observations by Guerin et al. [47], that the sperm motility is improved by co-culture of human spermatozoa with either endometrial or oviductal epithelial cells.
In the future studies, it will be important to investigate whether the hormonal status regulates the expression of different CA isozymes – particularly CA XII – in the endometrium. Another interesting line of investigations would be to analyze the fertilization capacity of CA XII knockout mice as soon as they become available. One could hypothesize that endometrial CA isozymes are important factors, contributing to the appropriate bicarbonate concentration and pH balance in the cervical and endometrial mucus needed for normal fertilization process. Based on our recent studies, CA IX-deficient mice showed no apparent phenotypic changes linked to fertility [26]. Even more interesting from this point of view is that CA XII may be an important isozyme present in the endometrium, and therefore, CA XII knockout mice will be very attractive targets for reproductive physiological studies.
Conclusions
The present paper demonstrates for the first time the expression of transmembrane carbonic anhydrase isozymes IX and XII in the female murine reproductive tract. The data indicates that the endometrial epithelium is a prominent site for CA XII expression. The conservation of CA XII expression in the endometrium of different species (mouse and human) suggests a role for this isozyme in reproductive physiology.
Authors' contributions
All authors participated in the design of the study. PH, JL and SP collected the tissue samples. PH, JL, ET, PK and SP drafted the manuscript. PH, JL and SP performed the western blotting. SPas, JP, AW and WSS provided the antibodies. PH, JL and SP participated in the immunohistochemical staining. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by grants from Sigrid Juselius Foundation, Academy of Finland, Bayer Corporation, Slovak Grant Agencies VEGA (2/3055) and APVT (51-005802), and National Institutes of Health (GM34182, DK40163).
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| 15488153 | PMC526762 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Oct 17; 2:73 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-73 | oa_comm |
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-2-81548557110.1186/1477-9560-2-8Case ReportTraumatic deep vein thrombosis in a soccer player: A case study Echlin Paul S [email protected] Ross EG [email protected] Douglas B [email protected] Harsha P [email protected] Providence Athletic Medicine, Providence Hospital and Medical Centers, 47601 Grand River Avenue, Suite 101, Novi Michigan, United States of America 483742 Primary Care Research Unit, Department of Family and Community Medicine, Sunnybrook and Women's College Health Sciences Centre, 2075 Bayview Avenue, #E-349, Toronto, Ontario, Canada M4N 3M53 Department of Family and Community Medicine, University of Toronto, 263 McCaul Street, Toronto, Ontario, Canada M5T 1W74 Department of Public Health Sciences, University of Toronto, 12 Queen's Park Crescent W., Toronto, Ontario, Canada M5S 1A85 Department of Family Medicine, Indiana University, Long Hospital, Second Floor. 1110 West Michigan Street, Indianapolis Indiana, United States of America 46202-51026 Department of Family Medicine, Wayne State University, 15400 West McNichols, 2nd Floor, Detroit, Michigan, United States of America 482352004 14 10 2004 2 8 8 27 4 2004 14 10 2004 Copyright © 2004 Echlin et al; licensee BioMed Central Ltd.2004Echlin et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A 42 year-old male former semi-professional soccer player sustained a right lower extremity popliteal contusion during a soccer game. He was clinically diagnosed with a possible traumatic deep vein thrombosis (DVT), and sent for confirmatory tests. A duplex doppler ultrasound was positive for DVT, and the patient was admitted to hospital for anticoagulation (unfractionated heparin, warfarin). Upon discharge from hospital the patient continued oral warfarin anticoagulation (six months), and the use of compression stockings (nine months). He followed up with his family doctor at regular intervals for serial coagulation measurements, and ultrasound examinations. The patient's only identified major thrombotic risk factor was the traumatic injury. One year after the initial deep vein thrombosis (DVT) the patient returned to contact sport, however he continued to have intermittent symptoms of right lower leg pain and right knee effusion.
Athletes can develop vascular injuries in a variety of contact and non-contact sports. Trauma is one of the most common causes of lower extremity deep vein thrombosis (DVT), however athletic injuries involving lower extremity traumatic DVT are seldom reported. This diagnosis and the associated risk factors must be considered during the initial physical examination. The primary method of radiological diagnosis of lower extremity DVT is a complete bilateral duplex sonography, which can be augmented by other methods such as evidence-based risk factor analysis. Antithrombotic medication is the current standard of treatment for DVT. Acute thrombolytic treatment has demonstrated an improved therapeutic efficacy, and a decrease in post-DVT symptoms.
There is a lack of scientific literature concerning the return to sport protocol following a DVT event. Athletic individuals who desire to return to sport after a DVT need to be fully informed about their treatment and risk of reoccurrence, so that appropriate decisions can be made.
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Introduction
Athletes are susceptible to a variety of vascular injuries, secondary to either repetitive motion, or high-speed collisions [1]. The differential diagnosis for lower extremity trauma in sport seldom invites a diagnosis of vascular injury, such as a deep vein thrombosis (DVT). Failure of the physician to recognize a vascular injury can have catastrophic limb or life threatening (pulmonary embolism) implications. The epidemiology, diagnosis, treatment, and recurrence of DVT, as well as the prevention of post-thrombotic symptoms are the most current areas of clinical research. Research-based guidelines concerning an athlete's return to sport after a DVT is an important area for future investigation.
Case Report
A 42 year old Polish born male former semi-professional soccer player was seen on May 16th, 2003 in the emergency department, with the chief complaint of right leg pain. The patient had been playing soccer 10 days prior to this visit, and recalled a traumatic "tackle" injury to the posterior area of his right lower extremity. He denied experiencing any sensation of tearing or popping in the right knee during the index trauma, and was able to complete the game with only minor discomfort. On day 3 post-injury the patient noted severe pain in his knee and calf with ambulation. The patient visited his primary doctor on post-injury day 8 and was diagnosed with a right lower extremity soft tissue injury. A right lower extremity echo-doppler ultrasound (US), and a semi-quantitative D-dimer automated latex procedure were ordered to rule out a vascular disorder. The US investigation demonstrated a DVT in the distal femoral, popliteal, and distal calf veins, with a heterogenous mass (5 cm × 3 cm × 4 cm, resembling a hematoma) without a doppler signal in the right popliteal fossa. The D-dimer result was also positive for a suspected thrombosis (1.0–2.0 ug/ml; range = <0.25 ug/ml). The patient was instructed by his physician to proceed immediately to the emergency department for further evaluation and treatment.
The past medical and family history of the patient was non-contributory for a history of thrombophilia or other thrombotic major risk factors. The patient had a remote (11 years old) surgical history of a right-sided inguinal hernia that could have created scar tissue contributing to vascular obstruction and stasis. The initial emergency department examination demonstrated an exquisitely tender right calf with a 3 cm difference in mid-calf girth (10 cm. distal from each inferior patellar pole); a 1+ right knee supra patellar effusion; and a palpable popliteal mass with visible ecchymosis. Laboratory tests (CBC, Lytes, PT, PTT, ESR, CPK, Anti-throbomin, Factor V Leiden, Lupus Screen, ANA, Anti-Cardiolipin, Protein C, and Protein S) were negative for metabolic, hematological or familial abnormalities. A repeat US investigation confirmed the results of the previous outpatient results. The patient was anticoagulated simultaneously with unfractionated heparin and Warfarin sulfate. A multiview plain film x-ray examination of the right lower extremity demonstrated no fracture, dislocation, or bony mass.
A magnetic resonance image (MRI) of the right knee was done several days after admission, to verify a torn right knee meniscal cartilage that had been previously diagnosed. The official MRI radiological report included a small free-edge tear of the posterior horn root junction of the lateral meniscus, chondromalasia (lateral patella and lateral femoral articular cartilage), and a moderate joint effusion with a bursal cyst or dilated semimembranous-gastronemius bursa.
Anticoagulation was achieved on day 6 of the patient's hospitalization. He was discharged on 5 mg of warfarin per day, with instructions to continue the use of compression stockings. The patient was also advised to follow up with his primary physician for regular monitoring, and to avoid contact or collision activities during anticoagulation.
The patient was maintained on warfarin for six months, with weekly physician monitoring (symptoms, PT, INR) for the first three months post-injury. The monitoring interval was changed to once per month for the remainder of the treatment period. Hematologic investigations (APTT, PT, INR, Cardiolipin antibody, C-reactive protein, Lupant anticoagulant, Factor V Leiden, Antithrombin, ANA, Protein C, Protein S, and RPR) were obtained three months post injury. There were no contributory thrombophilic factors found in these investigations. Laboratory levels of Protein C activity 22% (range = 70–140%), Protein S activity 48% (range = 75–140%), INR 2.57 (range = 0.88–1.12), and PT 27.5 sec (range = 9.6–12.0 sec); APTT 38.5 sec (range = 23.4–35.4 sec) were found to be appropriately reactive to the anticoagulant therapy.
The patient underwent two arthrocentesis procedures to remove small amounts of serous fluid from the joint, and each time was injected with a lidocaine/corticosteroid combination. US examinations after the hospitalization period failed to demonstrate a recurrence or new onset of DVT, however residual echogenic material characteristic of a chronic thrombus was demonstrated in the popliteal vein. Compression stocking use was maintained after hospital discharge, and was discontinued after nine months. The patient returned to soccer after anticoagulation, with a full understanding of his increased risk of DVT recurrence.
One-year post injury the patient continued to suffer from intermittent right lower extremity discomfort and swelling often unrelated to activity. An elective arthroscopy was recently performed on the patient's right knee to investigate his long-standing meniscal disruption and effusion. The arthroscopy demonstrated several areas of arthrosis (patellar lateral and medial facets, lateral and medial femoral condyles), and a torn lateral meniscus. Appropriate partial lateral menisectomy and debridement, and chondroplasty of the areas of arthrosis were preformed. An arthroscopic examination of the posterior compartment demonstrated a small cleft-like area just medial to the semimembranosis where the Baker's cyst likely originated. The patient returned to the orthopedist one week post-op with a large (150 cc's) hemarthrosis that was aspirated from the knee. He was requested to follow-up in one month for re-evaluation.
Discussion
This case study illustrates the importance of considering deep vein thrombosis in the diagnosis of sport-related extremity trauma. DVT is classically related to venous stasis, intimal injury, and coagulation diathesis (Virchow's triad). The estimated incidence of DVT from all causes is 0.5 to 1.6 per 1000 persons per year, and may be an underestimation due to the number of DVT that are asymptomatic [2].
Standard risk factors for DVT are immobilization, pregnancy, recent surgery (particularly orthopedic), malignancy, older age, smoking, coagulation deficits or hypercoagulable states, connective tissue disorders, sex steroid administration, severe dehydration, and major trauma. Bates et al. [3] presented a table of the estimated relative risk (RR) for individual DVT risk factors. These factors include inherited conditions (e.g. Factor V Leiden, RR = 50, Antithrombin deficiency, RR = 25, Protein C and S deficiency, RR = 10); acquired conditions (e.g. major surgery or trauma, RR = 5–200; history of venous thromboembolism, RR = 50); and hereditary, environmental, or idiopathic conditions (e.g. hyperhomocysteinemia, and elevated levels of Factor VIII, RR = 3: elevated levels of Factor IX, RR = 2.3).
Coagulation diathesis through congenital or acquired thrombophilia may promote coagulation [3]. Coagulation deficits in previously healthy athletes are becoming increasingly identified through laboratory tests, and must be considered as contributing factors for DVTs [4-7]. Hilberg et al. [6] found that the risk of hereditary exists in elite athletes, corresponds to the general population. These authors proposed that countermeasures (e.g. early anticoagulation during periods of immobilization/injury; single dose of low molecular weight heparin and/or leg exercises on long-distance flights; and avoiding hemoconcentration with adequate hydration) for athletes who are carriers of a congenital coagulation deficit [6].
The testing for hypercoagulable states in an individual after a single episode of thrombosis is a costly, yet routine procedure in many centers. The common assumption that an identified presence of a thrombophilic abnormality increases the risk of recurrence, and justifies prolonged therapy is without clear supportive evidence. A review of the current literature concerning the treatment of individuals with coagulation deficits concludes that there is no clear evidence that modifying treatment because of an identified hypercoaguable state alters the outcome, or that more intensive therapy is required in patients with laboratory evidence of thrombophillia [3].
Exercise is thought to act as a protective mechanism against thrombosis, due to the controlled balance between the exercise activated coagulation and fibrinolytic pathways [8]. Upper extremity thrombosis that is not related to primary diseases or well known risk factors are rare (2–4% of DVTs).This type of thrombosis has been documented in a variety of sports as effort thrombosis or "Paget-Schroetter's syndrome" [9-14]. This syndrome is been described as a primary thrombosis of the subclavicular and axillary veins, usually proceeded by a strenuous effort or repetitive action involving retroversion and hyperabuction of the extremity [10]. Vascular compression by adjoining bone, ligament and muscle or resulting intimal traumas have been documented as contributing factors toward the development of upper and lower extremity thrombosis [15-27].
Lower extremity DVT with a traumatic sporting injury in otherwise healthy active adults is seldom mentioned in the medical literature [16-29]. This lack of reported cases of this type of thrombosis may be due to either underreporting or incorrect diagnosis. Very few cases of sport-related lower extremity DVT involved direct externally trauma [29,30]. There is one case report (Finnish language) that specifically related DVT development to soccer-related trauma [30], and one case report of lower extremity DVT in a soccer player with coagulation deficiencies [31]. There is also one case report in the literature of a traumatic popliteal thrombosis in a hockey player, which resulted in a fatal pulmonary embolism (PE) [29].
The popliteal, posterior tibial and peroneal veins are susceptible to intimal trauma by the sudden hyperextension and torsion that the lower extremity experiences in a soccer "kick" or "tackle" motion. The popliteal arteries and veins are susceptible to direct, sheering, and muscular compressive forces due to their anatomical position, especially with rapid knee hyperextension or anterior dislocation [13,22].
The literature demonstrates the importance and efficacy of a complete bilateral duplex sonography as the primary method of DVT diagnostic investigation [32]. US findings can be augmented by other methods (e.g. evidence-based risk factor analysis) [33,34]. A review of the current literature also suggests the need for comprehensive evidence-based guidelines concerning the use of radiological diagnostic investigations of suspected DVT [35].
Anticoagulation is effective in preventing DVT propagation and PE, but has no chemical fibrinolytic activity. This type of therapy allows for intrinsic fibrinolysis to occur. Radiographically demonstrable clot lysis occurs in only 50% of anticoagulated patients, and the incidence of complete resolution is less than 5%. Intrinsic fibrinolysis that occurs slowly does not preserve the function of the venous valves, which become fibrotic and fixed after a few weeks of being trapped in clot [36].
The symptoms experienced by individuals without complete clot resolution include heavy or achy legs, edema, throbbing paresthesia, purities, numbness, stiffness, and difficulty standing or ambulating. Postthrombotic syndrome (PTS) is characterized by brawny edema of the leg, stasis dermatitis, hyperpigmentation, induration, ulceration and chronic leg pain. This syndrome is associated with an extraordinary level of chronic pain and disability, and approximately 40% of the total cost of treating DVT is spent on PTS [36].
Zeigler et al. [37] investigated the long-term clinical outcome of individuals with a first DVT. These authors found that 82% of the patients suffered from recurrent symptoms, with a mean follow-up period of 6.6 years. Four level DVT, calf vein thrombosis, recurrence of ipsilateral DVT, and a non-sufficient oral anticoagulation are of prognostic significance for developing clinically relevant symptoms within 10 to 20 years after the first DVT [37].
There is growing evidence that the early lysis provided by thrombolytic therapy is more likely to preserve valve function, decreasing the likelihood of DVT recurrence, and the occurrence of PTS [38,39]. Recent trials of new antithrombotic agent used an endpoint of 'symptomatic recurrent DVT', which was defined as the combination of persistent or recurrent symptoms along with the radiographic evidence of primary clot progression or new thrombus formation. The rate of symptomatic recurrent DVT was reported to be between 4–7%, and does not reflect those individuals who simply continue to be symptomatic after the primary event [35].
The general knowledge concerning quality of life and burden of illness in patients with persistent post-DVT symptoms is limited. This issue is especially important to the athletic patient, as participation in sport is usually an extremely important component of quality of life. For routine monitoring of outcomes in chronic venous disorders there are questionnaires that are available [40,41]. Hedner et al. [42] have recently developed an instrument that measures health and treatment-related quality of life factors in DVD patients.
The athlete's primary concern upon the initial DVT diagnosis is return to play. The issue of return to sport after a lower extremity DVTs has only been addressed only once in the literature concerning return to non-contact sport [43]. General guidelines for sedentary individuals allow for a gradual return to return to daily activities over a six week period [43], with no contact activities allowed during the period of anticoagulation. Roberts and Christie [43] provided a theoretical framework, based on the natural history of animal models for the safe and expeditious return of the athlete. These authors suggested a protocol that combines a graduated return to activity and anticoagulation therapy with regular physician based reevaluation [43].
An athlete who wants to return to a contact or collision sport should be informed of the possible increased risk of recurrent DVT that he or she may face, above the current estimates derived from the general population. There is no current evidence in the literature that investigates the specific risk factor of a traumatic collision, and the recurrence of a DVT. This lack of evidence suggests that the patient and physician should work together to make an informed return to play decision involving the patient's current individual risk profile, the likelihood of DVT recurrence, athletic goals, and the perceived importance of the particular sport to quality of life.
The potential limitations of this case study include the lack of testing for prothrobin mutation, and fibrynolitic parameters (level of tPA, PAI-1 or PAI-1 polymorphism 4G/5G).
Competing Interests
The author(s) declare that they have no competing interests.
Author Contributions
PE developed, researched, wrote and revised the case study; RU assisted in study development and manuscript revision; DM assisted in manuscript development and revision; HJ assisted in manuscript development and revision.
Acknowledgements
The authors would like to acknowledge the contribution Jefferey E. Michaelson MD, Ms. Elaine Skopelja MALS, Tsveti P. Markova MD, James Rosebolt MD, Madeleine L. Echlin, Alexia D. Estabrook MSLS, AHIP, Carole M. Gilbert MSLS, AHIP, and Don DeCenzo LTA in the preparation of this paper.
Dr. Upshur is supported by a New Investigator Award from the Canadian Institute of Health Research and a Research Scholar Award from the Department of Family Medicine and Community Medicine, University of Toronto.
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Constans J Boutinet C Salmi RS Saby JC Nelzy ML Baudouin P Sampoux F Marchand JM Boutami C Dehant V Pulci S Gauthier JP Cacareigt-Bourdenx V Barcat D Conri C Comparison of four clinical prediction scores for the diagnosis of lower limb deep venous thrombosis in outpatients Am J Med 2003 115 236 440 10.1016/S0002-9343(03)00432-7
Zierler BK Screening for acute dvt: optimal utilization of vascular diagnostic laboratory Sem Vasc Surg 2001 14 206 214 10.1053/svas.2001.25492
Baldwin ZK Comerota AJ Schwartz LB Catheter-directed thrombolysis for deep-vein thrombosis Vasc Endovasc Surg 2004 38 1 9
Ziegler S Schillinger M Maca TH Minar E Post-thrombotic syndrome after the primary event of deep venous thrombosios 10 to 20 years ago Thromb Res 2001 101 23 33 11342203 10.1016/S0049-3848(00)00370-4
Kahn SR Ginsberg JS The post-thrombotic syndrome: current knowledge, controversies, and directions for future research Blood Reviews 2002 16 155 165 12163001 10.1016/S0268-960X(02)00008-5
Burroughs KE New considerations in the diagnosis and therapy of deep vein thrombosis South Med J 1999 92 517 520 10342901
Mathias SD Prebil LA Putterman CG Chmiel JJ Throm RC Comerota AJ A health-related quality of life measure in patients with deep vein thrombosis: A validation study Drug Info J 1999 33 1173 1187
Lampling DL Scrotter S Kurz X Kahn SR Abenhaim L Evaluation of outcomes in chronic venous disorders of the leg: development of a scientifically rigorous, patient-reported measure of symptoms and quality of life J Vasc Surg 2003 37 410 419 12563215 10.1067/mva.2003.152
Hedner E Carlsson J Kulich KR Stigendal L Ingelgard A Wiklund I An instrument for measuring health-related quality of life in patients with deep vein thrombosis (DVT): development and validation of deep venous thrombosis quality of life (DVTQOL) questionnaire Health and Quality of Life Outcomes 2004 2 30 15214965 10.1186/1477-7525-2-30
Roberts WO Christie DM Return to training and competition after deep vein thrombosis Med Sci Sports Exer 1992 24 2 5
| 15485571 | PMC526763 | CC BY | 2021-01-04 16:36:20 | no | Thromb J. 2004 Oct 14; 2:8 | utf-8 | Thromb J | 2,004 | 10.1186/1477-9560-2-8 | oa_comm |
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-2-81548557110.1186/1477-9560-2-8Case ReportTraumatic deep vein thrombosis in a soccer player: A case study Echlin Paul S [email protected] Ross EG [email protected] Douglas B [email protected] Harsha P [email protected] Providence Athletic Medicine, Providence Hospital and Medical Centers, 47601 Grand River Avenue, Suite 101, Novi Michigan, United States of America 483742 Primary Care Research Unit, Department of Family and Community Medicine, Sunnybrook and Women's College Health Sciences Centre, 2075 Bayview Avenue, #E-349, Toronto, Ontario, Canada M4N 3M53 Department of Family and Community Medicine, University of Toronto, 263 McCaul Street, Toronto, Ontario, Canada M5T 1W74 Department of Public Health Sciences, University of Toronto, 12 Queen's Park Crescent W., Toronto, Ontario, Canada M5S 1A85 Department of Family Medicine, Indiana University, Long Hospital, Second Floor. 1110 West Michigan Street, Indianapolis Indiana, United States of America 46202-51026 Department of Family Medicine, Wayne State University, 15400 West McNichols, 2nd Floor, Detroit, Michigan, United States of America 482352004 14 10 2004 2 8 8 27 4 2004 14 10 2004 Copyright © 2004 Echlin et al; licensee BioMed Central Ltd.2004Echlin et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
A 42 year-old male former semi-professional soccer player sustained a right lower extremity popliteal contusion during a soccer game. He was clinically diagnosed with a possible traumatic deep vein thrombosis (DVT), and sent for confirmatory tests. A duplex doppler ultrasound was positive for DVT, and the patient was admitted to hospital for anticoagulation (unfractionated heparin, warfarin). Upon discharge from hospital the patient continued oral warfarin anticoagulation (six months), and the use of compression stockings (nine months). He followed up with his family doctor at regular intervals for serial coagulation measurements, and ultrasound examinations. The patient's only identified major thrombotic risk factor was the traumatic injury. One year after the initial deep vein thrombosis (DVT) the patient returned to contact sport, however he continued to have intermittent symptoms of right lower leg pain and right knee effusion.
Athletes can develop vascular injuries in a variety of contact and non-contact sports. Trauma is one of the most common causes of lower extremity deep vein thrombosis (DVT), however athletic injuries involving lower extremity traumatic DVT are seldom reported. This diagnosis and the associated risk factors must be considered during the initial physical examination. The primary method of radiological diagnosis of lower extremity DVT is a complete bilateral duplex sonography, which can be augmented by other methods such as evidence-based risk factor analysis. Antithrombotic medication is the current standard of treatment for DVT. Acute thrombolytic treatment has demonstrated an improved therapeutic efficacy, and a decrease in post-DVT symptoms.
There is a lack of scientific literature concerning the return to sport protocol following a DVT event. Athletic individuals who desire to return to sport after a DVT need to be fully informed about their treatment and risk of reoccurrence, so that appropriate decisions can be made.
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Introduction
Athletes are susceptible to a variety of vascular injuries, secondary to either repetitive motion, or high-speed collisions [1]. The differential diagnosis for lower extremity trauma in sport seldom invites a diagnosis of vascular injury, such as a deep vein thrombosis (DVT). Failure of the physician to recognize a vascular injury can have catastrophic limb or life threatening (pulmonary embolism) implications. The epidemiology, diagnosis, treatment, and recurrence of DVT, as well as the prevention of post-thrombotic symptoms are the most current areas of clinical research. Research-based guidelines concerning an athlete's return to sport after a DVT is an important area for future investigation.
Case Report
A 42 year old Polish born male former semi-professional soccer player was seen on May 16th, 2003 in the emergency department, with the chief complaint of right leg pain. The patient had been playing soccer 10 days prior to this visit, and recalled a traumatic "tackle" injury to the posterior area of his right lower extremity. He denied experiencing any sensation of tearing or popping in the right knee during the index trauma, and was able to complete the game with only minor discomfort. On day 3 post-injury the patient noted severe pain in his knee and calf with ambulation. The patient visited his primary doctor on post-injury day 8 and was diagnosed with a right lower extremity soft tissue injury. A right lower extremity echo-doppler ultrasound (US), and a semi-quantitative D-dimer automated latex procedure were ordered to rule out a vascular disorder. The US investigation demonstrated a DVT in the distal femoral, popliteal, and distal calf veins, with a heterogenous mass (5 cm × 3 cm × 4 cm, resembling a hematoma) without a doppler signal in the right popliteal fossa. The D-dimer result was also positive for a suspected thrombosis (1.0–2.0 ug/ml; range = <0.25 ug/ml). The patient was instructed by his physician to proceed immediately to the emergency department for further evaluation and treatment.
The past medical and family history of the patient was non-contributory for a history of thrombophilia or other thrombotic major risk factors. The patient had a remote (11 years old) surgical history of a right-sided inguinal hernia that could have created scar tissue contributing to vascular obstruction and stasis. The initial emergency department examination demonstrated an exquisitely tender right calf with a 3 cm difference in mid-calf girth (10 cm. distal from each inferior patellar pole); a 1+ right knee supra patellar effusion; and a palpable popliteal mass with visible ecchymosis. Laboratory tests (CBC, Lytes, PT, PTT, ESR, CPK, Anti-throbomin, Factor V Leiden, Lupus Screen, ANA, Anti-Cardiolipin, Protein C, and Protein S) were negative for metabolic, hematological or familial abnormalities. A repeat US investigation confirmed the results of the previous outpatient results. The patient was anticoagulated simultaneously with unfractionated heparin and Warfarin sulfate. A multiview plain film x-ray examination of the right lower extremity demonstrated no fracture, dislocation, or bony mass.
A magnetic resonance image (MRI) of the right knee was done several days after admission, to verify a torn right knee meniscal cartilage that had been previously diagnosed. The official MRI radiological report included a small free-edge tear of the posterior horn root junction of the lateral meniscus, chondromalasia (lateral patella and lateral femoral articular cartilage), and a moderate joint effusion with a bursal cyst or dilated semimembranous-gastronemius bursa.
Anticoagulation was achieved on day 6 of the patient's hospitalization. He was discharged on 5 mg of warfarin per day, with instructions to continue the use of compression stockings. The patient was also advised to follow up with his primary physician for regular monitoring, and to avoid contact or collision activities during anticoagulation.
The patient was maintained on warfarin for six months, with weekly physician monitoring (symptoms, PT, INR) for the first three months post-injury. The monitoring interval was changed to once per month for the remainder of the treatment period. Hematologic investigations (APTT, PT, INR, Cardiolipin antibody, C-reactive protein, Lupant anticoagulant, Factor V Leiden, Antithrombin, ANA, Protein C, Protein S, and RPR) were obtained three months post injury. There were no contributory thrombophilic factors found in these investigations. Laboratory levels of Protein C activity 22% (range = 70–140%), Protein S activity 48% (range = 75–140%), INR 2.57 (range = 0.88–1.12), and PT 27.5 sec (range = 9.6–12.0 sec); APTT 38.5 sec (range = 23.4–35.4 sec) were found to be appropriately reactive to the anticoagulant therapy.
The patient underwent two arthrocentesis procedures to remove small amounts of serous fluid from the joint, and each time was injected with a lidocaine/corticosteroid combination. US examinations after the hospitalization period failed to demonstrate a recurrence or new onset of DVT, however residual echogenic material characteristic of a chronic thrombus was demonstrated in the popliteal vein. Compression stocking use was maintained after hospital discharge, and was discontinued after nine months. The patient returned to soccer after anticoagulation, with a full understanding of his increased risk of DVT recurrence.
One-year post injury the patient continued to suffer from intermittent right lower extremity discomfort and swelling often unrelated to activity. An elective arthroscopy was recently performed on the patient's right knee to investigate his long-standing meniscal disruption and effusion. The arthroscopy demonstrated several areas of arthrosis (patellar lateral and medial facets, lateral and medial femoral condyles), and a torn lateral meniscus. Appropriate partial lateral menisectomy and debridement, and chondroplasty of the areas of arthrosis were preformed. An arthroscopic examination of the posterior compartment demonstrated a small cleft-like area just medial to the semimembranosis where the Baker's cyst likely originated. The patient returned to the orthopedist one week post-op with a large (150 cc's) hemarthrosis that was aspirated from the knee. He was requested to follow-up in one month for re-evaluation.
Discussion
This case study illustrates the importance of considering deep vein thrombosis in the diagnosis of sport-related extremity trauma. DVT is classically related to venous stasis, intimal injury, and coagulation diathesis (Virchow's triad). The estimated incidence of DVT from all causes is 0.5 to 1.6 per 1000 persons per year, and may be an underestimation due to the number of DVT that are asymptomatic [2].
Standard risk factors for DVT are immobilization, pregnancy, recent surgery (particularly orthopedic), malignancy, older age, smoking, coagulation deficits or hypercoagulable states, connective tissue disorders, sex steroid administration, severe dehydration, and major trauma. Bates et al. [3] presented a table of the estimated relative risk (RR) for individual DVT risk factors. These factors include inherited conditions (e.g. Factor V Leiden, RR = 50, Antithrombin deficiency, RR = 25, Protein C and S deficiency, RR = 10); acquired conditions (e.g. major surgery or trauma, RR = 5–200; history of venous thromboembolism, RR = 50); and hereditary, environmental, or idiopathic conditions (e.g. hyperhomocysteinemia, and elevated levels of Factor VIII, RR = 3: elevated levels of Factor IX, RR = 2.3).
Coagulation diathesis through congenital or acquired thrombophilia may promote coagulation [3]. Coagulation deficits in previously healthy athletes are becoming increasingly identified through laboratory tests, and must be considered as contributing factors for DVTs [4-7]. Hilberg et al. [6] found that the risk of hereditary exists in elite athletes, corresponds to the general population. These authors proposed that countermeasures (e.g. early anticoagulation during periods of immobilization/injury; single dose of low molecular weight heparin and/or leg exercises on long-distance flights; and avoiding hemoconcentration with adequate hydration) for athletes who are carriers of a congenital coagulation deficit [6].
The testing for hypercoagulable states in an individual after a single episode of thrombosis is a costly, yet routine procedure in many centers. The common assumption that an identified presence of a thrombophilic abnormality increases the risk of recurrence, and justifies prolonged therapy is without clear supportive evidence. A review of the current literature concerning the treatment of individuals with coagulation deficits concludes that there is no clear evidence that modifying treatment because of an identified hypercoaguable state alters the outcome, or that more intensive therapy is required in patients with laboratory evidence of thrombophillia [3].
Exercise is thought to act as a protective mechanism against thrombosis, due to the controlled balance between the exercise activated coagulation and fibrinolytic pathways [8]. Upper extremity thrombosis that is not related to primary diseases or well known risk factors are rare (2–4% of DVTs).This type of thrombosis has been documented in a variety of sports as effort thrombosis or "Paget-Schroetter's syndrome" [9-14]. This syndrome is been described as a primary thrombosis of the subclavicular and axillary veins, usually proceeded by a strenuous effort or repetitive action involving retroversion and hyperabuction of the extremity [10]. Vascular compression by adjoining bone, ligament and muscle or resulting intimal traumas have been documented as contributing factors toward the development of upper and lower extremity thrombosis [15-27].
Lower extremity DVT with a traumatic sporting injury in otherwise healthy active adults is seldom mentioned in the medical literature [16-29]. This lack of reported cases of this type of thrombosis may be due to either underreporting or incorrect diagnosis. Very few cases of sport-related lower extremity DVT involved direct externally trauma [29,30]. There is one case report (Finnish language) that specifically related DVT development to soccer-related trauma [30], and one case report of lower extremity DVT in a soccer player with coagulation deficiencies [31]. There is also one case report in the literature of a traumatic popliteal thrombosis in a hockey player, which resulted in a fatal pulmonary embolism (PE) [29].
The popliteal, posterior tibial and peroneal veins are susceptible to intimal trauma by the sudden hyperextension and torsion that the lower extremity experiences in a soccer "kick" or "tackle" motion. The popliteal arteries and veins are susceptible to direct, sheering, and muscular compressive forces due to their anatomical position, especially with rapid knee hyperextension or anterior dislocation [13,22].
The literature demonstrates the importance and efficacy of a complete bilateral duplex sonography as the primary method of DVT diagnostic investigation [32]. US findings can be augmented by other methods (e.g. evidence-based risk factor analysis) [33,34]. A review of the current literature also suggests the need for comprehensive evidence-based guidelines concerning the use of radiological diagnostic investigations of suspected DVT [35].
Anticoagulation is effective in preventing DVT propagation and PE, but has no chemical fibrinolytic activity. This type of therapy allows for intrinsic fibrinolysis to occur. Radiographically demonstrable clot lysis occurs in only 50% of anticoagulated patients, and the incidence of complete resolution is less than 5%. Intrinsic fibrinolysis that occurs slowly does not preserve the function of the venous valves, which become fibrotic and fixed after a few weeks of being trapped in clot [36].
The symptoms experienced by individuals without complete clot resolution include heavy or achy legs, edema, throbbing paresthesia, purities, numbness, stiffness, and difficulty standing or ambulating. Postthrombotic syndrome (PTS) is characterized by brawny edema of the leg, stasis dermatitis, hyperpigmentation, induration, ulceration and chronic leg pain. This syndrome is associated with an extraordinary level of chronic pain and disability, and approximately 40% of the total cost of treating DVT is spent on PTS [36].
Zeigler et al. [37] investigated the long-term clinical outcome of individuals with a first DVT. These authors found that 82% of the patients suffered from recurrent symptoms, with a mean follow-up period of 6.6 years. Four level DVT, calf vein thrombosis, recurrence of ipsilateral DVT, and a non-sufficient oral anticoagulation are of prognostic significance for developing clinically relevant symptoms within 10 to 20 years after the first DVT [37].
There is growing evidence that the early lysis provided by thrombolytic therapy is more likely to preserve valve function, decreasing the likelihood of DVT recurrence, and the occurrence of PTS [38,39]. Recent trials of new antithrombotic agent used an endpoint of 'symptomatic recurrent DVT', which was defined as the combination of persistent or recurrent symptoms along with the radiographic evidence of primary clot progression or new thrombus formation. The rate of symptomatic recurrent DVT was reported to be between 4–7%, and does not reflect those individuals who simply continue to be symptomatic after the primary event [35].
The general knowledge concerning quality of life and burden of illness in patients with persistent post-DVT symptoms is limited. This issue is especially important to the athletic patient, as participation in sport is usually an extremely important component of quality of life. For routine monitoring of outcomes in chronic venous disorders there are questionnaires that are available [40,41]. Hedner et al. [42] have recently developed an instrument that measures health and treatment-related quality of life factors in DVD patients.
The athlete's primary concern upon the initial DVT diagnosis is return to play. The issue of return to sport after a lower extremity DVTs has only been addressed only once in the literature concerning return to non-contact sport [43]. General guidelines for sedentary individuals allow for a gradual return to return to daily activities over a six week period [43], with no contact activities allowed during the period of anticoagulation. Roberts and Christie [43] provided a theoretical framework, based on the natural history of animal models for the safe and expeditious return of the athlete. These authors suggested a protocol that combines a graduated return to activity and anticoagulation therapy with regular physician based reevaluation [43].
An athlete who wants to return to a contact or collision sport should be informed of the possible increased risk of recurrent DVT that he or she may face, above the current estimates derived from the general population. There is no current evidence in the literature that investigates the specific risk factor of a traumatic collision, and the recurrence of a DVT. This lack of evidence suggests that the patient and physician should work together to make an informed return to play decision involving the patient's current individual risk profile, the likelihood of DVT recurrence, athletic goals, and the perceived importance of the particular sport to quality of life.
The potential limitations of this case study include the lack of testing for prothrobin mutation, and fibrynolitic parameters (level of tPA, PAI-1 or PAI-1 polymorphism 4G/5G).
Competing Interests
The author(s) declare that they have no competing interests.
Author Contributions
PE developed, researched, wrote and revised the case study; RU assisted in study development and manuscript revision; DM assisted in manuscript development and revision; HJ assisted in manuscript development and revision.
Acknowledgements
The authors would like to acknowledge the contribution Jefferey E. Michaelson MD, Ms. Elaine Skopelja MALS, Tsveti P. Markova MD, James Rosebolt MD, Madeleine L. Echlin, Alexia D. Estabrook MSLS, AHIP, Carole M. Gilbert MSLS, AHIP, and Don DeCenzo LTA in the preparation of this paper.
Dr. Upshur is supported by a New Investigator Award from the Canadian Institute of Health Research and a Research Scholar Award from the Department of Family Medicine and Community Medicine, University of Toronto.
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Zierler BK Screening for acute dvt: optimal utilization of vascular diagnostic laboratory Sem Vasc Surg 2001 14 206 214 10.1053/svas.2001.25492
Baldwin ZK Comerota AJ Schwartz LB Catheter-directed thrombolysis for deep-vein thrombosis Vasc Endovasc Surg 2004 38 1 9
Ziegler S Schillinger M Maca TH Minar E Post-thrombotic syndrome after the primary event of deep venous thrombosios 10 to 20 years ago Thromb Res 2001 101 23 33 11342203 10.1016/S0049-3848(00)00370-4
Kahn SR Ginsberg JS The post-thrombotic syndrome: current knowledge, controversies, and directions for future research Blood Reviews 2002 16 155 165 12163001 10.1016/S0268-960X(02)00008-5
Burroughs KE New considerations in the diagnosis and therapy of deep vein thrombosis South Med J 1999 92 517 520 10342901
Mathias SD Prebil LA Putterman CG Chmiel JJ Throm RC Comerota AJ A health-related quality of life measure in patients with deep vein thrombosis: A validation study Drug Info J 1999 33 1173 1187
Lampling DL Scrotter S Kurz X Kahn SR Abenhaim L Evaluation of outcomes in chronic venous disorders of the leg: development of a scientifically rigorous, patient-reported measure of symptoms and quality of life J Vasc Surg 2003 37 410 419 12563215 10.1067/mva.2003.152
Hedner E Carlsson J Kulich KR Stigendal L Ingelgard A Wiklund I An instrument for measuring health-related quality of life in patients with deep vein thrombosis (DVT): development and validation of deep venous thrombosis quality of life (DVTQOL) questionnaire Health and Quality of Life Outcomes 2004 2 30 15214965 10.1186/1477-7525-2-30
Roberts WO Christie DM Return to training and competition after deep vein thrombosis Med Sci Sports Exer 1992 24 2 5
| 15516266 | PMC526764 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Oct 29; 1:35 | latin-1 | Retrovirology | 2,004 | 10.1186/1742-4690-1-35 | oa_comm |
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Reprod HealthReproductive Health1742-4755BioMed Central London 1742-4755-1-61550713710.1186/1742-4755-1-6ReviewGenomic imprinting and assisted reproduction Paoloni-Giacobino Ariane [email protected] J Richard [email protected] Department of Molecular Genetics and Biochemistry, University of Pittsburgh, W1007 Biomedical Science Tower, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA2004 26 10 2004 1 6 6 11 8 2004 26 10 2004 Copyright © 2004 Paoloni-Giacobino and Chaillet; licensee BioMed Central Ltd.2004Paoloni-Giacobino and Chaillet; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Imprinted genes exhibit a parent-of-origin specific pattern of expression. Such genes have been shown to be targets of molecular defects in particular genetic syndromes such as Beckwith-Wiedemann and Angelman syndromes. Recent reports have raised concern about the possibility that assisted reproduction techniques, such as in vitro fertilization or intracytoplasmic sperm injection, might cause genomic imprinting disorders. The number of reported cases of those disorders is still too small to draw firm conclusions and the safety of these widely used assisted reproduction techniques needs to be further evaluated.
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Introduction
The first in vitro fertilization (IVF) baby was born in 1978 and intracytoplasmic sperm injection (ICSI) was introduced in 1992 for the treatment of male infertility. Both these techniques have been continually amended and access to them improved for infertile couples. Indeed, assisted reproduction now accounts for 1% to 3% of births in developed countries [1]. Until recently, these techniques were considered accurate substitutes for natural oocyte fertilization, and were therefore regarded as safe. However, reports of children conceived by assisted reproduction techniques (ART), and presenting with congenital anomalies have been published over the last 3 years. Even though the number of reported cases indicating a link between ART and congenital anomalies is still small, the safety of these techniques needs to be evaluated. In particular, the relationship between ART and the occurrence of imprinting defects needs to be clarified.
Epigenetics and DNA methylation
Epigenetic modifications are reversible changes of the DNA methylation pattern and chromatin structure that can affect gene expression. In many instances, epigenetic changes governing gene expression can be passed from cell to cell or from parent to offspring. Epigenetic modifications themselves might therefore explain how environmental factors modulate gene expression without affecting the genetic code. The most researched epigenetic phenomenon is DNA methylation [2].
DNA methylation is a covalent modification in which methyl groups are added to cytosine bases located 5' of guanosines (within cytosine-phospho-guanine (CpG) dinucleotides sequences). Methylation is catalyzed by the DNA cytosine-5-methyltransferase (DNA-MTase) enzyme family. Methylation induces changes in chromatin structure and is generally associated with silencing of gene expression, thus providing a way to control gene expression [3]. Indeed, methylation patterns are the result of complex interactions between de novo methylation, the maintenance of existing methylation and demethylation [4].
Imprinting
Genomic imprinting is an epigenetic phenomenon by which the expression of a gene is determined by its parental origin. Only one allele of an imprinted gene is expressed. Imprinting is controlled by DNA methylation in such a way that a difference in methylation between the maternal and paternal alleles correlates with the different expression of the two parental alleles.
It is estimated that the total number of imprinted genes in the human and mouse genomes ranges between 100 and 200 [5]. Imprinted genes are more often grouped into clusters than scattered throughout the genome and this organization most likely reflects a coordinated way of gene regulation in a chromosomal region [6]. Two features are characteristic, although not specific, to imprinted genes. The first one is the unusual richness in CpG islands onto which imprinted patterns of methylation are placed, and the second one is the presence of clustered direct repeats near or within the CpG islands [7].
Imprinting in development
In order to ensure that every generation receives the appropriate sex-specific imprint, the genome undergoes reprogramming. Epigenetic reprogramming has been shown to occur during gametogenesis and during preimplantation development [6]. During the development of primordial germ cells (PGC), imprinted methylation patterns are removed by a mechanism of erasure [8]. Both, passive and active demethylation may occur, although no active demethylating enzymes have yet been identified. The timing of erasure in PGCs is thought to be crucial. Studies in mice showed that erasure occurred when primordial germ cells enter into the gonads [8,9]. Erasure is followed by the establishment of sex-specific patterns of methylation during gametogenesis. Imprint establishment during gametogenesis occurs at different times in the male and female germ lines. In males it is completed by the haploid (meiotic) phase of spermatogenesis whereas in females imprint acquisition occurs in oocytes around the time of completion of the first meiotic division [5]. Furthermore, it seems that at least in oocytes, methylation might be acquired at different times (asynchronous) for different genes [5]. Epigenetic reprogramming is important for accurate development, as it controls expression of early embryonic genes, cell cleavage and cell determination in the early embryo [10].
Further genome reprogramming occurs during the preimplantation embryonic stage with epigenetic changes taking place through demethylation in non-imprinted genes in maternal and paternal genomes. This is followed by a genome-wide methylation at the time of implantation. The different stages of imprint establishment, maintenance and manipulations possibly disturbing them are illustrated in Figure 1. Genomic imprinting defects might indeed occur at any stage of the reprogramming process, such as during imprinting erasure, acquisition or maintenance.
Figure 1 ART and possible imprinting defects. Possible interactions between different steps of assisted reproduction procedures and imprint establishment or maintenance through different stages of development. PGC: primordial germ cell.
The main consequence of the sex-specific establishment and maintenance of imprinted methylation patterns is the creation of maternal- and paternal-allele methylation differences (differentially methylated domains or DMDs) in or around imprinted genes. A primary DMD is established during gametogenesis and secondary DMDs develop during embryogenesis, most likely due to a direct influence of a nearby primary DMD [11].
Imprinted genes are implicated in the regulation of embryonic and fetal growth, as well as many aspects of placental function, including placental growth and the activity of transplacental transport systems [12]. Indeed, in ruminants, such as sheep and cattle, a particular overgrowth syndrome known as "large offspring syndrome" (LOS) was reported after in vitro culture of embryos. LOS is caused by abnormal methylation of the IGF2R gene [13]. Imprinted genes are also involved in postnatal behavior development. Based on the functions of imprinted genes, disruption of normal imprinting can have predictable consequences such as embryonic death, excessive, defective or impaired fetal growth.
Imprinting defect syndromes in human
Several human syndromes are known to be associated with defects in gene imprinting, including Prader-Willi, Angelman, Beckwith-Wiedemann, Silver-Russell and Albright hereditary oseodystrophy syndromes [1]. Aberrant imprinting might also play a role in cancers and neuro-behavioral disorders such as autism.
The Beckwith-Wiedemann syndrome (BWS), whose frequency in the general population is about 1/14,000, is characterized by somatic overgrowth, congenital malformations and a predisposition to embryonic neoplasia. The majority of cases occur sporadically. In up to 60% of sporadic cases, the epigenetic changes occur at differentially methylated regions within 11p15.5 in a region of approximately 1 Mb. This region contains an imprinted cluster of at least 12 genes, including the paternally expressed genes IGF2 and KCNQ1OT1, and the maternally expressed genes H19, CDKN1C and KCNQ1 [14]. Approximately 25 to 50% of BWS patients have biallelic expression of the IGF2 gene, and some of these cases exhibit loss of imprinting (LOI) of IGF2 which is dependent on hypermethylation changes of H19 [14]. Approximately 50% of sporadic BWS have a loss of methylation associated to a LOI at KCNQ1OT1, an untranslated RNA within the KCNQ1 gene [15]. Some BWS cases exhibit LOI for KCNQ1OT1 as well as LOI for IGF2 [14]. It has been shown in BWS patients that aberrant methylation of KCNQ1OT1 is specifically associated with overgrowth and congenital defects, whereas aberrant methylation of H19 is specifically associated with an increased risk of developing tumors [16].
The Prader-Willi and Angelman syndromes (PWS/AS) are typical examples of imprinting dysregulations leading to severe neuro-behavioral disturbances. Their frequencies in the general population are approximately 1/10,000 and 1/15,000, respectively. The domain involved in these two pathologies is a 2 Mb domain on the 15q11–13 chromosomal region, including genes as SNRPN, UBE3A, ZNF127, IPW and NDN. The small percentage of AS cases (<5%) associated with methylation defect involves loss of methylation within the SNRPN imprinting center (IC) and defective expression or silencing of maternally expressed genes within this region. However, the methylation defect associated with PWS involves methylation within the SNRPN IC and a defective expression or silencing of paternally expressed genes within the same region. The IC comprises 2 regulatory regions: the PWS-shortest region of overlap (SRO) and the AS-SRO [17]. PWS-SRO and AS-SRO seem to operate in a stepwise way to establish imprinting during the early developmental stages [18]. Indeed, imprinting at the AS-SRO might cause maternal allele-specific repression of the PWS-SRO, preventing activation of the corresponding genes [17].
In addition, imprinting may have a wider impact on neurological development and behavior. Some reports suggest parent-specific imprinting defect in common neuro-behavioral disorders. Autism, bipolar affective disorder, schizophrenia [19] and other complex neuro-behavioral phenotypes such as alcohol abuse and audiogenic seizures [20] may be linked to imprinting disturbances. The transmission of abnormalities has been shown to be dependent upon which parent transmits the disease susceptibility. Such parent-of-origin effects on disease manifestation may be explained by a number of genetic mechanisms, one of them being genomic imprinting [21]. For instance, a lower age of onset of symptoms following paternal inheritance of one subtype of schizophrenia and following maternal inheritance of Tourette's syndrome suggests that imprinted genes are involved in the pathophysiology of these syndromes. Similarly, parent-specific components for late-onset Alzheimer's disease (paternal-specific component) or familial neural tube defects (maternal-specific component) have been described [20].
Cases of defective imprinting in ART conceptions
Prior to the establishment of sex-specific imprints in male and female germ cell lineages, imprints are erased. After erasure of the pre-existing imprints, the timing of acquisition of imprints is significantly different between the two germ lines [6]. In the female germ line, methylation occurs in the postnatal growth phase while oocytes are arrested at the diplotene stage of prophase I [22], whereas during spermatogenesis, methylation takes place before meiosis [23]. Maternal imprints are continually established as oocytes mature in females, and paternal imprints are established as long as spermatogonia proliferate in males. Thus, paternal imprints seem to be established earlier than maternal ones. It has been shown that this sex-specific methylation is intrinsic and cell-autonomous, and is not due to any influence of the genital ridge somatic cells, or gonadal environment on the primordial germ cells [24]. Imprinting defects in the course of assisted reproduction could theoretically occur during several stages of the methylation erasure/re-methylation process in male and female germ cells as well as during the early stages of in vitro embryonic development.
The first baby conceived by IVF was born 26 years ago. Intracytoplasmic sperm injection (ICSI), developed approximately 10 years ago, was seen to be the reproductive solution for severe male infertility. Several studies have established the general safety of both IVF and ICSI [25]. Nevertheless, it was recently reported that IVF and ICSI may be associated with an increased risk of major birth defects. Schieve et al. [26] studied 42 463 infants conceived with assisted reproductive techniques and reported a higher occurrence of low (less-than-or-equal 2500 g) and very low (less-than 1500 g) birth weight in this group compared to the control population of children naturally conceived. Hansen et al. [27] in a study on 837 infants conceived by IVF and 301 infants conceived by ICSI, reported rates of major birth defects (musculoskeletal, cardiovascular, urogenital, gastrointestinal, central nervous system, metabolic and poorly defined ones), as high as 9.0% for IVF and 8.6% for ICSI conceptions, compared to 4.2% reported for natural conceptions. A possible link with imprinting disturbances was not considered by the authors. These results were in part due to the increase in multiple pregnancies, known to be associated with ART, but also due to a higher rate of low birth weight babies among singleton pregnancies. In addition to these associated defects, a higher incidence of sex-chromosome aneuploidy has also been reported in ART conceptions [27].
DeBaun et al. [28] recently reported 7 cases of BWS conceived by ART, 6 of those showing an imprinting defect at KCNQ1OT1 or H19. By comparing this rate of ART-conceived BWS to the rate of ART in the general population during the same time period, sporadic cases of BWS were approximately six times more likely to have been conceived by ART than by natural conception. The authors suggested that causative factors may include the in-vitro culture conditions or the exposure of the gametes or embryos to specific media or growth factors.
Maher et al. reviewed a different set of sporadic BWS cases and looked for an association with ART [29]. Six out of the 149 BWS cases examined were conceived by ART, and 2 of these had a KCNQ1OT1 loss of imprinting as the causative molecular defect. Indeed, when compared to the incidence in the general population, ART had a four-fold greater likelihood of being associated with BWS than natural conception. The cases reported by DeBaun et al. [28] and Maher et al. [29] were recruited through registries of BWS patients. However, parents with BWS babies born after ART may be more likely to join BWS registries, which could introduce bias when using these registries.
Recently, a case-control study analyzed the frequency of BWS in 1'316'500 live births and 14'894 babies born after an IVF procedure [30]. The risk of BWS was reported to be 9 times higher in the IVF population compared to the general population.
Cox et al. [32] and Orstavik et al. [33] reported a total of 3 children with Angelman syndrome conceived by ICSI. In all 3 cases, AS was due to loss of imprinting within SNRPN gene at 15q11–13. Considering that the occurrence of AS in the general population is about 1/15,000 and that <5% of cases are due to epigenetic imprinting defects, these reports suggest that the predominant abnormalities seen in ART are epigenetic rather than genetic.
However, no evidence of abnormal methylation patterns at 15q11–13, the locus linked to the pathogenesis of AS and PWS, was found in 92 children conceived by ICSI [31].
Why might ART be harmful for the imprints
For assisted reproduction by intracytoplasmic sperm injection (ICSI), the injection of a spermatozoon into the ovum by micro-manipulation bypasses several of the steps involved in fertilization. However, in male germ cells, it seems that the paternal imprints are well established in the mature, meiotic stages of spermatogenesis. Furthermore, round spermatid microinjections have confirmed that paternal imprints are completely established in primary spermatocytes [34]. This point is relevant to the recent use of ICSI using round spermatids. Manning et al. [35] have analyzed the methylation pattern in immature testicular sperm cells at different developmental stages at the 15q11–13 imprinted region and reported that the ejaculated spermatozoa and elongated spermatids had completed the establishment of paternal methylation imprints. However, spermatozoa used for ICSI generally originate from men with abnormal semen parameters that may have had adversely affected the establishment of imprints. Moreover, immature spermatozoa for ICSI can also be directly collected from the testes of infertile males. It has been hypothesized that spermatozoa from men with fertility problems contain a higher number of gametes with chromosomal abnormalities [36]. A defect in gene imprinting can be considered as a possible sperm abnormality. Indeed, a recent report has analyzed the imprinting of two opposite imprinted genes (MEST and H19) in spermatozoon DNA from normozoospermic and oligozoospermic patients. The data presented suggest an association between abnormal genomic imprinting and hypospermatogenesis [37]. Theoretically, it is possible that freezing of mature sperm or the cryoprotectants used might disturb the established male imprints in mature spermatozoa or round spermatids.
Women with a variety of fertility problems, such as ovarian failure and/or hormonal disturbances, may be more prone to produce gametes with inherent imprinting defects because of the establishment of maternal imprints during the final phase of oocyte growth and meiotic maturation.
Although biologically plausible, this is purely speculative at the moment.
In addition to the theoretical possibility that there may be innate defects in oocytes used in ART, the in vitro treatment of oocytes and embryos during ART procedures might affect the establishment of imprints in female germ cells. For example, superovulation or in vitro maturation of oocytes might affect the establishment of the complete array of normal maternal imprints. Oocytes used for assisted reproduction usually originate from women who undergo hormonal hyperstimulation protocol followed by fertilization in vitro. It is not clear to date if the clinical use of high doses of gonadotrophins might alter imprint acquisition. Gonadotrophins might cause the premature release of immature oocytes that have not completed the establishment of their imprints, and establishment may not be completed during in vitro maturation. Shi and Haaf [38] determined the possible incidence of abnormal methylation patterns in mice embryos from superovulated compared to non-superovulated female mice. An immunostaining method was used to assess the overall extent of genomic cytosine methylation and reported abnormal methylation patterns in 2-cell embryos from superovulated females as compared to non-superovulated ones. Kerjean et al [39] explored in mice whether maternal imprinting progresses normally when oocytes are cultured in vitro. The authors analyzed the DMDs of 3 imprinted genes and reported that indeed in vitro culture affected imprint establishment and might lead to loss of methylation at certain imprinted loci, such as IGF2R and gain of methylation at other loci, such as H19. However, to our knowledge, no data concerning the possible effects of ovarian hyperstimulation on imprinting in humans is available yet.
Potential disruption of normal imprinting could result from the in vitro manipulation of early stage embryos. In vitro culture with the use of slightly different culture media led to decreased fetal viability and imprinting disturbances in mice. Doherty et al. [40] first reported the differential affects of culture media in preimplantation mouse embryos at the H19 imprinted gene. The loss of methylation at H19 gene was associated with culture in Whitten's media, resulting in LOI in the imprinting control domain upstream of the start of H19 transcription. Khosla et al. [41] examined mouse preimplantation mouse embryos cultured in different culture media and transferred into recipient mothers. Fetal development as well as the expression pattern of imprinted genes, including the IGF2 and H19 genes, was influenced by the addition of fetal calf serum (FCS) in the culture media. The mechanism by which culture media and other gamete or embryo handling might induce defects and lack of maintenance of methylation at imprinted loci is not clear. It may be due to the facilitation of removal of methyl groups on cytosine bases or the disturbance of the gamete development leading to incompleteness of imprint erasure and/or establishment [10]. Furthermore, cryopreservation of embryos could potentially affect the cytoskeleton, chromatin structure and the availability of methylating and/or demethylating enzymes during preimplantation development. However, it is not known at present if culture of human preimplantation embryos in different media or over longer periods – might lead to disturbances in genomic imprinting.
Disturbances in imprinting could affect the germline cells of the embryo conceived by assisted reproduction and the problems of imprinting might occur in the offspring of the subsequent generation [10]. Follow-up of these individuals may give important information about the possible risks associated with ART.
Imprinting and placenta
A critical way of regulating intrauterine development is through placental function and growth. Most imprinted genes are expressed in fetal and placental tissues, and are involved in fetal growth [12]. In general, paternally expressed imprinted genes enhance fetal growth whereas maternally expressed imprinted ones suppress it [6]. Among the genes expressed in the placenta, the MASH2 gene was shown to regulate the development of spongiotrophoblast [42]. Igf2 transcripts are found specifically in the labyrinthine trophoblast [43], and ASCL2 is a transcription factor expressed in the spongiotrophoblast and labyrinthine layers [5]. Indeed, mice with deletions of IGF2 and ASCL2 genes showed fetal growth restriction and death during embryonic development [43,42].
In humans, several imprinting disorders are associated with intrauterine growth restriction (IUGR) [44]. Studies on human placental imprinted genes and on the different roles of the maternally and paternally expressed genes are certainly needed to understand the placenta's role in normal embryonic and fetal development. Furthermore, analyses of placental samples obtained after ART conceptions might provide answers to some important questions about the possible links between ART and genomic imprinting.
Conclusion
Concern has been raised about the possible increased incidence of genetic syndromes due to imprinting defects in children conceived by assisted reproduction. In particular, experimental reports in mice have raised the question that some of the steps involved in these techniques, such as ovarian hyperstimulation or certain culture media for in vitro culture of embryos might be detrimental to the formation of genomic imprints. In order to be able to adequately counsel infertile couples enquiring about ART, solid evidence from large, well-designed studies as well as cautious long-term evaluation of the safety of these techniques need to be available. Although the unraveling of the mechanisms underlying genomic imprinting is only at the beginning, there is a clear need to investigate and better understand the regulation of this process during fecundation and embryogenesis.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Both authors contributed to the writing of this review and both read and approved the final manuscript.
Acknowledgements
APG acknowledges the Fondation Suisse pour les Bourses en Médecine et Biologie and the Eugenio Litta Foundation.
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| 15507137 | PMC526765 | CC BY | 2021-01-04 16:38:10 | no | Reprod Health. 2004 Oct 26; 1:6 | utf-8 | Reprod Health | 2,004 | 10.1186/1742-4755-1-6 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1651550714010.1186/1471-2105-5-165Methodology ArticleIdentification and utilization of inter-species conserved (ISC) probesets on Affymetrix human GeneChip® platforms for the optimization of the assessment of expression patterns in non human primate (NHP) samples Wang Zhining [email protected] Mark G [email protected] Martin E [email protected] Alma [email protected] Maryanne T [email protected] Henry M Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland 20850, USA2 Division of Retrovirology, Walter Reed Army Institute of Research, Washington, D. C. 20850, USA3 Bioqual, Rockville, MD 20850, USA2004 26 10 2004 5 165 165 15 7 2004 26 10 2004 Copyright © 2004 Wang et al; licensee BioMed Central Ltd.2004Wang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
While researchers have utilized versions of the Affymetrix human GeneChip® for the assessment of expression patterns in non human primate (NHP) samples, there has been no comprehensive sequence analysis study undertaken to demonstrate that the probe sequences designed to detect human transcripts are reliably hybridizing with their orthologs in NHP. By aligning probe sequences with expressed sequence tags (ESTs) in NHP, inter-species conserved (ISC) probesets, which have two or more probes complementary to ESTs in NHP, were identified on human GeneChip® platforms. The utility of human GeneChips® for the assessment of NHP expression patterns can be effectively evaluated by analyzing the hybridization behaviour of ISC probesets. Appropriate normalization methods were identified that further improve the reliability of human GeneChips® for interspecies (human vs NHP) comparisons.
Results
ISC probesets in each of the seven Affymetrix GeneChip® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were identified for both monkey and chimpanzee. Expression data was generated from peripheral blood mononuclear cells (PBMCs) of 12 human and 8 monkey (Indian origin Rhesus macaque) samples using the Focus GeneChip®. Analysis of both qualitative detection calls and quantitative signal intensities showed that intra-species reproducibility (human vs. human or monkey vs. monkey) was much higher than interspecies reproducibility (human vs. monkey). ISC probesets exhibited higher interspecies reproducibility than the overall expressed probesets. Importantly, appropriate normalization methods could be leveraged to greatly improve interspecies correlations. The correlation coefficients between human (average of 12 samples) and monkey (average of 8 Rhesus macaque samples) are 0.725, 0.821 and 0.893 for MAS5.0 (Microarray Suite version 5.0), dChip and RMA (Robust Multi-chip Average) normalization method, respectively.
Conclusion
It is feasible to use Affymetrix human GeneChip® platforms to assess the expression profiles of NHP for intra-species studies. Caution must be taken for interspecies studies since unsuitable probesets will result in spurious differentially regulated genes between human and NHP. RMA normalization method and ISC probesets are recommended for interspecies studies.
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Background
Microarray studies on non human primates (NHP) have been used to address viral pathogenesis [1,2], neurological disorders [3], development [4] and phylogenetic studies [5-7]. Due to the lack of species-specific microarray platforms for NHP, researchers have used GeneChip® platforms built using human sequence information. An underlying assumption in such studies is that transcripts of humans and NHP are highly conserved, and probe sequences designed to detect human genes will detect their orthologs in NHP samples. It is estimated that chimpanzees (Pan troglodytes) and humans shared 98.77 % DNA similarity [8]. While this statistic is widely quoted and believed, Britten [9] reported that the divergence between humans and chimpanzees to be about 5%. Anzai and colleagues [10] compared the chimpanzee MHC region (1,750,601 bp) with the human HLA region (1,870,955 bp), and concluded that the similarity drops to 86.7% if insertions and deletions were taken into account. All these analyses are based on genomic DNA sequences; however, for microarray studies on the transcriptome, the similarity of RNA transcripts is the primary concern. A single gene does not necessarily generate a single transcript. Splicing variants are very common in the human [11,12], and humans and NHPs may use different splicing strategies in some genes. Therefore, it is necessary to re-assess the reliability of human GeneChips® for NHP expression analysis.
Few published studies employing human GeneChip® platforms for NHP expression profiling have robustly addressed the quantitative aspects of cross platform performance. Vahey and colleagues [1] used the HuGeneFL GeneChip® and demonstrated that there was no significant difference in the dynamic range of the raw fluorescence distribution for equivalent amounts of human cRNA and macaque cRNA hybridized to the chip. Chismar and colleagues [13] used the U95Av2 GeneChip® platform and compared the expression patterns of humans with that of the rhesus macaque. They concluded that the percentage of 'present' calls observed in the transcriptome of macaque brain is lower than that of human brain, and that this is especially true for genes with lower signal intensity. Caceres and colleagues [5] used the HG-U95Av2 arrays to identify upregulated genes in the human cortex compared with those of the NHPs. Since sequence divergence could lead to an underestimation of expression levels in NHPs, they excluded 4572 probes that exhibited different hybridization behaviour between two sets of samples in order to reduce false positives. However, this analysis is solely based on probe signal intensities. A more robust way to assess the utility of human GeneChip® platforms for the study of expression profiles in NHP is to employ a sequence analysis approach.
In this study, we address the power of human GeneChip® platforms to assess expression patterns in NHP samples by: a) identifying ISC probesets based on sequence analysis; b) assessing intra (within NHP species)- and interspecies (between NHP and human samples) reproducibility of GeneChip® data; and c) applying appropriate normalization methods to improve interspecies reproducibility.
Results and discussion
Identification of ISC probesets
When a probe sequence on the human GeneChip® hybridizes with the transcriptome of a NHP, there are three possible outcomes: 1) it hybridizes with the ortholog of the NHP; 2) it cross-hybridizes with a non-ortholog transcript, or 3) it fails to hybridize due to sequence divergence. In Affymetrix GeneChip® system, a probeset is composed of 11–20 probes and each probe is a 25-mer oligo. We identified probes on the human GeneChip that are complementary to ESTs in NHP postulating that these probes would hybridize most optimally with the transcripts of NHP. We defined a probeset as an ISC probeset if it had at least two complementary probes. The rationale used to define the criterion that defines an ISC probeset is described in the methods section. The procedure used to generate ISC probesets is shown in Figure 1 and described in methods section. ISC probesets in each of the seven Affymetrix human GeneChip® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were generated for both monkey and chimpanzee. Detailed information about each ISC probeset such as probe sequence, GenBank accession and the position and degree of matching is provided in the supplemental materials (Additional File 1,Additional File 2,Additional File 3,Additional File 4). Table 1 displays a summary of the statistical characteristics of ISC probesets. Not surprisingly, there were more ISC probesets for monkey (Macaca mulatta) than for chimpanzee (Pan troglodytes). This is not because monkey EST sequences are more similar to human sequences than chimpanzee EST sequences, but because we have a much greater amount of EST sequences available for monkey. At the time of writing this manuscript, there were 33,474 monkey ESTs available, while there were only 6,943 ESTs available for chimpanzee. As the number of defined ESTs will increase in the future, additional ISC probesets could be identified for both monkey and chimpanzee using this method.
Table 1 The number of ISC probesets in various human GeneChip® platforms
Human GeneChip® platforms Probes / probeset Total number of probesets (genes*) The number of ISC probesets (genes)
Monkey Chimpanzee
HG-FL 20 7129 (5435) 1036 (891) 422 (362)
HG-Focus 11 8793 (8466) 1179 (1136) 523 (511)
HG-U95Av2 16 12625 (9203) 1505 (1267) 586 (511)
HG-U95B 16 12620 (9948) 561 (497) 256 (236)
HG-U133A 11 22283 (13624) 2676 (1991) 1102 (861)
HG-U133B 11 22646 (16119) 886 (773) 406 (363)
HG-U133 Plus2.0 11 54675 (29963) 3636 (2704) 1529 (1190)
* Refer to the number of unique UniGene clusters.
Figure 1 Algorithm for identifying ISC probesets in Affymetrix Human GeneChip® platforms. In the Affymetrix platform, a probe is a 25-mer oligo. A set of 11–20 probes forms a probeset. An ISC probeset is defined as having at least two probes that are complementary to ESTs in NHP. A perfect probeset is the one that all of its probes are complementary to ESTs in NHP.
It is not uncommon, especially in the U133Plus2.0 platform, that multiple probesets target the same gene. For example, in the U133A and the U133 Plus 2.0 GeneChip®s, there are three probesets (217028_at, 211919_s_at and 209201_x_at) that target the gene CXCR4 at different positions in its transcript. In order to address this redundancy issue, we converted the number of probesets into the number of unique UniGene clusters based on the GeneChip® annotation file provided by Affymetrix Website [18]. While a UniGene cluster does not necessarily correspond to a unique gene, it is a reasonable way to assess probeset redundancy. As shown in Table 1, the Focus GeneChip® and the U133Plus2.0 GeneChip® have the lowest and highest frequency of redundant probesets for a given gene, respectively.
The U133Plus2.0 is the most current version of human GeneChip® from Affymetrix and covers the human genome most extensively. Figure 2 displays the distribution of probesets on the human chromosomes. The yellow bars represent the distribution of all probesets on the U133Plus2.0 platform, and the blue and red bars represent the distribution of ISC probesets for monkey and chimpanzee, respectively. As shown in Figure 2, ISC probesets for both monkey and chimpanzee are distributed throughout the genome, from chromosome 1 to chromosome 22, including the two sex chromosomes X and Y. The percentage of ISC probesets on each chromosome is roughly proportional to that of the total probesets.
Figure 2 Distribution of ISC probesets on human chromosomes. The yellow bars represent the distribution of all probesets in GeneChip® U133Plus2.0 platform. The blue and red bars represent the distribution of ISC probesets for monkey (Macaca mulatta) and chimpanzee (Pan troglodytes), respectively.
Intra- and interspecies reproducibility of detection calls
The qualitative detection call (present / absent) output from MAS5.0 was the initial approach used to examine the reproducibility of GeneChip® data observed in intra- and interspecies samples. The intra-species reproducibility is displayed in Figure 3A and 3B for human samples and monkey samples, respectively. As shown in Figure 3A, 66% of probesets showed 100% reproducibility across 12 human replicates, being either present in all samples (24%) or absent in all samples (42%). Similarly, among 8 monkey samples, 69% of probesets showed 100% reproducibility, being either present in all samples (12%) or absent in all samples (57%) (Figure 3B). Although the percentage of absent calls in monkey samples (57%) is higher than those in human samples (42%), the detection call itself is consistent across replicates. In other words, an absent call caused by sequence divergence will be reliably repeated across monkey samples. This result suggests that it is feasible to use the human GeneChip® for NHP intra-species studies.
Figure 3 Intra-species reproducibility of detection calls. A: Reproducibility among human samples. B: Reproducibility among monkey (Rhesus macaque) samples. 1 P, 2 P ... 12 P represent 1, 2 ...12 present calls among all samples. Absent = no present calls in any sample. N = 12 human and 8 monkey samples.
In contrast, if human GeneChip® platforms are used to compare the expression pattern of humans with those of NHPs, care must be taken in the interpretation of data. If we consider a probeset as being expressed when 50% or more of replicates have present calls, then 3445 (2059+1386) and 2321 (2059+262) probesets are expressed in the PBMC fraction of humans and monkeys, respectively (Figure 4A). Approximately 40% (1386/3445) of probesets being detected in human PBMCs are not detected in the monkey. Due to the close evolutionary relationship between human and monkey, one would not expect that 40% of genes expressed in human PBMCs are not expressed in monkey PBMCs. This observation suggests that a subset of human probesets failed to properly hybridize with the orthologs of monkey. Based on expression data alone, however, it is difficult to distinguish a genuine absent call from a spurious absent call resulting from sequence divergence. ISC probesets can help to distinguish spurious from genuine absent calls. As shown in Figure 4B, of 868 ISC probesets that were detected in human PBMCs, only 216 (24.9%) are not detected in monkey PBMCs. The interspecies discordance is reduced significantly for ISC probesets (Fisher's exact test p < 2.2e-16). It is important to point out that ISC probesets will significantly reduce, but not eliminate interspecies discordance as it requires only a minimum of two complementary probes. It can be postulated that a perfect probeset in which all of its probes were complementary to ESTs of NHP would provide the ultimate reduction in discordance. However, the identification of such probesets is limited by currently available sequence information.
Figure 4 Interspecies reproducibility of detection calls. A: Venn diagram of the number of expressed probesets in human and monkey (Rhesus macaque) samples; B: Venn diagram of the number of expressed ISC probesets in human and monkey (Rhesus macaque) samples.
Intra- and interspecies reproducibility of signal intensities
To assess the intra- and interspecies reproducibility of GeneChip® signal intensities, a matrix that contains signal intensities of 3445 expressed probesets across 20 samples (12 human and 8 monkeys) was created. Probesets that are not expressed in human PBMCs were excluded in this analysis. Pair-wise correlation coefficients were calculated for all 20 samples (20C2 = 190 combinations in total). The correlation coefficients were visualized using heat spectrum graphs where colors ranging from red to white correspond to correlation coefficients of 0.5 to 1.0, respectively. In figure 5A, the cells in the diagonal line are all white as they represent samples correlating with themselves with a correlation coefficient of 1.0. The highest correlations were found among human replicates (lower left corner), followed by monkey replicates (upper right corner). The lowest correlations were found in interspecies comparisons (bottom right corner). The means and standard deviations of human-human, monkey-monkey and human-monkey correlation coefficients are 0.92 ± 0.013, 0.85 ± 0.039 and 0.65 ± 0.044, respectively. If the low correlation coefficients of human-monkey are caused by unsuitable probesets, then ISC probesets should have higher correlation coefficients. Figure 5B displayed the correlation coefficients of the same 20 samples as Figure 5A, but limited to ISC probesets. As shown in Fig 5B, the colors are much less red than those in Figure 5A, indicating higher correlation coefficients. The means and standard deviations of correlation coefficients of ISC probesets for human-human, monkey-monkey and human-monkey are 0.95 ± 0.0094, 0.92 ± 0.023 and 0.80 ± 0.026, respectively. The greatest improvement (0.65 to 0.80) in correlation coefficients are observed in the human-monkey comparison (Figure 5A and 5B) using the ISC probesets. This data suggests that a subset of problematic probesets interfered with interspecies comparison, and the ISC probesets could be used to improved interspecies reproducibility.
Figure 5 Intra- and interspecies reproducibility of expression signal intensities. Pair-wise correlation coefficients of 20 samples (12 human and 8 monkey (Rhesus macaque) samples) were calculated for expressed probesets (Figure 5A) and for expressed ISC probesets (Figure 5B). Correlation coefficients are visualized using colors of a heat spectrum (red=correlation coefficient of 0.5; white = correlation coefficient of 1.0). The graphs are symmetric along the diagonal lines. The diagonal line represents samples correlating with themselves, with a correlation coefficient of 1.0 (white). The means and standard deviations of correlation coefficients of human-human, monkey-monkey and human-monkey are shown in the bottom left, upper right and bottom right of each graph, respectively. A: Correlation coefficients calculated based on all expressed probesets. B: Correlation coefficients calculated based on expressed ISC probesets.
The effect of normalization methods on interspecies reproducibility
Different normalization methods have been shown to significantly affect GeneChip® data variation [14-17]. We compared three different normalization methods: MAS5.0, RMA [14-16] and dChip [17], to evaluate the effect of normalization methods on interspecies reproducibility. Both RMA and dChip methods normalize GeneChip® data at the probe level using a non-linear algorithm while MAS5.0 normalizes data at probeset level using linear scaling. Sequence divergence usually leads to one or very few probes in a probeset being problematic while the majority of probes in that probeset may still work reasonably well. If the variation generated from these problematic probes were normalized, the interspecies reproducibility should improve. Figure 6 showed the interspecies correlation coefficients using three different normalization methods. The average signal intensities of 8 monkey samples were given on the ordinate and that of 12 human samples on the abscissa. The RMA normalization method improved interspecies reproducibility the most for both expressed probesets and ISC probesets. As shown in the Figure 6A,6B,6C, correlation coefficients for expressed probesets using MAS5.0, dChip and RMA were 0.725, 0.821 and 0.893, respectively. Similarly, in Figure 6D,6E,6F, correlation coefficients for ISC probesets using MAS5.0, dChip and RMA were 0.850, 0.879 and 0.921, respectively. For the same normalization method, ISC probesets exhibited higher correlation coefficients than those of expressed probesets (horizontal comparison such as Figure 6A vs. Figure 6D). Use of the RMA normalization method in conjunction with the use of ISC probesets optimized the correlation coefficient between human and monkey. The resulting correlation coefficient of 0.92 is equivalent to the human-human correlation using the MAS5.0 normalization method (Figure 6F and Figure 5A).
Figure 6 The effect of normalization methods on interspecies reproducibility. A, B and C: MAS5.0, dChip and RMA normalization for expressed probesets. D,E, and F: MAS5.0, dChip and RMA normalization for expressed ISC probesets. x-axes and y-axes are average expression intensities of 12 human samples and 8 monkey (Rhesus macaque) samples, respectively.
Conclusions
This paper presents a comprehensive analysis of probe sequences and GeneChip® expression data as applied to the derivation of meaningful expression profile data from NHP. The utility of the human Affymetrix GeneChip® for the assessment of expression profiles in NHP depends on the experimental design and on the approach to data normalization and analysis. Our observations suggest that: 1) it is feasible to use the human GeneChip® in the evaluation of expression profiles of NHP samples for intra-species comparisons; 2) use of ISC probesets and RMA normalization are recommended for interspecies studies; and 3) with the increasing amount of ESTs of NHP, additional ISC probesets (and perfect probesets) will be identified in the near future.
Methods
Sequence data source
Affymetrix GeneChip probe sequences were downloaded from Affymetrix website [18]. The ESTs (Expressed Sequence Tags) of monkey (Macaca mulatta) and chimpanzee (Pan troglodytes) were downloaded from NCBI website [19].
Identification of ISC probesets
Stand alone BALST program was downloaded from NCBI website [19]. Perl script was written to automatically run BLAST search between GeneChip® probe sequences and monkey /chimpanzee EST sequences. The length of a probe sequence is always 25 nucleotides while the number of probes in a probeset varies from 11 to 20 depending on GeneChip® platforms (see Table 1). A certain degree of mismatch between a probe sequence and ESTs is allowed. If a probe has at least 23 nucleotides complementary to at least one EST sequence, this probe is designated as a complementary probe. If a probeset has at least two complementary probes, we defined this probeset as an ISC probeset. If all probes of a probeset are complementary probes, this probeset is called a 'perfect' probeset. The rationale for the definition of ISC probesets is as follows: 1) since each probe is a 25-mer oligo, the probability of random matching of one probe is 4-25 thus, the probability of random matching of two probes goes down to 4-50, being exponentially reduced; 2) in comparison with an RT-PCR experiment, the primer length is equivalent to our probe length, and two primers (one forward and one backward) usually generate a unique sequence in a whole genome; 3) a probe sequence on the Affymetrix GeneChip® is a well designed sequence with a single probe hybridizing with a unique transcript in whole transcriptome; and 4) since the EST sequences in NHP are very limited so far, most of them do not cover whole transcript such that a false negative could be generated if we require all the probes in a probeset being complementary to known ESTs. In order to convert probeset IDs to UniGene IDs and map them onto chromosomes, probeset annotation files were downloaded from Affymetrix website [18]. No animals or human samples were used for the purpose of this analysis. Affymetrix datasets used in this analysis are from other approved ongoing projects in our lab. The procedure used to process these samples was previously published [1].
Briefly, peripheral blood from healthy human and NHP (Indian origin Rhesus macaque) was collected and peripheral blood mononuclear cells (PBMCs) were separated by Histopaque-Ficoll (Sigma) gradient centrifugation. RNA preparation, Hybridization, staining and scanning of the GeneChip® was carried out as described by Vahey et al. [1]. Animal and human samples were handled identically throughout the process. All 20 samples (12 human and 8 rhesus macaques) were hybridized to Affymetrix's HG-Focus GeneChip®. Signal values and detection calls (present or absent) for all samples were determined by using MAS5.0 (Affymetrix Inc. Santa Clara, California). Signal values were scaled to the default target signal intensity of 500). A matrix of detection calls (present, absent and marginal) and a matrix of signal intensities for all samples across all probesets were constructed. A gene must exhibit 50% or more of 'present' calls in all samples to be considered 'expressed'. In this study, an expressed probeset in human is a probeset that has 6 or more present calls among 12 human samples. Similarly, an expressed probeset in monkey means there were 4 or more present calls among 8 monkey samples. The signal intensities output from MAS5.0 were log2 transformed. Model-based normalization was performed using dChip version 1.3 [17]. The output signal intensities were log2 transformed. RMA (Robust Multichip Average) normalization [14-16] was carried out using BioConductor package Affy_1.2.30 [20]. The rma() function in the package was used at its default setting, that is, 'RMA' background correction, 'quantile normalization', 'PM only model' and 'median polish summarization'. By default, the signal intensities were already log2 transformed.
Intra- and interspecies correlation coefficients of signal intensities were calculated by built in function 'cor' in statistical package R version 1.9.0. [21]. Visualization of correlation coefficients matrix was done by the function 'image'. The function 'heat.colors' was used to create heat-spectrum (red to white) and set color scales between 0.5 (red) and 1.0 (white).
Abbreviations
NHP: non human primate
EST: expressed sequence tag
ISC: inter-species conserved
RMA: robust multi-chip average
MAS5.0: Microarray suite version 5.0
Authors' contributions
ZW developed the original hypotheses, performed the bioinformatics analyses to test them and drafted the manuscript. MV provided critical input on design and execution of the laboratory experiments and with ZW interpreted the data sets and revised the manuscript. ML conducted all aspects of the animal handling including the harvest of well characterized primary samples. MN and AA are technical staff who extracted the nucleic acid and performed the laboratory portions of the microarray experiments. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for monkey (Macaca mulatta) in GeneChip® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 2
There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for monkey (Macaca mulatta) in GeneChip® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 3
There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for chimpanzee (Pan troglodytes) in GeneChip® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 4
There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for chimpanzee (Pan troglodytes) in GeneChip® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Acknowledgements
The authors thank Dr. Deborah L. Birx, Director of the Military HIV-1 Research Program, for support of this effort and Drs. Nelson Michael and Christian Ockenhouse for helpful discussions. This work was supported in part by Cooperative Agreement no. W81XWH-04-2-0005 between the U.S. Army Medical Research and Materiel Command and the Henry M. Jackson Foundation for the Advancement of Military Medicine.
The opinions or assertions contained herein are the private views of the authors, and are not to be construed as official, or as reflecting the views of the Department of the Army or the Department of Defence.
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| 15507140 | PMC526766 | CC BY | 2021-01-04 16:36:38 | no | BMC Bioinformatics. 2004 Oct 26; 5:165 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-165 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1651550714010.1186/1471-2105-5-165Methodology ArticleIdentification and utilization of inter-species conserved (ISC) probesets on Affymetrix human GeneChip® platforms for the optimization of the assessment of expression patterns in non human primate (NHP) samples Wang Zhining [email protected] Mark G [email protected] Martin E [email protected] Alma [email protected] Maryanne T [email protected] Henry M Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland 20850, USA2 Division of Retrovirology, Walter Reed Army Institute of Research, Washington, D. C. 20850, USA3 Bioqual, Rockville, MD 20850, USA2004 26 10 2004 5 165 165 15 7 2004 26 10 2004 Copyright © 2004 Wang et al; licensee BioMed Central Ltd.2004Wang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
While researchers have utilized versions of the Affymetrix human GeneChip® for the assessment of expression patterns in non human primate (NHP) samples, there has been no comprehensive sequence analysis study undertaken to demonstrate that the probe sequences designed to detect human transcripts are reliably hybridizing with their orthologs in NHP. By aligning probe sequences with expressed sequence tags (ESTs) in NHP, inter-species conserved (ISC) probesets, which have two or more probes complementary to ESTs in NHP, were identified on human GeneChip® platforms. The utility of human GeneChips® for the assessment of NHP expression patterns can be effectively evaluated by analyzing the hybridization behaviour of ISC probesets. Appropriate normalization methods were identified that further improve the reliability of human GeneChips® for interspecies (human vs NHP) comparisons.
Results
ISC probesets in each of the seven Affymetrix GeneChip® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were identified for both monkey and chimpanzee. Expression data was generated from peripheral blood mononuclear cells (PBMCs) of 12 human and 8 monkey (Indian origin Rhesus macaque) samples using the Focus GeneChip®. Analysis of both qualitative detection calls and quantitative signal intensities showed that intra-species reproducibility (human vs. human or monkey vs. monkey) was much higher than interspecies reproducibility (human vs. monkey). ISC probesets exhibited higher interspecies reproducibility than the overall expressed probesets. Importantly, appropriate normalization methods could be leveraged to greatly improve interspecies correlations. The correlation coefficients between human (average of 12 samples) and monkey (average of 8 Rhesus macaque samples) are 0.725, 0.821 and 0.893 for MAS5.0 (Microarray Suite version 5.0), dChip and RMA (Robust Multi-chip Average) normalization method, respectively.
Conclusion
It is feasible to use Affymetrix human GeneChip® platforms to assess the expression profiles of NHP for intra-species studies. Caution must be taken for interspecies studies since unsuitable probesets will result in spurious differentially regulated genes between human and NHP. RMA normalization method and ISC probesets are recommended for interspecies studies.
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Background
Microarray studies on non human primates (NHP) have been used to address viral pathogenesis [1,2], neurological disorders [3], development [4] and phylogenetic studies [5-7]. Due to the lack of species-specific microarray platforms for NHP, researchers have used GeneChip® platforms built using human sequence information. An underlying assumption in such studies is that transcripts of humans and NHP are highly conserved, and probe sequences designed to detect human genes will detect their orthologs in NHP samples. It is estimated that chimpanzees (Pan troglodytes) and humans shared 98.77 % DNA similarity [8]. While this statistic is widely quoted and believed, Britten [9] reported that the divergence between humans and chimpanzees to be about 5%. Anzai and colleagues [10] compared the chimpanzee MHC region (1,750,601 bp) with the human HLA region (1,870,955 bp), and concluded that the similarity drops to 86.7% if insertions and deletions were taken into account. All these analyses are based on genomic DNA sequences; however, for microarray studies on the transcriptome, the similarity of RNA transcripts is the primary concern. A single gene does not necessarily generate a single transcript. Splicing variants are very common in the human [11,12], and humans and NHPs may use different splicing strategies in some genes. Therefore, it is necessary to re-assess the reliability of human GeneChips® for NHP expression analysis.
Few published studies employing human GeneChip® platforms for NHP expression profiling have robustly addressed the quantitative aspects of cross platform performance. Vahey and colleagues [1] used the HuGeneFL GeneChip® and demonstrated that there was no significant difference in the dynamic range of the raw fluorescence distribution for equivalent amounts of human cRNA and macaque cRNA hybridized to the chip. Chismar and colleagues [13] used the U95Av2 GeneChip® platform and compared the expression patterns of humans with that of the rhesus macaque. They concluded that the percentage of 'present' calls observed in the transcriptome of macaque brain is lower than that of human brain, and that this is especially true for genes with lower signal intensity. Caceres and colleagues [5] used the HG-U95Av2 arrays to identify upregulated genes in the human cortex compared with those of the NHPs. Since sequence divergence could lead to an underestimation of expression levels in NHPs, they excluded 4572 probes that exhibited different hybridization behaviour between two sets of samples in order to reduce false positives. However, this analysis is solely based on probe signal intensities. A more robust way to assess the utility of human GeneChip® platforms for the study of expression profiles in NHP is to employ a sequence analysis approach.
In this study, we address the power of human GeneChip® platforms to assess expression patterns in NHP samples by: a) identifying ISC probesets based on sequence analysis; b) assessing intra (within NHP species)- and interspecies (between NHP and human samples) reproducibility of GeneChip® data; and c) applying appropriate normalization methods to improve interspecies reproducibility.
Results and discussion
Identification of ISC probesets
When a probe sequence on the human GeneChip® hybridizes with the transcriptome of a NHP, there are three possible outcomes: 1) it hybridizes with the ortholog of the NHP; 2) it cross-hybridizes with a non-ortholog transcript, or 3) it fails to hybridize due to sequence divergence. In Affymetrix GeneChip® system, a probeset is composed of 11–20 probes and each probe is a 25-mer oligo. We identified probes on the human GeneChip that are complementary to ESTs in NHP postulating that these probes would hybridize most optimally with the transcripts of NHP. We defined a probeset as an ISC probeset if it had at least two complementary probes. The rationale used to define the criterion that defines an ISC probeset is described in the methods section. The procedure used to generate ISC probesets is shown in Figure 1 and described in methods section. ISC probesets in each of the seven Affymetrix human GeneChip® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were generated for both monkey and chimpanzee. Detailed information about each ISC probeset such as probe sequence, GenBank accession and the position and degree of matching is provided in the supplemental materials (Additional File 1,Additional File 2,Additional File 3,Additional File 4). Table 1 displays a summary of the statistical characteristics of ISC probesets. Not surprisingly, there were more ISC probesets for monkey (Macaca mulatta) than for chimpanzee (Pan troglodytes). This is not because monkey EST sequences are more similar to human sequences than chimpanzee EST sequences, but because we have a much greater amount of EST sequences available for monkey. At the time of writing this manuscript, there were 33,474 monkey ESTs available, while there were only 6,943 ESTs available for chimpanzee. As the number of defined ESTs will increase in the future, additional ISC probesets could be identified for both monkey and chimpanzee using this method.
Table 1 The number of ISC probesets in various human GeneChip® platforms
Human GeneChip® platforms Probes / probeset Total number of probesets (genes*) The number of ISC probesets (genes)
Monkey Chimpanzee
HG-FL 20 7129 (5435) 1036 (891) 422 (362)
HG-Focus 11 8793 (8466) 1179 (1136) 523 (511)
HG-U95Av2 16 12625 (9203) 1505 (1267) 586 (511)
HG-U95B 16 12620 (9948) 561 (497) 256 (236)
HG-U133A 11 22283 (13624) 2676 (1991) 1102 (861)
HG-U133B 11 22646 (16119) 886 (773) 406 (363)
HG-U133 Plus2.0 11 54675 (29963) 3636 (2704) 1529 (1190)
* Refer to the number of unique UniGene clusters.
Figure 1 Algorithm for identifying ISC probesets in Affymetrix Human GeneChip® platforms. In the Affymetrix platform, a probe is a 25-mer oligo. A set of 11–20 probes forms a probeset. An ISC probeset is defined as having at least two probes that are complementary to ESTs in NHP. A perfect probeset is the one that all of its probes are complementary to ESTs in NHP.
It is not uncommon, especially in the U133Plus2.0 platform, that multiple probesets target the same gene. For example, in the U133A and the U133 Plus 2.0 GeneChip®s, there are three probesets (217028_at, 211919_s_at and 209201_x_at) that target the gene CXCR4 at different positions in its transcript. In order to address this redundancy issue, we converted the number of probesets into the number of unique UniGene clusters based on the GeneChip® annotation file provided by Affymetrix Website [18]. While a UniGene cluster does not necessarily correspond to a unique gene, it is a reasonable way to assess probeset redundancy. As shown in Table 1, the Focus GeneChip® and the U133Plus2.0 GeneChip® have the lowest and highest frequency of redundant probesets for a given gene, respectively.
The U133Plus2.0 is the most current version of human GeneChip® from Affymetrix and covers the human genome most extensively. Figure 2 displays the distribution of probesets on the human chromosomes. The yellow bars represent the distribution of all probesets on the U133Plus2.0 platform, and the blue and red bars represent the distribution of ISC probesets for monkey and chimpanzee, respectively. As shown in Figure 2, ISC probesets for both monkey and chimpanzee are distributed throughout the genome, from chromosome 1 to chromosome 22, including the two sex chromosomes X and Y. The percentage of ISC probesets on each chromosome is roughly proportional to that of the total probesets.
Figure 2 Distribution of ISC probesets on human chromosomes. The yellow bars represent the distribution of all probesets in GeneChip® U133Plus2.0 platform. The blue and red bars represent the distribution of ISC probesets for monkey (Macaca mulatta) and chimpanzee (Pan troglodytes), respectively.
Intra- and interspecies reproducibility of detection calls
The qualitative detection call (present / absent) output from MAS5.0 was the initial approach used to examine the reproducibility of GeneChip® data observed in intra- and interspecies samples. The intra-species reproducibility is displayed in Figure 3A and 3B for human samples and monkey samples, respectively. As shown in Figure 3A, 66% of probesets showed 100% reproducibility across 12 human replicates, being either present in all samples (24%) or absent in all samples (42%). Similarly, among 8 monkey samples, 69% of probesets showed 100% reproducibility, being either present in all samples (12%) or absent in all samples (57%) (Figure 3B). Although the percentage of absent calls in monkey samples (57%) is higher than those in human samples (42%), the detection call itself is consistent across replicates. In other words, an absent call caused by sequence divergence will be reliably repeated across monkey samples. This result suggests that it is feasible to use the human GeneChip® for NHP intra-species studies.
Figure 3 Intra-species reproducibility of detection calls. A: Reproducibility among human samples. B: Reproducibility among monkey (Rhesus macaque) samples. 1 P, 2 P ... 12 P represent 1, 2 ...12 present calls among all samples. Absent = no present calls in any sample. N = 12 human and 8 monkey samples.
In contrast, if human GeneChip® platforms are used to compare the expression pattern of humans with those of NHPs, care must be taken in the interpretation of data. If we consider a probeset as being expressed when 50% or more of replicates have present calls, then 3445 (2059+1386) and 2321 (2059+262) probesets are expressed in the PBMC fraction of humans and monkeys, respectively (Figure 4A). Approximately 40% (1386/3445) of probesets being detected in human PBMCs are not detected in the monkey. Due to the close evolutionary relationship between human and monkey, one would not expect that 40% of genes expressed in human PBMCs are not expressed in monkey PBMCs. This observation suggests that a subset of human probesets failed to properly hybridize with the orthologs of monkey. Based on expression data alone, however, it is difficult to distinguish a genuine absent call from a spurious absent call resulting from sequence divergence. ISC probesets can help to distinguish spurious from genuine absent calls. As shown in Figure 4B, of 868 ISC probesets that were detected in human PBMCs, only 216 (24.9%) are not detected in monkey PBMCs. The interspecies discordance is reduced significantly for ISC probesets (Fisher's exact test p < 2.2e-16). It is important to point out that ISC probesets will significantly reduce, but not eliminate interspecies discordance as it requires only a minimum of two complementary probes. It can be postulated that a perfect probeset in which all of its probes were complementary to ESTs of NHP would provide the ultimate reduction in discordance. However, the identification of such probesets is limited by currently available sequence information.
Figure 4 Interspecies reproducibility of detection calls. A: Venn diagram of the number of expressed probesets in human and monkey (Rhesus macaque) samples; B: Venn diagram of the number of expressed ISC probesets in human and monkey (Rhesus macaque) samples.
Intra- and interspecies reproducibility of signal intensities
To assess the intra- and interspecies reproducibility of GeneChip® signal intensities, a matrix that contains signal intensities of 3445 expressed probesets across 20 samples (12 human and 8 monkeys) was created. Probesets that are not expressed in human PBMCs were excluded in this analysis. Pair-wise correlation coefficients were calculated for all 20 samples (20C2 = 190 combinations in total). The correlation coefficients were visualized using heat spectrum graphs where colors ranging from red to white correspond to correlation coefficients of 0.5 to 1.0, respectively. In figure 5A, the cells in the diagonal line are all white as they represent samples correlating with themselves with a correlation coefficient of 1.0. The highest correlations were found among human replicates (lower left corner), followed by monkey replicates (upper right corner). The lowest correlations were found in interspecies comparisons (bottom right corner). The means and standard deviations of human-human, monkey-monkey and human-monkey correlation coefficients are 0.92 ± 0.013, 0.85 ± 0.039 and 0.65 ± 0.044, respectively. If the low correlation coefficients of human-monkey are caused by unsuitable probesets, then ISC probesets should have higher correlation coefficients. Figure 5B displayed the correlation coefficients of the same 20 samples as Figure 5A, but limited to ISC probesets. As shown in Fig 5B, the colors are much less red than those in Figure 5A, indicating higher correlation coefficients. The means and standard deviations of correlation coefficients of ISC probesets for human-human, monkey-monkey and human-monkey are 0.95 ± 0.0094, 0.92 ± 0.023 and 0.80 ± 0.026, respectively. The greatest improvement (0.65 to 0.80) in correlation coefficients are observed in the human-monkey comparison (Figure 5A and 5B) using the ISC probesets. This data suggests that a subset of problematic probesets interfered with interspecies comparison, and the ISC probesets could be used to improved interspecies reproducibility.
Figure 5 Intra- and interspecies reproducibility of expression signal intensities. Pair-wise correlation coefficients of 20 samples (12 human and 8 monkey (Rhesus macaque) samples) were calculated for expressed probesets (Figure 5A) and for expressed ISC probesets (Figure 5B). Correlation coefficients are visualized using colors of a heat spectrum (red=correlation coefficient of 0.5; white = correlation coefficient of 1.0). The graphs are symmetric along the diagonal lines. The diagonal line represents samples correlating with themselves, with a correlation coefficient of 1.0 (white). The means and standard deviations of correlation coefficients of human-human, monkey-monkey and human-monkey are shown in the bottom left, upper right and bottom right of each graph, respectively. A: Correlation coefficients calculated based on all expressed probesets. B: Correlation coefficients calculated based on expressed ISC probesets.
The effect of normalization methods on interspecies reproducibility
Different normalization methods have been shown to significantly affect GeneChip® data variation [14-17]. We compared three different normalization methods: MAS5.0, RMA [14-16] and dChip [17], to evaluate the effect of normalization methods on interspecies reproducibility. Both RMA and dChip methods normalize GeneChip® data at the probe level using a non-linear algorithm while MAS5.0 normalizes data at probeset level using linear scaling. Sequence divergence usually leads to one or very few probes in a probeset being problematic while the majority of probes in that probeset may still work reasonably well. If the variation generated from these problematic probes were normalized, the interspecies reproducibility should improve. Figure 6 showed the interspecies correlation coefficients using three different normalization methods. The average signal intensities of 8 monkey samples were given on the ordinate and that of 12 human samples on the abscissa. The RMA normalization method improved interspecies reproducibility the most for both expressed probesets and ISC probesets. As shown in the Figure 6A,6B,6C, correlation coefficients for expressed probesets using MAS5.0, dChip and RMA were 0.725, 0.821 and 0.893, respectively. Similarly, in Figure 6D,6E,6F, correlation coefficients for ISC probesets using MAS5.0, dChip and RMA were 0.850, 0.879 and 0.921, respectively. For the same normalization method, ISC probesets exhibited higher correlation coefficients than those of expressed probesets (horizontal comparison such as Figure 6A vs. Figure 6D). Use of the RMA normalization method in conjunction with the use of ISC probesets optimized the correlation coefficient between human and monkey. The resulting correlation coefficient of 0.92 is equivalent to the human-human correlation using the MAS5.0 normalization method (Figure 6F and Figure 5A).
Figure 6 The effect of normalization methods on interspecies reproducibility. A, B and C: MAS5.0, dChip and RMA normalization for expressed probesets. D,E, and F: MAS5.0, dChip and RMA normalization for expressed ISC probesets. x-axes and y-axes are average expression intensities of 12 human samples and 8 monkey (Rhesus macaque) samples, respectively.
Conclusions
This paper presents a comprehensive analysis of probe sequences and GeneChip® expression data as applied to the derivation of meaningful expression profile data from NHP. The utility of the human Affymetrix GeneChip® for the assessment of expression profiles in NHP depends on the experimental design and on the approach to data normalization and analysis. Our observations suggest that: 1) it is feasible to use the human GeneChip® in the evaluation of expression profiles of NHP samples for intra-species comparisons; 2) use of ISC probesets and RMA normalization are recommended for interspecies studies; and 3) with the increasing amount of ESTs of NHP, additional ISC probesets (and perfect probesets) will be identified in the near future.
Methods
Sequence data source
Affymetrix GeneChip probe sequences were downloaded from Affymetrix website [18]. The ESTs (Expressed Sequence Tags) of monkey (Macaca mulatta) and chimpanzee (Pan troglodytes) were downloaded from NCBI website [19].
Identification of ISC probesets
Stand alone BALST program was downloaded from NCBI website [19]. Perl script was written to automatically run BLAST search between GeneChip® probe sequences and monkey /chimpanzee EST sequences. The length of a probe sequence is always 25 nucleotides while the number of probes in a probeset varies from 11 to 20 depending on GeneChip® platforms (see Table 1). A certain degree of mismatch between a probe sequence and ESTs is allowed. If a probe has at least 23 nucleotides complementary to at least one EST sequence, this probe is designated as a complementary probe. If a probeset has at least two complementary probes, we defined this probeset as an ISC probeset. If all probes of a probeset are complementary probes, this probeset is called a 'perfect' probeset. The rationale for the definition of ISC probesets is as follows: 1) since each probe is a 25-mer oligo, the probability of random matching of one probe is 4-25 thus, the probability of random matching of two probes goes down to 4-50, being exponentially reduced; 2) in comparison with an RT-PCR experiment, the primer length is equivalent to our probe length, and two primers (one forward and one backward) usually generate a unique sequence in a whole genome; 3) a probe sequence on the Affymetrix GeneChip® is a well designed sequence with a single probe hybridizing with a unique transcript in whole transcriptome; and 4) since the EST sequences in NHP are very limited so far, most of them do not cover whole transcript such that a false negative could be generated if we require all the probes in a probeset being complementary to known ESTs. In order to convert probeset IDs to UniGene IDs and map them onto chromosomes, probeset annotation files were downloaded from Affymetrix website [18]. No animals or human samples were used for the purpose of this analysis. Affymetrix datasets used in this analysis are from other approved ongoing projects in our lab. The procedure used to process these samples was previously published [1].
Briefly, peripheral blood from healthy human and NHP (Indian origin Rhesus macaque) was collected and peripheral blood mononuclear cells (PBMCs) were separated by Histopaque-Ficoll (Sigma) gradient centrifugation. RNA preparation, Hybridization, staining and scanning of the GeneChip® was carried out as described by Vahey et al. [1]. Animal and human samples were handled identically throughout the process. All 20 samples (12 human and 8 rhesus macaques) were hybridized to Affymetrix's HG-Focus GeneChip®. Signal values and detection calls (present or absent) for all samples were determined by using MAS5.0 (Affymetrix Inc. Santa Clara, California). Signal values were scaled to the default target signal intensity of 500). A matrix of detection calls (present, absent and marginal) and a matrix of signal intensities for all samples across all probesets were constructed. A gene must exhibit 50% or more of 'present' calls in all samples to be considered 'expressed'. In this study, an expressed probeset in human is a probeset that has 6 or more present calls among 12 human samples. Similarly, an expressed probeset in monkey means there were 4 or more present calls among 8 monkey samples. The signal intensities output from MAS5.0 were log2 transformed. Model-based normalization was performed using dChip version 1.3 [17]. The output signal intensities were log2 transformed. RMA (Robust Multichip Average) normalization [14-16] was carried out using BioConductor package Affy_1.2.30 [20]. The rma() function in the package was used at its default setting, that is, 'RMA' background correction, 'quantile normalization', 'PM only model' and 'median polish summarization'. By default, the signal intensities were already log2 transformed.
Intra- and interspecies correlation coefficients of signal intensities were calculated by built in function 'cor' in statistical package R version 1.9.0. [21]. Visualization of correlation coefficients matrix was done by the function 'image'. The function 'heat.colors' was used to create heat-spectrum (red to white) and set color scales between 0.5 (red) and 1.0 (white).
Abbreviations
NHP: non human primate
EST: expressed sequence tag
ISC: inter-species conserved
RMA: robust multi-chip average
MAS5.0: Microarray suite version 5.0
Authors' contributions
ZW developed the original hypotheses, performed the bioinformatics analyses to test them and drafted the manuscript. MV provided critical input on design and execution of the laboratory experiments and with ZW interpreted the data sets and revised the manuscript. ML conducted all aspects of the animal handling including the harvest of well characterized primary samples. MN and AA are technical staff who extracted the nucleic acid and performed the laboratory portions of the microarray experiments. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for monkey (Macaca mulatta) in GeneChip® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 2
There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for monkey (Macaca mulatta) in GeneChip® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 3
There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for chimpanzee (Pan troglodytes) in GeneChip® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Additional File 4
There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for chimpanzee (Pan troglodytes) in GeneChip® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e- values and matching identities, in that order.
Click here for file
Acknowledgements
The authors thank Dr. Deborah L. Birx, Director of the Military HIV-1 Research Program, for support of this effort and Drs. Nelson Michael and Christian Ockenhouse for helpful discussions. This work was supported in part by Cooperative Agreement no. W81XWH-04-2-0005 between the U.S. Army Medical Research and Materiel Command and the Henry M. Jackson Foundation for the Advancement of Military Medicine.
The opinions or assertions contained herein are the private views of the authors, and are not to be construed as official, or as reflecting the views of the Department of the Army or the Department of Defence.
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| 15491493 | PMC526767 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Oct 18; 5:81 | latin-1 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-81 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-241550423610.1186/1471-2296-5-24Research ArticleFamily doctors' involvement with families in Estonia Oona Marje [email protected] Ruth [email protected] Margus [email protected] Heidi-Ingrid [email protected] Dept of Polyclinic and Family Medicine, University of Tartu, Puusepa 1a, 50406 Tartu, Estonia2 Dept of Internal Medicine, University of Tartu, Puusepa 6, 51014 Tartu, Estonia2004 25 10 2004 5 24 24 21 6 2004 25 10 2004 Copyright © 2004 Oona et al; licensee BioMed Central Ltd.2004Oona et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Family doctors should care for individuals in the context of their family. Family has a powerful influence on health and illness and family interventions have been shown to improve health outcomes for a variety of health problems. The aim of the study was to investigate the Estonian family doctors' (FD) attitudes to the patients' family-related issues in their work: to explore the degree of FDs involvement in family matters, their preparedness for management of family-related issues and their self-assessment of the ability to manage different family-related problems.
Methods
A random sample (n = 236) of all FDs in Estonia was investigated using a postal questionnaire. Altogether 151 FDs responded to the questionnaire (response rate 64%), while five of them were excluded as they did not actually work as FDs.
Results
Of the respondents, 90% thought that in managing the health problems of patients FDs should communicate and cooperate with family members. Although most of the family doctors agreed that modifying of the health damaging risk factors (smoking, alcohol and drug abuse) of their patients and families is their task, one third of them felt that dealing with these problems is ineffective, or perceived themselves as poorly prepared or having too little time for such activities. Of the respondents, 58% (n = 83) were of the opinion that they could modify also relationship problems.
Conclusions
Estonian family doctors are favourably disposed to involvement in family-related problems, however, they need some additional training, especially in the field of relationship management.
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Background
There are significant differences in the way how primary health care is organised in Europe [1]. Estonia was one of the first Eastern European countries where modern general practice was implemented [2]. Previously, the primary health care system functioned according to the Soviet model, which was basically a specialist-oriented system [3]. In the 1990s, there occurred a transition to a more personal, comprehensive continuous care on the primary level. In 1991, the training of family doctors was launched, both 3-year postgraduate residence training, as well as the retraining of currently practising primary care physicians through attending different courses at the University of Tartu parallel with their everyday practice. The courses on family practice covered such topics as special features of family practice, common clinical problems in family practice, diagnostic strategies, teamwork, ethical issues, prevention and health promotion [3]. For the population, the most important change was the introduction of the patient's list system for FD's: persons choose their own FD by registering in a patient list. Currently, all primary health care physicians in Estonia are trained family doctors who are able to provide a wide scope of medical services for their patients and fulfilling gate-keeping function for specialized medical care.
Irrespective of the health system, general practitioners/family doctors should care for individuals in the context of their family [1]. Family has a powerful influence on health and illness and family interventions have been shown to improve health outcomes for a variety of health problems [4]. However, there are differences in the physicians' involvement with families. Doherty and Baird have described five levels of physician involvement with families [5]. In Estonia, it has been aimed that a family doctor should work at least at level three, i.e. he or she has to communicate appropriate medical information and advice to family members, to be aware of gross family dysfunctions and to deal with the family members' feelings and concerns related to the condition of the patient [5,6]. It may differ in different countries what is expected and valued in general practice care [7]. What constitutes good medical care is determined culturally within a specific historical and geographic context [8].
In Estonia, a decade has passed after the new speciality, family doctor, was introduced into the health care system. The aim of the present study was to investigate the attitude of FDs in Estonia to family-oriented general practice [9]: FDs' awareness of various family-related matters of their patients, FDs' preparedness for management of family-related issues and FDs' self-assessment of the ability to manage different problems (substance abuse, relationship problems) in the family.
Methods
A 21-item questionnaire was designed for the study. The items were developed by researchers considering the aims of the study.
First, the FDs were asked whether their patients have registered on the list by families or not and whether they regard it as appropriate that the family should be cared by one doctor, or whether they think that children should have a separate primary care physician. The FDs rated their opinion on the degree of involvement with various problems in the family: they should deal only with the treatment and counselling of a particular patient, or to cooperate also with family members, in addition to treatment of a particular patient, or they should deal also with emotional and relationship problems of the family members. The questions about FDs' awareness of various issues related to their patients' families such as familial diseases and diseases of family members, financial coping, relationships in the family, living conditions (overcrowding), drug addiction, alcohol abuse, smoking and leisure activities were inquired on a three-step scale : yes, in the case of each patient; yes, in certain cases; no, it is not necessary. FDs' were asked to self-estimate their ability to manage problems in families such as substance abuse and relationship problems. Questions about the FDs' ability to manage problems in families were open-ended, for example: how do you assess your possibilities as a FD to influence relationship problems in the family? If you assess that you cannot influence them, please specify why? If you assess you can influence them, please specify how? Also, the FDs were asked to estimate whether their professional training for dealing with the problems of families is adequate or inadequate. Several questions were related to sociodemographic characteristics (sex, age) and professional history (character and size of practices and length of service in primary health care). The questionnaire was piloted for clarity and relevance in a group of five FDs, and minor changes were introduced.
In February 2002, the questionnaire was mailed to 236 FDs. a random sample of family doctors of Estonia. The random sample of FDs was formed by choosing the name of every 3rd doctor, in alphabetical order, from the list of doctors who had passed residency or retraining courses in family medicine by that time in Estonia (n = 715). A note of reminder and a new questionnaire was sent to the non-responders 4 weeks after the first mailing. Of the 236 mailed questionnaires, 151 were returned after two mailings (64%), five of them were excluded as the respondents did not actually work as FDs. Thus, altogether 146 questionnaires were included in the study, of these 124 were fully completed, while in 22 cases some of the answers (1 to 3 per questionnaire) were missing.
The data were analysed using SPSS for the Windows version 10. The chi-square test was used to test the differences in the proportions, all p-values calculated were two-tailed, the p-values higher than 0.05 were considered non-significant (NS).
All open questions were analysed as follows: all statements expressing motivation for or indicating problems with dealing with family issues were marked. Further, all similar expressions were grouped under one category. Proceeding from this, the key problems relevant to the study were identified [10].
Results
Respondents' characteristics
The mean age of the respondents was 46 (± 8) years, the mean length of the period during which they had worked in primary health care was 18 (± 9) years and the mean size of the list was 1800 (± 513) patients. Of the respondents 55% worked in urban areas, 40% in rural areas, and 5% worked in both areas. The majority of the doctors (92%) were female. The age and sex distribution of the respondents and non-respondents did not differ significantly.
Individual versus family registration in patient lists
A total of 90 (62%) FDs were of the opinion that it was good to have the same FD for the whole family, 31 (21%) responded that it was preferable that every family member chooses a FD on the basis of personal preference and 25 (17%) thought that children should have a primary health care physician other than adults of the same family. Of the FDs, 119 (82%) responded that most patients were registered in their lists by families.
The degree of involvement of FDs in family matters
Of the respondents 15 (10%) were of the opinion that FDs should deal only with the health problems of concrete patients without involvement of family members, 94 (65%) responded that, besides managing the health problems of patients, FDs should communicate and cooperate with family members, and 36 (25%) thought that apart from the previously mentioned issues, FDs should deal with the family members' emotional and relationship problems.
FDs' belief about the necessity for awareness of different family matters
Over 70% of Estonian FDs agreed that in the case of all patients, it is necessary to be aware of drug addiction in the family, diseases of family members, living conditions and alcohol abuse in the family, while the remainder believed that they should be aware of these issues on certain occasions. Of the respondents, over a third thought that the FD should always be aware of relationships in the family and 12% thought that the FD should always be aware of leisure activities of their patients. However, between 60 to 75% believed that FDs should be aware of these issues in certain occasions (Figure 1). Very few FDs responded that patients had never actively sought FDs to discuss family relations (4 out of 143) or health risks in the family (5 out of 145), while 44 (30%) of the respondents stated that patients commonly addressed them to discuss family relations, and 34 (23%) reported that it was common to discuss the health risks associated with the familial diseases.
Figure 1 Percentage distribution of the FDs' answers to the question: "Is it necessary to be aware of the following issues related to their patients' families?"
Preparedness for management of family-related issues
The respondents valued highly their preparedness to counsel for harmful habits: 104 (71%) of the respondents felt that their preparedness was adequate. Regarding the other issues, less than half of the respondents considered their training adequate (Table 1).
Table 1 FDs' self-assessment of their preparedness for management of different family related issues
Issues Preparedness adequate n (%) Preparedness inadequate n (%)
Training in counselling for harmful habits 104 (71%) 42 (29%)
Training in counselling for the health risks associated with hereditary diseases 63 (43%) 82 (57%)
Training in relationships counselling 39 (27%) 106 (73%)
p < 0.0001
FDs' self-assessement of the ability to manage different problems (substance abuse, relationship problems) in family
Altogether 142 FDs responded to the question about their ability to reduce the use of harmful substances (alcohol, tobacco, drugs) in families. One hundred (70%) of the respondents reported that this was within the scope of their ability, while the majority (n = 71) stated that the methods used were advice and counselling, but also referral to specialists, use of specific medications and suggestions regarding appropriate reading material. However, 16 FDs admitted that the efficacy of their work in this field was low.
Nearly one-third of the respondents (30%) estimated that they were not able to reduce the use of harmful substances in their patients' families. The analysis of the open-ended questions identifed some key problems:
• Low motivation of patients.
• Socio-economic reasons for substance abuse.
• Limited time for consultation.
• Inadequate preparedness for management of these issues.
One hundred and forty-three FDs responded to the question about their influence on relationship problems in their patients' families, 83 (58%) of them were of the opinion that they were able to modify these problems. In most cases, FDs used advice and counselling (n = 52), but they also cooperated with specialists as the psychotherapist, family therapist, psychiatrist or social worker. Of the family doctors 60 (42%) thought that they were not able to influence the patients' relationship problems. In the analysis of the open questions, the doctors identified several key problems:
• Limited time.
• Lack of special training.
• Patients do not address FDs with their problem.
• Patients themselves deny the existence of the problem.
• These are the patients' private issues in which physicians could not intervene.
The FDs who were sure that they were able to modify the patients' harmful habits as well as family relationships were more likely to estimate their preparedness for the management of these issues as adequate. Among the doctors who reported that their preparedness for counselling for lifestyle issues was adequate, 76% (n = 76) believed that they were able to treat harmful habits, versus 57% (n = 24) of those who reported that their preparedness for such issues was not adequate (p < 0.05); in the case of relationship problems, the respective percentages were 41% (n = 34) versus 9% (n = 5) (p < 0.0001). There were found no other significant determinants among the sociodemographic or work related factors.
Discussion
The present study addressed the family doctors' opinions about their involvement in the patients' family issues. This is the first study of this kind conducted in Estonia, a country where family doctors were introduced into the health care system ten years ago. There is yet no definite agreement as to what are the appropriate, ideal or minimal levels of family orientation that family doctors should have [9]. Several studies have shown that the frequency of discussing family issues varies significantly [11-13]. The limitation of the study was that the response rate was quite low, 64%. However, the age and sex distribution of the respondents corresponds to that of the Estonian family doctors in general [14].
Our study revealed that care of patients in the context of the family is an important issue for FDs and that Estonian family doctors have good possibilities to take care of the whole family. Although all patients have the right to choose an individual family doctor, the FDs who responded to the questionnaire were sure that family members mostly have one and the same family doctor. This is concordant with the results of a recent survey among patients according to which 74% of the respondents reported that they had one and the same family physician for the whole family [15]. In Estonia, similar with the other Eastern Europe countries shift from separate pediatric and adult primary care system to family doctors system occurred in the 1990s [16]. Only 17% of the FDs in our study were of the opinion that children and adults should have different primary care doctors.
In our study, altogether 90% of the family doctors thought that they should communicate and cooperate with family members in management of the health problems of patients. It is a good result considering that family medicine is a new and developing speciality in Estonia. At the same time, most family doctors are not yet ready to deal with the family members' emotional and relationship problems. Primary care is an important early intervention site of most serious relationship issues, domestic violence, etc [17]. However, studies conducted in other communities have identified that physicians need more continuing education concerning these topics [18,19]. Currently, a two-day course on domestic violence and child abuse, which are serious problems also in Estonia [20], is included in the postgraduate residence training curriculum.
Concerning the family doctors' attitudes to the importance of awareness of their patients' family-related issues, the results indicated that the awareness of drug addiction, diseases of family members, living conditions of the family and alcohol problems in the family were considered the most essential. The awareness of the patients' relationship problems, economic problems and leisure activities was not so highly valued. A recent survey among patients in Estonia revealed that they were also more disposed to involve the family physician in such problems as harmful habits and diseases in the family, but they were less willing to share the relationship problems [15]. This can reflect the current situation in Estonia where the number of drug users as well as alcohol users has significantly increased during the last five years [21]. Lately, much attention has been paid to the problem by politicians, doctors and the mass media.
The low willingness to be aware of the patients' relationship problems is partly related to insufficient preparedness in this field, as the doctors who considered themselves to be adequately prepared to tackle relationship issues were also more often willing to do this. From another point of view, family issues, especially relationships and economic situation, are always delicate topics and require consideration of the patient's attitude to corresponding activities. It has been shown that patients vary considerably in their preferences for physician inquiries into such problems as social functioning, psychosocial issues and health risks. Also, it may reflect cultural differences: in the Nordic countries biomedical talk is more common, while in the southern regions psychosocial dialogue is prevalent [22].
Although most of the family doctors agreed that modifying of the health damaging risk factors (smoking, alcohol and drug abuse) of their patients was their task, they also felt that management of these problems was ineffective, or they perceived themselves as poorly prepared, or had lack of time for such activities. This shows that despite the fact that family doctors are becoming increasingly more aware of their role, there exists the actual need to improve their instruments for handling lifestyle related and psychosocial problems. In practice, both individual and family–centered working methods are needed, while the choice depends on the patient's problems and needs [23]. Management of such issues requires development of new interviewing strategies and different ways to use the visit time more effectively [24]. Finnish experience shows that after completing an education programme, the family doctors' became more family-oriented and family doctors satisfaction with their work was also increased [25].
Conclusions
The results of the present study allow to conclude that Estonian family practitioners are favourably disposed to involvement in family-related problems, but they need additional training especially in the field of relationship management.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors participated in the design of the study. MO and RK carried out the data collection, performed the data analyses and drafted the manuscript. All authors participated in the discussion of the drafts. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by a grant No. 0821 from the Research and Development Council of Estonia.
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| 15504236 | PMC526768 | CC BY | 2021-01-04 16:28:59 | no | BMC Fam Pract. 2004 Oct 25; 5:24 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-24 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-5-261550714510.1186/1471-2350-5-26Research ArticleCLC-2 single nucleotide polymorphisms (SNPs) as potential modifiers of cystic fibrosis disease severity Blaisdell Carol J [email protected] Timothy D [email protected] Augustus [email protected] Penelope [email protected] Eugene R [email protected] O Colin [email protected] Department of Pediatrics, School of Medicine, University of Maryland, Bressler 10–019, 655 W. Baltimore St., Baltimore, Maryland, 21201 USA2 Center for Human Genomics, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, North Carolina, 27157 USA3 School of Medicine, University of Maryland, Howard Hall 324, Baltimore, Maryland, USA4 Department of Genetics, School of Medicine, University of Maryland, Howard Hall 596, 660 W. Redwood St., Baltimore, Maryland, USA2004 26 10 2004 5 26 26 22 5 2004 26 10 2004 Copyright © 2004 Blaisdell et al; licensee BioMed Central Ltd.2004Blaisdell et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Cystic fibrosis (CF) lung disease manifest by impaired chloride secretion leads to eventual respiratory failure. Candidate genes that may modify CF lung disease severity include alternative chloride channels. The objectives of this study are to identify single nucleotide polymorphisms (SNPs) in the airway epithelial chloride channel, CLC-2, and correlate these polymorphisms with CF lung disease.
Methods
The CLC-2 promoter, intron 1 and exon 20 were examined for SNPs in adult CF dF508/dF508 homozygotes with mild and severe lung disease (forced expiratory volume at one second (FEV1) > 70% and < 40%).
Results
PCR amplification of genomic CLC-2 and sequence analysis revealed 1 polymorphism in the hClC -2 promoter, 4 in intron 1, and none in exon 20. Fisher's analysis within this data set, did not demonstrate a significant relationship between the severity of lung disease and SNPs in the CLC-2 gene.
Conclusions
CLC-2 is not a key modifier gene of CF lung phenotype. Further studies evaluating other phenotypes associated with CF may be useful in the future to assess the ability of CLC-2 to modify CF disease severity.
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Background
Although greater than 1000 mutations of the CF gene product, CFTR are known, none of these can be used to make predictions about the occurrence of common complications, the severity, or course of pulmonary disease. The identification of a gene, which modifies the phenotypic expression of CF would be very important for understanding this complex disease. Because CF is a disease of chloride transport in respiratory epithelia, alternative chloride channels present in the airway may be able to partially compensate for the CF defect.
CLC-2 is one candidate alternative chloride channel in respiratory epithelia. Localization to the luminal surface of the airway and perinatal downregulation of CLC-2 in mammalian lung suggests a role in lung morphogenesis [1,2]. Persistent expression of CLC-2 mRNA and protein in tissues unaffected in CF suggests that CLC-2 may compensate for defects in CFTR expression [1]. CLC-2 has the capacity to conduct chloride in mature respiratory epithelia [3,4]. The rat CLC-2 promoter has SP-1 domains that are important for gene regulation [5]. A splice variant of CLC-2 skipping exon 20 occurs in rat lung, suggesting that alternative splicing may have functional significance in this tissue [6]. Because investigation of human CLC-2 genomic structure would be important for studies of gene regulation, we sought to identify single nucleotide polymorphisms in potential regulatory domains of human CLC-2. Genomic DNA was isolated from CF adults with severe and mild obstructive lung disease in order to determine if SNPs segregate with CF lung phenotype.
Methods
CLC-2 protein expression in CF nasal polyps
Nasal polyps from CF patients were obtained at the time of elective surgery from 1989 to 1996. Genotypes of CF mutations for each patient was available, but not clinical status, according to approval by the Johns Hopkins Medical Institution Review Board. At harvest, the tissue was washed 3 times in HBSS, and incubated at 4°C overnight in Protease XIV (Sigma). Epithelial cells were isolated by gentle agitation and filtered through a 70-μm nylon cell strainer (Becton Dickinson; Franklin Lakes, NJ). Cells were grown on 1% collagen coated 35 mm dishes for 1 week. Cell lysates were prepared using 2% sodium dodecyl sulfate (SDS) at 65°C and a cell scraper. Equivalent amounts of total protein from primary CF nasal polyp cultured cell lysates were loaded onto an SDS-polyacrylamide gel electrophoresis (PAGE) system, electrophoresed and transferred to a nitrocellulose membrane. CLC-2 protein levels were detected using the polyclonal chicken anti-CLC-2 antibody and the enhanced chemiluminescent reaction as previously described [2].
Population studied for CLC-2 polymorphisms
Variable expression of CLC-2 protein in nasal cell lysates (see Results) suggested that CLC-2 is differentially expressed in adults and that examination of human CLC-2 genomic structure would be important to investigate this differential expression. Identification of volunteers for nasal epithelial cell culture was not permitted with the original IRB consent process. Therefore, a cohort of CF patients was recruited for polymorphism analysis. A review of the Johns Hopkins Medical Institution CF center database was conducted in 1998 to identify patients that had reached adulthood (age > 18 years), homozygous for the most common CF genotype delF508, so that the affect of various CFTR genotypes would not affect the investigation of CLC-2 polymorphisms. Status of obstructive lung disease was defined using most recent pulmonary function studies. Those patients with spirometry FEV1 ≤ 40% predicted were classified as severe, those with spirometry FEV1 ≥ 70% predicted as mild in order to classify 2 severity levels of CF lung disease. Of 74 eligible subjects (age > 18 years, del F508 homozygous), 43 had FEV1 ≥ 70%, 9 had FEV1 = 41–69%, and 22 had FEV1 ≤ 40%; 31 were recruited during routine visits to the CF center from June 1998 to January 2000. With informed consent, participants provided blood samples for genomic DNA isolation. This study was approved by the Institutional Review Board at Johns Hopkins Medical Institution. DNA was isolated from lymphocytes using standard procedures.
Identification of CLC-2 polymorphisms
The genomic structure of rat CLC-2 has been previously published [5,6]and has important sites for gene regulation. The human CLC-2 genomic sequence, however, was largely unknown at the start of this study. Promotors are an important site to examine for SNPs, which might affect regulation of a gene. The first intron of a gene also can function as an important regulatory domain. Because the rat lung has a splice variant that deletes exon 20 [6] due to an unusually high CT content in the upstream intron 19 and a rare AAG acceptor site, this region was also examined for polymorphisms.
Primer pairs were thus chosen from rat [accession gi|4406230] and human CLC-2 sequence [accession S7770] to amplify the promoter, intron 1 and exon 20 from adult CF subjects homozygous for delF508. Sequencing of the human CLC-2 promoter initially from one human genomic sample was performed by polymerase chain reaction using the 5'-flanking region of rat hpolE1 (dTCC GGG TCA ATA TCC TTC ACA TCG), which is approximately 2000 base pairs upstream from the rat CLC-2 coding sequence [5] and the 3'-hCLC-2 promoter primer (dCGC CCG TGG CTC CAT CCC TTC), which corresponds to sequence from the N-terminus of the hCLC-2 coding region [accession S7770 [7]]. PCR amplification was performed using the MasterAmp™ PCR Optimization Kit buffer J (Epicentre Technologies, Madison, WI) due to the high GC rich content of this region in the rat [5]. The amplified product was cloned into the TA cloning Vector (Invitrogen), plasmid DNA grown in E. coli, and DNA isolated using the Mini-prep kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The sequence of this ~2000 bp product yielded genomic DNA for design of primer pairs that would yield overlapping PCR-amplified DNA fragments of the promoter.
Sequencing and identification of human CLC-2 promoter polymorphisms in 15 CF patients with severe obstructive lung disease (FEV1 ≤ 40% predicted) and 16 CF patients with mild disease (FEV1 ≥ 70% predicted) were performed by polymerase chain reaction using overlapping primers designed from the initial hCLC-2 clone. The only product that yielded a SNP was amplified using primers 15F dGTC CCA GGA GTA GAC TTC C and 16R dCAC TGC CCT CTG GCC TC providing a 760 base pair product, using cycling conditions of 94°C 6 mins, 35 cycles of 94°C 30 s, 59°C 30 s, 72°C 30 s and 72°C 6 min. A nested reaction with 20 uM primers 17F dTCC CCT CCG GCC TAC CCC TTC CGG T and 18R dGGA AGG ATT CGG AGA GGG TTG GGG C amplified both a 150 and 300 bp product using Epicentre MasterAmp™ buffer J (Madison, WI) with cycling conditions 94°C 6 mins, 35 cycles of 94°C 30 s, 64°C 30 s, 74°C 30 s and 74°C 6 min.
Because regulation of a gene may occur also through its first intron we amplified this region from all subjects using primers 1F' dCGC TGC AGC ACG AGC AGA C and 1R' dCCC AAG GTC CTG AGT GTA CC, which yielded a product 2273 bp product. Cycling conditions were 95°C 6 mins, 35 cycles of 95°C 30 s, 63°C 30 s, 72°C 3 minutes and 72°C 6 min. Finally, because exon 20 is alternatively spliced in rat lung [6] we examined whether or not SNPs existed in this region including parts of exon 19 and 21 and the intervening introns using primers 20F dGCC TCT TCT GTG GCA GTC C and 20R dCTT CAG GGC TCA TCT CGC C using PCR amplification conditions of 92°C 6 mins, 30 cycles of 92°C 30 s, 55°C 30 s, 72°C 30 s and 72°C 6 min These amplify a 481 bp fragment covering the 3' end of E19 to 5' end of E21.
With PCR amplification of all 31 genomic CF samples using primers listed in Table 1, the presence of amplified products was confirmed on agarose gels. Amplified DNA and primers were separated using Millipore filters. The purified PCR products were sequenced in both directions using the same primers used for amplification and Big Dye cycle sequencing kit (ver. 2 or 3.1, ABI) in accordance with the manufacturer's instructions. The fluorescently labeled products were separated and detected using either an ABI 377 or 3700 or 3730xl Automatic Sequencer (ABI). The trace files were read using Phred [8,9] and Phrap [10]. Each potential polymorphism was confirmed by visual inspection.
Table 1 Primers used to amplify CLC-2 polymorphisms
Primer oligomer expected size (bp)
rat hpolE1 dTCC GGG TCA ATA TCC TTC ACA TCG
2128
hClC-2 promoter dCGC CCG TGG CTC CAT CCC TTC
15F dGTC CCA GGA GTA GAC TTC C
760 bp
16R dCAC TGC CCT CTG GCC TC
17F dTCC CCT CCG GCC TAC CCC TTC CGG T
147 + 300 bp
18R dGGA AGG ATT CGG AGA GGG TTG GGG C
Intron 1F dCGC TGC AGC ACG AGC AGA C
2273
Intron 1R dCCC AAG GTC CTG AGT GTA CC
Exon 20F dGCC TCT TCT GTG GCA GTC C
481
Exon 20R dCTT CAG GGC TCA TCT CGC C
Results
Expression of CLC-2 protein in CF nasal cells
CLC-2 protein is nearly undetectable in postnatal rat lung [2], however we hypothesized that postnatal expression of CLC-2 in CF individuals might confer a protective advantage for the respiratory epithelium of CF individuals. We examined human CLC-2 protein expression using lysates from primary nasal cells obtained from elective polypectomy of CF patients with a variety of CFTR mutations. Similar amounts of total protein from nasal lysates electrophoresed on an SDS-PAGE system had variable amounts of CLC-2 protein detected [figure 1]. High levels of CLC-2 protein were expressed in some lysates, but CLC-2 protein was nearly undetectable in others suggesting that CLC-2 expression is variably regulated in humans. CFTR genetic mutation information was available for these patients and did not correlate with levels of CLC-2 protein expressed [figure 1]. In addition, the expression of ClC-2 protein was diminished in transformed bronchial epithelial IB3-1 cells [11] (lane 10, figure 1), which were derived from primary nasal epithelial cells of a subject with delF508/ W1282X (lane 8, figure 1). While data about the genetic mutations of the CFTR were available on these patients, information about their clinical status was not according to an agreement with the Johns Hopkins Institutional Review Board.
Figure 1 ClC-2 expression by Western blot of nasal polyp lysates from CF adults with the following genotypes: Lanes 1,3,6 dF508/dF508; Lane 2: dF508/d559T; Lane 4: unknown; Lane 5: S549N/R553X; Lane 7,9: dF508/unknown; Lane 8: F508/W1282X.; Lane 10, IB3-1 cell line, genotype F508/W1282X. Arrow identifies CLC-2 bands.
Single nucleotide polymorphisms in CLC-2
In order to minimize the confounding of genotype, race and age, all individuals were homozygous for delF508 mutation of CFTR, Caucasian, and over 17 years old. FEV1% determined 2 cohorts, one with mild CF lung disease with average FEV1% of 77.4 ± 3.18 SEM (Table 2, n = 16, 9 male). The group with severe lung disease had an average FEV1% of 35.6 ± 3.13 SEM (n = 15, 9 male). The mean age of the mild and severe groups was not significantly different (22.6 ± 1.37 years vs. 24.7 ± 1.56 years mean ± SEM). Because CLC-2 expression could be regulated through the promoter, for each patient's DNA, we amplified the CLC-2 promoter, primers that produced overlapping sequences that were examined for SNPs. In addition, intron1 and exon 20 were investigated for SNPs because of their potential role in CLC-2 expression.
Table 2 Demographics of study subjects.
FEV1 Gender Age (years) (± SEM)
Severe 35.6 (3.13) 9 M / 6 F 24.7 (1.56)
Mild 77.4 (3.18) 9 M / 7 F 22.6 (1.37)
Promotor
PCR amplified a 2128 bp promotor product confirmed by agarose gel. Sequence comparison revealed that bp 21 to 2128 of the amplified sequences was compatible with bp 317320 to 319427 of ref|NT_0292533|Hs_29412 and that there were no differences between the two sequences. Examination of these products determined that the upstream region was RPB8 exons 1–3 of the human gene polr2H (gi|8052522|) as expected from the rat genomic structure [5]. Human CLC-2 promoter is 69% GC rich and contains 4 GC boxes in the 225 bp upstream from the ATG start site (sequence to submit to GenBank). This area is very similar to rat ClC-2 promotor, where binding of transcription factors Sp1 and Sp3 occurs [5]. Human CLC-2 promotor sequence is very conserved with as much as 82% sequence identity with rat (gi 4406230) and 77% with mouse (gi 28494743). Guinea pig genomic sequence (gi 5001715) aligns with approximately 100 bp of the terminal end of human CLC-2 promoter and rabbit (gi 642465) only with 19 bp upstream of the coding sequence (Figure 2a and 2b). One G/A polymorphism was identified in the 5' upstream sequence of human CLC-2. This SNP is -693 relative to the ATG start site of hCLC-2 (figure 2b, asterisk, genbank S7770), and has not previously been identified. The -693 G/A polymorphism is a putative AP-2 binding site, predicted by TESS and MATINSPECTOR [12,13], which may affect regulation of the gene.
Figure 2 Diagram of alignment of human CLC-2 promoter and mammalian homologues (H = human, R = rat, M = mouse, GP = guinea pig, and Rb = rabbit. CLC-2 translation initiation site in all 5 species is denoted by "start". One single nucleotide polymorphism (SNP) is present at nt -693 (human). Hpol is a polymerase whose gene product is on the complementary strand, upstream from the CLC-2 promoter.
There were five subjects with severe CF lung disease (FEV1 < 40%), who had the genotype A/G, whereas eleven had G/G at position -693 (Table 3). Of the individuals with mild CF lung disease (FEV1 > 70%), 6 had A/G and 9 had G/G. By Fisher's test analysis there was no difference in the frequency of the promotor polymorphism between the severe and mild groups (p = 0.72).
Table 3 Promotor & Intron 1 hClC-2 polymorphisms
Promotor Intron 1
-693 358 427 1089 1909
FEV1 <40 AG (5) GG (13) AA (13) TT (9) GG (15)
GG (11) GC (2) AG (2) CT (6) GC (0)
FEV1 >70 AG (6) GG (11) AA (11) TT (6) GG (12)
GG (9) GC (3) AG (3) CT (10) GC (2)
P-value 0.72 0.32 0.32 0.21 0.22
Intron 1
The first intron of human CLC-2 was amplified and the 2273 bp product confirmed by gel electrophoresis. This sequence correlates with bp 319453 to 321725 of ref|NT_0292533|Hs_29412. Human CLC-2 intron 1 has regions with as much as 74% sequence identity with rat (gi 2873366) and 85% with mouse (gi28494743) (Figure 3a and 3b). Examination of 31 human CF samples revealed four SNPs: 358 G/C, 427 A/G, 1089 T/C and 1909 G/C (Figure 3a). There is complete linkage disequilibrium between SNP 358 and 427. Two CF subjects with severe lung disease (FEV1 < 40%) had 358 G/C, 2 had 427 A/G, and 6 had 1089 C/T, 0 had 1909 G/C (Table 3). Of the mild subjects (FEV1 > 70%), 3 of 14 had 358 G/C, 3 of 14 had 427 A/G, 10 of 16 had 1089 C/T, and 2 of 14 had 1909 G/C. By Fisher's test analysis there was no difference in the frequency of any one of the intron 1 polymorphisms between the severe and mild groups (Table 3, p = 0.32, 0.32, 0.21, and 0.22 for SNPs 358, 427, 1082 and 1902 respectively).
Figure 3 Sequence alignment of human, rat, mouse, guinea pig, and rabbit CLC-2 promoter. Site of human SNP at position -693 shown with asterisk. Conserved GC boxes underlined.
Exon 20
Primers used to examine the potential exon 20 splice variant region in hCLC-2 amplified a 481 bp fragment that correlates with bp 328123 to 328556 of human genomic sequence NT_0292533 and 2446 to 2617 of hCLC-2 cDNA (accession S7770). There were no SNPs identified using all 31 patient samples.
Conclusions
With an autosomal recessive pattern of inheritance, CF was long considered a monogenic disease with 1 mutant allele inherited from each parent. While CF neonatal screening is offered in several states of the U.S., counseling of families has been difficult, because CF genotyping does not easily predict onset and severity of pulmonary complications [14]. Strategies to identify modifier genes for the CF phenotype are important for defining disease prognosis and developing new strategies to prevent progression of the disease.
There are several chloride conductances, which have been characterized in the mammalian lung: the cAMP-dependent cystic fibrosis transmembrane conductance regulator (CFTR) [15], the Ca++-dependent chloride channel (CaCC) [16-18], the outwardly rectifying chloride channel (ORCC) [19], the purinergic receptor-mediated chloride channel [20,21], and the voltage- and volume-regulated, ClC family of chloride channels [3,22-24].
One or more of the chloride channels present in the respiratory epithelium may be able to partially compensate for defects in another. For example, there was no lung pathology in the first CF knock-out mouse models, where there is enhanced activity of a Ca++-dependent chloride channel [25-27], however lung disease is present when alternative chloride channels are absent [26]. The CF mouse, however, develops severe intestinal disease leading to premature death, which has been attributed to inadequate secretion via alternative chloride channels. Ca++-dependent chloride conductance is low in the intestine of the CF knock-out mouse. To take advantage of alternative chloride channels in the lung, UTP analogues have been used to stimulate chloride secretion in CF individuals via the purinergic receptor-mediated chloride channels [20,21].
One member of the ClC family of chloride channels may also be an alternative chloride conductance in the airway epithelium. We have demonstrated that CLC-2 mRNA and protein are abundantly expressed in the fetal lung [1,2] and that acidic pH can activate chloride secretion [3,22]. CLC-2 mRNA and protein are much higher in brain and kidney compared to tissues that are more severely affected by defective CFTR (lung, intestine, liver) [1] suggesting that CLC-2 expression may protect against disease manifestations in certain tissues. CLC-2 immunolocalizes to the apical surface of the respiratory epithelium [2,22], consistent with the potential to function as a chloride channel in a secretory organ. In this study, we have shown that several CF subjects do express CLC-2 protein as adults (figure 1), unlike in rats [2]. In single channel recordings, overexpression of CLC-2 in a CF bronchial epithelial cell line demonstrated that chloride secretion can be enhanced [3]. While the CLC-2 knock-out mouse has degeneration of the retina and testes [28], loss of CLC-2 function has not been associated with lung disease. To date overexpression of CLC-2 has not been described in an animal model to determine if this channel can be upregulated and serve as a potential therapeutic target for CF.
In this study of 31 CF subjects, we identified 5 single nucleotide polymorphisms that have not previously been described for human CLC-2. One of these is -693 relative to the ATG start site of hCLC-2 (Genbank S7770). The -693 G/A polymorphism is a putative AP-2 binding site, predicted by TESS and MATINSPECTOR [12,13] and may be important for regulation of the gene. This polymorphism was no more frequent in the CF subjects with mild lung disease compared with the subjects with severe lung disease.
In the rat, SP-1 sites are important for gene regulation [5,29,30]. ClC-2 expression in the lung is developmentally downregulated at birth [2] and is dependent on Sp binding to GC boxes in the ClC-2 promotor [5]. These GC boxes are highly conserved in human and rat, suggesting they are important sites for gene regulation. Phosphorylation of Sp-1 decreases its DNA binding activity and coincides with the downregulation of CLC-2 expression [5]. SNPs in the conserved GC boxes were not identified in the subjects of this study.
We also identified 4 polymorphisms in hCLC-2 intron1. These did not appear more or less frequently in the mild CF subjects. Two of the polymorphisms were in complete linkage disequilibrium. The polymorphisms were not identified in areas that were highly conserved in rat or mouse. While splice variants of exons may be affected by intron/exon boundaries, we did not find any polymorphisms in the region of the rat exon 20 splice variant [6].
These findings suggest that CLC-2 is not regulated differently at the genomic level in relatively healthy CF adults. Lack of an association in this study does not exclude the possibility that CLC-2 plays a role in modifying the CF phenotype as might be suggested by the variability of CLC-2 protein expression in primary respiratory epithelial cells from CF subjects in this study. Although we were limited by inadequate power with a small sample size and because phenotypic contrast was low, our data suggest that gene regulation of CLC-2 in relation to polymorphisms in regulatory domains does not play a major role in protection against CF lung disease. Studies which rely on recruitment of small numbers of patients have been shown to detect a difference when a strong relationship is present [31]. Another limitation may be in the selection of FEV1 at a single point in time, rather than using rate of decline of FEV1. Other studies of CF modifier genes have similarly found difficulty in confirming a candidate gene, which also relied on FEV1 at one time point. In addition, the effect on lung phenotype may occur at an earlier stage of CF lung disease, and examination of adults only as in this study may have limited our ability to detect a difference. The polymorphisms identified in this report should facilitate further investigation of CLC-2 regulation. While we did examine subjects with the same CF genotype (namely delF508 homozygous), measures of ion transport (sweat chloride, nasal potential difference), time to colonization with Pseudomonas, and frequency of pneumonia, should be taken into account in future studies.
The identification of candidate genes, which may modify CF lung disease is important so that new therapies may be developed. Multi drug resistant genes have recently been identified that provide some "protection" to the CF lung phenotype [32]. Ion transport dysfunction of CFTR and the channels it regulates, however, may not be the only determinant of disease severity. Many have suggested that inflammatory mechanisms may also impact disease progression and survival in CF individuals [33]. Other classes of candidate genes possibly related to CF phenotype include tumor necrosis factor alpha (TNF-α), nitric oxide synthase (NOS), alpha 1-antitrypsin, mannose-binding lectin [34], and other ion channels such as the basolateral K+ channels [17,35-38].
Lastly, gene expression and function may be independent of genomic polymorphisms, as suggested by our data demonstrating variable expression of hClC-2 protein in CF nasal polyps and must also be considered as a mechanism whereby, CLC-2 could alter the course of CF disease. Haug et al. recently identified mutations in the CLC-2 coding region that are associated with idiopathic generalized seizures in humans [39]. No lung disease was reported from loss of function of CLC-2, so presumably CLC-2 is not critical for the function of mature respiratory epithelium when CFTR is present. A ClC-2 knock-out mouse shows severe degeneration of the retina and testes, but no evident lung disease [28,40]. While there has been no report of ClC-2 lung abnormality in these mice, they do not replicate the human seizure disorder and mouse models do not exclude the possibility of a role in airway epithelial ion transport. For example, initial studies of CF knock out mice also suggested no discernible lung disease that mimics CF in humans [41,42]. The activation of CLC-2 currents by acidic pH, suggests that alterations of key regulatory domains of the channel may affect function. There is disagreement about whether or not a specific region of the N-terminus of CLC-2 is the sensor for acid and voltage regulation [43-45].
This study provides important information about the human CLCN2 genomic organization. Several polymorphisms of key regulatory domains of CLCN2 were identified in a cohort of subjects with cystic fibrosis, who carry the same CF genotype. While we have found no significant association of CLC-2 polymorphisms with FEV1 % predicted in adulthood, further study of potential polymorphisms in CF subjects at an earlier age and investigation of potential mutations in the coding region of CLC-2 that would lead to enhanced transepithelial chloride transport would be necessary to determine if CLC-2 can modify CF.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
CB provided overall study design, analysis, and drafted the manuscript. TH designed sequencing methods and analyzed alignment. AS and PB conducted experiments. EB contributed to the design of the study and OS designed sequencing methods and provided analysis including statistics.
All authors read and approved the final manuscript.
Figure 4 Diagram of alignment of human CLC-2 intron1 and mammalian homologues (H = human, R = rat, and M = mouse. Four single nucleotide polymorphisms (SNP) are present at nt 358, 427, 1089, and 1909 (human).
Figure 5 Sequence alignment of human, rat, and mouse CLC-2 promoter (nt 26-1488).
Figure 6 Sequence alignment of human, rat, and mouse CLC-2 promoter (nt 1489-2138). Site of human SNP at position 1909 shown with asterisk.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This work was funded by the National Heart, Lung, and Blood Institute Grant K08-HL-03469 (CJB). We would like to thank Pamela Zeitlin for her encouragement and support of this project and Shijian Chu for his thoughtful review.
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| 15507145 | PMC526769 | CC BY | 2021-01-04 16:31:07 | no | BMC Med Genet. 2004 Oct 26; 5:26 | utf-8 | BMC Med Genet | 2,004 | 10.1186/1471-2350-5-26 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-351550713910.1186/1471-244X-4-35Case ReportIncreased deep sleep in a medication-free, detoxified female offender with schizophrenia, alcoholism and a history of attempted homicide: Case report Lindberg Nina [email protected] Pekka [email protected] Pirjo [email protected] Eila [email protected] Hanna [email protected] Markku [email protected] Matti [email protected] Institute of Biomedicine, Department of Physiology, University of Helsinki, Helsinki, Finland2 Institute of Clinical Medicine, Department of Psychiatry, University of Helsinki, Finland3 Vanha Vaasa Hospital, Vaasa, Finland2004 26 10 2004 4 35 35 2 9 2004 26 10 2004 Copyright © 2004 Lindberg et al; licensee BioMed Central Ltd.2004Lindberg et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Psychiatric sleep research has attempted to identify diagnostically sensitive and specific sleep patterns associated with particular disorders. Both schizophrenia and alcoholism are typically characterized by a severe sleep disturbance associated with decreased amounts of slow wave sleep, the physiologically significant, refreshing part of the sleep. Antisocial behaviour with severe aggression, on the contrary, has been reported to associate with increased deep sleep reflecting either specific brain pathology or a delay in the normal development of sleep patterns. The authors are not aware of previous sleep studies in patients with both schizophrenia and antisocial personality disorder.
Case presentation
The aim of the present case-study was to characterize the sleep architecture of a violent, medication-free and detoxified female offender with schizophrenia, alcoholism and features of antisocial personality disorder using polysomnography. The controls consisted of three healthy, age-matched women with no history of physical violence. The offender's sleep architecture was otherwise very typical for patients with schizophrenia and/or alcoholism, but an extremely high amount of deep sleep was observed in her sleep recording.
Conclusions
The finding strengthens the view that severe aggression is related to an abnormal sleep pattern with increased deep sleep. The authors were able to observe this phenomenon in an antisocially behaving, violent female offender with schizophrenia and alcohol dependence, the latter disorders previously reported to be associated with low levels of slow wave sleep. New studies are, however, needed to confirm and explain this preliminary finding.
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Background
Female violent behaviour has been studied less than that of men. This is partly because women commit fewer crimes than men [1], but also because female aggression is traditionally carried out in private and domestic settings [2]. Even though the rate of violent crimes among women appears to be increasing [3,4], female homicide can still be seen as a rare phenomenon. A psychotic disorder has been observed in ca 30 % of homicidal women [5], and schizophrenia has been reported to increase the risk for homicidal violence [6]. Alcohol is often present in severe violent crimes [7] and the risk for homicidal behaviour is extremely high in women with both schizophrenia and alcohol dependency [8]. Schizophrenia patients with antisocial personality disorder represent a special high-risk subgroup that is vulnerable to severe substance abuse, psychiatric impairment and aggression as well as to legal problems [9].
Human sleep consists of two main components: rapid eye movement (REM) sleep, and non-REM sleep, the latter divided into stages 1–4 (S1–S4). Stage 3 sleep (S3) and stage 4 sleep (S4) in non-REM sleep are defined as slow-wave sleep (SWS), also called delta sleep or deep sleep. In normal sleep, REM and non-REM sleep periods alternate cyclically. Though the exact functions of the different sleep stages are unknown, it is generally accepted that SWS is the physiologically significant, refreshing part of sleep. Feelings of unwellness, either somatic or psychiatric, are frequently associated with decreased SWS.
Serious sleep disturbances are associated both with schizophrenia and alcoholism. Typical findings in both disorders are long sleep latency, reduced sleep length, long periods of waking after sleep onset, abnormalities in REM parameters as well as decreased SWS [10-12]. The reduction in SWS tends to persist after the clinical remission of psychotic symptoms [13], and it has been suggested that reduced SWS is the prevailing alteration in the sleep of patients with schizophrenia [14], although Hoffmann et al. [15] challenged this view in their recent study.
The aim of the present study was to characterize the sleep architecture in a severely violent, medication-free, detoxified woman with schizophrenia, alcoholism and a history of attempted homicide. She is the first subject in a larger study concerning sleep structure in women with this type of comorbidity.
Case presentation
Participants
The patient
The patient was a 23-year-old woman charged with attempted manslaughter. She was ordered by the court to undergo a pre-trial forensic psychiatric examination which took place in a maximum security state mental hospital. The trial records and all available background information were reviewed. Both of her parents were alcoholics and she was taken to protective custody under the age of one year. Her grandmother suffered from schizophrenia and her mother had been in a psychiatric hospital after a suicide attempt. The patient used to shoplift before the age of 15 and later she stole money from time to time. She also worked as a prostitute to earn money. She started to abuse alcohol at the age of 15. She had several boyfriends but has never married and has no children. Her personality was noticed to change before the age of 17. She started to have obsessive-compulsive behaviour, paranoid thoughts as well as depressive symptoms. She tried to kill herself by hanging and by several drug intoxications. She brutally killed her own pet. She was never hospitalized before the forensic mental examination but she irregularly visited an outpatient clinic. Despite psychiatric treatment, she impulsively tried to kill her male friend with whom she was drinking. During the psychiatric examination the diagnoses – schizophrenia paranoid type, alcohol dependence and features of antisocial personality disorder – were made by a senior forensic psychiatrist using the structured clinical interview for DSM-IV, SCID I and II [16,17]. Neither waking EEG nor brain MRI (1.5T) disclosed any abnormality. She had no somatic disorders. She had finished high school and vocational school, and was within average intelligence (WAIS-IQ total 109). She was not sentenced, but was ordered by the Finnish National Board of Medico-Legal Affairs to stay in the state mental hospital as a criminally insane patient. The sleep recordings were performed during the psychiatric examination period and the patient was completely medication-free and had abstained from alcohol and drugs for six months.
The controls
The control group consisted of three 23-year-old female students without criminal records or a history of physical violence. They were healthy with no signs of somatic, psychiatric, or neurological disorders. As part of a psychiatric interview, the SCID-non-patient version [18] was filled in. To exclude general diseases that could affect sleep, blood tests (including serum prolactin, thyroid function, kidney and liver function) and electrocardiograms were taken and they were within normal range, both in the patient and in controls. No history of alcohol abuse or dependence was detected in controls. The controls were asked to avoid alcohol, drugs or medication two weeks prior to the sleep examinations. Caffeine and nicotine consumption was neither restricted nor recorded.
Written consent was obtained from all participants after the study procedure had been fully explained to them. The study was approved by the local human ethics committee.
Sleep examination
Polysomnography
Polysomnography (PSG) was recorded over two consecutive nights but only the second night was considered for the study. The study patient slept in a single room within the department, while controls slept in the hospital guest room. All participants were allowed to sleep as long as they wanted to. Recordings took place on an ambulatory basis; the participant had a portable recording device (Embla, Flaga hf, Reykjavik, Iceland) that was connected to the recording electrodes. The recordings were performed using the standard Rechtschaffen-Kales method [19]. The high-pass filter was 0.5 Hz and the low-pass filter 45 Hz, with a sampling rate of 100 Hz. Commercial software (Somnologica, version 2.0, Flaga hf, Reykjavik, Iceland) was used for scoring and calculation of sleep parameters. Sleep onset was defined as the first occurrence of three consecutive epochs (90 sec) of stage 1 (S1) or other sleep stages. The following parameters were calculated: time in bed, sleep latency, sleep period (time in bed – sleep latency), wake after sleep onset, total sleep time (sleep period – wake after sleep onset), sleep efficiency (total sleep time/sleep period), number of awakenings, REM latency and percentage amounts of different sleep stages (S1–S4 %, REM %). All data for the analysis were scored by the same scorer (NL), not blinded to the patient group.
Sleep diary
A sleep diary was used for one week during the study period to ensure a normal sleep-wake rhythm and to exclude the effects of daytime naps. The participant filled in the time of retiring to bed, estimated time of falling asleep and time of awakening in the morning for each consecutive night as well as daytime naps.
Results and discussion
The patient's PSG recording with reduced sleep length, long sleep latency, and a high number of awakenings after sleep onset as well as reduced sleep efficiency (Table 1) is very typical for patients with schizophrenia or alcoholism. Reduced REM sleep latency has been attributed to cholinergic hyperactivity secondary to increased dopaminergic tone in schizophrenia [20]. The most striking finding in the sleep recording was the high amount of deep sleep (SWS: 41.8%, S4 sleep: 26.1%). One of the consistent alterations in normal ageing is a decrease of SWS. The sleep patterns of children are typically characterized by high amounts of SWS, but a quantitative decrease occurs during puberty. As ageing proceeds, a gradual decline in SWS is observed [21]. In normal young adult individual SWS generally constitutes about 13 to 23% and S4 sleep about 10 to 15% of sleep [22], and the amounts of these sleep stages in controls of the present study are in good agreement with this.
Table 1 Polysomnography parameters of a homicidal woman with schizophrenia, alcoholismand features of antisocial personality disorder and of three healthy female controls. REM = rapid eye movement sleep, S1–S4 = sleep stages 1–4, SWS = slow wave sleep.
patient controls mean (SD) control 1 control 2 control 3
time in bed (min) 475.5 562.0 (10.00) 552.0 562.0 572.0
sleep latency (min) 56.0 19.0 (7.26) 18.5 26.5 12.0
sleep period (min) 419.5 543.0 (14.76) 533.5 535.5 560.0
awakenings (n) 14 7.7 (0.58) 7 8 8
wake after sleep onset (min) 56.0 30.3 (2.02) 30.0 28.5 32.5
total sleep time (min) 363.5 512.7 (12.97) 503.5 507.0 527.5
sleep efficiency (%) 86.7 94.4 (0.25) 94.4 94.7 94.2
REM latency (min) 52.5 85.8 (5.01) 81.0 85.5 91.0
S1 (%) 3.3 6.9 (0.90) 6.0 7.8 6.8
S2 (%) 33.2 53.1 (1.66) 51.3 53.3 54.6
S3 (%) 15.7 7.8 (1.41) 7.7 6.5 9.3
S4 (%) 26.1 11.6 (2.07) 14.0 10.7 10.2
SWS (%) 41.8 19.5 (2.25) 21.7 17.2 19.5
REM (%) 21.7 20.6 (1.35) 21.0 21.7 19.1
The finding concerning SWS is completely contrary to previous sleep reports in patients with schizophrenia as well as with alcohol dependence. The patient had abstained from alcohol for several months before the PSG, and this raises the question if the result could be explained with a withdrawal state. However, reduced SWS is also associated also with alcohol withdrawal and the phenomenon has been reported to be extremely long lasting [11]. Even after one or two years of abstinence, the sleep records of alcoholics had partly normalized but the percentage of S4 sleep still remained at lowered levels [23]. Thus, alcohol abstinence cannot explain the finding.
MRI disclosed no post-traumatic signs in the brain substance of the patient. Waking EEG was also within normal limits. Minor head injuries (concussions) are, however, frequent events among people with alcoholism. Kaufman et al. [24] demonstrated a chronic sleep disturbance several years after a minor head injury in a non-selected population. They found lower sleep efficiency, and more awakenings lasting more than three minutes, but no changes in S3 or S4 sleep compared with healthy controls.
The patient had a history of violent acts and her lifestyle was described to be antisocial and unstable and she was diagnosed as having features of antisocial personality disorder in addition to schizophrenia and alcohol dependence. The authors are not aware of previous sleep studies in patients with both schizophrenia and antisocial personality disorder. In a PSG study among habitually violent male offenders with antisocial personality disorder as a primary diagnosis, increased amounts of SWS and S4 sleep were observed as compared with age-matched healthy men [25], which is in agreement with the present case-study. Furthermore, the male offenders with severe conduct disorder preceding ASP had higher amounts of deep sleep than men with only mild or moderate conduct disorder [26]. Adult antisocial personality disorder, childhood conduct disorder [27] and childhood ADHD [28] are the only psychiatric disorders reported to be associated with increased deep sleep. Whether the phenomenon observed in these three disorders reflects specific brain pathology, or a delay in the normal development of sleep patterns in the course of ageing, is still an open question and needs to be clarified.
All patients with schizophrenia should not be considered to be violent, although there are minor subgroups of schizophrenic patients among whom the risk for violence is remarkably high. It has been estimated that this increase in risk could be associated with the paranoid form of schizophrenia, coexisting substance abuse and antisocial behaviour [6]- the disorders, which our patient has. Clozapine, an atypical antipsychotic with significant anti-aggressive effects [29] is widely used in institutions like state mental hospitals where habitually violent patients with schizophrenia are treated. Interestingly, clozapine has been shown to significantly reduce the amount of S4 sleep both in healthy controls [30] and in patients with schizophrenia [31]. Future research is needed to clarify this association and the mechanisms behind this phenomenon.
Conclusions
Increased deep sleep has been associated with antisocial behaviour with severe aggression. The authors were able to observe this phenomenon in an antisocially behaving, violent female offender with schizophrenia and alcohol dependence, the latter disorders previously reported to be related to low levels of SWS. New studies are, however, needed to confirm and explain this preliminary finding.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
This manuscript was prepared by a multidisciplinary team consisting of:
NL, generated the idea for this case report, interviewed the controls, scored the sleep recordings, and prepared the manuscript together with the team.
PT, had a substantial contribution in theoretical background and processing of the present study as expert in sleep research
PT, reviewed all material concerning the patient as expert in forensic psychiatry
ES, participated in processing of the manuscript as expert in forensic psychiatry
HP, participated in the processing of the manuscript as expert in forensic psychiatry and female violence
ME had a central role in planning the study design as well as in formulating the theoretical background of the present study. He also allocated financial resources to this project and helped solving practical problems of the study project
MV, supervised and participated with great impact in all stages of this manuscript
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Written consent was obtained from the patient for publication of the study.
==== Refs
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Eronen M Mental disorders and homicidal behavior in female subjects Am J Psychiatry 1995 152 1216 1218 7625476
Mueser KT Drake RE Ackerson TH Alterman AI Miles KM Noordsy DL Antisocial personality disorder, conduct disorder, and substance abuse in schizophrenia J Abnorm Psychol 1997 106 473 477 9241949 10.1037//0021-843X.106.3.473
Benson KL Zarcone VP Kryger MH, Roth T, Dement WC Schizophrenia In Principles and Practice of Sleep Medicine 2000 3 Philadelphia: WB Saunders Company 1159 1167
Gillin JC Drummond SPA Kryger MH, Roth T, Dement WC Medication and Substance Abuse In Principles and Practice of Sleep Medicine 2000 3 Philadelphia: WB Saunders Company 1176 1195
Goder R Boigs M Braun S Friege L Fritzer G Aldenhoff JB Hinze-Selch D Impairment of visuospatial memory is associated with decreased slow wave sleep in schizophrenia J Psychiatr Res 2004 38 591 9 15458855 10.1016/j.jpsychires.2004.04.005
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Hoffmann R Hendrickse W Rush AJ Armitage R Slow-wave activity during non-REM sleep in men with schizophrenia and major depressive disorders Psychiatry Res 2000 95 215 25 10974360 10.1016/S0165-1781(00)00181-5
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| 15507139 | PMC526770 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Oct 26; 4:35 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-35 | oa_comm |
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BMC Pulm MedBMC Pulmonary Medicine1471-2466BioMed Central London 1471-2466-4-101549407410.1186/1471-2466-4-10Case ReportTracheal adenoid cystic carcinoma masquerading asthma: A case report Kokturk Nurdan [email protected] Sedat [email protected] Cuneyt [email protected] Haluk [email protected] Gazi University School of Medicine, Department of Pulmonary Medicine, Ankara, Turkey2 Gazi University School of Medicine, Department of Thoracic Surgery, Ankara, Turkey2004 19 10 2004 4 10 10 23 5 2004 19 10 2004 Copyright © 2004 Kokturk et al; licensee BioMed Central Ltd.2004Kokturk et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Tracheal tumors are often misdiagnosed as asthma and are treated with inhaled steroids and bronchodilators without resolution.
Case Presentation
Here, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported. The possibility of the presence of localized airway obstruction was raised when the flow-volume curve suggesting fixed airway obstruction, was obtained.
Conclusion
The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized obstructions should be ruled out before establishing the diagnosis of asthma.
==== Body
Background
Tracheal tumors are uncommon and often overlooked until they reach to an advanced stage. The presenting symptoms are typically prolonged cough and wheezing that can be misdiagnosed as asthma [1]. Therefore, making precise diagnosis of tracheal tumor may be extremely challenging.
Here, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported.
Case presentation
A 29 year-old man was referred to our hospital with a 2-year history of paroxysmal attacks of dyspnea, dry cough and wheezing. He had smoked 2 packs/day cigarettes for 3 years and has been ex-smoker for 5 years. He experienced frequent sudden-onset coughing episodes followed by the development of dyspnea and wheezing a year ago. He was previously diagnosed with difficult asthma and treated with high dose inhaled corticosteroids (1600 μg budesonide) and bronchodilators. Since he was unresponsive to the therapy, he has applied to several institutions for multiple times to seek medical attention.
On admission, no stridor, wheezing and cyanosis were present and the general appearance was good. Vital signs were as follows: temperature 37°C, respiratory rate 20/min, pulse 96 beats/min, blood pressure 140/70 mmHg. The chest examination was unremarkable. The results of the routine laboratory analysis, including complete blood cell count, chemistry, arterial blood gas, urinalysis and chest x-ray were within normal ranges. On spirometric examination, flow-volume curve displayed suggestive fixed airway obstruction. Forced vital capacity (FVC) was 122 % of predicted, forced expiratory volume in one second (FEV1) was 31 % of predicted and FEV1/FVC was 21 % (Figure 1). In order to exclude the possibility of upper airway obstruction, a work-up of computerized tomography (CT) of the chest and fiberoptic bronchoscopy (FOB) was obtained. The CT scan illustrated a solid, polipoid intratracheal mass originating from the right side of the trachea at 4 centimeter proximal of the carina (Figure 2a). FOB revealed a smooth, round mass of 2 cm in diameter originating from the right lateral side of the trachea. The lesion was occupying approximately 50 % of the lumen (Figure 3). It localized at 4th centimeter distal to larynx. Histopathological diagnosis was adenoid cystic carcinoma of the trachea.
Figure 1 Flow-volume curve displays suggestive fixed airway obstruction
Figure 2 a) Chest CT scan displays a polypoid mass occupying 50 % of the lumen. b) Control CT scan displays resolution of the tumor
Figure 3 Bronchoscopic examination reveals polypoid mass originating from trachea with a 50 % obstruction of the lumen.
The patient underwent resection surgery. At the operational site, there were severe adhesions between the mediastinal surface and the trachea. Therefore, a conservative surgery was performed. The tumor was seen on the right anterolateral wall of the trachea being expanded submucosally from the carina to the proximal end of the trachea.
The patient underwent adjuvant radiation therapy after the operation. CT scan of the neck revealed resolution of the tumor (Figure 2b). Now, 3 months after the operation, the patient has remained well.
Conclusions
Primary tracheal tumors are rare with the incidence of less than 0.1 % [2,3]. The majority of tracheal tumors in adults are malignant and the most common ones are squamous cell carcinoma and adenoid cystic carcinoma (cylindroma). Tumors of the larynx and lungs are respectively, 75 and 180 times more common than malignant tracheal tumors [3,4]. Benign tracheal tumors such as lipoma, hamartoma and neurilemmoma are much more rare than malignant tracheal tumors [2,5,6].
Clinical manifestations of tracheal tumors are developed as a consequence of tumor bulk and location. Patients with tracheal tumor often have exertional shortness of breath, prolonged cough or a new onset of wheezing, which is frequently misdiagnosed as asthma. Patients are usually initially managed accordingly. However, tumor may occlude 75 % of the lumen before leading symptoms. In the literature, most of the reports highlight that there is always a remarkable delay of establishing accurate diagnosis as a result of misdiagnosis of asthma [1,5,7]. Pearson et al have reported a 2-month to 2-year delay in diagnosis in their series from Toronto General Hospital [8]. Therefore, adult-onset asthma that increases the severity despite the adequate therapy should alert one to the possibility of a central obstructing lesion [1]. In such patients, a flow-volume curve may provide extremely valuable data and may lead the clinician toward accurate diagnosis.
Another diagnostic challenge of tracheal tumor is the fact that it can occasionally be visualized by plain chest X-ray. CT scan of the chest or magnetic resonance imaging may yield more valuable data on the site and length of the tracheal lesion [1]. The cornerstone diagnostic modality is bronchoscopy [1].
In the presenting report, the patient had been mistakenly diagnosed with difficult asthma because of the presence of uncontrolled asthmatic symptoms and poor lung functions despite the use of high doses corticosteroids. Nevertheless, the flow-volume curve typically displayed localized fixed obstruction of central airways. This led us to have a work-up of CT scan and FOB to rule out upper airway obstructions.
Adenoid cystic carcinomas are smooth, firm, and well-circumscribed lesions. These tumors grow extremely slowly. Patients have been known to survive for 10 to 15 years with multiple lung metastases. Spread tends to occur submucosally [1].
The optimal therapeutic approach is surgical resection and reconstruction. The surgeon should be aware of the fact that the apparent gross margin of the tumor is usually still involved with the tumor cells so that the resection should be done at least 1 cm beyond the gross tumor margin. Postoperative irradiation was recommended by most of the authors [1,9]. Long-term postoperative follow-up is important to discover recurrences.
In this case, postoperative irradiation on curative doses has been applied for a month. Control bronchoscopic examination revealed near-complete remission.
The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized airway obstructions should be rule out before establishing the diagnosis of asthma.
Competing interest
The author(s) declare that they have no competing interests.
Authors' contributions
HT has seen the patient and made the diagnosis of tracheal obstruction, participated in the design of the manuscript. NK has seen the patient in the clinical, followed the patient and drafted the manuscript. SD and CK have performed the surgery and followed the patient. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The patient who has been illustrated in this report has given a signed written consent for the publication of the study.
==== Refs
Allen MS Malignant Tracheal Tumors. Mayo Clin Proc 1993 68 680 4 8394484
Tastepe AI Kuzucu A Demircan S Liman TS Demirag F Surgical Treatment of Tracheal Hamartoma. Scand Cardiovasc J 1998 32 239 241 9802143 10.1080/14017439850140030
Pando Pinto JM Vega Cuadri A Montero Garcia C Blasco Huelva A Primary Carcinoma Of The Trachea. Report of 2 Cases. An Otorrinolaringol Ibero Am 2000 27 595 604 11200556
Stack PS Steckler RM Tracheal Neurilemmoma: Case Report and Review Of The Literature. Head And Neck 1990 12 436 439 2211106
Turay UY Ergun P Topcu S Kurul C Aydogdu M Demirag F Erdogan Y A Case Of Tracheal Neurilemmoma Treated As Bronchial Asthma. Turkish Respir J 2002 3 79 82
Tayama K Takal E Ueda T Yano T Ichinose Y Tracheal Lipoma Obstructing The Right Main Bronchus: Report Of A Case. Surg Today 1996 26 1017 1019 9017967 10.1007/BF00309965
Parrish RW Banks J Fennerty AG Tracheal Obstruction Presenting As Asthma. Postgrad Med J 1983 59 775 776 6318209
Pearson FG Todd TRJ Cooper JD Experience With Primary Neoplasms Of The Trachea And Carina. J Thorac Cardiovasc Surg 1984 88 511 516 6090818
Grillo HC Mathisen DJ Primary Tracheal Tumors: Treatment and Results. Ann Thorac Surg 1990 49 69 77 2153371
| 15494074 | PMC526771 | CC BY | 2021-01-04 16:30:10 | no | BMC Pulm Med. 2004 Oct 19; 4:10 | utf-8 | BMC Pulm Med | 2,004 | 10.1186/1471-2466-4-10 | oa_comm |
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BMC DermatolBMC Dermatology1471-5945BioMed Central London 1471-5945-4-151550930410.1186/1471-5945-4-15Research ArticleAn evaluation of UV protection imparted by cotton fabrics dyed with natural colorants Sarkar Ajoy K [email protected] Design and Merchandising, Colorado State University, Fort Collins, Colorado, USA2004 27 10 2004 4 15 15 9 7 2004 27 10 2004 Copyright © 2004 Sarkar; licensee BioMed Central Ltd.2004Sarkar; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The ultraviolet properties of textiles dyed with synthetic dyes have been widely reported in literature. However, no study has investigated the ultraviolet properties of natural fabrics dyed with natural colorants. This study reports the Ultraviolet Protection Factor (UPF) of cotton fabrics dyed with colorants of plant and insect origins.
Methods
Three cotton fabrics were dyed with three natural colorants. Fabrics were characterized with respect to fabric construction, weight, thickness and thread count. Influence of fabric characteristics on Ultraviolet Protection Factor was studied. Role of colorant concentration on the ultraviolet protection factor was examined via color strength analysis.
Results
A positive correlation was observed between the weight of the fabric and their UPF values. Similarly, thicker fabrics offered more protection from ultraviolet rays. Thread count appears to negatively correlate with UPF. Dyeing with natural colorants dramatically increased the protective abilities of all three fabric constructions. Additionally, within the same fabric type UPF values increased with higher depths of shade.
Conclusion
Dyeing cotton fabrics with natural colorants increases the ultraviolet protective abilities of the fabrics and can be considered as an effective protection against ultraviolet rays. The UPF is further enhanced with colorant of dark hues and with high concentration of the colorant in the fabric.
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Background
High, short-term exposure to ultraviolet radiation (UVR) from the sun causes sunburns and long-term exposure leads to skin cancer. The National Toxicology Program, U.S. Department of Health and Human Services has classified UVR as a known human carcinogen [1]. The American Cancer Society estimates that more than one million cases of skin cancer cases are diagnosed each year in the United States [2]. In 2002, an estimated 54,200 new cases of melanoma skin cancer alone were diagnosed [2]. A primary reason for the increased incidence of skin cancers is attributed to ozone depletion. Each one percent decrease in ozone concentration is predicted to increase the rate of skin cancer by two percent to five percent [3]. The United States Environmental Protection Agency estimates that ozone depletion will lead to between three and fifteen million new cases of skin cancer in the United States by the year 2075. Other reasons for the skin cancer epidemic can be traced to lifestyle changes such as excessive exposure to sunlight during leisure activities, for example, playing outdoors and swimming in the case of children and golfing and fishing in the case of adults. In the case of agricultural and other outdoor workers, exposure to the sun is an occupational hazard as they have no choice about the duration of their exposure to the sun [3-5].
The ultraviolet radiation (UVR) band consists of three regions: UV-A (320 to 400 nm), UV-B (290 to 320 nm), and UV-C (200 to 290 nm). UV-C is totally absorbed by the atmosphere and does not reach the earth. UV-A causes little visible reaction on the skin but has been shown to decrease the immunological response of skin cells [3]. UV-B is most responsible for the development of skin cancers [3].
Other than drastically reducing exposure to the sun, the most frequently recommended form of UV protection is the use of sunscreens, hats, and proper selection of clothing. Unfortunately, one cannot hold up a textile material to sunlight and determine how susceptible a textile is to UV rays. Even textiles which seem to be non-light transmitting may pass significant amounts of erythema-inducing UV irradiation [4]. Therefore, knowledge of the factors that contribute to the protective abilities of textiles is vital. Important factors include fiber composition, fabric construction and wet-processing history of the fabric such as color and other finishing chemicals that may have been applied to the textile material.
To the author's knowledge, no study has investigated the ultraviolet properties of natural fabrics dyed with natural colorants. A plethora of previous studies have concluded that good UVR protection can be provided by synthetic fibers dyed with high concentrations of synthetic dyes. However, synthetic fibers such as polyester are hydrophobic and are generally not deemed to be comfortable for wear especially in warm weather. According to a report in America's Textile Industries [6] natural fibers are back in demand. The emergence and popularity of a more natural way of life as reflected in a return to organic farming and natural foods has now extended into textiles where the resurgence of natural fibers and natural dyes is on the increase [6,7]. It is hoped that data from the present study will be useful for dermatologists advising patients regarding the UV-protective properties of clothing made from natural fibers and dyed with natural colorants.
In this study, cotton fabrics were dyed with three natural colorants of plant and insect origin. Fabrics were characterized with respect to fabric construction, weight, thickness and thread count. Ultraviolet Protection Factor (UPF) was measured using a labsphere® UV-100 F Ultraviolet Transmission Analyzer. The effect of colorant concentration on the ultraviolet protection factor was examined via color strength analysis using a HunterLab ColorQuest XE® spectrophotometer.
Methods
Three fabrics were chosen to cover the gamut of basic weave constructions. They were a bleached, mercerized plain weave cotton fabric, a bleached mercerized cotton twill and a desized and bleached cotton sateen. Fabric weight was measured according to ASTM Test Method D3776-96 [8]. Fabric thickness was measured according to ASTM Test Method D1777-96 [8]. Thread counts were measured according to ASTM D3775-98 [8].
Natural plant colorants used were madder (Rubia tinctorum) and indigo (Indigofera tinctoria) and the natural colorant of insect origin was cochineal (Dactylopius coccus). Since natural dyes do not have affinity for cellulosic fibers an alum mordant was used to impart affinity. Fabrics were mordanted prior to dyeing by treating with alum at boil for 45 minutes. The liquor ratio was 1:40 and alum concentration was 10% on weight of the fabric. After mordanting, fabric was squeezed thoroughly and dyed.
Madder and cochineal dyeings were done in stainless steel canisters of an Atlas launder-ometer using 2%, 4% and 6% dye on weight of fabric. The liquor-goods ratio was 40:1. Fabrics were introduced into the dyeing solutions at room temperature. Temperature was raised to the boil and dyeing continued at the boil for 60 minutes. After dyeing, fabrics were rinsed in deionized water, washed using a non-ionic detergent and air-dried. Three replications were done for each colorant and at each dye concentration.
Dyeing with indigo was done in the following manner. Indigo dye was made into a paste and solubilized using sodium hydroxide and sodium hydrosulfite. Fabrics were introduced into dyebaths containing 2%, 4% and 6% dye on weight of fabric. The liquor-goods ratio was 40:1. After thirty minutes of dyeing the fabrics were removed and oxidized by drying in air. The fabrics were then rinsed in deionized water and washed using a non-ionic detergent and dried.
Direct and diffuse UV transmittance through a fabric is the crucial factor determining the UV protection of textiles [9]. Ultraviolet protection factor (UPF) is the scientific term used to indicate the amount of Ultraviolet (UV) protection provided to skin by fabric. UPF values are analogous to SPF values the only distinction being that SPF values for sunscreens are determined through human testing whereas UPF values are based on instrumental measurements [10]. UPF is defined as the ratio of the average effective UV irradiance calculated for unprotected skin to the average UV irradiance calculated for skin protected by the test fabric [5,10]. The higher the value, the longer a person can stay in the sun until the area of skin under the fabric becomes red [5,10]. An effective UVR dose (ED) for unprotected skin is calculated by convolving the incident solar spectral power distribution with the relative spectral effectiveness function and summing over the wavelength range 290-400 nm. The calculation is repeated with the spectral transmission of the fabric as an additional weighting to get the effective dose (EDm) for the skin when it is protected. The UPF is defined as the ratio of ED to EDm and calculated as follows [11]:
where:
Eλ = erythemal spectral effectiveness
Sλ = solar spectral irradiance in Wm-2nm-1
Tλ = spectral transmittance of fabric
Δλ = the bandwidth in nm
λ = the wavelength in nm
UPF's were measured in vitro using a labsphere® UV-100 F Ultraviolet Transmission Analyzer according to standard AS/NZ 4399:1996 [12]. Fabrics with a UPF value in the range 15 – 24 were classified as having "Good UV Protection"; when the UPF values were between 25 and 39 fabrics were classified as having "Very Good UV Protection" and "Excellent UV Protection" classification was used when the UPF was 40 or greater [13]. In no event was a fabric assigned a UPF rating greater than 50.
Measured UPF values were also correlated to the color strength of the dyed fabrics. Color strength was evaluated using K/S values generated by a HunterLab ColorQuest XE diffuse/8° spectrophotometer. K/S is a function of color depth and is represented by the equation of Kubelka and Munk (Equation 2). Higher the value of K/S greater is the color strength [14,15].
where R is the reflectance of the dyed fabric; K is the sorption coefficient, and S is the scattering coefficient. The spectrophotometer was standardized for a 1 inch diameter specimen viewing aperture in reflectance – specular included mode. Illuminant D65 and CIE 10-degree observer were used. During measurements, fabric samples were held flat and securely using a spring-loaded sample clamp. Three measurements were taken on each dyed fabric with the fabric rotated between measurements.
Results and discussion
Fabric characterization parameters and UPF values prior to dyeing are listed in Table 1. Based on the classification parameters referenced previously the plain weave fabric and the sateen weave fabric cannot be rated as offering any degree of protection since their UPF values were less than 15. The undyed twill weave fabric with a UPF of 19.2 is rated as having Good UV Protection. The UPF values of the undyed fabrics can be explained in terms of fiber composition and fabric construction. In terms of fiber composition it is known that undyed bleached cotton, linen, acetate, and rayon fabrics afford poor protection against UV radiation [16]. Fabric construction parameters of weight and thickness show a positive correlation with UPF values. Higher the weight and thicker the fabric, higher is the degree of protection afforded by the fabric. Accordingly, the twill weave fabric with a weight of 235 g/m2 and a thickness of 0.069 centimeters has the highest UPF value followed by the sateen weave fabric which weighed 235 g/m2 and was 0.061 centimeters thick. The plain weave fabric with a weight of 120 g/m2 and a thickness of 0.035 centimeters offers no protection against transmittance of UV rays. The positive correlation between fabric weight and fabric thickness with UPF values can be explained with reference to porosity. Porosity is a measure of tightness of weave and is also called as Coverfactor. Cover factor is defined as the percentage area occupied by warp and filling yarns in a given fabric area [4,17]. The closer the weave, the more is the percentage area occupied by the yarns and more opaque is the fabric to UV radiation. Cover factor is increased by an increase in weight per unit area. Heavier fabric minimizes UV transmission by virtue of having smaller spaces between yarns thus blocking more radiation [3,17]. A related variable is thickness. Thicker, denser fabrics transmit less UV radiation and have a higher cover factor [10]. The data also reveals a negative correlation between thread count and UPF. Higher the thread count, lower is the degree of protection afforded by the fabric. The plain weave fabric with a thread count of 205 had a UPF of 3.2 whereas the twill weave fabric with a thread count of 81 had a UPF of 19.2 with the sateen weave between the two with a thread count of 106 and a UPF of 13.3. A possible explanation for the negative correlation between thread count and UPF is the fact that fabrics that are thinner tend to contain finer yarns and therefore have the highest thread counts [10]. In other words thickness and thread count are inversely correlated a point substantiated by the values in Table 1.
Table 1 Fabric characterization parameters and % UV transmittance of undyed fabrics
Weight, g/m2 Thickness, cm. Thread Count (per inch) UPF UV Protection Class
Plain weave 120 0.035 205 3.8 No Class
Twill weave 258 0.069 81 19.2 Good
Sateen weave 235 0.061 106 13.3 No Class
The percent UV transmittance data in the presence and absence of colorants for the plain weave fabric is shown in Figure 1. It is noted that since the relative erythemal spectral effectiveness is higher in the UV-B region compared to the UV-A region, the UPF values depend primarily on transmission in the UV-B region. Undyed plain weave fabric had significant transmittance and consequently a very low UPF value of 3.8. UPF values and protection categories of the plain weave fabric dyed with the different colorants are listed in Table 2. As is evident from the transmission data and the corresponding UPF values all colorants used in the study caused a dramatic reduction in UV radiation transmission through the plain weave fabric. The increase in UPF values in the presence of colorant was especially significant for the cochineal and indigo dyed samples which were classified as having Very Good (UPF values between 25 and 39) to Excellent UV Protection (UPF values 40 or greater). Madder dyed samples could be classified as having Good UV Protection (UPF values between 15 and 24) to Very Good UV Protection. Compared to cochineal and indigo, madder is a paler color and therefore these results agree with previous data reported by Reinert et al. [18] who showed that pale colored fabrics of cotton, silk, polyamide, and polyamide/elastan gave less protection against intense UV radiation. The results also show that UPF values for colorants applied at higher concentrations gave higher UPF values. For example, the UPF of the plain weave fabric at a 2% indigo on weight of fabric was 43.1 and that increased to greater than UPF 50 at an indigo concentration of 6%. We agree with Gies et al. [11] who indicated that dyeing fabrics in deeper shades and darker colors improves sun protection properties. Thus although the studies by Reinert at al. and Gies et al. were done with synthetic dyes their conclusions seem to hold with natural colorants as well.
Figure 1 UV transmission of plain weave fabric in the absence and presence of colorants.
Table 2 UPF values, protection class and K/S values of plain weave fabric dyed with natural colorants at different concentrations
Colorant UPF UV Protection Class K/S
Plain weave 2% Madder 11.1 No Class 0.20
4% Madder 15.8 Good 0.28
6% Madder 16.6 Good 0.38
2% Cochineal 28.5 Very Good 0.63
4% Cochineal 34 Very Good 0.79
6% Cochineal 36.6 Very Good 0.99
2% Indigo 43.1 Excellent 1.78
4% Indigo > 50 Excellent 2.56
6% Indigo > 50 Excellent 3.02
The K/S values of the dyed fabrics which are a measure of color depth seem to support the claim that higher color depths increases UPF values. For example, in the case of the madder dyed samples when the K/S value increased from 0.20 to 0.38 the UPF values rose from 11.1 to 16.6. However, the relationship of K/S with UPF is limited to the same fabric type and the results cannot be generalized across fabrics of different weave structures. A primary reason for this observation is the acknowledgement that UPF values are dependent on a multitude of fabric construction factors such as pores in the fabric, thickness, and weight in addition to processing parameters such as dyeing and finishing. Another probable reason is the dependence of K/S on the absorbing properties of colorants in the visible region of the spectrum and that may not influence the absorption characteristics of colorants in the UV region.
The percent UV transmittance data in the presence and absence of colorants for the twill weave fabric is shown in Figure 2. UPF values and protection categories for the dyed twill weave fabric are shown in Tables 3. The twill weave fabric which prior to dyeing was rated as offering Good UV Protection moved to the Excellent UV Protection classification irrespective of the colorant and the concentration of the dye used. Again, it was found that dark colors within the same fabric type transmit less UV radiation than light colors and consequently have higher UPFs.
Figure 2 UV transmission of twill weave fabric in the absence and presence of colorants.
Table 3 UPF values, protection class and K/S values of twill weave fabric dyed with natural colorants at different concentrations
Colorant UPF UV Protection Class K/S
Twill weave 2% Madder > 50 Excellent 0.27
4% Madder > 50 Excellent 0.44
6% Madder > 50 Excellent 0.59
2 % Cochineal > 50 Excellent 0.82
4% Cochineal > 50 Excellent 1.70
6% Cochineal > 50 Excellent 1.89
2% Indigo > 50 Excellent 2.33
4% Indigo > 50 Excellent 3.76
6% Indigo > 50 Excellent 4.00
Table 4 shows the UPF values and protection categories for the dyed sateen weave fabric. The percent UV transmittance data in the presence and absence of colorants for the sateen weave fabric is shown in Figure 3. The increase in UPF values of the sateen weave dyed fabrics was dramatic in the sense that the sateen which prior to dyeing could not be rated (UPF < 15) achieved the Excellent UV Protection classification by virtue of its UPF values increasing by more than a factor of four (UPF > 50). This result was true for all colorants and at all dye concentrations. Again, as was the case with the dyed plain weave fabric, the color strength (K/S) of the cochineal and indigo dyed twill and sateen fabrics were higher than the color strength of the madder dyed fabrics conclusively establishing that indigo and cochineal colorants resulted in deeper colors on the fabrics.
Table 4 UPF values, protection class and K/S values of sateen weave fabric dyed with natural colorants at different concentrations
Colorant UPF UV Protection Class K/S
Sateen weave 2% Madder > 50 Excellent 0.25
4% Madder > 50 Excellent 0.36
6% Madder > 50 Excellent 0.59
2% Cochineal > 50 Excellent 1.78
4% Cochineal > 50 Excellent 1.87
6% Cochineal > 50 Excellent 2.42
2% Indigo > 50 Excellent 1.66
4%Indigo > 50 Excellent 2.05
6% Indigo > 50 Excellent 2.40
Figure 3 UV transmission of sateen weave fabric in the absence and presence of colorants.
Conclusions
Fabric weight and thickness are important predictors of UPF values for undyed cotton fabrics. In general, it was found that increase in weight and thickness increased the UPF though the relationship was not linear. UPF of undyed fabrics was significantly enhanced by dyeing with natural colorants especially for fabrics such as the plain weave and the sateen weave fabrics that displayed no protective abilities in the undyed state. The degree of protection imparted after dyeing was a function of the concentration of the colorant in the fabric. Within the same fabric type, as the percentage depth of shade increased so did the UPF values. In addition, darker colors such as indigo provide better protection on account of higher UV absorption. Based on the results of this study it can be theorized that plain, twill or sateen weave cotton fabrics dyed with natural colorants can provide good protection against ultraviolet rays with the only condition being that either the color has to be a dark hue or the concentration of the colorant in the fabric has to be high.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AKS conceived the study, carried out the dyeing and testing and drafted the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This research was supported by the U.S. Department of Agriculture Multi-State Research Project NC-170 (Mediating exposure to environmental hazards through textile systems) via a grant by the Colorado Agricultural Experiment Station, under Project COL00217.
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Glover B Are natural colorants good for your health? Are synthetic ones better? Textile Chemist and Colorist 1995 27 17 20
American Society of Testing and Materials Annual Book of ASTM Standards 2001 West Conshohocken, PA, USA
Hoffman K Laperre J Avermaete A Altmeyer P Gambichler T Defined UV protection by apparel textiles Archives of Dermatology 2001 137 1089 1094 11493104
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Standards Australia/Standards New Zealand AS/NZS 4399 1996
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| 15509304 | PMC526772 | CC BY | 2021-01-04 16:29:15 | no | BMC Dermatol. 2004 Oct 27; 4:15 | utf-8 | BMC Dermatol | 2,004 | 10.1186/1471-5945-4-15 | oa_comm |
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BMC BiotechnolBMC Biotechnology1472-6750BioMed Central London 1472-6750-4-221548259510.1186/1472-6750-4-22Research ArticleReduction of arsenic content in a complex galena concentrate by Acidithiobacillus ferrooxidans Makita Mario [email protected]ón Margarita [email protected] Benito [email protected]ópez Alejandro [email protected] Erasmo [email protected] Centro de Investigación en Materiales Avanzados, Miguel de Cervantes 120, 31109 Chihuahua, México2 Instituto Tecnológico de Chihuahua II, Chihuahua, México3 Universidad Autónoma de Nuevo León, Monterrey, México2004 13 10 2004 4 22 22 2 3 2004 13 10 2004 Copyright © 2004 Makita et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Bioleaching is a process that has been used in the past in mineral pretreatment of refractory sulfides, mainly in the gold, copper and uranium benefit. This technology has been proved to be cheaper, more efficient and environmentally friendly than roasting and high pressure moisture heating processes. So far the most studied microorganism in bioleaching is Acidithiobacillus ferrooxidans. There are a few studies about the benefit of metals of low value through bioleaching. From all of these, there are almost no studies dealing with complex minerals containing arsenopyrite (FeAsS). Reduction and/or elimination of arsenic in these ores increase their value and allows the exploitation of a vast variety of minerals that today are being underexploited.
Results
Arsenopyrite was totally oxidized. The sum of arsenic remaining in solution and removed by sampling represents from 22 to 33% in weight (yield) of the original content in the mineral. The rest of the biooxidized arsenic form amorphous compounds that precipitate. Galena (PbS) was totally oxidized too, anglesite (PbSO4) formed is virtually insoluble and remains in the solids. The influence of seven factors in a batch process was studied. The maximum rate of arsenic dissolution in the concentrate was found using the following levels of factors: small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system. It was also found that ferric chloride and carbon dioxide decreased the arsenic dissolution rate. Bioleaching kinetic data of arsenic solubilization were used to estimate the dilution rate for a continuous culture. Calculated dilution rates were relatively small (0.088–0.103 day-1).
Conclusion
Proper conditions of solubilization of arsenic during bioleaching are key features to improve the percentage (22 to 33% in weight) of arsenic removal. Further studies are needed to determine other factors that influence specifically the solubilization of arsenic in the bioleaching system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others. At. ferrooxidans was able to completely oxidize the minerals present during the arsenic bioleaching. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized. The process of bioleaching is expected to be influenced by mechanisms that still need to be established due to the diversity of the minerals involved and by the presence of traces of metals in the concentrate. The increase in pulp density generates a decrease in the dissolved arsenic concentration. This decrease is greater in runs where air was not injected to the system. The maximum rate of arsenic dissolution in the concentrate was found using; small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system. The effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system. The presence of CO2 is associated to the decrease in arsenic dissolution.
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Background
The term bioleaching refers to the bacterial conversion of an insoluble metal (usually a metal sulfide, e.g., CuS, NiS, ZnS) into a soluble form (usually the metal sulfate e.g., CuSO4, NiSO4, ZnSO4). When this happens, the metal is extracted into water [1,2]. The first bacterium discovered that was able to oxidize minerals was Acidithiobacillus ferrooxidans (At. ferrooxidans, previously Thiobacillus ferrooxidans), a gram-negative, acidophilic, chemolithoautotrophic, non-spore forming rod. Nutritionally, typical At. ferrooxidans isolates are considered obligate autotrophs. At. ferrooxidans is able to use either ferrous iron or a wide variety of reduced inorganic sulfur species as an electron donor compounds. It is also able to grow using ferric iron as an electron acceptor, provided that an electron donor, such as reduced inorganic sulfur compound is present. Energy is derived from the oxidation of reduced iron and sulfur compounds, including ferrous ion, sulfide, elemental sulfur and thiosulfate, with final oxidation products being ferric ion and sulfate [3,4]. Other microorganisms considered important in commercial mineral biooxidation processes are: Acidithiobacillus thiooxidans, Acidithiobacillus caldus, Leptospirillum ferrooxidans, and Acidiphilium acidophilum [3].
Bioleaching has emerged as a simpler, safer, and less expensive process than other alternatives for most limestone, granitic, or other host rocks that have secondary replacement of pyritic minerals containing metal values. In recent years, biooxidation has shown itself to require less capital, reduced operating cost, and less skilled operating and maintenance personnel than the traditional pressure oxidation or roasting techniques [5].
This technology has been used for treating specific mineral ores, mainly copper and gold bearing ores [6-8]. Moreover, bacterial leaching in acid medium has been successfully applied in: uranium metallurgy [9]; silver, gold and lead recovery [10]; zinc [11]; and new processes have been developed for cobalt recovery [12,13].
A complex sulfide ore is an association of galena (PbS), sphaelerite (ZnS) and chalcopyrite (CuFeS2), disseminated in a pyritic matrix. Besides of lead, zinc and copper as valuable metals, such deposits may contain significant quantities of silver, gold, arsenic, antimony, bismuth and mercury. Numerous economically important deposits of these ores exist in the world [14].
Complex ores are often characterized by particularly fine intergrowth of the mineral values. Due to these specific mineralogical characteristics, it is necessary to finely grind and concentrate the ore prior to the solubilization of the valuable metals. To obtain separate concentrates by selective flotation involves high unit-cost, poor quality of the concentrates and relatively low overall recoveries [15].
Arsenic is a major impurity present in numerous sulfide deposits. The presence of arsenic in mineral concentrates drastically diminishes their value, and results in two types of problems. On one hand, arsenic produces metallurgical problems, making difficult the metal extraction and the recovery of a final product of high purity. On the other hand, arsenic is regarded as a highly toxic contaminant resulting in environmental problems due to its atmospheric release and possible water contamination associated to the processing of arsenic bearing ores and concentrates [16].
Furthermore, this element is generally toxic for microorganisms and its dissolution could inhibit bacterial activity in the bioleaching process. It has been shown that high concentrations of arsenic in solution inhibit bacterial growth, with As(III) reported to inhibit bacteria to a greater degree than As(V) [17].
The processing of complex arsenic bearing ores and concentrates requires a good understanding of the mechanisms involved previous to the design and development of an adapted sequence of process units. In particular, a complete chemical and mineralogical characterization of the ore is essential from both biological and metallurgical points of view in order to determine the possible inhibition problems and the requirement for effluent treatment processes [12].
The interest in the study of bioleaching of arsenic-containing minerals, started two decades ago with a publication dealing with the degradation of arsenopyrite (FeAsS) through At. ferrooxidans in gold-arsenic concentrates. This study established the economic feasibility of this technology and its potential for alleviating some environment related problems [18]. Later, other researchers [19] studied the physical changes occurring in the mineral-bacteria interphase in pure crystals of arsenopyrite. They found that bacterial oxidation is characterized by three stages that coincide with the phases of the general bacterial growth and that the amorphous arsenates produced are deposited over the crystal surface, thus interrupting the bioleaching process.
There have been other studies aimed to determine the influence of several factors over the arsenic bioleaching in gold minerals and concentrates. These studies have been mainly focused to maximize gold extraction and considered bioleaching as a pretreatment in the cyanidation process, consequently leaving the importance of arsenic dissolution and extraction in a second term.
Ubaldini et al [20] studied the arsenopyrite bioleaching over a refractory mineral using a mixture of At. ferrooxidans and At. thiooxidans. They achieved an increase in gold extraction from 55.3% to 96.8% using the following conditions; pulp density 20%, pH 2, stirring conditions 200 rpm, temperature 30°C, time 7 days. Another research group used a domestic strain of At. ferrooxidans and a gold refractory mineral to study the influence of a magnetic field, surfactant addition and the presence of Ag+, Bi3+, Co2+ y Hg2+ ions over the bioleaching process [21]. They found a reduction to 120 hours to leachate 60% of arsenic using magnetized water and the addition of tween-80 surfactant and Ag+ ion [21].
As indicated above, previous studies are focused in using bioleaching as a technique to eliminate arsenic in high-value minerals such as gold concentrates. However, there is no reported work on arsenic elimination or reduction in minerals of low value such as lead.
This work is aimed to reduce the arsenic content in complex concentrates of galena and to generate preliminary data to allow us to conduct further studies to understand the complex behavior of the bioleaching process. This preliminary study deals with the influence of main factors in batch bioleaching over the arsenic solubilization from a complex lead concentrate.
The main factors influencing the biooxidative treatment were tested using a two level fractional factorial plan of experiments (27–4), and they were: surface area, pulp density, carbon dioxide bubbling, air bubbling, 9 K medium addition, FeCl3 addition, and two different At. ferrooxidans strains.
Results and discussion
Chemical and mineralogical characteristics of the concentrate
The chemical composition of the concentrate, determined by atomic absorption spectrometry (AAS) and AAS Hydride System for arsenic was: 3.85% As, 14.75% Fe, 23.53% S, 49.76% Pb, 2.48% Zn, 0.51% Sb, 0.29% Cu, 0.28% Bi. The remaining 4.56% being other elements such as: Cd, Ca, Ag, K, Mn, Na, Ni, Ba, Mo, Sn, Si, O.
Table 1 shows major phases in the concentrate determined by X-ray diffraction (XRD).
Photographs of prepared mounts (Figures 1, 2 and 3) show several mineral species and some mineral associations found in the concentrate.
Studies dealing with bioleaching through At. ferrooxidans have provided great amount of basic knowledge about this process and have been useful in the understanding of the physicochemical and microbiological aspects of this phenomenon [3,22].
Biooxidative dissolution of arsenopyrite [19,23-29] and the parameters for adapting the bacteria to arsenic have also been studied [17,30]. However, a major disadvantage of these studies is the fact that they were performed using chemicals reagents or pure crystals of pyrite and/or arsenopyrite, synthetic or manually sorted with the aid of a microscope and therefore, they do not represent the conditions and complexity involved in the bioleaching treatment of concentrates.
There are only a few of studies dealing with arsenic-bearing ores and concentrates of natural sources [12,14,31,32]. It is desirable that the trend of today's studies of bioleaching of natural ores be aimed to understand the factors influencing these phenomena if this technology is expected to reach industrial importance. However, it is clear that the complexity of some sulfide minerals are due to the association among species and to the coexistence of inclusion forms of many of these sulfide materials. These two issues (complexity and inclusions) are important factors that may inhibit the process and produce non-successful intrinsic behavior of the bioleaching mineral systems [33].
The concentrated used in this study was highly complex as can be seen in Figures 1, 2 and 3. Also in these Figures the relative composition of the mineral species and their associations described in Table 1 are shown. The complex array of compositions showed in these Figures means that the process of bioleaching is expected to be influenced by mechanisms that still need to be established by the diversity of the mineral species involved and by the presence of traces of metals in the concentrate.
Mineral species oxidized
XRD analysis of the residual solid from bioleaching (Figure 4) shows only the presence of crystalline phase anglesite (PbSO4). Chemical analysis performed through AAS resulted in the presence of residual arsenic. This indicates that all crystalline species present in the concentrate were completely oxidized through the bioleaching process. This means that all arsenic present in arsenopyrite was either solubilized (22 to 33%) or precipitated in amorphous compounds (67 to 78%). Also, galena (PbS) was completely oxidized to anglesite. Only a very small portion of anglesite remained in solution (25 mg/L, see Figure 5) while, the main phase of this appeared as a solid precipitate. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized. This fact opens a window of a great potential for separating lead from other elements that often are present along in the mineral concentrates [2].
Formation of precipitates during biooxidation
Formation of precipitates of arsenic and iron has been commonly observed with the use of At. ferrooxidans [34]. Several other compounds have also been observed in solid state during the bioleaching of arsenopyrite, among these are; ferric arsenate, elemental sulfur, amorphous ferric arsenate FeAsO4·xH2O, jarosite KFe3(SO4)2(OH)6 and scorodite FeAsO4·2H2O [19,25-27].
It has been reported that the elemental sulfur and ferric arsenate are accumulated in the surface of the grains forming a coating barrier that avoids the contact between the mineral and the Fe+3 ions in the indirect oxidation, inhibiting the leaching of the mineral species [19,27]. The formation of precipitates of the jarosite-type are prevented by a pH control to levels lower than 1 [33], while the formation of S0 can be avoided by the use of bacterial consortia in which sulfur-oxidant bacteria such as At. caldus [28] or At. thiooxidans are included [3].
In this study the precipitation of amorphous arsenic compounds was important during the bioleaching process. In samples taken at the end of the experiment there was a difference in the arsenic content in the digested mineral residue, which was treated by a hydrochloric acid digestion, to the non-digested residue (see Table 2). This last, suggests the presence of two types of arsenic compounds in the residue, probably amorphous ferric arsenates and jarosite-type precipitates due to the acidic conditions used in the experiment (pH ≥ 2). However, other studies are needed using this kind of concentrates in order to determine the type and amount of precipitates formed and the kinetics involved in this process.
Significant factors in arsenic dissolution
Arsenic bioleaching results for the eight experimental runs (Table 3) are shown in Figure 6. In this plot a great deal of scattered data among experimental runs is observed, this means that still a great deal of variability exists among the effect of the factors considered in this study.
Table 4 shows the results of the least-squares-multiple-regression model fitting to arsenic dissolution data using time and factors as explanatory variables. With the exception of At. ferrooxidans strains, all of the other factors resulted to be highly significant in its influence to the arsenic dissolution. The resulting model was the following (Minitab 13.0):
Arsenic = 52.0 - 66.3 Pulp Density - 32.6 Surface Area - 21.1 Ferric chloride - 12.5 Carbon dioxide + 29.3 Air + 29.3 9 K Medium + 3.00 Strain + 3.94 Days. (1)
With a determination coefficient of R2 = 0.834
The negative sign in some of the variables of the model indicates that in order to maximize bioleaching of arsenic, these factors must be kept in low levels. These were denoted with a zero value in the model. Thus, the levels of factors that should be considered in the model are the following: pulp density at 10%, low surface area, no FeCl3 and CO2 addition, air and 9 K Medium addition and any strain present.
Best run
One of the main disadvantages for the commercial use of bioleaching is the slow nature of this process, which is due mainly to the relative slow growth rate of the bacteria [5,35], to the long period of time needed for the bacteria to adapt to the mineral environment and particularly to mineral complexes [31] and to inhibition problems due to the products of the bioleaching [36].
In Figure 6, Run 1 presents the highest arsenic concentration in the leachate (approximately 200 mg/L). The combination of the previously established levels of factors for this run are shown in Table 3 and coincides with the required combination of levels to maximize the arsenic dissolution as stated in the model of the above Equation (1).
The graphical pattern of the arsenic bioleaching for Run 1 (Figure 7) shows some features that are potentially adequate for the use of this combination in reduction of arsenic content in future studies. Some of these features are: the lack of an adaptation period (lag-time), a very pronounced slope and that the stability of the system is reached in a relatively short time (four days) compared to the other experimental runs.
The data for this run (Run 1) were fitted through a third order polynomial linear regression model and statistically analyzed over a 95% confidence interval (see Figure 7). The model of arsenic present in solution in Run 1 is the following (Minitab 13.0):
Arsenic = 100.16 + 11.487 Days - 0.4705 Days2 + 0.006 Days3 (2)
With a determination coefficient of R2 = 0.6585
Besides of all these convenient features in Run 1, the percentage of bioleached arsenic is still low. Proper conditions of solubilization of arsenic during bioleaching are key feature to improve the percentage (yield) of arsenic removal. Therefore, further studies are needed in order to determine the other factors (not considered in this work) that influence specifically the solubilization of arsenic in the bioleaching system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others.
General evolution of arsenic in the leachate
When time (days) was used as the only independent variable, data collected in all runs were adjusted to a third order polynomial regression model and the analysis is shown in Figure 8. The resulted general equation of this model is the following (Minitab 13.0):
Arsenic = 23.888 - 1.0597 Days + 0.5161 Days2 - 0.0131 Days3 (3)
The determination coefficient of this model was R2 = 0.3237, which represents a value relatively low due to the absence of the other significant factors (pulp density, CO2, 9 K medium, particle size, air injection and ferric chloride added).
It is worth to note that this model resembles the well known general three-phase bacterial growth model.
Effect of pulp density
The pulp density effect is shown in Figure 9. Here a greater arsenic dissolution is achieved with a low level of 10% of solids. This result is in agreement with results reported by other researchers working with complex concentrates of copper and zinc [14], complex sulfides of copper, lead and zinc [37] and copper concentrates [38,39]. In all of these studies greater levels of leaching were achieved with low pulp densities. The reduction in the bioleaching rate can be due to the fact that at higher concentrations of solids causes an increase in the friction between particles, and probably avoiding the adhesion between the particle and bacteria [40]. This friction may consequently cause some mechanical damage to the cell [41].
In the case of arsenic, it is possible that a high pulp density may cause a greater interaction between the dissolved arsenic and other products of biooxidation. Thus, increasing the rate of formation of precipitates and consequently decreasing the concentration of arsenic in solution.
In our study it was found a strong interaction between the pulp density and the air injection with respect to the arsenic removal. The ANOVA analysis of this interaction is shown in Table 5, and the plot in Figure 10. The increase in pulp density generates a decrease in the dissolved arsenic concentration in all runs. However, this decrease is greater in runs where air was not injected to the system.
Effect of surface area
The effect of surface area (particle size) can be observed in Figure 11. Here a greater arsenic dissolution is presented when the particle surface area is small, in other words, where bigger particles are present in the system. One would expect that a greater exposed surface area to the microbial attack would reflect an increase of the arsenic dissolution. However, this unexpected result is in agreement with other studies reported in the literature [42,43]. Explanations for this phenomenon are in the sense that it is possible for the bacteria to preferentially attack some sites formed during the solids grinding process [42]. Other study of the leaching of chalcopyrite reported that the rate of leaching increased with the use of larger particles sizes [42]. They suggested that this was due to a greater efficiency of bacteria attachment to the particles. Other explanations are focused on disregarding the associated physical factors such as surface area and stressing the specific and non specific interactions. Among the specific are the ionic and hydrogen bonds, and chemical and protein interactions. Non-specific are hydrophobic interactions such as surface free energy and electrostatic [44].
A possible explanation for the behavior observed in this study is based in the great complexity of the mineral concentrate that may enhance the specific interactions. These interactions are affected by the particles of smaller sizes where the greater surface area leaves each particle exposed to a greater amount of different mineral species. These species increase the complexity of the medium thus affecting directly the solubilization of arsenic during the bioleaching. The complexity of the medium due to small size particles can be further increased when a high pulp density is used as pointed out in the previous section. These two issues, complexity and pulp density, combined in the conditions above mentioned may produce a negative effect over the arsenic dissolution.
Effect of ferric chloride
In the present study the effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system as observed in Figure 12. Information on this effect is limited in the literature with only some studies reporting the influence of the ion chloride on the sulfur and iron oxidation during bioleaching of some chemical species using At. ferrooxidans and /or At. thiooxidans [45-47].
Effect of carbon dioxide
It is well known that autotrophic organisms such as At. ferrooxidans require CO2 for its growth. This is due to the fact that these bacteria are able to use CO2 as its only source of carbon for their biosynthetic reactions. CO2 fixation is usually achieved through the so-called Calvin-Benson-Bassham cycle [48]. In Figure 13 it can be seen that the arsenic dissolution is reduced when CO2 is bubbled into the system. This behavior is in agreement with studies reported in literature that established that the consumption of carbon by the bacteria decreases at high CO2 concentrations implying the formation of intracellular bicarbonates [49]. Other authors coincide in that the amount of CO2 present in the air is enough to withstand the bacterial growth [14,25].
In our study, it was found a strong interaction between the two levels of CO2 and air into the system. The ANOVA analysis of this interaction is also shown in Table 6 and Figure 14. However, in our study the presence of CO2 according to the results shown in Figure 13 is associated to the decrease in arsenic dissolution. At this time, it is difficult to establish the influence of the presence and concentration of CO2 on the dissolution of arsenic. Therefore, further studies oriented to establish the relationship between the dissolved oxygen and the concentration of CO2 in the system are needed in order to elucidate this effect.
Effect of air
Acidithiobacillus ferrooxidans is an aerobic microorganism that uses oxygen as a final electron acceptor during the oxidation process. However, in absence of oxygen it is still able to grow in the presence of inorganic reduced sulfur compounds by using the ferric ion as an alternative electron acceptor [50]. In these conditions, however, its growth is slower [48]. In the present study, it is not surprising that the arsenic dissolution was increased with the aeration of the system as can be observed in Figure 15. As it was shown previously, the availability of oxygen in the medium decreases when the pulp density increases and also when CO2 is admitted into the system. Again a more definite study targeted to measure dissolved oxygen in the system may bring light into the effect of air during the arsenic bioleaching.
Effect of 9 K medium
The 9 K medium is the source of nitrogen and phosphorous [51] needed for bacterial growth. These two elements are not available in the sulfide minerals. Therefore, they need to be provided during the bioleaching process. Figure 16 shows the effect of the addition of 9 K medium. As expected, the arsenic bioleaching is increased when 9 K medium is used.
Effect of strain
In Figure 17 differences of two At. ferrooxidans strains used in this study are shown. Strain T1 presents the common bacterial model of growth in three stages, while strain T18 presents a straight model that requires less adaptation time. This can be due to the fact that T18 was previously grown in systematically increased arsenic concentrations as discussed in the material and methods section of this paper.
Continuous culture dilution rate calculus
Data generated from the batch arsenic bioleaching were used as a first approximation for the calculation of the dilution rate D for a future design of a continuous bioleaching system. The procedure used consisted in to derive the third order polynomial equations (2) and (3) with respect to time and plot each equation versus the arsenic concentration in the system. Then to draw a straight line from the origin to the maximum point in each curve (Figure 18). The slope of this line was then defined as dilution rate in agreement with the equation of product balance in a continuous culture vessel in steady state [52,53] as shown in equation (4):
where
F = Rate of medium flow through the vessel (volume/time)
V = Volume of the vessel (volume)
D = F/V Dilution rate (time-1)
Pn-1 = Inner product concentration
Pn = Outer product concentration
dPn/dt = Total variation of the product concentration in the vessel
(dPn/dt)production= Product concentration variation due to the production in the vessel
Rearranging Eq. (4):
In steady state, dPn/dt = 0 and assuming that Pn-1 = 0 in a single vessel,
From which
Equation (7) represents a straight line through the origin having a slope of D (dilution rate) and corresponds to the straight line plotted in Figure 18.
Using this method, the calculated dilution rate for the general model of equation (3) is 0.088 days-1, while for run 1 in equation (2) is of 0.103 days-1 (not shown in the plot). However, these dilution rates are still relatively small compared to the dilution rates used in industrial scale processes (0.05–0.6 h-1) [53].
Conclusions
Since the bioleaching rate and the levels of leached arsenic are limited (22–33% of the originally present in the concentrate), proper conditions of solubilization of arsenic during bioleaching is the key feature to improve the percentage (yield) of arsenic removal. Therefore, further studies are needed in order to determine the other factors (not considered in this work) that influence specifically the solubilization of arsenic in the bioleached system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others.
The performance of the bacteria used in this study (At. ferrooxidans) was able to completely oxidize the minerals present during the arsenic bioleaching. Since, galena (PbS) was completely oxidized to anglesite (PbSO4) with only a very small portion of anglesite remaining in solution, while the main phase of this appeared as a solid precipitate. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized.
The complex array of compositions contained in the concentrate, employed in this study, means that the process of bioleaching is expected to be influenced by mechanisms that still need to be established due to the diversity of the mineral species involved and by the presence of traces of metals in such concentrate.
The precipitation of amorphous arsenic compounds was important during the bioleaching process. Results suggest the presence of two types of arsenic compounds contained in the residue, probably amorphous ferric arsenates and jarosite-type precipitates due to the acidic conditions used in the experiment (pH ≥ 2).
The increase in pulp density generates a decrease in the dissolved arsenic concentration. However, this decrease is greater in runs where air was not injected to the system.
The maximum rate of arsenic dissolution in the concentrate was found using small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system.
The effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system.
The presence of CO2 according to the results is associated to the decrease in arsenic dissolution. Further studies oriented to establish the relationship between the dissolved oxygen and the concentration of CO2 in the system are needed in order to elucidate this effect.
Arsenic dissolution was increased with the aeration of the system. The availability of oxygen in the medium decreases when the pulp density increases and also when CO2 is admitted into the system. A study using dissolved oxygen measurements is needed in the future to determine the effect of air during the arsenic bioleaching.
Methods
Chemical and mineralogical analysis
The flotation concentrate was obtained from La Soledad mine (Parral, Chihuahua, México). Chemical analysis was carried out by atomic absorption spectrometry through AAS (GBC Avante Σ), arsenic was determined by AAS Hydride System. The major phases in the concentrate were determined by X-ray diffraction (Siemens D5000). Mineral samples were mounted in polyester resin blocks using approximately 0.2 g per mount and surface was polished. Mounts were examined using a microscope (Olympus AX70) and photographs were taken at various sites of each sample.
Design of experiments
One of the most useful types of multifactor experiment is the 2k factorial series. In this series, there are k factors each at two levels. Hence there are a total of 2k treatments in the full factorial set. This series is particularly useful in the exploratory stages of an investigation because it permits the examination of a fairly large number of factors and their interactions in a trial of reasonable size [54].
In almost all experiments the investigator would like to reduce the number of observations required for a complete factorial. If certain assumptions can be met the use of fractional factorials is a most efficient technique to reduce the number of observations and still obtain the desired information. The usual fractional factorial is still orthogonal, which means that certain effects are estimated independently of one other [55].
A major use of fractional factorial designs is in screening experiments. These are experiments in which many factors are considered with the purpose to identifying only the important variables that affect the response and their interactions. The factors that are identified as important are then investigated more thoroughly in subsequent experiments. A fractional factorial of the 2k design containing 2k-p runs is called a 1/2p fraction of the 2k or, more simply, a 2k-p fractional factorial design. It is possible to construct this type of designs for investigating up to k = N-1 factors in only N runs. If k = N-1 the fractional factorial design is said to be saturated. Of particular importance is a very useful saturated fractional factorial design for studying seven factors in eight runs; that is, the 27–4 design. This design is a one-sixteenth fraction of the 27 [56].
This was the design used in this work to test the factors influencing the biooxidative treatment. The objective was to maximize the arsenic solubilization. The factors, levels and runs are presented in Table 3.
Factors and levels in experimental runs
Pulp density
The bioleaching experiments were carried out using two solid concentrations. The low level for pulp density was settled as 10 % w/v; the high level as 20% w/v.
Surface area
The lead concentrate was washed with distilled water; the mineral suspension was wet sieved using a 75 μm sieve in order to obtain two fractions. Both of them were dried and analyzed for specific surface area by a laser scattering particle sizer (Malvern Master Sizer 2000). The specific surface area for fractions >75 μm and <75 μm were 0.42 and 1.65 m2/g and were established as low and high levels respectively.
Ferric chloride
Addition of ferric chloride at concentration of 150 mg per liter of liquid medium was settled as high level, no ferric chloride addition was low level.
Carbon dioxide
Low level: no carbon dioxide bubbling; high level: carbon dioxide flow from a compressed cylinder, injected into the mixture at a rate of 0.2 vol/vol min-1.
Air
No air bubbling was considered as low level; no sterile air bubbles injected into the liquid-concentrate mixture at a rate of 0.3 vol/vol min-1 was high level.
9 K Medium
Pure distilled water as culture medium was established as low level; the use of 9 K medium [51], which contained (per liter of distilled water) 3.0 g of (NH4)2SO4, 0.5 g of MgSO4·7H2O, 0.1 g of KCl, 0.5 g of K2PO4, 0.01 g of Ca(NO3)2 was settled as high level. The pH was adjusted to 5.7 with sulfuric acid.
Strains
A native Acidithiobacillus ferrooxidans wild type strain termed T1 [57], isolated from a domestic mining site acid drainage, was used as a low level; and an arsenic-resistant strain, called T18, derived from T1 by serial transfers to flasks containing increasing arsenic amounts, was used as high level. T18 is able to grow at arsenic concentration as high as 1800 mg l-1 [57]. The strains were cultured in a rotary shaker incubator (30°C, 175 rpm), in a medium containing 44.22 g FeSO4, 3.0 g (NH4)2SO4, 0.5 g KH2PO4, 0.5 g MgSO4· 7H2O, 0.1 g KCl, 0.01 g Ca(NO3)2 per liter, adjusted to pH 2.0 with sulfuric acid. After bacterial growth for ten days, cultures were filtered and the clear liquid was used as inoculum (20% v/v).
Conditions of cultivation and sampling
The eight biooxidation runs (Table 3) were conducted in 1000 ml Pyrex culture flasks containing 500 ml of mixture, placed in a rotary shaker incubator (30°C, 175 rpm). The pH was maintained to 2.0 with sulfuric acid. The experiment was monitored each 48 h. After a short period for sedimentation of solid particles, each flask was sampled extracting 2 ml of clear leachate in which total arsenic, lead and iron concentrations were determined. The liquid extracted was compensated by the addition of distilled water or 9 K medium. Little hoses were submerged in liquid mixture to inject compressed air, carbon dioxide, or both. The experiment was carried out for 28 days; this is twice the time needed to reach the stability of arsenic concentration in a previous laboratory test.
At the end of the experiment the resulting pulp in each run was filtered and the bioleached mineral was washed with distilled water and dried in a stove at 40°C. Two grams of this material were taken and were submitted to digestion using 10 ml of HCl 0.6 N during 3 hours at room temperature. The digested mineral was filtered, washed with distilled water and dried at 40°C. In both cases the arsenic content was determined through chemical analysis.
Data analysis
Table 3 shows the saturated experimental design used to carry out the experiment, the eight trials provide a total of seven degrees of freedom for the entire experiment, allocated to seven columns of two levels, each column having one factor assigned (Pulp density, Surface area, Ferric chloride, Carbon dioxide, Air, 9 K medium, Strain). All columns provide four tests under the low level of the factor and four tests under the high level of the factor. This is one of the features that provides the orthogonality among all the columns (factors) [58]. Orthogonality permits the comparison between low and high levels for each factor in their ability to dissolve arsenic. Comparison was performed by fitting a multiple regression model to arsenic dissolution data, to make the model function, all seven factors were treated as dummy variables [59] taking the low level as 0 and high level as 1 (Table 7). Time in days was included in the model as the only true quantitative factor. Model fitted is:
Arsenic = β0 + β1 Pulp Density + β2 Surface Area + β3 Ferric chloride + β4 Carbon dioxide + β5 Air + β6 9 K Medium + β7 Strain + β8 Days. (8)
Where
Arsenic Expected value of arsenic concentration in mg l-1 in leachate
β0, β1,... β8 Regression coefficients
As the experiment was monitored each 48 hours, and arsenic concentration was determined in leachate, arsenic dissolution data gathered during the bioleaching experiment were analyzed as time series (time as independent variable) by multiple regression to fit the third order polynomial model:
Arsenic = β0 + β1t + β2t2 + β3t3 (9)
Where
Arsenic Expected value of arsenic concentration in mg l-1 in leachate
t Time in days of bioleaching
β0, β1, β2, β3 Regression coefficients
Software
Analytical procedures and graphing was performed using MS Excel or Minitab 13.0
Authors' contributions
MM designed and performed the experimental runs and prepared the manuscript. ME performed the statistical analysis. BP provided the bacterial strains and helped in the culture production. AL performed the technical revision of the manuscript. EO got the funds and provided the tutoring during the development of this work. All authors read and approved the final manuscript.
Acknowledgements
The authors wish to express sincere gratitude to the Consejo Nacional de Ciencia y Tecnología CONACYT (México) for funding this research through project CONACYT-DAIC 34223-B.
Figures and Tables
Figure 1 Photography of a site in surface of a mount G: Galena; S: Sphaelerite; A: Arsenopyrite; P: Pyrite
Figure 2 Photography of a site in surface of a mount G: Galena; S: Sphalerite; A: Arsenopyrite; H: Hematite; Q: Quartz
Figure 3 Photography of a site in surface of a mount S: Sphaelerite; A: Arsenopyrite; P: Pyrite; Pr: Pyrrotite
Figure 4 XRD of the residual solid from bioleaching
Figure 5 Lead Solubilized
Figure 6 Arsenic Solubilized
Figure 7 Third order model fit and confidence interval for Run 1
Figure 8 Third order model fit for all data
Figure 9 Effect of Pulp Density
Figure 10 Interaction plot Pulp density – Air
Figure 11 Effect of Surface Area
Figure 12 Effect of Ferric Chloride
Figure 13 Effect of Carbon Dioxide
Figure 14 Interaction plot Carbon dioxide – Air
Figure 15 Effect of Air
Figure 16 Effect of 9 K medium
Figure 17 Effect of Strain
Figure 18 Dilution rate calculus
Table 1 Mineral species and associations
Mineral species and associations Relative proportion
Galena (free) 50.41
Galena + Arsenopyrite 13.95
Pyrite (free) 5.42
Galena + Sphaelerite 4.60
Arsenopyrite (free) 3.87
Pyrrotite (free) 3.09
Sphaelerite 3.09
Galena + Pyrite 2.33
Galena + Sphaelerite + Arsenopyrite 1.55
Galena + Arsenopyrite with inclusions of Galena 1.55
Other complex associations 10.14
Table 2 Arsenic content in mineral residue before and after digestion with hydrochloric acid
Run Residue before digestion % arsenic Residue after digestion % arsenic Difference % arsenic
1 3.5208 2.6332 0.8876
2 3.5751 2.7105 0.8646
3 3.6283 3.0034 0.6249
4 3.6452 2.9911 0.6541
5 3.5291 2.5907 0.9384
6 3.5325 2.5821 0.9504
7 3.6334 2.9436 0.6898
8 3.6021 2.9784 0.6237
Table 3 The 27–4 design used.
Pattern
Run
Pulp Density %
Surface Area
Ferric Chloride
Carbon Dioxide
Air
9 K Medium
Strain
----+++ 1 10 Low No added No added Added Added T18
--++--+ 2 10 Low Added Added No added No added T18
-+-+-+- 3 10 High No added Added No added Added T1
-++-+-- 4 10 High Added No added Added No added T1
+--++-- 5 20 Low No added Added Added No added T1
+-+--+- 6 20 Low Added No added No added Added T1
++----+ 7 20 High No added No added No added No added T18
+++++++ 8 20 High Added Added Added Added T18
Table 4 Regression analysis results
Predictor Coefficient T value Probability
Constant 52.0270 6.76 0.000
Pulp density % -66.3470 -14.05 0.000
Surface area -32.5570 -6.89 0.000
Ferric chloride -21.0730 -4.46 0.000
Carbon dioxide -12.5350 -2.65 0.009
Air 29.3000 6.20 0.000
9 K medium 29.3010 6.20 0.000
Strain 3.0020 0.64 0.526
Days 3.9396 14.41 0.000
Table 5 Analysis of variance for interaction Pulp density – Air
Source DF SS MS F P
Air 1 4714 4714 1.92 0.168
Pulp Density 1 132058 132058 53.84 0.000
Interaction 1 25775 25775 10.50 0.002
Error 116 284501 2453
Total 119 447028
DF: Degrees of Freedom; SS: Sum of Squares; MS: Mean Square; F: Fisher Statistic; P: Probability
Table 6 Analysis of variance for interaction Carbon dioxide – Air
Source DF SS MS F P
Air 1 4714 4714 1.92 0.168
Carbon dioxide 1 25755 25775 10.50 0.002
Interaction 1 132058 132058 53.84 0.000
Error 116 284501 2453
Total 119 447028
DF: Degrees of Freedom; SS: Sum of Squares; MS: Mean Square; F: Fisher Statistic; P: Probability
Table 7 Factors coded as dummy variables
Pattern
Run
Pulp Density
Surface Area
Ferric Chloride
Carbon Dioxide
Air
9 K medium
Strain
Results As mg/l
----+++ 1 0 0 0 0 1 1 1 R1
--++--+ 2 0 0 1 1 0 0 1 R2
-+-+-+- 3 0 1 0 1 0 1 0 R3
-++-+-- 4 0 1 1 0 1 0 0 R4
+--++-- 5 1 0 0 1 1 0 0 R5
+-+--+- 6 1 0 1 0 0 1 0 R6
++----+ 7 1 1 0 0 0 0 1 R7
+++++++ 8 1 1 1 1 1 1 1 R8
Results of As in leachate (mg/l) were determined each 48 hours, to finally have the results of this table 15 times during the 28 days of the experiment
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| 15482595 | PMC526773 | CC BY | 2021-01-04 16:02:57 | no | BMC Biotechnol. 2004 Oct 13; 4:22 | utf-8 | BMC Biotechnol | 2,004 | 10.1186/1472-6750-4-22 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-331550069510.1186/1471-244X-4-33Research ArticleMental health first aid training of the public in a rural area: a cluster randomized trial [ISRCTN53887541] Jorm Anthony F [email protected] Betty A [email protected]'Kearney Richard [email protected] Keith BG [email protected] Centre for Mental Health Research, Australian National University, Canberra, ACT 0200, Australia2 School of Psychology, Australian National University, Canberra, ACT 0200, Australia2004 23 10 2004 4 33 33 8 8 2004 23 10 2004 Copyright © 2004 Jorm et al; licensee BioMed Central Ltd.2004Jorm et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
A Mental Health First Aid course has been developed which trains members of the public in how to give initial help in mental health crisis situations and to support people developing mental health problems. This course has previously been evaluated in a randomized controlled trial in a workplace setting and found to produce a number of positive effects. However, this was an efficacy trial under relatively ideal conditions. Here we report the results of an effectiveness trial in which the course is given under more typical conditions.
Methods
The course was taught to members of the public in a large rural area in Australia by staff of an area health service. The 16 Local Government Areas that made up the area were grouped into pairs matched for size, geography and socio-economic level. One of each Local Government Area pair was randomised to receive immediate training while one served as a wait-list control. There were 753 participants in the trial: 416 in the 8 trained areas and 337 in the 8 control areas. Outcomes measured before the course started and 4 months after it ended were knowledge of mental disorders, confidence in providing help, actual help provided, and social distance towards people with mental disorders. The data were analysed taking account of the clustered design and using an intention-to-treat approach.
Results
Training was found to produce significantly greater recognition of the disorders, increased agreement with health professionals about which interventions are likely to be helpful, decreased social distance, increased confidence in providing help to others, and an increase in help actually provided. There was no change in the number of people with mental health problems that trainees had contact with nor in the percentage advising someone to seek professional help.
Conclusions
Mental Health First Aid training produces positive changes in knowledge, attitudes and behaviour when the course is given to members of the public by instructors from the local health service.
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Background
Community surveys have shown that the public in many countries have poor mental health literacy [1]. Many people cannot recognise mental disorders correctly, they differ from mental health professionals in their beliefs about causes and the most effective treatments, and they have stigmatizing attitudes which hinder recognition and appropriate help-seeking. This lack of mental health literacy limits the uptake of evidence-based treatments and leads to lack of support for people with mental disorders from others in the community.
To help improve mental health literacy, a Mental Health First Aid training course has been developed. This course uses the first aid model that has been successfully applied to training members of the public to help in accidents and emergencies [2]. The Mental Health First Aid course is designed to give skills to provide initial help in mental health crisis situations and for on-going mental health problems. The course teaches a five-step approach to first aid: 1. Assess risk of suicide or harm, 2. Listen non-judgmentally, 3. Give reassurance and information, 4. Encourage person to get appropriate professional help, and 5. Encourage self-help strategies. These steps are applied to depression, anxiety disorders, psychosis and substance use disorders. In addition, participants are given specific instruction on how to help in the following mental health crisis situations: a suicidal person, a person having a panic attack, a person who has experienced a traumatic event, and a psychotic person threatening violence.
An initial uncontrolled evaluation of the course involved comparing the first 210 participants at the beginning and end of the course, and at 6 months follow-up [3]. The course was found to produce improvement in ability to recognize a mental disorder in a case vignette, to change beliefs about treatment to be more like those of health professionals, to decrease social distance from people with mental disorders, to improve confidence in providing help to others, and to increase the amount of help actually provided.
The next stage in the evaluation of Mental Health First Aid involved a randomized controlled trial with 301 employees of two large government departments [4]. Participants were assigned to either receive the course immediately or were placed on a wait-list for 5 months and received the training after the trial was completed. The trial found a number of benefits, including greater confidence in providing help to others, greater likelihood of advising people to seek professional help, improved concordance with health professionals about treatments, and decreased social distance from people with mental disorders. A surprising finding was that the course improved the mental health of the participants themselves, even though they were not recruited to have mental health problems and no therapeutic benefit was promised. The mental health benefits of the course had not been assessed in the earlier uncontrolled trial.
This study involved an "efficacy" trial in that it was carried out under fairly ideal conditions which permitted rigorous experimental control. There was only one instructor who was the originator of the Mental Health First Aid course and very experienced, the trial was carried out in a workplace setting where employees were allowed time off to participate, the participants were a relatively well educated group of civil servants, and it was possible to randomly allocate participants relatively easily. In order to evaluate the course under more typical circumstances, we have now carried out a second trial. This "effectiveness" trial involved members of the public in a large rural area of Australia, who were taught by trained Mental Health First Aid instructors from the local health service. As in the previous trial, participants who received training were compared to a wait-list control group. Participants were randomized by Local Government Area clusters rather than individually because (1) there might have been contamination of information provided across allocated groups (2) the wait list group might have been difficult to maintain if others in the same locality were seen to be receiving training, and (3) individual randomization in some small communities may not have produced sufficient numbers to run a course.
The reason for basing the trial in a rural area is that people living in rural Australia are less likely to receive general practitioner services for common mental disorders and also have more limited access to specialist mental health services [5,6]. There is therefore a greater need to develop community capacity to support those with mental disorders.
Methods
The details of this trial have been reported according to the CONSORT statement for cluster randomized trials [7].
Participants
Eligible participants were residents of the catchment area of the New South Wales (Australia) Southern Area Health Service who were over 17 years of age, who volunteered for training in response to publicity, who were available over the period of the trial, and who were willing to receive interviews assessing trial outcomes. Participants had to volunteer as individuals rather than as a group (e.g. a whole workplace). Publicity took the form of talks to community groups, newspaper ads, a press release and radio interviews.
Eligible clusters were the 16 Local Government Areas (cities or shires) in the catchment area of the Southern Area Health Service in 2003. This catchment is located in south-east New South Wales, runs approximately 370 km from north to south and approximately 160 km from east to west, and had a population of 194,435 in 2001. The Local Government Areas varied from popular coastal areas to farming communities to rural towns and ranged in population size from less than 5000 to over 50,000.
Intervention
Participants received a nine-hour Mental Health First Aid course, in three weekly sessions of three hours each. Training was administered in the local area in groups of up to 25 participants, with a minimum of 10 participants per course. As documentation of the intervention, there is a lesson plan for each session and a participants' manual containing material that was given to take away [2]. All instructors were given training and a teaching kit of lesson plans, videos, books, master copies of handouts and a set of transparencies. Educators received a one-week training program in how to conduct Mental Health First Aid courses and subsequent supervision in running a course. They were trained by Betty Kitchener who devised the Mental Health First Aid course. The course teaches how to help people in the crisis situations of being suicidal, having a panic attack, being exposed to a traumatic event, or in an acute psychotic state. The symptoms, risk factors and evidenced-based treatments (medical, psychological, alternative and self-help) for the mental disorders of anxiety, depressive and substance use and psychotic disorders are also taught. Figure 1 shows the five steps of providing mental health first aid taught in the course. Participants received training either immediately (experimental Local Government Areas) or after 6 months on a wait-list (control Local Government Areas).
Figure 1 The five steps in providing mental health first aid.
Training was administered by educators who were recruited from the staff of the Southern Area Health Service. Expressions of interest to become Mental Health First Aid instructors were sought from staff of the Area Health Service and associated community organisations. Five Mental Health first Aid instructors were recruited from a pool of 10 applicants for these positions. All the instructors had experience in mental health work and also a background in training, working with communities or health promotion work. A project coordinator with experience in mental health and health promotion (Ms Karen Peterson), who was employed to work on the project half time, also trained as an instructor. The same instructors taught courses in each paired Local Government Area, so that this factor did not differ between the immediate and wait-list Local Government Areas. The coordinator monitored a sample of courses taught during the trial to assess fidelity to the lesson plans. A fidelity checklist of topics that had to be covered was developed for each session. Four of the instructors had all three course sessions checked, while one of the instructors only had two sessions checked. The percentage of topics covered correctly was 100% for four of the instructors and 81% for one of the instructors.
Objectives
The hypotheses were that individuals trained in Mental Health First Aid, when compared to wait-list controls, would have increased knowledge of mental disorders and their treatments, decreased social distance, increased confidence in providing help, and that they would provide greater help to people experiencing mental health problems.
Outcomes
Outcomes were measured in January–February of 2003 (the pre-test assessment), the courses were run for the intervention group in March–April of 2003, and outcomes were measured again in July–August 2003 (the follow-up assessment). The wait-list control group received courses in September–October 2003, after the follow-up assessment was completed.
All outcomes were measured at the individual level by telephone interview. The interview content was based on the questionnaire used in the uncontrolled trial of Mental Health First Aid [3]. The pre-test interview covered the following: whether the participant had ever experienced a mental health problem (yes/no), whether a family member had ever experienced a mental health problem (yes/no), the participant's confidence in helping someone (five-point scale from 1. not at all to 5. extremely), contact in the last six months with anyone with a mental health problem (yes/no), how many people, whether any help offered (yes/no), what type of help (open-ended question), recognition of the problem in a case vignette (randomly assigned to be a case of depression or one of schizophrenia), what participant would do to help if they knew the person in the vignette (this "mental health first aid intention" involved the presence or absence of 8 elements, arrived at by a qualitative analysis of a sample of the responses, and added up to give a scorefrom 0–8), ratings of the likely helpfulness of a range of interventions for the person in the vignette (scored to give a scale of percentage agreement with mental health professionals about treatment [3]), a social distance scale relating to the person in the vignette [8], whether the participant had had a problem like the one in the vignette, whether a family member had had a problem like the one in the vignette, participant's reason for doing the course, and sociodemographic characteristics of the participant (age, gender, education, non-English speaking background, aboriginality). The follow-up questionnaire was the same as the pre-test questionnaire except that it omitted the sociodemographic questions.
All outcomes were measured by a scripted telephone interview administered by professional interviewers. In order to reduce the length of the interview, participants were individually randomly assigned to receive either a depression vignette or a schizophrenia vignette, with the same questions asked in respect to each vignette. The interviewers were provided with an ID, name and phone number of each participant and knew whether they were giving the first or second interview to the participant. While they were not told whether the participant was in the experimental or control group, information about which group they were assigned to was given at the end of the interview script. As far as was practical given the very different sizes of the Local Government Area pairs, the same interviewers interviewed participants in each pair.
Sample size determination
For power calculations and sample size determination, a conservative assumption was made that the waitlist control group would show improvements, possibly due to increased awareness of mental health issues, of about 50% of that of the experimental group. This corresponds to effect sizes in the range 0.28–0.31 for changes on scales and in the range 0.02–0.04 for changes in identifying the correct diagnosis. Sample size estimates using nQuery Advisor software [9] indicated that a sample size of 200 participants in each of the two groups would be sufficient to detect differences with power of at least 80% in 2-sided tests at the 0.05 level. Clustering effects of individuals in 16 Local Government Areas involved design effects of unknown magnitude in the analysis. It was assumed that these would be of the order of 20%, so that a total achieved sample sizes of 250 in each group would be sufficient to detect differences with 80% power.
Randomization: Sequence generation
Randomization to immediate participation or wait-list was at the level of Local Government Area. The Local Government Areas were matched in pairs to have similar population and social characteristics. The variables used for matching were population size, interior vs coastal location, and an index of population education/occupation. The first listed LGA of each pair was assigned to the immediate or wait-list group at random, using the Random Integers option of Random.org [10] to generate a 1 or a 2 for each pair. For LGA pairs receiving a 1, the first member of the pair received immediate training, while for those receiving a 2 it was the second member of the pair.
Each individual participant was randomly assigned a variable (values of 1 or 2) to determine which case vignette they received during their interviews. This was done using the Random Integers option of Random.org [10]. Those assigned a 1 received the interview based on a vignette of a person who is depressed and those assigned 2 received a vignette of a person with schizophrenia.
Randomization: Allocation concealment
Allocation was on the basis of cluster. In other words, the participant's Local Government Area determined whether they received immediate or wait-list training. Participants were not informed about their allocation to immediate or wait-list training until the end of their baseline interview.
Randomization: implementation
Local Government Areas were matched in pairs and Anthony Jorm assigned these randomly to immediate training or wait-list. Participants were not able to attend a class from outside their own Local Government Area. There was a recruitment period for all Local Government Areas which was organized by the coordinator Karen Peterson. The coordinator and the participants who were recruited were blind to the allocation of the Local Government Area during the recruitment period. Anthony Jorm revealed the allocation to Karen Peterson after the recruitment period ended. Karen Peterson then organized class times either immediately or after a waiting period, depending on the allocation of each Local Government Area in the pair.
Randomization: Blinding (masking)
At the time of the baseline interview, the participants did not know whether they were in an immediate or wait-list Local Government Area. However, interviewers had information at the end of the interview script telling whether the participant was assigned an immediate class or had to wait. Blinding of participants was not possible at subsequent interviews. Participants knew whether or not they had received training. While interviewers were not told the allocation of the participants in subsequent interviews, this might have become obvious during the interview if participants mentioned whether or not they had done the course. Interviewers were given a scripted interview to minimize any bias in the assessment due to knowledge of allocation.
Ethics
Ethical approval for the study was given by the Australian National University Human Research Ethics Committee and by the ethics committee of the South Western Sydney Area Health Service.
Statistical methods
For outcomes measured on a numeric scale, the change from pre-test to follow-up was analysed using linear regression. For binary outcomes, individuals scoring the same at pre-test and at follow-up were not used, and for those who changed, the direction of change was analysed as a binary outcome using logistic regression. Standard errors and p-values were adjusted for the cluster design using the Huber-White "sandwich" variance estimator, treating the 16 LGAs as the clusters. Analyses were corrected for differences between the LGA pairs by including this as an 8-level fixed-effect factor in the regression models. Missing data were imputed using best-subsets regression. All analysis was done using Stata version 8.2 [11].
Results
Recruitment and Participant flow
Recruitment of participants took place in October and November of 2002. Figure 2 shows the number of participants and clusters at each stage of the trial.
Figure 2 Flow diagram of the number of participants and clusters at each stage of the trial.
Baseline data
Table 1 shows the characteristics of each group at the cluster and individual level. The two groups appear to be well matched in terms of sociodemographic characteristics and in history of mental health problems in self and family. However, there was a significant difference in reason for doing the course, with more people in the control group doing it for work reasons.
Table 1 Baseline characteristics for each group given at the individual and cluster levels.
Mental Health First Aid group Control group P-value
Local Government Area characteristics at baseline
Number 8 8
Population size: 1.0
<5,000 3 3
5,000–9,999 2 1
10,000–19,999 1 2
20,000–29,999 1 0
30,000–39,999 1 2
Number of participants in each area (smallest to largest) 9,17,18,29,30,48,100,165 8,9,12,16,28,50,53,161
Individual participant characteristics at baseline
Number 416 337
Mean age (years) 47.14 47.97 0.42
Number (%) men 79 (19.0) 57 (16.9) 0.40
Number (%) with university degree 85 (20.6) 81 (24.1) 0.36
Number (%) aboriginal 11 (2.6) 10 (3.0) 0.40
Number (%) non-English speaking background 5 (1.2) 7 (2.1) 0.12
Reason for doing course: 0.011
Relating to workplace/voluntary work 180 (43.3) 188 (55.8)
Relating to family/close friends 56 (13.5) 29 (8.6)
Relating to own mental health status 20 (4.8) 10 (3.0)
Duty as a citizen 49 (11.8) 44 (13.1)
Just interested 111 (26.7) 66 (19.6)
Note: P-values are adjusted for clustering by Local Government Area
Numbers analyzed
The data were analyzed by an intention-to-treat approach, with single imputation used for missing data. As shown in Figure 2, the number of participants analyzed was the same as the number randomly allocated.
Outcomes and estimation
Tables 2 and 3 show the changes found for the dichotomous and continuous outcome measures respectively and the P-value of the comparison between the Mental Health First Aid and control group on these changes. From pre-test to follow-up a significantly larger percentage of the Mental Health First Aid group than the control group changed from not reporting experiencing a mental health problem to reporting experiencing one, from incorrectly to correctly diagnosing the case vignette and from reporting not offering help to a person with a mental health problem to reporting offering help. The Mental Health First Aid group changed significantly more than the control group in their agreement with health professional about treatment, in the degree of reduction in reported social distance from the person in the vignette and in their confidence in providing help.
Table 2 Changes in dichotomous outcome measures.
Outcome Mental Health First Aid group Control group OR (95% CI) P-value
Mental health problems in self
Pre-test 154 (37%) 118 (35%)
Follow-up 172 (41%) 118 (35%)
Change (95% CI) 4% (2 to 6) 0% (-3 to 3) 0.548 (0.304, 0.986), P = 0.045
Mental health problems in family
Pre-test 233 (56%) 183 (54%)
Follow-up 277 (67%) 205 (61%)
Change (95% CI) 11% (4 to 17) 7% (2 to 11) 0.575 (0.318, 1.037), P = 0.066
Correct diagnosis of vignette
Pre-test 282 (68%) 249 (74%)
Follow-up 337 (81%) 255 (76%)
Change (95% CI) 13% (8 to 19) 2% (0 to 4) 0.311 (0.250, 0.387), P < 0.001
Help offered to person with mental health problem
Pre-test 305 (73%) 256 (76%)
Follow-up 340 (82%) 270 (80%)
Change (95% CI) 8% (4 to 13) 4% (-2 to 10) 0.602 (0.380, 0.953), P = 0.031
Professional help advised to person with mental health problem
Pre-test 81 (19%) 71 (21%)
Follow-up 104 (25%) 73 (22%)
Change (95% CI) 6% (3 to 8) 1% (-4 to 5) 0.734 (0.452, 1.191), P = 0.21
Note: P-values and confidence intervals are adjusted for clustering by Local Government Area
Table 3 Changes in continuous outcome measures.
Outcome Mental Health First Aid group Control group Treatment effect (95% CI), P-value
Agreement with health professionals about treatment
Pre-test mean (SEM) 60.55 (3.89) 69.46 (2.18)
Follow-up mean (SEM) 74.74 (1.91) 70.81 (2.27)
Change (95% CI) 14.19 (9.53 to 18.85) 1.35 (-6.04 to 8.75) 11.77 (5.98, 17.56), P = 0.001
Social distance
Pre-test mean (SEM) 8.13 (0.24) 8.06 (0.13)
Follow-up mean (SEM) 7.59 (0.17) 7.90 (0.20)
Change (95% CI) -0.53 (-0.99 to -0.08) -0.17 (-0.41 to 0.07) -0.26 (-0.49, -0.03), P = 0.032
Mental health first aid intention
Pre-test mean (SEM) 1.81 (0.04) 1.88 (0.04)
Follow-up mean (SEM) 1.83 (0.03) 1.85 (0.07)
Change (95% CI) 0.02 (-0.11 to 0.15) -0.03 (-0.15 to 0.08) 0.06 (-0.00, 0.12), P = 0.066
Confidence in providing help
Pre-test mean (SEM) 3.13 (0.08) 3.17 (0.07)
Follow-up mean (SEM) 3.39 (0.05) 3.21 (0.07)
Change (95% CI) 0.27 (0.11 to 0.42) 0.04 (-0.02 to 0.11) 0.21 (0.10, 0.33) P = 0.001
Number of people in contact with who had mental health problem
Pre-test mean (SEM) 3.97 (0.31) 4.56 (0.20)
Follow-up mean (SEM) 3.89 (0.30) 4.34 (0.29)
Change (95% CI) -0.08 (-0.64 to 0.49) -0.22 (-0.83 to 0.40) 0.22 (-0.18, 0.63) P = 0.25
Note: Standard errors of the mean (SEM), confidence intervals and P-values are adjusted for clustering by Local Government Area
The intraclass correlations for the continuous outcomes were: for agreement with health professionals about treatment, 0.15 (95% confidence interval 0.01, 0.29); for number of people in contact with that had a mental health problem, 0.02 (0, 0.06); for confidence in providing help, 0.03 (0, 0.07); for mental health first aid intention, 0.002 (0, 0.02); and for social distance, 0.04 (0, 0.08). Thus for all but one outcome, the correlation was small, justifying our assumption of a modest design effect.
Adverse events
Given that an educational intervention was evaluated with a non-clinical sample, there was no justification for a systematic inquiry into adverse events. Informally, no adverse events were reported.
Discussion
This study has found that the Mental Health First Aid training produced a number of significant changes in participants compared to a wait-list control group. A number of changes related to how people responded to a vignette of a person with either depression or schizophrenia. We found that there was greater recognition of the disorders in a vignettes, increased agreement with health professionals about which interventions are likely to be helpful, decreased social distance towards the people portrayed in the vignettes. These changes were seen equally with both vignettes. There was also a non-significant trend for those in the trained group to have more ideas for how to help the person in the vignette if it had been someone they knew.
Other outcomes with significant changes related more directly to the provision of mental health first aid. There was increased confidence in providing help to others and an increase in help actually provided. There was no change in the number of people with mental health problems that trainees had contact with or in the percentage advising someone to seek professional help.
One potential concern of Mental Health First Aid training is that it will lead to over-diagnosis of life problems as mental disorders. In previous trials we have found no evidence that the training affects the perception that the participant or their family have mental health problems [3,4]. By contrast, in the present study there was a significant increase in the percentage who perceived themselves as having a mental health problem and a non-significant trend for an increased perception of family members as having mental health problems. However, in absolute terms the changes were not so great as to be a concern and may, in fact, reflect accurate re-labelling.
These findings are similar to those of the earlier efficacy trial. However, the courses were taught by instructors who were not the originators of the Mental Health First Aid program under conditions which more closely approximate those that are typical in practice. The findings are therefore more generalizable than those reported previously.
While the more typical conditions of this trial are an advantage for generalizability, they produced greater practical difficulties in running the trial. An important weakness was that attendance data on participants were not collected by some of the instructors. We are therefore uncertain what proportion of the participants received the complete training course. A similar problem was determining the adherence of the instructors to the curriculum. We were able to carry out some formal observation of the instructors'adherence to a list of topics covered by the curriculum and found 100% adherence for most of the instructors, but one had only 81% adherence.
Another limitation of this study is that we did not directly measure the mental health of participants. In the earlier trial, we unexpectedly found a mental health benefit and this requires replication. The reason that a mental health measure was not included was that we did not have the results of the earlier trial at the time we designed this one. Another factor was the limited time available in the telephone interviews used to assess outcomes.
We used an intention-to-treat approach to the data. Whereas many trials use a last observation carried forward approach to handle missing post-test data, we used data imputation by best-subsets regression. This approach is likely to give better estimates than conventional approaches to missing data even when the missing-at-random assumption is not met [12].
Since this and the earlier trials were started, the Mental Health First Aid course has been extended from 9 to 12 hours on the basis of consistent requests from trainees for a longer course. The longer course does not add new content, but rather extends the time available to deal with each topic. We have yet to evaluate whether this extension adds to the effectiveness of the training.
Conclusions
A nine-hour Mental Health First Aid training produces positive changes in knowledge, attitudes and behavior when the course is given to members of the public by instructors from the local health service. This finding shows that the effects of the course are generalizable beyond its originators and when run under typical conditions.
Competing interests
BAK and AFJ were the developers of the Mental Health First Aid course.
Authors' contributions
AFJ was involved in securing funding for the study, had a major role in the design of the study, co-developed the evaluation questionnaire, contributed to the data analysis and had a major role in writing the manuscript.
BAK was involved in securing funding for the study, developed and taught the Mental Health First Aid Instructor course, had a role in the design of the study, co-developed the evaluation questionnaire, organized the outcome assessment and had a minor role in writing the manuscript.
ROK was involved in securing funding for the study, had a role in the design of the study, had a major role in planning and managing the trial's implementation in its initial stages, recruited and supervised the study staff, established and maintained organisational support in the Southern Area, and had a role in the writing of the manuscript.
KBGD had a major role in the data analysis and a minor role in writing the manuscript.
All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Thanks to Karen Peterson who coordinated the implementation of the Mental Health First Aid courses within the Southern Area Health Service, to Kelly Blewitt for assistance with data management, to Ailsa Korten for help with the power analysis, to the five telephone interviewers, to Mr Greg Alridge Area Director of the Southern Area Mental Health Service and Dr Ian White Director of the Southern Area Health Promotion Unit for assisting with organisation support for the trial, and to the five instructors: Len Kanowski, Jennie Lampard, Tina Philip, Karen Peterson and Tracie Storay. Funding was provided by the Health Promotion Demonstration Research Grants Scheme from the New South Wales Department of Health, a National Health and Medical Research Council Research Fellowship and Program Grant, and a grant from ACT Health and Community Care.
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| 15500695 | PMC526774 | CC BY | 2021-01-04 16:33:02 | no | BMC Psychiatry. 2004 Oct 23; 4:33 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-33 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-231549149810.1186/1471-2210-4-23Research ArticleSerum adiponectin as a biomarker for in vivo PPARgamma activation and PPARgamma agonist-induced efficacy on insulin sensitization/lipid lowering in rats Yang Baichun [email protected] Kathleen K [email protected] Lihong [email protected] Kevin M [email protected] Lisa G [email protected] Judi A [email protected] Deborah A [email protected] Jay C [email protected] Stephen A [email protected] Gregory L [email protected] Departments of Molecular Pharmacology, GlaxoSmithKline, Research Triangle Park, NC 27709, USA2 Metabolic Diseases, GlaxoSmithKline, Research Triangle Park, NC 27709, USA3 Quantitative Expression, GlaxoSmithKline, Research Triangle Park, NC 27709, USA2004 18 10 2004 4 23 23 18 6 2004 18 10 2004 Copyright © 2004 Yang et al; licensee BioMed Central Ltd.2004Yang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
PPARγ agonists ameliorate insulin resistance and dyslipidemia in type 2 diabetic patients. Adiponectin possesses insulin sensitizing properties, and predicts insulin sensitivity of both glucose and lipid metabolism. In diet-induced insulin resistant rats and ZDF rats, the current studies determined the correlation between PPARγ agonist-upregulated fatty acid binding protein(FABP3) mRNA in adipose tissue and PPARγ agonist-elevated serum adiponectin, and the correlation between PPARγ agonist-elevated serum adiponectin and PPARγ agonist-mediated efficacy in insulin sensitization and lipid lowering.
Results
Parallel groups of SD rats were fed a high fat/sucrose (HF) diet for 4 weeks. These rats were orally treated for the later 2 weeks with vehicle, either PPARγ agonist GI262570 (0.2–100 mg/kg, Q.D.), or GW347845 (3 mg/kg, B.I.D). Rats on HF diet showed significant increases in postprandial serum triglycerides, free fatty acids (FFA), insulin, and area under curve (AUC) of serum insulin during an oral glucose tolerance test, but showed no change in serum glucose, adiponectin, and glucose AUC. Treatment with GI262570 dose-dependently upregulated adipose FABP3 mRNA, and increased serum adiponectin. There was a positive correlation between adipose FABP3 mRNA and serum adiponectin (r = 0.7350, p < 0.01). GI262570 dose-dependently decreased the diet-induced elevations in triglycerides, FFA, insulin, and insulin AUC. Treatment with GW347845 had similar effects on serum adiponectin and the diet-induced elevations. There were negative correlations for adiponectin versus triglycerides, FFA, insulin, and insulin AUC (For GI262570, r = -0.7486, -0.4581, -0.4379, and -0.3258 respectively, all p < 0.05. For GW347845, r = -0.6370, -0.6877, -0.5512, and -0.3812 respectively, all p < 0.05). In ZDF rats treated with PPARγ agonists pioglitazone (3–30 mg/kg, B.I.D.) or GW347845 (3 mg/kg, B.I.D.), there were also negative correlations for serum adiponectin versus glucose, triglycerides, FFA (for pioglitazone, r = -0.7005, -0.8603, and -0.9288 respectively; for GW347845, r = -0.9721, -0.8483, and -0.9453 respectively, all p < 0.01).
Conclusions
This study demonstrated that (a) PPARγ agonists improved insulin sensitivity and ameliorated dyslipidemia in HF fed rats and ZDF rats, which were correlated with serum adiponectin; (b) Serum adiponectin was positively correlated with adipose FABP3 mRNA in GI262570-treated rats. These data suggest that serum adiponectin can serve as a biomarker for both in vivo PPARγ activation and PPARγ agonist-induced efficacy on insulin resistance and dyslipidemia in rats.
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Background
Type 2 diabetes mellitus (T2D) and the metabolic syndrome are characterized by resistance to the action of insulin in peripheral tissues, including skeletal muscle, liver, and adipose. Activation of the peroxisome proliferator-activated receptor gamma (PPARγ) improves insulin sensitivity and lowers circulating levels of glucose, triglycerides and free fatty acids without stimulating insulin secretion in rodent models of T2D [1,2]. PPARγ agonists also alleviate peripheral insulin resistance in humans, and have been effectively used in treatment of T2D patients [3-5]. Fatty acid binding protein(FABP3), adipocyte lipid binding protein(aP2) and lipoprotein lipase (LPL)are response genes of PPARγ and are indicators for in vivo PPARγ activation in adipose tissue [6-9].
Adiponectin, an adipose-specific plasma protein, possesses insulin sensitizing and anti-atherogenic properties [10]. It has been well documented that plasma adiponectin is lower in obese subjects than in lean subjects, lower in diabetic patients than in non-diabetic patients [10-13], and is negatively correlated with body weight, visceral fat mass, and resting insulin level [11,12]. Hotta et al also reported that adiponectin decreased in parallel with the progression of T2D in rhesus monkeys, and there is a strong correlation between plasma adiponectin and systemic insulin sensitivity [14]. Studies by Maeda et al showed that adiponectin knockout mice developed hyperglycemia and hyperinsulinemia while on HF diet, which was reversed by adenoviral-mediated adiponectin expression [15]. Exogenous adiponectin also lowered hepatic glucose production during a pancreatic euglycemic clamp [16], and increased post-absorptive insulin-mediated suppression of hepatic glucose output [10]. The PPARγ agonist, class of insulin sensitizer, has the marked effect of up-regulating serum adiponectin. Combs et al reported that the PPARγ agonist rosiglitazone increased plasma adiponectin in db/db mice [17]. Yang et al reported rosiglitazone increased plasma levels of adiponectin in type 2 diabetic patients [18]. Tschritter et al analyzed the associations between plasma adiponectin and insulin sensitivity and serum lipid parameters in nondiabetic individuals, and concluded that plasma adiponectin predicts insulin sensitivity of both glucose and lipid metabolism [19].
While PPARγ agonists increase plasma adiponectin and adiponectin levels predict insulin sensitivity, there is not a clear demonstration of the relationships among PPARγ agonist-increased adiponectin and PPARγ agonist-mediated efficacy on insulin sensitivity/in vivo PPARγ activation. Therefore, the current studies were designed to define these relationships and assess serum adiponectin as a biomarker for in vivo PPARγ activation and PPARγ agonist-induced efficacy on insulin sensitization and lipid lowering.
Results
High fat/sucrose (HF) diet induced changes in SD rats
Rats on the HF diet for 4 weeks showed marked insulin resistance and dyslipidemia, indicated by significant increases in postprandial serum levels of triglycerides, free fatty acids, insulin, and area under curve (AUC) for serum insulin during OGTT. But the HF diet did not cause changes in postprandial serum glucose or OGTT glucose AUC compared with rats on normal diet, consistent with an insulin resistant, pre-diabetic phenotype. Serum adiponectin level in rats on HF diet was slightly higher than that in normal diet rats at week 2, but back to the same level at week 4 (Table 1).
Table 1 HF diet induced changes in SD rats.
Normal diet HF diet
Triglyceride (mg/dL)
Prior to start 95.0 ± 8.7 115.6 ± 13.7
2 weeks diet 100.4 ± 7.6 446.2 ± 38.7**++
4 weeks diet 121.7 ± 9.1 388.1 ± 44.5**++
Free fatty acid (mEq/L)
Prior to start 0.36 ± 0.05 0.41 ± 0.04
2 weeks diet 0.38 ± 0.05 0.56 ± 0.04**++
4 weeks diet 0.24 ± 0.02 0.67 ± 0.06**++
Glucose (mg/dL)
Prior to start 166.8 ± 5.3 162.8 ± 4.3
2 weeks diet 170.0 ± 11.3 174.1 ± 2.5
4 weeks diet 176.6 ± 2.8 167.0 ± 4.0
Post-prandial insulin (ng/ml)
Prior to start 0.71 ± 0.11 1.06 ± 0.15
2 weeks diet 1.26 ± 0.29 2.72 ± 0.47**++
4 weeks diet 1.23 ± 0.22 2.39 ± 0.35**++
Insulin AUC during OGTT
4 weeks diet 241.6 ± 19.5 528.6 ± 84.9**
Glucose AUC during OGTT
4 weeks diet 7360 ± 416 7533 ± 496
Serum adiponectin (μg/ml)
Prior to start 3.59 ± 0.25 3.53 ± 0.25
2 weeks diet 3.64 ± 0.32 4.96 ± 0.41*
4 weeks diet 3.75 ± 0.40 4.35 ± 0.40
*p < 0.05 vs Before diet, **p < 0.01 vs Before diet, ++p < 0.01 vs Normal diet.
PPARγ agonist on adiponectin in SD rats
As showed in Fig. 1, treatment of SD rats on HF diet with GI262570 for 2 weeks dose-dependently increased serum adiponectin, and upregulated adipose FABP3 mRNA without effect on housekeeper genes 18S, β-actin, and cyclophilin. There was a positive correlation between adipose FABP3 mRNA and serum adiponectin (Pearson Correlation Coefficients 0.7350, p < 0.01). A marked increase in serum adiponectin was also observed in GW347845-treated HF fed SD rats (30.93 ± 0.45 vs 4.86 ± 0.30 μg/ml in vehicle. p < 0.01).
Figure 1 Efeects of PPARγ agonist GI262570 on serum adiponectin level (a), adipose FABP3 mRNA level (b), and the correlation between serum adiponectin and adipose FABP3 mRNA. SD rats were on HF diet for 4 weeks. GI262570 was oral dosed for the later 2 weeks. Mean ± SEM. N = 5–8 in each group. *p < 0.05 vs vehicle. **p < 0.01 vs vehicle.
PPARγ agonist-increased serum adiponectin and PPARγ agonist-mediated efficacy on insulin sensitivity and lipid lowering
Treatment of rats on HF diet with GI262570 for 2 weeks significantly decreased the diet-induced elevations in postprandial serum triglycerides, free fatty acids, insulin, and insulin AUC in a dose-dependent manner (Fig. 2). Treatment with GW347845 showed a qualitatively similar effect to that of GI262570 treatment (Table 2). There were negative correlations for adiponectin versus triglycerides, free fatty acids, insulin, and insulin AUC (For GI262570, r = -0.7486, -0.4581, -0.4379, and -0.3258; p < 0.005, 0.005, 0.01 and 0.05 respectively, Fig. 3; For GW347845, r = -0.6370, -0.6877, -0.5512, and -0.3812, p < 0.01, 0.01, 0.01 and 0.05 respectively, Table 2).
Figure 2 Effects of PPARγ agonist GI262570 on serum insulin, triglycerides, free fatty acids, and insulin AUC during OGTT. SD rats were on HF diet for 4 weeks. GI262570 was oral dosed for the later 2 weeks. Mean ± SEM. N = 7–9 in each group.
Table 2 Effect of GW347845 (3 mg/kg, B.I.D.) in rats on HF diet.
Triglycerides (mg/dL) FFA (mEq/L) Serum Insulin (ng/ml) Insulin AUC (min × ng/ml)
Normal diet 98.6 ± 8.6 0.26 ± 0.03 1.34 ± 0.19 241.0 ± 22.8
Diet-Vehicle 455.6 ± 94.2** 0.65 ± 0.07** 1.88 ± 0.16* 356.9 ± 25.3**
Diet-GW347845 139.4 ± 14.8++ 0.37 ± 0.03++ 1.16 ± 0.08++ 267.9 ± 34.2
Corr. Coeff. -0.637 -0.6877 -0.5512 -0.3812
Vs adiponectin p < 0.01 p < 0.01 p < 0.01 p < 0.05
Corr. Coeff.: Pearson Correlation Coefficient. *p < 0.05 vs Normal diet. **p < 0.01 vs normal diet. ++p < 0.01 vs diet-vehicle. N = 7–8 in each group.
Figure 3 Correlation between PPARγ agonist GI262570 (0.2–100 mg/kg)-elevated serum adiponectin and GI262570-decreased serum insulin, triglycerides, free fatty acids, and insulin AUC during OGTT in HF fed SD rats.
PPARγ agonists in Zucker rats
Compared with Zucker lean rats, ZDF rats had higher serum insulin, glucose, TG, FFA, but similar serum adiponectin levels. Treatment of ZDF rats with PPARγ agonist pioglitazone or GW347845 for 2 weeks resulted in significantly lower serum glucose, triglycerides, free fatty acids, and modestly lower serum insulin, compared to vehicle treatment. Both pioglitazone and GW347845 markedly increased serum adiponectin in ZDF rats (Table 3). There were also negative correlations for serum adiponectin versus glucose, TG, FFA (for pioglitazone, r = -0.7005, -0.8603, and -0.9288 respectively; for GW347845, r = -0.9721, -0.8483, and -0.9453 respectively, all p < 0.01).
Table 3 Effect of pioglitazone and GW347845 in ZDF rats.
Insulin (ng/ml) Glucose (mg/dL) Triglycerides (mg/dL) FFA (mEq/L) Adiponectin (μg/ml)
ZDF lean rats
Vehicle 0.3 ± 0.1 158 ± 4 82 ± 7 0.31 ± 0.02 10.2 ± 0.5
ZDF rats
Vehicle 2.9 ± 0.5** 525 ± 25** 912 ± 97** 0.62 ± 0.03** 10.0 ± 1.1
Pioglitazone (mg/kg, B.I.D)
3 2.5 ± 0.6+ 214 ± 64+ 251 ± 81++ 0.26 ± 0.09++ 44.0 ± 7.7++
10 2.8 ± 0.5 154 ± 17++ 129 ± 19++ 0.16 ± 0.01++ 60.0 ± 1.5++
30 2.3 ± 0.4+ 154 ± 9++ 129 ± 16++ 0.14 ± 0.01++ 63.0 ± 0.8++
GW347845 (mg/kg, B.I.D)
3 1.7 ± 0.2++ 147 ± 6++ 95 ± 10++ 0.12 ± 0.01++ 66.1 ± 0.6++
**p < 0.01 vs ZDF lean rats. + p < 0.05 vs Vehicle-treated ZDF rats. ++p < 0.01 vs Vehicle-treated ZDF rats. N = 6–12 in each group.
Discussion
Adiponectin possesses insulin sensitizing and anti-atherogenic properties [10]. In most clinical reports, primate studies, and genetic models, serum adiponectin level had been reported to be negatively correlated with body weight, visceral fat mass, and resting insulin level [10-13]. The present study showed that rats fed a HF diet had significantly higher serum insulin and lipids with in 2 weeks, which indicates insulin resistance. However, serum adiponectin level was not decreased by the diet up to 4 weeks. We have subsequently kept rats on the HF diet for up to 20 weeks, and observed a slight increase (instead of decrease) in serum adiponectin level (data not shown). Our data may suggest that the HF diet-induced insulin resistance happened much early than diet-induced change in serum adiponectin. Our data is consistent with studies by Naderali EK et al [19]. In their report, 16 weeks of high fat/glucose diet resulted in significantly higher body weight, fat pad masses, plasma leptin, and higher plasma level of adiponectin, besides higher levels of plasma TG and FFA.
PPARγ is a member of the PPAR family of the nuclear receptor superfamily [6]. PPARγ agonists increase insulin sensitivity and circulating adiponectin [1,2,17,18]. The response genes of PPARγ for in vivo PPARγ activation include LPL, AP2 and FABP3 [6,7,9]. The current study demonstrated that as in other species the PPARγ agonist GI262570 upregulated serum adiponectin level and adipose FABP3 mRNA level in SD rats in a dose-dependent manner. Interestingly, there is a positive correlation between PPARγ full agonist-upregulated serum adiponectin level and adipose FABP3 mRNA level, demonstrating the serum adiponectin level could be a biomarker for in vivo PPARγ activation.
We did perform parallel experiments to check mRNA levels of PPARγ response genes FABP3, aP2 and LPL in epididymal fat. We found that basal level of FABP3 mRNA was very low compared to aP2 and LPL (FABP3:LPL:aP2 = ~1:250:2500), and that PPARγ agonist GI262570 dose-dependently increased FABP3 mRNA. AP2 was abundant in epididymal fat tissues, and was only slightly increased by GI262570 in a non-dose-dependent manner (data not shown). LPL was decreased in high fat diet fed rats, which was reversed by GI262570 but not dose-dependently (data not shown). With in vivo chronic exposure, the effect of PPARγ agonists on gene expression is difficult to separate from the effects on differentiation. In general we find aP2 a better marker of adipocyte differentiation than PPARγ activation. Since PPARγ agonist-mediated action in vivo may vary with organs/tissues (such as liver vs fat; subcutaneous fat vs omental or epididymal fat) [20,21] and duration of treatment, all PPARγ response genes may not be changed in the same manner in one tissue following chronic treatment. Therefore the authors consider that the dose-dependently GI262570 upregulated FABP3 mRNA in epididymal fat caught in the present study is of value for quantitative in vivo PPARγ activation. Thus the correlation data using FABP3 mRNA is of value.
Adiponectin has been demonstrated to have an insulin sensitizing effect [10]. Circulating adiponectin levels were positively correlated with insulin sensitivity, measured both by an euglycemic-hyperinsulinemic clamp and estimated by an oral glucose tolerant test, were negatively correlated with fasting lipids [22]. The PPARγ agonist rosiglitazone increased plasma level of adiponectin, decreased fasting plasma glucose and HBA1C, and ameliorated insulin resistance in type 2 diabetic patients [18]. However, the relationship between PPARγ agonist-increased circulating adiponectin and PPARγ agonist-induced efficacy on insulin resistance has not been studied. The current study showed that PPARγ agonists increased serum levels of adiponectin, ameliorated insulin resistance and lipid profile in both diet-induced insulin resistant rats and ZDF rats. There is a correlation between PPARγ agonist-increased serum adiponectin level and PPARγ agonist-induced efficacy in insulin sensitivity/lipid lowering. These data provide a link between PPARγ agonist-elevated circulating adiponectin level and PPARγ agonist-mediated efficacy in insulin sensitivity and lipid lowering, and indicate that serum adiponectin level could be a biomarker for in vivo PPARγ efficacy.
Other adipokines, such as leptin, are important in obesity and insulin resistance. Unlike adiponectin, leptin is positively correlated with fat amount, mass and percentage [23]. It has been reported that PPARγ agonists inhibit the expression and function of leptin [24,25]. Our unpublished study showed that high fat diet resulted in insulin resistance and higher serum leptin level in rats. Treatment of these insulin resistant rats with PPARγ agonist GW7845 improved insulin sensitivity, but did not affect serum leptin level. Therefore leptin is not considered to be a marker for PPARγ efficacy. There are indices for in vivo PPARγ activation (i.g., adipose FABP3 mRNA), or for in vivo PPARγ efficacy on insulin sensitization (i.g., serum insulin and glucose). These indices can not be used to represent both in vivo PPARγ activation and in vivo PPARγ efficacy on insulin sensitization. It is well known that circulating adiponectin increases insulin sensitivity [10], is decreased in T2D patients [10-13], and is negatively correlated with insulin resistance [22]; PPARγ agonists increase insulin sensitivity as well as circulating adiponectin [17,18]. The correlations, serum adiponectin vs adipose FABP3 mRNA and serum adiponectin vs insulin/lipids, in our study demonstrated that serum adiponectin is a good biomarker for both in vivo PPARγ activation and in vivo PPARγ efficacy on insulin sensitization.
Conclusions
These studies demonstrated that in both diet-induced and genetic rat models of insulin resistant (metabolic) syndrome the full PPARγ agonists GI262570, GW347845, and pioglitazone significantly elevated serum adiponectin levels, increased adipose transcription of the PPARγ response gene FABP3, and were efficacious as expected. This is the first demonstration of correlation among PPARγ agonist-increased serum adiponectin, PPARγ agonist response gene mRNA, and PPARγ agonist-mediated efficacy in insulin sensitivity and lipid lowering. These data indicate that serum adiponectin can serve as a biomarker for both in vivo PPARγ activation and PPARγ agonist-induced efficacy in rats.
Methods
Experimental animal and protocols
All procedures performed were in compliance with the Animal Welfare Act and U.S. Department of Agriculture regulations, and were approved by the GlaxoSmithKline Animal Care and Use Committee. Male caesarian derived Sprague Dawley rats (SD, 225–250 g) (Charles River, Indianapolis, IN) were fed rodent chow Purina 5001 (Harlan Teklad, Indianapolis, IN). Male Zucker diabetic fatty (ZDF) and male Zucker lean rats (8 weeks old) (Genetic Models, Indianapolis, IN) were fed Formulab Diet 5008 (PMI Feeds, Richmond, IN). After an adaptation period of 1 week, SD rats were fed a HF diet (TD88137, Containing 34.146% sucrose. 42% of calories from fat. Harlan Teklad, Indianapolis, IN) for 4 weeks. SD Rats fed chow Purina 5001 served as normal diet control. SD rats on HF diet were treated with vehicle (0.5% hydroxypropyl methylcellulose and 0.1% Tween 80), PPARγ agonist GI262570 [7,26-28] (0.2, 2, 20, or 100 mg/kg, QD), or PPARγ agonist GW347845 (3 mg/kg, BID) for the last 2 weeks. ZDF rats were gavaged twice daily for 14 days with vehicle, PPARγ agonist pioglitazone [4] (3, 10, or 30 mg/kg), or PPARγ agonist GW347845 (3 mg/kg) [29,30]. Zucker lean rats were gavaged twice daily for 14 days with vehicle. One day prior to the end of dosing (after 13 days of dosing), serum was obtained from tail vein of SD rats for determining postprandial levels of glucose, insulin, triglycerides, free fatty acids, and adiponectin. The SD rats were then implanted with a jugular cannula. Oral glucose tolerant tests (OGTT) were performed in these SD rats after 14 days of dosing. At the end of the study, SD rats were euthanized with CO2. White adipose tissue (WAT, epididymal fat pad) were saved for determining mRNA levels of PPARγ response gene FABP3. In Zucker rats, serum was collected after 2 weeks of dosing for determining postprandial levels of glucose, insulin, triglycerides, free fatty acids, and adiponectin. Zucker rats were then euthanized with CO2.
Determination of postprandial serum chemicals
Serum glucose, triglycerides, and free fatty acids were measured using Ilab600 Clinical Chemistry System (Instrumentation Laboratory).
Determination of serum adiponectin
Serum adiponectin of SD rats was determined by using adiponectin RIA kit (Linco Research, MO), according to the manufacture's instruction. Serum adiponectin of ZDF rats was determined by using adiponectin ELISA kit (B-Bridge International, CA), according to the manufacture's instruction.
Jugular vein cannulation
Under anesthesia with isoflurane, surgical site was prepared using standard aseptic technique (with Hiboclens® Chlorhexidine Gluconate, Zeneca Pharmaceuticals, Delaware). A longitudinal incision was made over the right external jugular vein. 5–10 mm of the vein was exposed by blunt dissection. Jugular cannula (Access™ Technologies, IL) was inserted into the vein for about 1 inch. The cannula was secured using sterile sutures. The cannula was routed subcutaneously, exteriorized between the scapulae. The cannula was then filled with dextrose-heparin solution (50:50), and heat sealed.
OGTT
Rats implanted with jugular cannula were fasted overnight. The following morning, dextrose (0.5 g/ml in water, 2 g/kg body weight) was administered by oral gavage. Blood samples (0.3 ml/time) were obtained from the jugular cannula before gavage, 10, 20, 30, 45, 60, 90 and 120 min after gavage. Blood glucose was immediately measured by using Elite® XL Glucometer (Bayer, Tarrytown, NY). Serum was collected for insulin measurement. Area under curves (AUCs) for glucose and insulin during OGTT were calculated by using WinNonlin™ Noncompartmental Model 200.
Determination of insulin level
Serum insulin of SD rats level was determined using Rat Insulin ELISA kit (Crystal Chem Inc, IL), according to the manufacture's instruction. Serum insulin level of ZDF rats was determined using Igen's M-SERIES M-8 Analyzer (Igen International, Inc., Gaithersburg, MD).
Determination of FABP3 mRNA level in white adipose tissue by real time PCR
Total RNA in epididymal fat pad was isolated by the TRIZOL® method [31]. All RNA samples were DNased using the DNA-free™ kit (Ambion – according to protocol). The samples were then quantitated by RiboGreen™ (Molecular Probes – according to protocol). GAPDH gene expression was analyzed in the absence of reverse transcriptase to ensure the samples were free of genomic DNA. The samples were then converted to cDNA using the High Capacity cDNA Archive Kit (Applied Biosystems – according to protocol). Samples were diluted to a final concentration of 5 ng/ul of cDNA. PCR results were generated using the 5' nuclease assay (TaqMan) [32] and the ABI 7900 Sequence Detection System (Applied Biosystems, Foster City, CA). Primers and probe for FABP3 are: Forward-GTCGTGACACTGGACGGAGG; Reverse-TTCCCATCACTTAGTTCCCGTG; Probe-CAGAAGTGGGACGGGCAGGAGACTACG. The primers and probe for Cyclophilin are: Forward-TATCTGCACTGCCAAGACTGA; Reverse-CCACAATGCTCATGCCTTCTTTCA; Probe-CCAAAGACCACATGCTTGCCATCCA. A master mixture was utilized which included 900 nM each of the forward and reverse primers, 100 nM probe, and 1 × PCR master mix (Applied Biosystems). The PCR reaction consisted of 12.5 ng of cDNA in a 12.5 ul total reaction volume. The PCR cycling conditions were 95°C for 10 minutes, and 40 cycles of 95°C for 15 seconds and 60°C for 1 minute.
Statistical analysis
There was a minimum of 5 rats for each data point. Data are presented as mean ± SEM. Correlation between two parameters and the significant level of correlation were analyzed by Pearson correlation analysis. Differences between vehicle and treated groups were analyzed by two-way ANOVA. P less than 0.05 was taken to be significant.
List of abbreviations
QD: Once a day
BID: Twice a day
PCR: Polymerase Chain Reaction
GAPDH: Glyceraldehyde-3-Phosphate Dehydrogenase
ANOVA: Analysis of Variance
Authors' contributions
BY is the principal investigator. LC, LGC, JM, and DW participated in the in vivo experiments. KC and JS performed the real time PCR. KB, SS and GP participated in study design and manuscript preparation.
Acknowledgements
We thank Qiming Liao for his assistance in statistical analysis, and Jane Binz for her assistance in measuring serum glucose, triglycerides, and free fatty acids.
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| 15491498 | PMC526775 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 18; 4:23 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-23 | oa_comm |
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BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-181549810210.1186/1471-2261-4-18Research ArticleThe use of warfarin in veterans with atrial fibrillation Bravata Dawn M [email protected] Karen [email protected] Sue [email protected] Lawrence M [email protected] Clinical Epidemiology Research Center, VA Connecticut Healthcare System, 950 Campbell Avenue 11C-2, West Haven, CT 06516, USA2 Department of Internal Medicine, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT 06516, USA3 Department of Quality Management, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT 06516, USA4 Department of Neurology, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT 06516, USA5 Yale University School of Medicine, 333 Cedar Street, Room IE-61 SHM, New Haven, CT 06520-8088, USA2004 21 10 2004 4 18 18 10 5 2004 21 10 2004 Copyright © 2004 Bravata et al; licensee BioMed Central Ltd.2004Bravata et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Warfarin therapy is effective for the prevention of stroke in patients with atrial fibrillation. However, warfarin therapy is underutilized even among ideal anticoagulation candidates. The purpose of this study was to examine the use of warfarin in both inpatients and outpatients with atrial fibrillation within a Veterans Affairs (VA) hospital system.
Methods
This retrospective medical record review included outpatients and inpatients with atrial fibrillation. The outpatient cohort included all patients seen in the outpatient clinics of the VA Connecticut Healthcare System during June 2000 with a diagnosis of atrial fibrillation. The inpatient cohort included all patients discharged from the VA Connecticut Healthcare System West Haven Medical Center with a diagnosis of atrial fibrillation during October 1999 – March 2000. The outcome measure was the rate of warfarin prescription in patients with atrial fibrillation.
Results
A total of 538 outpatients had a diagnosis of atrial fibrillation and 73 of these had a documented contraindication to anticoagulation. Among the 465 eligible outpatients, 455 (98%) were prescribed warfarin. For the inpatients, a total of 212 individual patients were discharged with a diagnosis of atrial fibrillation and 97 were not eligible for warfarin therapy. Among the 115 eligible inpatients, 106 (92%) were discharged on warfarin.
Conclusions
Ideal anticoagulation candidates with atrial fibrillation are being prescribed warfarin at very high rates within one VA system, in both the inpatient and outpatient settings; we found warfarin use within our VA was much higher than that observed for Medicare beneficiaries in our state.
Atrial fibrillationwarfarinanticoagulationpreventive medicineguideline adherence
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Background
Warfarin therapy is highly effective for the prevention of ischaemic stroke in atrial fibrillation [1]. Despite the accepted benefit of warfarin therapy, several reports have indicated that warfarin therapy is underutilized even in ideal anticoagulation candidates with atrial fibrillation. Most studies have reported rates of use between 13–60% [2-8]. For example, a national study of inpatient Medicare beneficiaries with atrial fibrillation demonstrated that approximately 55% of patients were discharged on warfarin [9].
Many of the previous studies about the use of warfarin in atrial fibrillation have focused on the prescription of warfarin on discharge from an acute hospitalization. Since some patients may be discharged from the hospital with a plan to begin warfarin therapy as an outpatient, these prior studies may have underestimated the use of warfarin for patients with atrial fibrillation. The Veterans Affairs (VA) Healthcare System is a useful setting for studying the use of warfarin therapy in both the inpatient and outpatient arenas because the electronic medical record contains prescription medication data as well as the inpatient and outpatient medical records (including progress notes, laboratory data, radiology reports, and other consult reports).
The objective of this study was to examine the use of warfarin in both inpatients and outpatients with atrial fibrillation within a VA setting. Specifically, we used the same methodology as the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation [10], so that we could compare rates of warfarin use in ideal anticoagulation candidates with atrial fibrillation from one VA system to those in the private sector.
Methods
We assembled two retrospective cohorts of patients to evaluate both inpatients and outpatients with atrial fibrillation. The medical records of both the inpatients and the outpatients were reviewed to confirm the diagnosis of atrial fibrillation, to identify any exclusion criteria, and to determine if patients were being prescribed warfarin.
Diagnosis of atrial fibrillation
Using the criteria developed by the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation [10], a physician's documentation of the diagnosis of atrial fibrillation was required for inclusion (electrocardiogram data were not used to make the diagnosis of atrial fibrillation). For the outpatients, Physicians' Current Procedural Terminology (CPT) codes were used to identify potential patients with a diagnosis of atrial fibrillation. For the inpatients, the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) discharge diagnosis code (427.31) was used to identify potential patients with a diagnosis of atrial fibrillation. Chart review was used to confirm the diagnosis for both the inpatients and the outpatients; a physician's documentation of atrial fibrillation in a progress note, a consult note, the discharge summary, or the problem list was needed for confirmation.
Medical record abstraction was conducted by two of the authors (KR, SK) using standard definitions. All of the exclusions were reviewed by three of the authors (KR, SK, DMB), a sample of the charts of patients with an exclusion criteria was re-abstracted (DMB), and any disagreements were resolved by consensus.
Cohort descriptions
The outpatient cohort included all of the patients seen in the outpatient clinics of the VA Connecticut Health Care System during the month of June 2000 with a diagnosis of atrial fibrillation. Outpatient clinics include both primary care and subspecialty clinics. Some of these clinics have a particular interest in the care of patients with atrial fibrillation such as cardiology and anti-coagulation clinics, however, most of the clinics do not have such a special interest (e.g., mental health, physical therapy, dermatology, endocrinology).
The inpatient cohort included all patients discharged from the VA Connecticut Health Care System West Haven Campus with any discharge diagnosis of atrial fibrillation (primary or secondary diagnosis) during the period of October 1, 1999 through March 31, 2000. Some of the patients in the inpatient cohort were readmitted during our study period; this report includes data from individual patients for their first hospital stay.
Exclusion criteria
The Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation project developed a set of exclusion criteria to identify ideal candidates for warfarin therapy; we used this exclusion criteria for the current study. Patients were excluded if they met one or more of the following: current sinus rhythm; bleeding disorder; endocarditis or pericarditis (within 6 months); seizures; intracranial hemorrhage; intracranial surgery or biopsy; lone atrial fibrillation; dual chamber pacemaker; alcohol or drug abuse; allergy to warfarin; hepatic failure; schizophrenia or active psychotic disorder; comfort care or terminal illness with life expectancy less than 6 months; un-repaired intracranial aneurysm; extensive metastatic cancer; brain cancer; malignant hypertension; peptic ulcer disease; hemorrhage; documentation that the patient refused warfarin therapy; prior complication or allergy related to past use of warfarin; or physician documentation of a rationale for not prescribing warfarin, including risk for bleeding, risk for falls, mental status impairment, liver disease, arthritis requiring non-steroidal anti-inflammatory medications or aspirin, pending surgery or other invasive procedure, terminal illness, patient's inability to obtain necessary blood work, or history of patient's non-adherence to warfarin [10]. Patients in intermittent or paroxysmal atrial fibrillation were included in the study, however, patients who were noted to be in current sinus rhythm and for whom atrial fibrillation was not a current problem were not included. For example, a patient with atrial fibrillation in the setting of an acute myocardial infarction or post-coronary bypass grafting for whom the atrial fibrillation was not a current medical problem was not included.
Warfarin prescription
For all of the patients, warfarin prescription was determined from the medical record. Patients receiving warfarin from the VA pharmacy were readily identified from the VA pharmacy component of the medical record. Patients receiving warfarin privately were identified from the progress notes. For inpatients, warfarin prescription was evaluated at the time of discharge from the hospital. For outpatients, warfarin prescription was evaluated at the time of examination of the medical record. Each outpatient medical record was examined in detail to determine the presence or absence of warfarin prescription. For those outpatients patients in whom this determination was difficult or if the data collector had a question about the patient, then the medical record was examined again by three of the authors to determine the presence or absence of warfarin prescription. This study received Institutional Review Board approval.
Statistical analysis
Student's t-tests were used to compare dimensional variables, and Fisher Exact and chi-square tests were used to assess binary variables. Two-sided p-values <0.05 were considered to be statistically significant. Exact binomial 95% confidence interval were calculated for the proportions of patients using warfarin in the inpatient and outpatient cohorts. The SAS System software release 6.12 (Cary, N.C.) was used for data analysis.
Results
Outpatient cohort
A total of 538 patients from the VA Connecticut outpatient clinics were identified as having a diagnosis of atrial fibrillation (age: 74.0 years mean ± 8.3 standard deviation; 529 [98%] men). Of these, 73 patients had one or more contraindication to anticoagulation (Table 1). Of the 465 eligible patients, 455 (98%; 95%CI 96–99%) were prescribed warfarin.
Table 1 Exclusion Criteria*
Characteristic Inpatients Outpatients
N = 212 N = 538
N (%)† N (%)†
Sinus rhythm 32 (15) 42 (8)
Death 21 (10) 1 (0.2)
History of gastrointestinal hemorrhage 18 (8) 6 (1)
Fall risk 14 (7) 4 (0.7)
Pacemaker 4 (2) 9 (2)
Lone atrial fibrillation 4 (2) 1 (0.2)
Terminal illness 4 (2) 0 (0)
Patient refused 3 (1) 6 (1)
History of intracranial hemorrhage 3 (1) 3 (0.6)
Transfer to outside facility 3 (1) 0 (0)
Multi-infarct dementia in comfort care patients 2 (0.9) 0 (0)
Warfarin held for procedure or surgery 2 (0.9) 0 (0)
Warfarin allergy 1 (0.5) 0 (0)
Failed to comply with warfarin protocol, warfarin stopped 1 (0.5) 1 (0.2)
History of seizures 1 (0.5) 0 (0)
Elective admission to begin sotalol and discontinue warfarin 1 (0.5) 0 (0)
Previous bleeding on warfarin 0 (0) 1 (0.2)
*These exclusion criteria were taken directly from the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation.10
†Note: some patients had more than one reason for not being prescribed warfarin.
The ten patients who were not prescribed warfarin did not differ from those who received warfarin with respect to age (mean age ± standard deviation; no warfarin: 76.6 ± 11.5, warfarin: 73.9 ± 8.1; p = 0.3). Among the ten patients who were not prescribed warfarin: one was a dialysis patient who received his medical care primarily from private physicians outside of the VA, he was eventually placed on warfarin; the medical record of one 90-years-old patient, who also received the majority of his health care from private physicians, indicated that his private physician had elected not to prescribe anticoagulation "because of age"; one patient had a history of alcohol use; and in the remaining 7 patients there was no documentation of a reason for why the warfarin had not been prescribed (3 of the 7 patients were receiving the majority of their medical care outside of the VA).
Inpatient cohort
A total of 212 individual patients were discharged with a diagnosis of atrial fibrillation (age: 72.9 years mean ± 9.9 standard deviation; 211 [99.5%] males). During the admission these 212 patients, 17 died during their hospitalization, 3 were transferred to a facility outside of the VA Connecticut Healthcare System, and 77 had one or more contraindication to anticoagulation. Of the 115 remaining eligible patients, 106 (92%; 95%CI 86–96%) were discharged on warfarin.
The nine patients who were not prescribed warfarin did not differ from those who were prescribed warfarin with respect to age (mean age ± standard deviation; no warfarin: 76.4 ± 7.4, warfarin: 72.6 ± 7.9; p = 0.2). To determine if some of the eligible inpatients who were not discharged on warfarin later received warfarin in the outpatient setting, we examined the outpatient records of the nine inpatients who were not discharged on warfarin: 5 died; 3 no longer receive care at our medical center (and no medication data were available); and for 1 patient, the medical record stated that he was offered warfarin therapy but that he refused to accept it. Similarly, we evaluated a sample of 50 eligible patients who had been discharged on warfarin therapy and examined their warfarin use post-discharge: 7 no longer receive care at our medical center (and no medication data are available); 4 were taken off of warfarin (3 because they were cardioverted as outpatients, and for 1 patient the warfarin was discontinued after an episode of bright red blood per rectum); and 2 patients have died.
Among the eligible inpatients 12/115 (10%) had history of prior stroke or transient ischemic attacks; 3/12 (25%) were not prescribed warfarin on discharge. No reasons were documented for why these patients were not given warfarin.
Unique patients
There was overlap between the inpatient and outpatient cohorts such that a total of 722 unique patients were identified among the 212 inpatients and the 538 outpatients.
Discussion
We found high rates of warfarin prescription in ideal anticoagulation candidates with atrial fibrillation treated within this VA system. A total of 561 of 580 ideal anticoagulation candidates (97%) were prescribed warfarin: 98% of ideal outpatient anticoagulation candidates and 92% of ideal inpatient anticoagulation candidates. These rates of warfarin use for atrial fibrillation are substantially higher than those reported previously from private sector academic and community hospitals. For example, as part of the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation, medical records were reviewed from a random sample of Medicare beneficiaries, hospitalized during the period 1998–1999, with any discharge diagnosis of atrial fibrillation from each state [9,10]. The exclusion criteria for the Medicare medical record review were the same as those used for the current study and were developed to select a cohort of atrial fibrillation patients who were "ideal" candidates for oral anticoagulation because they do not have any contraindications to oral anticoagulation [10]. Therefore, one would expect that the rates of warfarin use would be higher in ideal anticoagulation candidates than in a general population of patients with atrial fibrillation. Overall, the rate of warfarin prescription for ideal anticoagulation candidates with atrial fibrillation patients by state ranged from 31–65%, with a median of 55% in the Medicare study [9]. In Connecticut, 57% of eligible atrial fibrillation inpatients were discharged on warfarin [9]. Therefore, the inpatient rates observed in the current study of 90–92% are much higher than those observed for Medicare beneficiaries using similar methodology.
We report the anticoagulation rates from one VA healthcare system, and our findings may not be generalizable to other VA healthcare systems or to non-VA hospitals. Specifically, our results may not be generalizable to women with atrial fibrillation. Furthermore, given that the out-patient cohort for this study was obtained from a one-month sample, we may have selected patients who are more likely to be seen at an out-patient clinic, and therefore, our findings may also have limited generalizability to atrial fibrillation patients who do not require or who do not have access to regular out-patient clinical care. Although we have found higher rates of warfarin use than most of the previous studies in this area [2-9], our findings are similar to those reported by Gottlieb and Salem-Schatz [11] who found that 78.8% of atrial fibrillation patients in an HMO setting were receiving warfarin. Our findings are also similar to those reported by Bradley et al. from another VA health care system [12]. Bradley et al. demonstrated that 89% of patients without a contraindication to anticoagulation were prescribed warfarin [12].
Several possible factors might account for the high use of warfarin in VA hospitals. First, the actual rates of warfarin prescription may be higher in VA facilities where anticoagulation clinics are well established, the electronic medical record ensures that all services (primary care and consult services) have access to a patient's medical record, the staff has academic affiliations, and the VA culture embraces quality improvement and medical error reduction initiatives. Within the VA Connecticut Health Care System, pharmacist-directed anticoagulation clinics are available at two sites, in West Haven and Newington, Connecticut. Veterans who choose to obtain warfarin from the VA pharmacy are usually followed at one of these two anti-coagulation clinics. Veterans can elect to purchase warfarin from private pharmacies and have their anticoagulation intensity monitored privately (usually by their private internist or private cardiologist). Second, the higher rate may result from data collection differences between studies. Specifically, given the comprehensive VA electronic medical record, VA-based researchers may be able to identify more contraindications for anticoagulation. No quality improvement projects to increase the use of warfarin for patients with atrial fibrillation in the VA Connecticut Healthcare System were initiated during or immediately prior to the study period. A limitation of the current study is that we were unable to determine the specific reasons for why such a high rate of warfarin use was observed.
The retrospective nature of this study permitted us to evaluate clinical practices without altering physicians' behavior. However, this retrospective chart review may have limitations. First, we may not have identified those patients who are prescribed warfarin by private practitioners and obtain their warfarin from non-VA pharmacies. This would result in even higher rates of warfarin prescription than we have reported.
Second, we assembled our cohort using diagnosis codes for atrial fibrillation and did not use electrocardiographic data. Those patients who had atrial fibrillation by electrocardiogram, but who were not identified as having atrial fibrillation by their clinicians, would not have been included in this study. Because such patients are unlikely to receive warfarin our estimates of warfarin use are higher than would have been observed if we had used electrocardiography to identify atrial fibrillation patients. While many studies of the use of warfarin for atrial fibrillation have also assembled cohorts using diagnosis codes and not electrocardiographic data, the VA-based study by Bradley et al. used electrocardiographic criteria and their findings are similar to ours [12].
Third, some may argue that we excluded patients who would benefit from anticoagulation. For example, patients with atrial fibrillation and numerous other risk factors for stroke might benefit from anticoagulation despite the presence of a contraindication to anticoagulation such as a risk for falls. We chose to use the exclusion criteria developed for the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation [10] so that we could compare the rates of warfarin prescription observed within one VA system to those seen for Medicare beneficiaries.
Fourth, this study includes a total of 722 unique patients. Although some studies of warfarin use in patients with atrial fibrillation have included similar numbers of patients (e.g., N = 635 in the study of Medicare beneficiaries with ischemic stroke and atrial fibrillation by Brass, et al) [13], many studies have included much larger sample sizes (e.g., N = 11,699 in the study of Medicare beneficiaries with new-onset atrial fibrillation) [14]. Often, the studies with the largest sample sizes were secondary analyses of existing administrative datasets [14]. Future studies should be directed at evaluating the use of oral anticoagulation in veterans with atrial fibrillation using national VA data where both large sample sizes and nationally representative sampling are possible.
Conclusion
We conclude that high rates of adherence to treatment guidelines regarding the use of anticoagulation in patients with atrial fibrillation can be achieved. Our experience, and that of Bradley et al., indicates that high rates of warfarin use can be achieved across at least two VA settings [12].
Competing interests
The authors declare that they have no competing interests.
All of the authors are employed by the VA Connecticut Healthcare system. Dr. Bravata is supported by an Advanced Research Career Development Award from the Department of Veteran Affairs Health Services Research & Development Service.
Authors' contributions
All of the authors contributed to this manuscript, participated in the research design and manuscript preparation. Two of the authors (SK, KR) conduct the data collection. Three of the authors (DMB, SK, KR) reviewed the data and conducted the analyses.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15498102 | PMC526776 | CC BY | 2021-01-04 16:30:03 | no | BMC Cardiovasc Disord. 2004 Oct 21; 4:18 | utf-8 | BMC Cardiovasc Disord | 2,004 | 10.1186/1471-2261-4-18 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-321549149610.1186/1471-244X-4-32Research ArticleToxoplasma and coxiella infection and psychiatric morbidity: A retrospective cohort analysis Thomas Hollie V [email protected] Daniel Rh [email protected] Roland L [email protected] Glyn [email protected] Andy P [email protected] Department of Psychological Medicine, University of Wales College of Medicine, Heath Park, Cardiff, UK2 Communicable Disease Surveillance Centre, National Public Health Service, Wedal Road, Cardiff, UK3 Division of Psychiatry, University of Bristol, Cotham Hill, Bristol, UK4 Centre for Occupational and Health Psychology, School of Psychology, Cardiff University, Cardiff, UK2004 18 10 2004 4 32 32 15 7 2004 18 10 2004 Copyright © 2004 Thomas et al; licensee BioMed Central Ltd.2004Thomas et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
It has been suggested that infection with Toxoplasma gondii is associated with slow reaction and poor concentration, whilst infection with Coxiella burnetii may lead to persistent symptoms of fatigue.
Methods
425 farmers completed the Revised Clinical Interview Schedule (CIS-R) by computer between March and July 1999 to assess psychiatric morbidity. Samples of venous blood had been previously collected and seroprevalence of T. gondii and C. burnetii was assessed.
Results
45% of the cohort were seropositive for T. gondii and 31% were positive for C. burnetii. Infection with either agent was not associated with symptoms reflecting clinically relevant levels of concentration difficulties, fatigue, depression, depressive ideas or overall psychiatric morbidity.
Conclusions
We do not provide any evidence that infection with Toxoplasma gondii or Coxiella burnetii is associated with neuropsychiatric morbidity, in particular with symptoms of poor concentration or fatigue. However, this is a relatively healthy cohort with few individuals reporting neuropsychiatric morbidity and therefore the statistical power to test the study hypotheses is limited.
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Background
It has been suggested that infections with the zoonoses Toxoplasma gondii and Coxiella burnetii may lead to long term neuropsychiatric morbidity. More specifically, T. gondii infection is hypothesised to be associated with slow reaction and poor concentration [1,2] whilst C. burnetii infection is reported to be associated with persistent symptoms of fatigue for up to ten years following exposure [3-5]. Exposure to the parasitic protozoon T. gondii is common in the UK population as a whole (40% to 50%) whereas exposure to the rickettsia-like C. burnetii leading to Q fever is relatively rare in urban populations [6-8].
We have examined data previously collected from a farm-based occupational cohort recruited in three areas of England to test these hypotheses. Furthermore, given the high risk of suicide amongst farmers as an occupational group[9], we also investigated associations between infection with either organism and symptoms of depression or depressive ideas. Statistical associations were examined between measures of lifetime exposure to T. gondii and C. burnetii and current psychiatric morbidity measured by the Revised Clinical Interview Schedule (CIS-R).
Methods
Sample
A representative cohort of 606 farmers, farmworkers and family members has been recruited since 1991 in three areas of England to investigate occupational risk factors for zoonoses[10]. A random sample of farmers was drawn from the Ministry of Agriculture, Fisheries and Food June Agricultural Census lists of agricultural holdings[11], and each farmer could then nominate a further adult on the same farm holding (usually his wife). Seventy-seven per cent of the cohort were still enrolled in May 1998 and of these, 425 (91%) completed the CIS-R by computer between March and July 1999[12].
Psychiatric morbidity data
The computer-administered version of the CIS-R was used to assess the prevalence of symptoms of neurotic psychopathology in the week prior to interview[13]. The CIS-R is made up of fourteen sections, each covering a particular area of neurotic symptoms. For this study we utilized data from the sections relating to fatigue, concentration difficulties, depression and depressive ideas as assessment of psychiatric outcome. Individual symptoms are regarded as clinically relevant if they have a score of two or more (range zero to four, or five for section on depressive ideas). Summed scores from all fourteen sections range from zero to fifty seven, the overall threshold for clinically significant psychiatric morbidity is twelve. The time taken to complete the questionnaire ranged from ten to thirty minutes, due to the filtering nature of the questions.
As reported previously, the prevalence of clinically relevant levels of each of these neurotic symptoms was relatively low[9]. Fifteen percent of farmers reached the threshold for a clinically relevant level of symptoms of fatigue (n = 62). Approximately 5% of the farmers reported symptoms of either concentration difficulties (n = 22) or depressive ideas (n = 23), and 4% reported symptoms of depression (n = 18). Approximately 6% of farmers reported significant general psychiatric morbidity (n = 25).
Seroprevalence data
At enrolment, 10 ml of venous blood was taken from all subjects. Samples were screened for Toxoplasma gondii at Hereford PHL using the Eiken latex agglutination test (cut off titre 1/32). Seroconverters were confirmed by PHLS Toxoplasma Reference Laboratory at Swansea PHL using the dye test and by IgM ELISA. Serum IgG specific antibody levels for Coxiella burnetii phase II antigen were estimated at Bristol Public Health Laboratory (PHL) using an indirect immunofluorescence test. Serum with a reciprocal titre of 32 or more were taken as positive. Seroprevalence data for T. gondii and C. burnetii were available for 370 and 422 individuals respectively.
Statistical analysis
All analyses allowed for clustering by farm holding in the survey data (Stata Version 6.0, StataCorp, College Station, TX, USA). Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression and were adjusted for sex and age in ten year bands.
Results
In total, 45% (95% CI 40% to 50%) of the cohort were seropositive for T. gondii and 31% (95% CI 26% to 36%) were positive for C. burnetii. Forty six individuals were seropostivie for both. Exposure to either infection did not differ significantly between men and women (T. gondii seroprevalence 47% vs. 41%; C. burnetii seroprevalence 32% vs. 28%). T. gondii seroprevalence increased significantly with age (<30 years 9%, 30–39 years 29%, 40–49 years 33%, 50–59 years 54%, 60–69 years 57%, 70+ years 56%; χ2 = 25.8, df = 5, P = 0.0002). C. burnetii seroprevalence was not associated with age (<30 years 38%, 30–39 years 28%, 40–49 years 36%, 50–59 years 27%, 60–69 years 29%, 70+ years 36%).
Those seropositive for T. gondii were not more likely to report symptoms reflecting clinically relevant levels of fatigue, concentration difficulties, depression, depressive ideas or overall psychiatric morbidity than those who were seronegative (Table 1). Similarly, evidence of infection by C. burnetii was not significantly associated with reporting of clinically relevant levels of any of these symptoms. Indeed, although based on very limited numbers, a greater percentage of farmers who were seronegative rather than seropositive for C. burnetii reported psychiatric symptoms. Furthermore, neither infection was associated with an increased risk of any studied psychiatric outcome after adjusting for age and sex (Table 2).
Table 1 Prevalence of self-reported psychiatric symptoms in relation to infection by T. gondii and C. burnetii
T. gondii C. burnetii
Seropositive Seronegative Seropositive Seronegative
N = 166 N = 204 N = 130 N = 292
N (%)
Fatigue 27 (16.3) 33 (16.2) 18 (13.9) 43 (14.7)
Concentration difficulties 9 (5.4) 13 (6.4) 3 (2.3) 19 (6.5)
Depression 7 (4.2) 11 (5.4) 3 (2.3) 15 (5.1)
Depressive ideas 10 (6.0) 13 (6.4) 5 (3.9) 18 (6.2)
Psychiatric morbidity 10 (6.0) 15 (7.4) 6 (4.6) 19 (6.5)
Table 2 Odds ratios for self-reported psychiatric symptoms in relation to T. gondii and C. burnetii
T. gondii C. burnetii
OR (95% CI) OR (95% CI)
Fatigue (n = 62)
Seronegative 1.00 1.00
Seropositive 1.18 (0.66–2.14) 0.92 (0.52–1.64)
Concentration difficulties (n = 22)
Seronegative 1.00 1.00
Seropositive 0.97 (0.41–2.28) 0.32 (0.10–1.06)
Depression (n = 18)
Seronegative 1.00 1.00
Seropositive 0.81 (0.29–2.30) 0.47 (0.13–1.67)
Depressive ideas (n = 23)
Seronegative 1.00 1.00
Seropositive 1.11 (0.44–2.77) 0.62 (0.23–1.69)
Psychiatric morbidity(n = 25)
Seronegative 1.00 1.00
Seropositive 0.88 (0.38–2.04) 0.71 (0.28–1.84)
Odds ratios account for sample clustering by farm holding
Odds ratios were adjusted for sex and age in 10-year bands (<30, 30–39, 40–49, 50–59, 60–69, 70+)
Discussion
This study does not provide evidence to support the hypothesis that infection by Toxoplasma gondii is associated with difficulties in concentration. Given the relatively common exposure to latent toxplasmosis in both the general population and in certain occupational cohorts, even a relatively weak association with neuropsychiatric outcome might be of potential public health interest. However, no strong evidence to support such an association has been reported to date.
Havlicek and colleagues [1] reported significantly longer reaction times for completion of a computerised version of a psychomotor test amongst 60 Toxoplasma-positive individuals compared to 56 Toxoplasma-negative individuals, although the difference in reaction time was only up to 17 miliseconds. The authors interpreted this possible behavioural change as a manipulation activity to promote transmission of the parasite; delayed reaction times in rodents could increase the chance of transmission into a definitive host such as the cat. Flegr and collagues [2] investigated this hypothesis further by comparing the seroprevalence of latent toxoplasmosis in 146 subjects involved in traffic accidents (assumed to have delayed reaction) and 446 members of the general public in the same geographical area. Subjects with latent toxoplasmosis were 2.65 times more likely (95% CI 1.76–4.01) to be identified as having a traffic accident than those who were seronegative. However the study was limited by the sampling of both cases and controls relying on availability of archived blood samples for serological testing of toxoplasmosis thus increasing the possibility of selection bias, together with inadequate consideration of possible confounders.
Furthermore there is no evidence from this study to support the hypothesis that infection by Coxiella burnetii is associated with symptoms of fatigue. Wildman and colleagues [5] have previously reported that symptoms of fatigue were more common in 77 individuals assessed ten years after exposure to Q fever in a UK outbreak than in matched unexposed controls (65% vs 35% P < 0.001). Their study benefited from a comprehensive assessment of fatigue, a matched design to control for age, sex and smoking status, and a relatively high response rate amongst those exposed to Q fever (84%). However only 36% of the matched controls participated in the study, suggesting a possible selection bias. Finally due to the nature of self-report questionnaires and knowledge of study hypotheses the possibility of response bias could not be ruled out.
Strengths and limitations
Exposure to both T. gondii and C. burnetii was common in this occupational cohort of farmworkers which is not surprising. Indeed the exposure to toxoplasma is common in the UK population as a whole (40% to 50%)[7], whereas occupational exposure is more important in the epidemiology of Q fever, with prevalence amongst farmworkers being two to three times higher than a comparison group of ambulance and police workers[8].
This survey benefited from using the CIS-R as a standardised assessment suitable for lay interviewers in assessing minor psychiatric disorder in an occupational setting. The computerised CIS-R assessment provides an easy, quick, inexpensive yet thorough assessment which is acceptable to interviewees and also helps to eliminate observer bias.
The prevalence of psychiatric symptoms was lower than expected in this cohort of farm workers[10], indicating that this is a relatively healthy occupational cohort with only very few individuals reporting clinically relevant levels of symptoms. Therefore unfortunately the statistical analyses are limited in power to test the study hypotheses. Although the response rate for the mental health survey was relatively high, it is possible that those subjects previously exposed to T. gondii or C. burnetii and who subsequently developed symptoms might have been more likely to drop out of the cohort either before recruitment or before this more recent survey ('healthy worker effect'). If this were the case we might underestimate the strength of association between exposure and psychiatric outcome. Finally we have no indicator of duration of infection for those individuals who are currently seropositive for either infectious agent which might affect the observed association if a long lag time is required between exposure and presentation of symptoms.
Conclusions
We do not provide any evidence that infection with Toxoplasma gondii or Coxiella burnetii is associated with neuropsychiatric morbidity, in particular with symptoms of poor concentration or fatigue.
List of abbreviations
T. gondii – Toxoplasma gondii
C. burnetii – Coxiella burnetii
CIS-R – Revised Clinical Interview Schedule
OR – odds ratio
95% CI – ninety five percent confidence interval
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
HT completed the statistical analyses and drafted the manuscript, DT and RS conceived of the PHLS Farm Cohort study and participated in its design and coordination, GL developed the psychiatric assessments and advised in their use, AS advised on the study hypotheses under investigation. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We thank all the participants of the study. We also thank the field workers and laboratory staff for their contribution to data collection. The PHLS Farm Cohort study was funded by the Health and Safety Executive.
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Havlicek J Gasova Z Smith AP Zvara K Flegr J Decrease of psychomotor performance in subjects with latent "asymptomatic" toxoplasmosis Parasitology 2001 122 515 520 11393824 10.1017/S0031182001007624
Flegr J Havlicek J Kodym P Maly M Smahel Z Increased risk of traffic accidents in subjects with latent toxoplasmosis: a retrospective case-control study BMC Infectious Diseases 2002 2 11 12095427 10.1186/1471-2334-2-11
Marmion BP Shannon M Maddocks I Storm P Penttila I Protracted debility and fatigue after acute Q fever Lancet 1996 347 977 978 8598796 10.1016/S0140-6736(96)91469-5
Ayres JG Flint N Smith EG Tunnicliffe WS Fletcher JJ Hammond K Ward D Marmion BP Post-infection fatigue syndrome following Q fever QJM 1998 91 105 123 9578893 10.1093/qjmed/91.2.105
Wildman MJ Smith EG Groves J Beattie JM Caul EO Ayres JG Chronic fatigue following infection by Coxiella burnetii (Q fever): ten-year follow-up of the 1989 UK outbreak cohort QJM 2002 95 527 538 12145392 10.1093/qjmed/95.8.527
Department for Environment, Food and Rural Affairs Zoonoses Report United Kingdom 2002 2003 London: HMSO
Walker J Nokes DJ Jennings R Longitudinal study of Toxoplasma seroprevalence in South Yorkshire Epidemiolol Infect 1992 108 99 106
Thomas DR Treweek L Salmon RL Kench SM Coleman TJ Mead D Morgan-Capner P Caul EO The risk of acquiring Q fever on farms: a seroepidemiological study Occup Environ Med 1995 52 644 647 7489053
Charlton J Trends and patterns in suicide in England and Wales International Journal of Epidemiology 1995 24 S45 52 7558551
Thomas DR Salmon RL Kench SM Meadows D Coleman TJ Morgan-Capner P Morgan KL Zoonotic illness -- determining risks and measuring effects: the association between current animal exposure and a history of illness in a well characterised rural population J Epidemiol Community Health 1994 48 151 155 8189169
MAFF Agricultural statistics UK 1989 1991 London: HMSO
Thomas HV Lewis G Thomas DRh Salmon RL Chalmers R Coleman TJ Kench SM Morgan-Capner P Meadows D Sillis M Softley P Mental health of British farmers Occupational and Environmental Medicine 2003 60 181 186 12598664 10.1136/oem.60.3.181
Lewis G Pelosi AJ Araya R Dunn G Measuring psychiatric disorder in the community: a standardized assessment for use by lay interviewers Psychological Medicine 1992 22 465 86 1615114
| 15491496 | PMC526777 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Oct 18; 4:32 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-32 | oa_comm |
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BMC Oral HealthBMC Oral Health1472-6831BioMed Central London 1472-6831-4-21551129510.1186/1472-6831-4-2Research ArticleGrouping of tooth surfaces by susceptibility to caries: a study in 5–16 year-old children Batchelor Paul A [email protected] Aubrey [email protected] Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK2004 28 10 2004 4 2 2 18 6 2004 28 10 2004 Copyright © 2004 Batchelor and Sheiham; licensee BioMed Central Ltd.2004Batchelor and Sheiham; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The decline in caries has slowed and this may be indicative of variation in the susceptibility of differing teeth to caries. This study tests the hypothesis that in children, there are groups of tooth sites that exhibit differences in caries susceptibility.
Methods
Probit analysis of caries data collected from a 4-year longitudinal study of 20,000 schoolchildren aged between 5 and 16 years in 10 differing locations in the United States.
Results
The development of dental caries within the mouth followed a fixed hierarchy indicating that tooth surfaces show variation in caries susceptibility. Certain teeth and tooth sites have similar susceptibilities and can be grouped, the sizes of the groups vary. The most susceptible group consists of six tooth surfaces: the buccal pits and occlusal fissured surfaces of the first molar teeth. The second group consisted of 12 sites on the second molar and premolar teeth. The group formed by the least susceptible sites included the largest number of tooth surfaces and consists of the majority of the lower anterior teeth and canines.
Conclusion
Variation in the caries susceptibility of tooth surfaces exists. Surfaces can be grouped according to caries susceptibility. An effect that reduces the cariogenic challenge of one of the sites within a group is likely to affect all the other sites within the particular group.
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Background
The decline in caries that has occurred in industrialized countries over the past 30 years has been accompanied by major changes in the pattern of caries within the mouth. While the absolute levels of disease have declined, a relatively higher proportion of pit and fissured surfaces and lower proportion of approximal and smooth surfaces are involved. An additional feature in the pattern of dental caries is the existence of a surface hierarchy in susceptibility to caries [1-4]. These authors have reported that the most susceptible surfaces are pit and fissured followed by approximal surfaces on posterior teeth, and the least susceptible, approximal surfaces on anterior teeth.
There is also a reported degree of symmetry both between the upper and lower jaws in the posterior sextants [5] and left and right side of the risk of caries. The concept is so well accepted that some survey systems for recording dental caries will only examine one side of the mouth and then double the score to give the total DMFS [6]. The fact that caries occurs bilaterally in the same type of tooth suggests that when a decline or increase in caries occurs, it presents in increments of 2.
For a given DMF-T or S, there is a specific pattern of caries within a population. A working rule is that 'As caries in the population declines, caries in the least susceptible surfaces (approximal and smooth surfaces) decreases considerably more than in the most susceptible surfaces (pits and fissures)'. This pattern is independent of the presence of fluoride [7].
Furthermore, changes in mean DMF scores are not linear, but 'stepped' [4]. This stepped model of the changing patterns of caries suggests certain groups of teeth and tooth sites may have a similar 'resistance' to caries. When the resistance of one site in a group is increased, for example by fluoride, then all sites with similar resistance levels will also benefit and not become carious. Indeed, the existence of groups by resistance may explain the rapid stepped rates of decline of caries in the past 20 years [8]. A major reason for this rapid decline can be explained by the fact that groups of teeth or tooth sites with similar propensity to decay respond as a group to increase of resistance or reduction in the challenge. This would lead to a marked decline in overall caries levels rather than a gradual reduction. The present study tests the hypothesis that in children, there are groups of tooth sites by caries susceptibility.
Methods
Study population
This study used the data from the National Preventive Dentistry Demonstration Programme (NPDDP) in the United States [9]. The NPDDP data set was used as it contains the most extensive and comprehensive longitudinal data available on caries preventive regimes using standardised DMF criteria that have been shown to be reliable through extensive critical analyses of the project. Perhaps most importantly for this project, the caries data range was very wide: both within the individual ethnic groups and according to water fluoridation status. The NPDDP included 20,052 children aged from 5 to 16 years of age from 10 locations in the USA: five fluoridated and 5 non-fluoridated communities. The mean DMF-S at the commencement of the programme was 2.43, and was 4.51 four years later. Preventive interventions were introduced that allowed an analysis of the changes in caries patterns with the decline in caries to be examined [9-11]. The caries surface data were selected for each child at the beginning of the study.
Statistical methods
To test the hypothesis probit analysis was used. Probit analysis is a method for examining any dose-response relationship where the dependent variable, i.e. caries, is dichotomous (caries/no caries). Since all tooth surfaces may not 'respond' in a similar manner, the problem must be formulated in terms of the proportion responding (diagnosed as caries) at each level of challenge. With probit analysis, any changes are constant in proportion so that changes on a log scale will also be constant. In a probit transformation each of the observed proportions were translated into the value of the standard normal curve below which the observed proportion of the area was found. For example, if half the subjects in a caries trial had one particular site carious, the probit value would be 0, since half the area in a standard normal distribution falls below a z score of 0.
When using standard normal values, negative scores can occur. To overcome this, the constant 5 was added. For example, if a particular surface scored 1 and half the subjects studied had caries, the probit value would be 0, since half of the area in a standard normal curve falls below a z score of 0. When the constant is added, the transformed value for the proportion becomes 5. If the observed proportion of individuals in whom the site was carious was 0.84, the probit value would be 0.34, i.e. a z score of 1, which would give a transformed value of 6. The actual proportion of each of the tooth sites recorded carious at each DMF-S level was calculated and replaced with the value of the standard normal curve below which the observed proportion of the area below the curve was found.
The logarithmic transformation of the data provides a linear relationship between the probability of an event occurring for any given value. The proportion of each surface within a population diagnosed as carious at a particular DMF-S score can be calculated. For example, take the occlusal surface of the first right lower molar. At a DMF of 1, a certain proportion of this site within a population would be carious. This proportion would increase for a DMF of 2, and so on.
For each DMF-S score, the percentage of tooth surfaces exhibiting caries was calculated to give a probability score of between 0 and 1. Subsequently, the log transformation of the probabilities for each DMF-S against the actual DMF-S score was plotted for every surface. The common reference value, of 0.5 outlined above was used to establish the susceptibility of each surface to a given caries challenge. As some random variation can be expected, the susceptibilities are grouped within bands rather than as individual sites.
To establish whether a hierarchy for caries susceptibility exists, the probability of finding each site carious for a given DMF-S score was calculated. The probability was calculated by adopting a common reference value for the proportion of the tooth sites that become carious. In the present study 0.5 was used to provide the most accurate value, although any value of proportion can be used. The question can then be phrased in terms of the value of the DMF-S at which 50% of the sites or teeth would be expected to have become carious. The probability scores derived are then aggregated to produce an overall picture of tooth and site susceptibilities.
The probability of an event occurring ranges from zero to 1. As the overall DMF score rises, the probabilities of any site being carious will change. For example, if a group of 128 individuals, each having a DMF-S score of 1 with all sites exhibiting the same propensity for caries. The distribution of sites affected within the mouth should be random, the probability for a particular site being carious would then be 1/128. If, however, the group all had a DMF-S of 128, the relative probability of finding a particular site carious remains the same, but this time the absolute probability changes from 1/128 to 1.
As the DMF score increases the probabilities alter. However, once a probability of 1 has been reached no further increase is possible. Thus, when examining changes in the distribution of caries at different DMF-S scores, the ratio between individual probability scores is unimportant. The crucial factor is the overall ranking exhibited by the probabilities for each site. The order of susceptibly will be determined by the relative values of the probabilities. Whether an individual site is twice as likely to become carious as another cannot be determined using this approach. However, certain sites may exhibit similar probabilities. For example, a particular site on the left hand side of the mouth may have a similar mean probability as the corresponding site on the right hand side. Other factors may influence the distribution of probabilities. For example, it has been suggested that fluoride has a more beneficial effect on approximal sites when compared to occlusal.
The probabilities derived are for each site for each individual and are then aggregated for the sample population. The aggregation of probabilities gives rise to a distribution, approximately normal in character, and the mean of this distribution is subsequently reported and used in the analyses.
Data analyses were performed using SPSS. The data files of the NPDDP were supplied by the Rand Corporation in ASCII format and subsequently read onto the mainframe system. Two of the five data files were utilised in this project: the master file containing the demographic information of each individual and the clinical file containing the status of each tooth site.
Results
Figure 1 shows the distribution of probabilities of caries grouped into 5 categories. The categories were formed by grouping together sites with similar probabilities of having experienced caries. The most susceptible groups of sites were defined as having a probability of being carious within the range 0.34 to 0.23, the next group 0.18 to 0.04, then 0.03 to 0.01, then 0.008 to 0.002 and, finally, the least susceptible sites which formed the remaining group (Figure 1).
Figure 1 Distribution of probabilities of site susceptibilities.
The emerging pattern indicates a left:right side symmetry with the propensity of attack similar for the two sides of the mouth. Furthermore, there is a degree of symmetry between the upper and lower jaws in the posterior sextants. For the anterior sextants, teeth in the upper jaw are more prone to attack than those in the lower jaw.
An important finding is the relative sizes of the groups by the probability of susceptible sites having experienced caries. The group with most susceptible sites consisted of six tooth surfaces: the pit and fissured surfaces of the first molar teeth. The second group consists of 12 sites on the second molar and premolar teeth. The fifth group, and least susceptible group, is the largest and consists of the majority of the lower anterior teeth and canines.
The groups, in order of susceptibility, allowing for some left:right asymmetry, were:
1. occlusal surfaces of 1st molars and buccal pits of lower 1st molars;
2. occlusal surfaces of 2nd molars and buccal surfaces of lower 2nd molars and occlusal surfaces of all 2nd premolars;
3. occlusal surfaces of 1st premolars, palatal surfaces of upper lateral incisors, approximal surfaces of 1st molars, lingual surfaces of lower 1st molars and buccal surfaces of upper 1st molars and palatal surfaces of upper 2nd molars;
4. all approximal surfaces of 2nd premolars, all approximal surfaces of upper 1st premolars, mesial and lingual surfaces of lower 2nd molars and distal and buccal surfaces of upper 2nd molars, approximal surfaces of upper central incisors, some approximal surfaces of upper and lower lateral incisors, all approximal surfaces of lower central incisors and distal approximals of upper canines, and approximal surfaces of 2nd molars; and
5. all surfaces of lower canines, buccal and mesial and labial aspects of upper canines, all smooth and approximal surfaces of lower 1st premolars, smooth surfaces of lower central incisors, approximal surfaces of lateral incisors (Figure 1).
The next analysis was to establish the probability of finding a particular surface type carious for each DMF score. The probability, by surface, was converted into a ratio as in Figure 1. Figure 2 combines the findings shown in Figure 1 with the patterns by tooth surface. To facilitate comparison by surface, the probability of each surface type was calculated, the total for each DMF-S score being equal to one. As the DMF score increased, the ratio of smooth to approximal to pit and fissured surfaces changes, although for the lower DMF scores, any major change in the ratios did not occur until a DMF-S of 9. Only then did the contribution of lesions on approximal or smooth surfaces make a significant contribution to the overall DMF-S.
Figure 2 Proportion of each tooth surface type affected by caries at each DMF-S score.
Discussion
This study shows that that a number of tooth sites exhibit similar susceptibilities to caries. Susceptibility of tooth sites is not only similar for homologous pairs but also for grouped sites. For example, at a DMF score of 1, six sites have very similar probabilities of being carious. These involve the pit and fissured surfaces of all first molar teeth. Most authors, whilst accepting a degree of left:right symmetry in caries attack, have assumed that it is either the same site affected on the left hand side of the mouth as on the right [12-16].
This study reported that precise symmetry of caries did not occur by site but rather that symmetry of caries existed by groups of sites. For example, in the anterior incisor region, where several sites have similar propensities, the left:right symmetry may have been lost because the mirror image site had not undergone cavitation. However, due to the similarities in susceptibilities, another site on the opposite or even the same side has cavitated. In both cases symmetry will be lost. What exists is groups of teeth by susceptibility to caries.
The concept of groups of susceptible sites has a number of important implications. If the application of a caries preventive strategy leads to a reduction in either the attack intensity or an increase in the resistance of the sites within a group to a value at which a particular site was protected then all sites in the group would also be protected. Depending upon the size of the group, several sites may well be protected. The existence of groups of sites goes some way to explain the stepped nature exhibited by changing patterns of caries. This may explain the apparent rapidity with which DMF levels decreased initially with the decline in caries reported in many industrialised countries since the 1970s. The adoption of a preventive strategy such as the use of fluoride toothpaste to reduce the attack intensity or increase in resistance for the least susceptible group, would lead to substantial savings in the total number of cavitated sites.
There is a change in the changing ratio of smooth and approximal sites on the one hand, and pit and fissured surfaces on the other, for different levels of DMF-S, confirming findings by Burt [17], Dummer et al., [18], and Vehkalahti et al., [19]. For example, at a DMF-S of 1, the ratio of pit and fissured to smooth or approximal surfaces is 99:1. At a DMF-S of 10, the ratio had changed to 3:1. However, the changing ratios should be considered against the overall decline in caries.
When considering the hierarchy of susceptibility by groups of sites the types of sites must be considered. With a change in the surface type ratio in which pit and fissured surfaces predominate to one in which approximal surface involvement becomes important, the consequences of the latter are smaller. The sites with a similar high propensity for caries attack at a DMF of 10, which includes a higher proportion of approximal surfaces, are not necessarily ten times as great as for an individual with a DMF of 1. This has important implications for planning preventive strategies. More effort would be required to reduce caries from low to very low levels than from high to low caries.
Finally, the mathematical relationships identified by Batchelor and Sheiham [20] describing the distribution of caries at the population level could be combined with the findings of this study, to develop a model to assess the impact of caries preventive strategies. The model would help provide a scientific basis on which to formulate caries preventive strategies.
Conclusions
The findings show that there is a hierarchy by tooth types and sites in the pattern of dental caries attack in children. While it was not possible to identify precisely the order that each tooth or site succumbed to caries, groups of sites or teeth can be placed in a hierarchy of risk of caries. This concept expands the proposals of a hierarchy of 'within mouth' zones [21-23]. More importantly, there are groupings of tooth sites by susceptibility to caries. The size of the groupings varies. The impact of preventive agents that increased the resistance or reduced the intensity of the challenge affects most sites within a particular group. The larger sized groups occur at high caries levels. Increasing their resistance or lowering the intensity of the caries challenge would lead to a substantial drop in cavitated sites providing the agent or agents offered sufficient protection for any single site in the larger groups.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
PAB conceived, undertook the design of the study, performed the statistical analysis and drafted the manuscript. AS participated in the study design and coordination and helped to draft the manuscript. Both authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15511295 | PMC526778 | CC BY | 2021-01-04 16:29:56 | no | BMC Oral Health. 2004 Oct 28; 4:2 | utf-8 | BMC Oral Health | 2,004 | 10.1186/1472-6831-4-2 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1554764110.1371/journal.pbio.0020326Community PageOtherNoneInternational Network for the Availability of Scientific Publications: Facilitating Scientific Publishing in Developing Countries Community PageSmart Pippa 11 2004 16 11 2004 16 11 2004 2 11 e326Copyright: © 2004 Pippa Smart.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The International Network for the Availability of Scientific Publications (INASP) was established in 1992 to bridge the information divide between the developed and developing world
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‘The most important element that restricts our researchers is access to information.’—Subbiah Arunachalam, India, 2003
The International Network for the Availability of Scientific Publications (INASP) was established by the International Council for Science in 1992 to provide support for networking between information providers and users, particularly to bridge the information divide between the developed and developing world. Since 1992 INASP has worked, in response to requests, to develop its activities for capacity building in information production, access, and use, with an overarching vision that all people are able to access and contribute information, ideas, and knowledge necessary for sustainable and equitable development.
To ensure that INASP activities are appropriate for the communities and cultures of the countries in which INASP works, local partners are used to build networks and to provide advice and support. Enhancing capacities is central to all activities, and local ownership and sensitivity to local conditions and opinions are of paramount importance. Here I highlight two aspects of our work: increasing access to information and supporting the visibility of regional journals within the global research community.
Programme for the Enhancement of Research Information
The growth and acceptability of the internet during the 1990s opened up tremendous possibilities for the dissemination of research information, providing many nations with access to information that had previously been out of their reach. Although internet connectivity remains problematic in many countries, it still offers great potential for bridging the information gap.
Arising from discussions with librarians and researchers in 1999 and 2000, the Programme for the Enhancement of Research Information was formally launched in 2002 after a two-year pilot programme. The Programme for the Enhancement of Research Information works in a two-phase manner: (1) providing and supporting access to international research information and (2) supporting and promoting access to nationally published research.
To support access to international information, INASP negotiates for heavily discounted or free access to online information from publishers and information providers in developed countries. Enabling access, however, does not guarantee that resources are used; both training and promotion are needed so that researchers and downstream information providers know how to make the best use of what is available. With this in mind, INASP has set up four series of training workshops, which have trained over 1,000 people from over 200 institutions in over 17 countries during the last two years. Although some of the training is undertaken by INASP staff, most is facilitated by in-country trainers.
Planning for long-term sustainability, INASP aims to hand over the tasks of negotiation, purchasing, and training to local consortia, associations, or networks.
African Journals OnLine
Although much needed, access to international resources can discriminate against nationally published scholarly information. This may be due to one of the following: a perception that local publications are lower quality, distribution problems and irregular publishing, or lack of online visibility. However, national publications provide vital access to potential collaborators and information about research on topics of local relevance—since 1998, INASP has provided support to indigenous research publications to enable them to survive and coexist with international information.
One specific project that exemplifies these aims is known as African Journals OnLine (AJOL). AJOL launched in 1998 in response to requests from African journals. Starting with only ten titles, it now includes 189 from 21 African countries. Access to the site, which includes tables of contents, article abstracts, and a homepage for each journal with information about editorial boards, guidelines for authors, and more, is entirely free. Participating journals report increased international submissions and increasing contact with international researchers.
Researchers make wide use of this service, and over 8,000 people have registered to use AJOL since it launched. Registration is optional and until March this year did not provide the user any benefit—it simply provided an indication of the number of people visiting and from which countries. However, since March 2004 anyone who registers can sign up to receive a free E-mail table of contents alert. Over 500 people have chosen to receive E-mail alerts from an average of four journals each, and the effect of these alerts is felt through the increase in document delivery requests.
AJOL does not currently host full text, but can provide full-text articles through a document delivery service (there are plans to load full-text articles on AJOL in the future). This service is provided to researchers from developing countries for free and to researchers elsewhere at a minimal charge (to cover costs, plus a small payment to the journal). In the first six months of 2004, over 700 articles were ordered and sent out.
Most journals publishing out of Africa are not run by commercial publishers, and most of them operate at a loss, subsidised by universities, associations, funding agencies, or a mixture of all three. Paid subscriptions to print journals are frequently a vital part of their economics, and the financial viability of many journals is constantly under threat. Originally, it was hoped that AJOL would increase subscriptions, thereby providing greater financial security to the journals. This has not been achieved and is unlikely to occur in the future. However, with increasing visibility, the value of the journals increases along with, hopefully, their importance to the supporting agencies and long-term sustainability.
Since AJOL is set up to provide support for the participating journals, the journals' opinions are constantly sought before any developments are undertaken, meaning AJOL is effectively “owned” by them. Although the service is not actively run by the journals themselves, all participating journals are considered to be part of a community, receiving regular E-mail contact and skills support. Many journal editors (who frequently undertake all the publishing activities) feel isolated and unskilled, and even though AJOL does not operate as an association for the journals, it encourages communication between the editors, and a sharing of experience.
The AJOL website was relaunched in March 2004 using open-source software called Open Journal System, originally developed in Canada by the Public Knowledge Project at the University of British Columbia and further developed for AJOL at Bristol University, United Kingdom. This software was written to enable a single journal to manage the entire publishing process online from submission to publishing. Being open source, the software can also easily be adapted and modified. The Public Knowledge Project set up the system with the developing world in mind: it operates efficiently at low bandwidths and is easy to use, with many guides and help functions built in.
For the users, this software offers sophisticated searching, E-mail alerts, and a space for each journal within AJOL for journal-specific information. It also now provides a range of management tools, which makes the service more efficient and enables it to grow.
Another important consideration for choosing this system was that individual journals can take over the responsibility for maintaining their own journal areas within AJOL via the Internet. Over 50 journals have expressed an interest in taking this on, and one publisher is already successfully maintaining its material. To support this, training workshops are currently in preparation. This development will assist the long-term sustainability of AJOL and also give the participating journals experience in managing and publishing content online.
In the near future, INASP will be including full text on AJOL and hopes to move the management of AJOL to an African organisation so that it will truly become an African gateway for published research. In addition, INASP continues to work with journals to strengthen their quality and sustainability, providing advice, training workshops, study tours, and a coordinating point for discussion and collaboration.
Outside Africa, INASP is working on similar developments in Nepal and the Caribbean and has received expressions of interest from Bangladesh, Sri Lanka, and Vietnam.
The Future of Journal Access
It is vital that researchers everywhere in the world have access to reliable, relevant information. At the same time, providing access to international literature needs to be balanced with supporting local publications to ensure that indigenous knowledge is not lost, but can take its place in the research community and contribute to the continuing development of science. Worldwide access to information is central to all INASP activities, and INASP supports policies and activities that work towards this.
Where to Find Out More
INASP: http://www.inasp.info
AJOL: http://www.ajol.info
Public Knowledge Project: http://pkp.ubc.ca
Pippa Smart is head of publishing and publishing initiatives for the International Network for the Availability of Scientific Publications, Oxford, United Kingdom. E-mail: [email protected]
Abbreviations
AJOLAfrican Journals OnLine
INASPInternational Network for the Availability of Scientific Publications
| 15547641 | PMC526779 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Nov 16; 2(11):e326 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020326 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020394Book Reviews/Science in the MediaNeuroscienceHomo (Human)Palmistry Book Review/Science in the MediaDayan Peter 11 2004 16 11 2004 16 11 2004 2 11 e394Hawkins J with Blakeslee S ( 2004 ) On intelligence .
New York : Times Books . 272 p (hardcover) 0805074562. US$25.00 Copyright: © 2004 Peter Dayan.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.A review of "On Intelligence", a book exploring brain function by entrepreneur Jeff Hawkins and science writer Sandra Blakeslee
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Is Michael Moore liberal America's Rush Limbaugh? If so, is he filling a much needed, or a much lamented, gap in turning issues that are really cast in pastel shades into Day-Glo relief? In this hale monograph, Jeff Hawkins (rendered by Sandra Blakeslee) plays exactly this role for theoretical neuroscience. As a pastel practitioner myself, but furtively sharing many of Hawkins' prejudices and hunches about computational modelling in neuroscience, I am caught between commendation and consternation.
Hawkins is an engineer, entrepreneur, and scientist who founded and led the companies Palm and then Handspring. He created, against what must have been considerable obstacles, the first widely successful PDA, and continued the development of this platform. He has thus amply earned a bully pulpit. The autobiographical segments of this book detail that, throughout his career, he has been interested in understanding how the brain works, using his substantial knowledge and intuition about the architecture and design of conventional computers as a counterpoint.
More recently, Hawkins has generously put his money where his ideas about mentation dictate, founding the Redwood Neuroscience Institute and also funding various conferences and workshops. The institute is dedicated to ‘studying and promoting biologically accurate mathematical models of memory and cognition.’Despite its youth, the Institute already has attracted notable attention as a centre for theoretical neuroscience. Hawkins' quest, and-depending on which statements of the book you read-its endpoint (‘… a comprehensive theory of how the brain works … describ[ing] what intelligence is and how your brain creates it’) or just its tipping point (‘join me, along with others who take up the challenge’), are the subject here.
There are really three books jostling inside the covers. One is the (highly abbreviated) autobiography. The history of modern computing is very brief and (at least judging by the sales) very glorious, and this story is most entertaining. Don't miss the wonderfully faux naive letter from Hawkins to Gordon Moore asking, in 1980, to set up a research group within Intel devoted to the brain. That Hawkins prospered in clear opposition to accepted wisdom is perhaps one of the key subtexts of the book.
The second, and rather less satisfying, book is about the philosophy of mind and the history of artificial intelligence and neural network approaches to understanding the brain and replicating cognition. With respect to the fields of artificial intelligence and neural nets, the text seems rather to be fighting yesterday's battles. The importance of learning, flexibility in representation and inference, and even decentralisation of control has been more than amply recognised in the inexorable rise of probabilistic approaches in both fields.
With respect to the philosophy of mind, there seems to be something of an enthusiast's disdain for the niceties of philosophical pettifogging, even arguing by assertion. The discussions at the end on creativity and consciousness all seem a bit gossamer. The book is somewhat careless about functionalism, a key doctrine for computational theorists about how brains give rise to minds. According to this doctrine, at least roughly, it is the functional roles of, and functional interactions among, the physical elements of brain that matter, and not their precise physical nature. If you can capture those functional aspects correctly, for instance, in a computer program, then you can (re-)create what's important about mental states. Functionalism licenses a form of inquiry into the computational jobs played by structures in the brain. However, although formally agreeing that ‘there's nothing inherently special or magical about the brain that allows it to be intelligent,’the book slips into statements such as ‘brains and computers do fundamentally different things,’which are, at best, unfortunate shorthand.
The book is a little apt to sneak plausible, but misleading, claims under the radar. Just to give one instance, it compellingly compares a six year old hopping from rock to rock in a streambed with a lumbering robot failing to do the same task. However, this is a bit unfair. One of Hawkins' self-denying ordinances is to consider the cortex pretty much by itself. As aficionados of the cerebellum (an evolutionarily ancient brain region with a special role in the organisation of smooth, precise, well-timed, and task-sensitive motor output) would be quick to point out, the singular role for the cortex in such graceful behaviour is rather questionable.
The third book is what I think is intended to be the real contribution. This contains a (not wholly convincing) attempt to conceptualise the definition of intelligence in terms of prediction rather than behaviour, and then to describe its possible instantiation in the anatomy (and mostly only the anatomy) of the cortex.
Unsupervised Learning
To situate Hawkins' suggestions, it is instructive to consider current models of how the cerebral cortex represents, and learns to represent, information about the world without being explicitly taught. Being a popular account, the book fairly breezes by these so-called unsupervised learning models (see Hinton and Ghahramani 1997; Rao et al. 2002), in which the neocortex is treated as a general device for finding relationships or structure in its input. The algorithms are called unsupervised since they have to work without detailed information from a teacher or a supervisor about the actual structure in each input. Rather, they must rely on general, statistical characteristics.
First, where does the structure in the inputs come from? For the sake of concreteness, think of the input as being something like movies on a television screen. Movies don't look like white noise, or ‘snow’, because of their statistical structure. For instance, in movies, pixel activities tend to change rather slowly over time, and pixels that are close to each other on the screen tend to have relatively similar activities at any given time. Neither of these is true of white noise. More technically, movies constitute only a tiny fraction of the space of all possible activations of all the pixels on your screen. They (and indeed real visual scenes) have a particular statistical structure that the cortex is supposed to extract.
What is the cortex supposed to do with this structure? The idea is that the cortex learns to model, or ‘parameterize’, it. Then, the activities of cortical cells over time for a particular input, for example, a particular face in a movie, indicate the values of the parameters associated with that face. Thereby the cortical activities represent the input. The parameters for a face might include one set for its physical structure (e.g., the separation between the eyes and whether it is more round or more square), another set for the expression, and yet others, too.
Cortical representations are thus intended to reflect directly the statistical structure in the input. Importantly, for inputs such as movies, this structure is thought to be hierarchical and, concomitantly, to provide an account of the observed hierarchical structure of sensory cortical areas. One source of hierarchical structure in movies is the simple fact that objects (such as the faces) have parts (such as eyes and cheeks) whose form and changes in form over time are interdependent. Another source of hierarchical structure is that the same face can appear in many different poses, under many different forms of illumination, and so on. Pattern theory (Grenander 1995), one of the parent disciplines of the field, calls these dimensions of variation deformations. Loosely, the deformations are independent of the objects themselves, and we might expect this independence to be reflected in the cortical representations. Indeed, there is neurophysiological evidence for just such invariant neural responses to deformations of a stimulus.
How does the cortex do all this? Of course, some fraction of this structure was built in over evolution. However, the unsupervised learning tradition concentrates on ontogenic adaptation, based on multiple presented input movies. An additional facet of the lack of supervision is that this adaptation is taken as not depending on any particular behavioural task.
Finally, what does this process allow the cortex to do? The whole representational structure is intended to support inference. Crudely, this involves turning partial or noisy inputs into the completed, cleaned-up patterns they imply, using connections between areas in the cortical hierarchy. Construed this way, probabilistic inference actually instantiates a very general form of computation. Crucially, over the course of the development of unsupervised learning methods, it has been realised that the best way to approach the extraction of input structure, and inference with it, is through the language and tools of probability theory and statistics. The same realisation has driven substantial developments in artificial intelligence, machine learning, computer vision, and a host of other disciplines.
Predictive Auto-Association
We can now return to the book. Hawkins compactly sums up his thesis in the following way. ‘To make predictions of future events, your neocortex has to store sequences of patterns. To recall appropriate memories, it has to retrieve patterns by their similarity to past patterns (auto-associative recall). And finally, memories have to be stored in an invariant form so that the knowledge of past events can be applied to new situations that are similar but not identical to the past.’In fact, to take the latter points first, the sort of auto-associative storage and recall to which Hawkins refers is a theoretically and practically hobbled version of unsupervised learning's probabilistic inference. Invariance is closely related to the deformations we described above in the context of pattern theory.
Unsupervised learning has certainly paid substantial attention to sequences of inputs and prediction, and to some good effect. For instance, (artificial) speech recognition programs are based on a probabilistic device called a hidden Markov model, which is a key element in a wealth of unsupervised learning approaches to prediction. However, despite heroic efforts, these modelling methods are incapable of capturing the sort of complex structure seen in inputs such as natural languages. They fail on phenomena like long-distance dependencies, for example, the agreement between the cases of subjects and verbs, which are rife. This does tend to offer a vaccine against Hawkins' otherwise infectious optimism.
Once place in which Hawkins goes beyond existing unsupervised learning models is in an extension to actions and control, and in an ascription of parts of the model to cortical anatomy. The hierarchical conception of cortex here goes all the way down to primary motor cortex (the neocortical area most directly associated with motor output). This allows auto-associative recall of sequences of past inputs and outputs to be used to specify actions that have formerly been successful. The discussion of this possibility is, unfortunately, rather brief. Central issues are omitted, such as the way that planning over multiple actions might happen. Also, the way that value is assigned to outcomes to determine success or failure is not discussed. The latter is widely believed to involve the neuromodulatory systems that lie below the cortex and that the book's cortical chauvinism leads it cheerfully to ignore.
By contrast, the book has a rather detailed description of how the model should map onto the anatomy of the cerebral cortex. Like many unsupervised learning modellers, Hawkins is a self-confessed ‘lumper’. He ignores huge swathes of complexity and specificity in cortical structure and connections in favour of a scheme of crystalline regularity. Though this will doubtless irk many readers (as will the lack of citations to some influential prior proponents such as Douglas and Martin [1991]), some (though not necessarily this) strong form of abstraction and omission is necessary to get to clear functional ideas. This part has interesting suggestions, such as a neat solution for a persistent dilemma for proponents of hierarchical models. The battle comes between cases in which information in a higher cortical area, acting as prior information, boosts activities in a lower cortical area, and cases of predictive coding, in which the higher cortical area informs the lower cortical area about what it already knows and therefore suppresses the information that the lower area would otherwise just repeat up the hierarchy. The proposed solution involves the invention (or rather prediction) of two different sorts of neurons in a particular layer of cortex.
Unsupervised learning models of cortex are without doubt very elegant. However, if pushed, purveyors of this approach will often admit to being kept awake at night by a number of critical concerns even apart from the difficulty of getting the models to work in interestingly rich sensory domains. Does the book provide computational Halcyon? First, the representations acquired by unsupervised learning are intended to be used for something-such as accomplishing more specific learning tasks, for example, making predictions of reward. However, most aspects of the statistical structure of inputs are irrelevant. This might be called the ‘carpet’problem: there is a wealth of statistical structure in the visual texture of carpets; however, this structure is irrelevant for almost any task. Capturing it might therefore (a) constitute a terrible waste of cortical representational power, or, worse, (b) interfere with, or warp, the parameterization of the aspects of the input that are important, making it harder to extract critical distinctions. The book does not address this issue, relying on there being enough predictive power to capture any and all predictions, including predictive characterisation of motor control.
Second, although our subjective sense is that we build a sophisticated predictive model of the entire sensory input, experiments into such phenomena as change blindness (Rensink 2002) show this probably isn't true. A classic example involves alternating the presentation of two pictures, which differ in some significant way (e.g., the colour of the trousers of one of the main protagonists). Subjects have great difficulty in identifying the difference between the pictures, even though (a) they are explicitly told to look for it, (b) they have the subjective sense that they have represented all the information in each picture, and (c) if the location of the change is pointed out, they see it as blindingly obvious. This, and other attentional phenomena, suggests that substantially less is actually represented than we might naively think. In fact, elaborate computations go into selecting aspects of the input to which the models might be applied, and sophisticated models of these computations, such as Li's salience circuit (2002), involve aspects of cortical anatomy and physiology ignored in the book.
As a final example of a spur to insomnia, unsupervised learners worry that Damasio (1994) might be somewhat right. That is, cool logic and hot emotion may be tightly coupled in a way that a model such as this that is rigidly confined to cortical processing, ignoring key subcortical contributions to practical decision making, will find hard to capture.
To sum up, in terms of the adage that genius is 1% inspiration and 99% perspiration, the book's enthymematic nature suggests that not quite enough sweat has been broken. Were it 1% inspiration and 99% aspiration, though, then the appealing call to arms for a new generation of modellers should more than suffice.
Funding was from the Gatsby Charitable Foundation. I am most grateful to a large number of colleagues for comments.
Peter Dayan is in the Gatsby Computational Neuroscience Unit at University College London, London, United Kingdom. E-mail: [email protected]
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References and Further Reading
Damasio AR Descartes' error: Emotion, reason, and the human brain 1994 New York Putnam 312
Douglas RJ Martin KA Opening the grey box Trends Neurosci 1991 14 286 293 1719675
Grenander U Elements of pattern theory 1995 Baltimore (Maryland) Johns Hopkins University Press 222
Hinton GE Ghahramani Z Generative models for discovering sparse distributed representations Philos Trans R Soc Lond B Biol Sci 1997 352 1177 1190 9304685
Li Z A saliency map in primary visual cortex Trends Cogn Sci 2002 6 9 16 11849610
Rao RPN Olshausen BA Lewicki MS Probabilistic models of the brain: Perception and neural function 2002 Cambridge (Massachusetts) MIT Press 324
Rensink RA Change detection Annu Rev Psychol 2002 53 245 277 11752486
Wolpert DM Ghahramani Z Flanagan JR Perspectives and problems in motor learning Trends Cogn Sci 2001 5 487 494 11684481
| 0 | PMC526780 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Nov 16; 2(11):e394 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020394 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1554764210.1371/journal.pbio.0020400PrimerCell BiologyMicrobiologyEubacteriaExploiting Thiol Modifications PrimerKiley Patricia J [email protected] Gisela 11 2004 16 11 2004 16 11 2004 2 11 e400Copyright: © 2004 Kiley and Storz.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Oxidative Stress Inactivates Cobalamin-Independent Methionine Synthase (MetE) in Escherichia coli
Protein Thiol Modifications Visualized In Vivo
Shut Down, Don't Stress Out
A New Way to Look at Oxidative Stress
Molecular oxygen may be necessary for life but with its beneficial properties comes formation of potentially toxic reactive oxygen species. One of the ways in which bacteria protect themselves is explained
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As the premier biological electron acceptor, molecular oxygen (O2) serves a vital role in fundamental cellular functions, including the process of aerobic respiration. Nevertheless, with the beneficial properties of O2 comes the inadvertent formation of reactive oxygen species, including superoxide (O−
2), hydrogen peroxide (H2O2), and hydroxyl radical (•OH); these differ from O2 in having one, two, and three additional electrons, respectively (Figure 1). Cells also encounter elevated levels of these reactive oxygen species when they are released by animals, plants, and insects as a defense against detrimental organisms such as microbial pathogens. Reactive oxygen species can damage cells in many ways: by inactivating proteins, damaging nucleic acids, and altering the fatty acids of lipids, which leads in turn to perturbations in membrane structure and function. The accumulation of this oxidative damage underlies the formation of many disease states in humans. It is postulated that tissue injury by these reactive oxygen species accumulates over a long period of time and plays roles in the aging process and the development of heart disease, diabetes, chronic inflammatory diseases, cancer, and several neurodegenerative diseases (Halliwell 1999).
Figure 1 Formation of Reactive Oxygen Species
The four-electron reduction of molecular O2 generates two molecules of H2O, which is O2 in its most reduced form. While this reduction normally occurs within the enzyme cytochrome oxidase, one-electron transfers to O2 also occur outside of cytochrome oxidase via inadvertent reactions with other reduced electron carriers, resulting in partially reduced and reactive forms of O2· H2O2 is also produced by the enzymatic or spontaneous dismutation of O2
−, and •OH is generated by the reaction of iron with H2O2 (the Fenton reaction). In addition, the reactive oxygen intermediates are produced by a variety of organisms as a defense against microbial invasion. (Illustration: Rusty Howson, sososo design)
Many organisms have evolved strategies to remove reactive oxygen species and repair damage, which have enabled them to prosper from the tremendous oxidizing potential of O2 without succumbing to oxidative damage. Bacteria, yeast, and mammalian cells all induce the synthesis of global regulatory responses to survive oxidative insults. The consequences of oxidative stress and the corresponding defense responses have been extensively studied in Escherichia coli. For ease of study in the laboratory, the stress responses are often provoked by the external addition of chemical oxidants that specifically elevate the levels of reactive oxygen species within cells, or by the use of mutant strains that disrupt the normal “homeostatic mechanisms” for removing reactive oxygen species or the damage they do. While this primer focuses on a particular set of protective and regulatory protein modifications induced by oxidative stress in E. coli, it should be noted that many of the same mechanisms are present in other organisms; some specific examples from other species will also be described.
The major target of O2
− damage identified in bacteria is a class of dehydratase enzymes that utilize [4Fe–4S] clusters to bind their substrate (Imlay 2003; Djaman et al. 2004). Since some of these enzymes function in the citric acid cycle (also called the Krebs cycle) and in amino acid biosynthesis, high levels of O2
− lead to a requirement for certain amino acids in growth media (Imlay and is well known Fridovich 1991). H2O2 for its role in oxidizing thiol (SH) groups of cysteinyl amino acid residues in proteins. Elevated levels of H2O2 also are associated with the oxidation of other amino acids, leading to the formation of methionine sulfoxide and a variety of carbonyls. Lastly, because of its extreme reactivity, •OH targets all of the major macromolecules of cells: RNA, DNA, protein, and lipids. The extent to which membrane lipids are targets appears to depend on the presence of polyunsaturated fatty acids in lipids, which are not as prevalent in bacteria as they are in mammals.
Many enzymes that protect against oxidative damage have been identified in E. coli (Imlay 2002, 2003). Three superoxide dismutases, each of which contain a different metal center and show different expression patterns and subcellular localization, catalyze the dismutation of O2
− to H2O2. While the superoxide dismutases eliminate O2
−, they also are a source of endogenously produced H2O2 in E. coli. The major enzymes involved in reducing H2O2 to H2O and O2 in E. coli are catalase and alkyl hydroperoxide reductase. There is no enzymatic mechanism for decreasing levels of •OH, produced from H2O2. Thus, levels of •OH will be directly proportional to levels of H2O2, and accordingly, catalase and alkyl hydroperoxide reductase activities are critical to oxidative stress survival.
Another component to the oxidative stress response is the reduction of oxidized thiols that arises through one of the mechanisms described below. The tripeptide glutathione and the thiol reductants glutaredoxin and thioredoxin are key to the restoration of thiols to their reduced state (SH) (Fernandes and Holmgren 2004). E. coli contains three glutaredoxins that utilize the reducing power of glutathione to catalyze the reduction of disulfide bonds (–S–S–) in the presence of NADPH and glutathione reductase. There are two thioredoxins in E. coli that also function to reduce disulfide bonds. Reduced thioredoxin is regenerated by thioredoxin reductase and NADPH. The fact that NADPH is required to maintain the reduced state of glutathione and thioredoxin indicates that the response to oxidative stress is coupled to the physiological status of core pathways that generate NADPH.
Regulatory Roles of Thiol Modifications
As mentioned above, proteins—in particular, the thiols of cysteines—are the major targets of H2O2. The reaction of cysteinyl thiolates with H2O2 can lead to the formation of different modifications, such as sulfenic acid (–SOH), sulfinic acid (–SO2H), and sulfonic acid (–SO3H), as well as disulfide bond formation (–S–S–) and glutathione conjugation (–S–GSH) (Jacob et al. 2004; Poole et al. 2004) (Figure 2). These modifications often alter the structure and function of the protein. Recent progress in this field points to a common chemistry in the reaction of H2O2 with thiolates through the initial formation of sulfenic acid. In the case of proteins that have a nearby cysteinyl residue, a disulfide bond forms between the two sulfur atoms. The sulfenated cysteinyl residue also can react with a cysteinyl residue on another protein or with glutathione, leading to a mixed disulfide. If no cysteinyl residue is nearby, the sulfenated cysteine can be further oxidized to sulfinic or sulfonic acid, or it can remain in the sulfenic acid state. All but the sulfinic and sulfonic acid modifications are readily reversible by reduction, using proteins such as thioredoxin or glutaredoxin; though sulfinic acid reductase activities have recently been identified in yeast and mammalian cells (denoted sulfiredoxin and sestrin, respectively) (Biteau et al. 2003; Budanov et al. 2004).
Figure 2 Thiol Modifications of Proteins
Formation of sulfenic acid from the reaction of H2O2 with protein thiolates leads to different protein modifications, depending on the protein. In proteins without a second sulfhydryl, the sulfenic acid (–SOH) may be stabilized (e.g., OhrR) or may react with reactive oxygen species to generate the further oxidized sulfinic (–SO2H) (e.g., thiolperoxidase; Tpx) and sulfonic acid (–SO3H) derivatives. Alternatively, if a second cysteinyl residue is in proximity within the same polypeptide (e.g., OxyR) or an associated protein (e.g., Yap1 and Orp1), a disulfide bond can form between the two sulfur atoms (–S–S–). Lastly, the sulfenated cysteinyl residue can react with glutathione (GSH), leading to a mixed disulfide (e.g., MetE). (Illustration: Rusty Howson, sososo design)
Given the reversible nature of most forms of thiol oxidation, it has been suggested that thiol modifications can play roles in signal transduction that are similar to protein phosphorylation/dephosphorylation (Sitia and Molteni 2004). In support of this model, there are several examples of proteins whose activities are modulated by thiol oxidation and reduction.
The first of these examples is the OxyR transcription factor, which upregulates peroxide defenses in E. coli and a variety of other bacteria. OxyR contains two critical cysteines that are oxidized to form an intramolecular disulfide bond when cells encounter peroxide stress (Zheng et al. 1998; Aslund et al. 1999). Disulfide bond formation is associated with a conformational change that alters OxyR binding to DNA and allows the protein to activate the transcription of genes encoding enzymes, such as catalase and the alkylhydroperoxide reductase, that destroy H2O2. Once the H2O2 concentration is decreased, OxyR is reduced and the system is reset. The unusually reactive cysteine in OxyR that is oxidized by H2O2 to form the sulfenic acid intermediate can clearly be nitrosylated and glutathionylated in vitro (Hausladen et al. 1996; Kim et al. 2002), but the in vivo relevance of these other modifications is questionable (Mukhopadhyay et al. 2004).
Two other examples of redox-regulated proteins are the E. coli chaperone protein Hsp33 (Jakob et al. 2000) and the Streptomyces coelicolor anti-sigma factor, RsrA (Li et al. 2003; Paget and Buttner 2003; Bae et al. 2004). For these proteins, the cysteine residues, which form intramolecular disulfide bonds, are in a reduced state when coordinated to a zinc ion (Zn2+), and zinc is released upon oxidation of the thiols. For both proteins, oxidation and zinc release are associated with an opening of the protein structure. For Hsp33, this structural change allows for dimerization and activates its chaperone activity (Graf et al. 2004). RsrA, on the other hand, dissociates from a promoter specificity factor of RNA polymerase (an extracytoplasmic-function-type alternative sigma factor) allowing the transcription of genes that permit recovery from the stress (Li et al. 2003; Bae et al. 2004). Among the target gene products is a thioredoxin, which reduces the disulfide bonds that form within oxidized RsrA. Presumably, reduction of the disulfide restores the binding of zinc and its inhibitory association with the sigma factor. Thus, the RsrA regulatory circuit provides another example, comparable to OxyR, in which the modification of a regulatory protein thiol group can be linked to a change in the transcriptional output of genes that remediate stress.
The peroxide-sensing repressor OhrR from Xanthomonas campestris pv. phaseoli (Panmanee et al. 2002) and Bacillus subtilus (Fuangthong and Helmann 2002) can be inactivated by H2O2 or by organic peroxides (ROOH) formed by the oxidation of a variety of organic molecules in the cell or in the environment. The B. subtilis OhrR transcription regulator contains only a single cysteine that forms a relatively stable sulfenic acid upon its reaction with H2O2 or organic peroxides (Fuangthong and Helmann 2002). Oxidation of the single cysteine leads to the dissociation of OhrR from its DNA binding site and the derepression of the gene encoding an organic hydroperoxidase that eliminates the initial oxidizing insult.
In this issue, Hondorp and Matthews (2004) provide an example of a thiol modification that protects an enzyme activity during oxidative stress. Their data suggest that when cells encounter oxidative stress, a key cysteinyl residue near the active site of methionine synthase (MetE) is glutathionylated. This modification blocks access of the substrate and prevents further synthesis of methionine. This finding is significant in that it presents a mechanism to reversibly preserve the function of a protein during oxidative challenge. By glutathionylating a single cysteinyl residue, the protein is protected from further oxidation of that cysteinyl residue to the irreversible sulfinic and sulfonic acid forms. Once the stress is removed, the mixed disulfide bond will be readily reduced, and access to the substrate restored.
Prevalence of Regulatory Thiol Modifications?
As illustrated by the examples above, an array of chemical modifications obtained by oxidizing cysteinyl residues has been exploited in combating oxidative stress. Yet it is important to note that not all cysteinyl residues of proteins are readily oxidized by oxidants such as H2O2. We do not currently understand all of the features that determine the reactivity of a particular thiol to H2O2 (Poole et al. 2004). The pKa of the thiolates clearly plays an important role, as thiolates are more reactive than their protonated counterparts. In addition, the contribution of protein environment to the stability of the oxidized products is also known to be a factor, but is not well understood. Given that many of the thiol modifications do not appear to be in equilibrium with the redox state of the cell, the features of the protein that determine the rate at which the modifications are formed are another important parameter.
The added complexity of the cysteine targets that compose part of a Zn binding site found for Hsp33 and RsrA raises questions about the function of the zinc. Perhaps Zn binding provides some additional control over the reactivity of the cysteine thiols, or perhaps the loss of the zinc facilitates conformational changes. Recently, the oxidative, stress-induced thioredoxin-2 from E. coli has also been shown to contain a H2O2-labile zinc site, although the loss of zinc does not change its reductase activity (Collet et al. 2003). Thus, the way this oxidatively labile Zn site affects thioredoxin function has yet to be established.
The extent of thiol oxidation within the cell remains another open question. The variety of modifications that arise from treatment with H2O2 and the experimental challenges associated with their detection has made it difficult to catalog all the proteins that are modified and all the types of modifications that exist. In this issue, Leichert and Jakob (2004) report a general method for detecting cellular proteins whose cysteinyl residues were modified after imposing an oxidative stress. Such an approach will greatly enhance our understanding of targets of oxidative stress. The method described by Leichert and Jakob also will be useful in detecting transient cysteine modifications.
The importance of monitoring transient changes in cysteines is highlighted by the recent finding that oxidation of the Yap1 activator of antioxidant genes in the yeast Saccharomyces cerevisiae requires a peroxidase denoted Gpx3 or Orp1 (Delaunay et al. 2002). In this case, H2O2 reacts with a cysteine in Orp1, forming an unstable sulfenic acid intermediate that then reacts with a cysteinyl residue of Yap1 to form an intermolecular disulfide. The disulfide undergoes an exchange with a second cysteine within Yap1 to form an intramolecular disulfide that locks Yap1 in a confirmation that masks the nuclear export signal (Wood et al. 2004). Thus, methods that allow the appearance of thiol modifications in cells to be monitored kinetically will greatly enhance our understanding of how cysteine residues become oxidized.
The examples mentioned here illustrate the versatile potential of thiol modifications. Given the reversibility of thiol oxidations and the wide range of structural constraints that can be imposed by the formation of a sulfenic or sulfinic acid or a disulfide bond, we predict there will be many more examples of regulation by thiol modification.
Patricia J. Kiley is in the Department of Biomolecular Chemistry, University of Wisconsin Medical School in Madison, Wisconsin, United States of America. Gisela Storz is in the Cell Biology and Metabolism Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, United States of America
Abbreviations
H2O2hydrogen peroxide
O2molecular oxygen
O2−superoxide
•OHhydroxyl radical
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1554764310.1371/journal.pbio.0020401PrimerNeuroscienceMammalsMolecules That Cause or Prevent Parkinson's Disease PrimerCookson Mark R 11 2004 16 11 2004 16 11 2004 2 11 e401Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Sensitivity to Oxidative Stress in DJ-1-Deficient Dopamine Neurons: An ES-Derived Cell Model of Primary Parkinsonism
DJ-1 Is a Redox-Dependent Molecular Chaperone that Inhibits α-Synuclein Aggregate Formation
A New Cell Model for Parkinson's Disease
An overview of the molecules and associated cell biology underlying neuron death in Parkinson's Disease
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The consequence of Parkinson's disease (PD) is well described: a progressive movement disorder that, whilst responding to symptomatic therapy, chronically disables its sufferers and adds an enormous economic burden in an aging society. We have some clues to the process underlying the disease from the snapshot provided by postmortem studies of diseased brains. Groups of neurons in specific brain regions are lost, notably those that produce dopamine in a part of the midbrain called the substantia nigra. Those neurons that do survive to the end of the disease course contain accumulations of proteins and lipids within their cytoplasm. Named after their discoverer, these “Lewy bodies” are one piece of evidence that protein aggregation is related to the ongoing disease process.
In contrast, the causes of PD are poorly defined except in those rare variant forms that are clearly genetic. Several families have been described where PD-like syndromes are inherited in either a dominant or recessive fashion, and four of the underlying genes have been identified. The precise relationships between these different syndromes are complex and are the subject of some controversy. For example, it is not clear whether all the genetic diseases given PARK nomenclature have Lewy bodies and should be considered “true” PD—the term parkinsonism is preferred for these syndromes (Hardy and Langston 2004). For the purposes of this primer, I will concentrate on the molecular biology of the genes linked to PD rather than disease etiology. However, my assumption is that symptoms of the disease are a reflection of neuronal dysfunction, and that in the disease state the balance between damage and survival tips in the direction of cell loss. Whilst dominant mutations overwhelm the ability of cells to survive, recessive mutations result in the absence of protective proteins and make the neuron grow weaker.
Aggregation of α-Synuclein in Neurodegeneration
On the detrimental side of the cell survival equation is the PD gene that was discovered first, α-synuclein. The synaptic protein encoded by this gene, α-synuclein, is prone to aggregation, and, as is the case for other aggregating proteins, mutations in α-synuclein are associated with dominantly inherited disease. Related to this, α-synuclein is a major protein component of Lewy bodies. The phenotype of patients with α-synuclein mutations varies from PD to a more diffuse Lewy body disease in which pathology is detected in the cerebral cortex and other areas of the brain. Mutations in α-synuclein include three point mutations (A30P, E46K, and A53T) and multiplication of the wild-type (normal) gene. All of these mutations increase the tendency of α-synuclein to aggregate, suggesting that disease is a consequence of protein aggregation. An interesting example is the triplication of the wild-type gene: toxicity and aggregation can both be driven by increased expression and are thus qualitative, not quantitative, effects (Singleton et al. 2003). The fact that the wild-type protein can aggregate suggests that the process is fundamentally similar for both inherited and sporadic PD in which wild-type α-synuclein is also present in Lewy bodies. Several commentators have suggested that non-genetic risk factors may also promote damage via their effects on (wild-type) α-synuclein conformation or aggregation (e.g., Di Monte 2003). This reinforces the notion that α-synuclein is central to the pathogenesis of both sporadic and familial PD.
There is some controversy about the exact nature of the toxic species produced by α-synuclein, as one point mutation (A30P) behaves differently from the others. Instead of forming fibrils, which are insoluble, high-molecular-weight species, A30P forms relatively soluble, partially aggregated species (Conway et al. 2000). These intermediate-sized protein aggregates are referred to as oligomers or protofibrils. Some authors have argued that since A30P causes disease, oligomers/protofibrils are the authentic toxic species. It is generally assumed that fibrils are the form of α-synuclein deposited into Lewy bodies, but whether Lewy bodies damage cells is controversial. One possibility is that by sequestering α-synuclein into this insoluble body and compartmentalizing the potentially toxic species away from possible targets in the cytoplasm, the Lewy body represents an attempt of the cell to protect itself (Olanow et al. 2004).
Whether the Lewy body is damaging or neuroprotective, there are clearly several possible targets for toxic α-synuclein within the cell. For example, aggregated α-synuclein can permeabilize cellular membranes and thus might damage organelles (Volles and Lansbury 2003). Mitochondrial function and synaptic transmission may be especially affected, and both of these can secondarily increase oxidative stress within the cytosol (Greenamyre and Hastings 2004). When overexpressed, mutant α-synuclein can inhibit the proteasome (Petrucelli et al. 2002), a multiprotein complex that degrades many unwanted or inappropriate proteins in cells. Mutant forms of α-synuclein also inhibit chaperone-mediated autophagy, another important protein turnover pathway that involves lysosomes (Cuervo et al. 2004). Between these two effects, it is likely that cells with aggregated α-synuclein will become less able to handle damaged or misfolded proteins. It is also possible that other cellular processes that we have not yet identified are affected by the presence of this protein that has such an innate tendency to aggregate.
Presumably, neurons require α-synuclein for their normal function and thus cannot simply dispense with this protein that has toxic properties, although mice in which the α-synuclein gene is knocked out have no obvious deficits (see Dauer and Przedborski [2003] for discussion).
Parkin, DJ-1, and PINK1 in Neuroprotection
Evolution has provided cells with many ways to protect themselves. As we will see, mutations that cause recessive diseases result in the loss of these neuroprotective functions. The genes involved in recessive parkinsonism are, in order of discovery, parkin, DJ-1, and PINK1. The three protein products of these genes all have different functions, thus implicating several different cellular functions in neuroprotection. Parkin is an E3 ubiquitin–protein ligase, promoting the addition of ubiquitin to target proteins prior to their degradation by the proteasome. The identification of parkin's function was facilitated by the observation that the protein contains a RING finger (Zhang et al. 2000), a common motif amongst this class of E3 enzymes. Several parkin substrates have been proposed, and at least two are damaging to neurons if they are allowed to accumulate (Dong et al. 2003; Yang et al. 2003). Therefore, our best evidence to date indicates that parkin benefits neurons by removing proteins that might otherwise damage the cell. In fact, expression of parkin is neuroprotective in a number of contexts, and there is even evidence for a beneficial effect of this E3 ligase on mitochondrial function (Shen and Cookson 2004).
Data on PINK1 are limited, but the protein contains two motifs that indicate its likely cellular role. At the amino-terminus of PINK1 is a mitochondrial-targeting sequence, and mitochondrial localization has been confirmed in the one study published to date (Valente et al. 2004). Most of the rest of PINK1 is a Serine/Threonine protein kinase domain, followed by a short carboxy-terminal region of unclear significance. The substrates of PINK1 have not yet been identified, but presumably phosphorylation of these substrates controls some critical function for neuronal survival. In their paper, Valente and colleagues show that PINK1 decreases damage to mitochondria induced by proteasome inhibition, but a recessive mutant PINK1 is unable to protect cells.
The discussion of protein functions gets more complicated in the case of DJ-1. Unlike parkin or PINK1, there are no motifs within DJ-1 that hint strongly at a single function. Instead, DJ-1 is a member of a large superfamily of genes with several different functions across species (Bandyopadhyay and Cookson 2004). These include proteases in thermophilic bacteria, transcription factors, and chaperones that promote protein refolding. Several research groups have published data in support of DJ-1 having one or more of these activities, including the report, published in this issue of PLoS Biology, that DJ-1 is a molecular chaperone that regulates α-synuclein, among other molecules (Shendelman et al. 2004). It is not yet firmly established which activity of DJ-1 is most relevant to recessive parkinsonism. The important function of DJ-1 might be unrelated to any of the above activities. For example, there are several roles of this protein in modulation of transcriptional responses, which may be critical in maintaining neuronal viability (Bonifati et al. 2003 and references therein)
DJ-1 is also known to be responsive to oxidative conditions, under which cysteine residues are oxidized to form cysteine-sulfinic acids. There is some discussion about which cysteine residue is oxidized; the most likely is cysteine 106, which is present in a nucleophile elbow in the protein. We have suggested that modifying this residue precludes DJ-1 oxidation under mild conditions and also blocks the neuroprotective activity of DJ-1 against mitochondrial toxicity (Canet-Aviles et al. 2004). Therefore, whatever the function of DJ-1, it seems to be related to oxidation. In support of this idea, cells with DJ-1 knocked out show increased sensitivity to oxidative stress (Yokota et al. 2003). Another study published in this issue of PLoS Biology shows that dopamine neurons differentiated from embryonic stem cells lacking functional DJ-1 are especially sensitive to oxidative stress (Martinat et al. 2004).
This discussion indicates that the genes responsible for recessive parkinsonism all have different functions but are all, in a broad sense, neuroprotective. A very difficult question to answer is whether this has anything to do with α-synuclein. We have shown that parkin can mitigate the toxicity of mutant α-synuclein (Petrucelli et al. 2002). Although there are reports that a proportion of α-synuclein is a parkin substrate (Shimura et al. 2001), most of the protein is not degraded by the ubiquitin-proteasome system. Recent evidence points, instead, to an important role of the lysosome, the other major pathway within cells for degrading unwanted proteins, in clearing α-synuclein (Cuervo et al. 2004). On balance, therefore, there is no direct evidence that parkin controls α-synuclein toxicity by an effect on protein levels within the cell. Furthermore, parkin does not just prevent α-synuclein toxicity: it is beneficial against several other stresses (discussed in Shen and Cookson 2004), leading to the possibility that this protein protects neurons against more than just the processes implicated in PD.
It has also been suggested that DJ-1 can prevent the accumulation of aggregated α-synuclein and that cysteine 53 is critical for this activity (Shendelman et al. 2004). However, DJ-1 is not just a chaperone for α-synuclein; it can also promote refolding of citrate synthase, glutathione transferase, and neurofilament light. Other research groups have reported similar findings (Olzmann et al. 2004), although there are differences between these studies in which cysteine residues are thought to be required for DJ-1 function. Given that there are some differences in these results, further clarification of the role for DJ-1 in α-synuclein-mediated toxicity is needed. More generally, we have to bear in mind that whether recessive parkinsonism has anything to do with α-synuclein is still an open question. What is clear is that some neurons rely on parkin, DJ-1, or PINK1 to protect themselves against the many stresses that they face. However, mutations in these genes do not cause generalized neurodegeneration; in fact, they tend to be more restricted and less progressive than, for example, α-synuclein mutations. This suggests, at least to my mind, that recessive mutations indicate something about the neurons that are damaged in these disorders. Why is this of more than academic importance? Perhaps by identifying the proximal events that are sufficient to cause a specific set of neurons to degenerate, we might begin to design therapies that address the underlying degeneration in PD and not just the consequences.
Figure 1 Molecules That Cause or Prevent Parkinson's Disease
(A) shows a simplified, linear view of the aggregation pathway of α-synuclein (in blue). The monomer of α-synuclein is a natively unfolded protein with several repeats, shown by dark bars on the monomer. The protein has an innate tendency to aggregate with other molecules of α-synuclein, first into oligomers (also known as protofibrils), then into fibrils. It is the fibrillar forms of α-synuclein that are deposited into the classic pathological structures of PD, Lewy bodies. There are several studies that suggest that the oligomeric intermediates are the major toxic species, although this is not certain. (B) shows the recessive mutations associated with parkinsonism and their possible relationships to subcellular targets, either mitochondria (left) or the proteasome (right). Insults to either of these can cause cellular damage and may interact. For example, proteasome inhibitors can cause mitochondrial damage, which can be antagonized by PINK1. Parkin can promote the turnover of proteasomal substrates, and DJ-1 can prevent mitochondrial damage.
Quite whether (B) relates to (A) is not clear, but recent results with DJ-1 imply that DJ-1 has chaperone activity towards oligomers of α-synuclein (see text). Although there is much to be done to resolve the order of these events, it is likely that, either alone or in concert, damage to multiple cellular pathways leads to neuronal dysfunction and, eventually, cell death.
Mark R. Cookson is with the Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America. E-mail: [email protected]
Abbreviation
PDParkinson's disease
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Bandyopadhyay S Cookson MR Evolutionary and functional relationships within the DJ1 superfamily BMC Evol Biol 2004 4 6 15070401
Bonifati V Rizzu P van Baren MJ Schaap O Breedveld GJ Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism Science 2003 299 256 259 12446870
Canet-Aviles RM Wilson MA Miller DW Ahmad R McLendon C The Parkinson's disease protein DJ-1 is neuroprotective due to cysteine-sulfinic acid-driven mitochondrial localization Proc Natl Acad Sci U S A 2004 101 9103 9108 15181200
Conway KA Lee SJ Rochet JC Ding TT Williamson RE Acceleration of oligomerization, not fibrillization, is a shared property of both alpha-synuclein mutations linked to early-onset Parkinson's disease: Implications for pathogenesis and therapy Proc Natl Acad Sci U S A 2000 97 571 576 10639120
Cuervo AM Stefanis L Fredenburg R Lansbury PT Sulzer D Impaired degradation of mutant alpha-synuclein by chaperone-mediated autophagy Science 2004 305 1292 1295 15333840
Dauer W Przedborski S Parkinson's disease: Mechanisms and models Neuron 2003 39 889 909 12971891
Di Monte DA The environment and Parkinson's disease: Is the nigrostriatal system preferentially targeted by neurotoxins? Lancet Neurol 2003 2 531 538 12941575
Dong Z Ferger B Paterna JC Vogel D Furler S Dopamine-dependent neurodegeneration in rats induced by viral vector-mediated overexpression of the parkin target protein, CDCrel-1 Proc Natl Acad Sci U S A 2003 100 12438 12443 14530399
Greenamyre JT Hastings TG Biomedicine. Parkinson's—Divergent causes, convergent mechanisms Science 2004 304 1120 1122 15155938
Hardy J Langston JW How many pathways are there to nigral death? Ann Neurol 2004 56 316 318 15349857
Martinat C Shendelman SB Jonason A Leete T Beal MF Sensitivity to oxidative stress in DJ-1-deficient dopamine neurons: An ES-derived cell model of primary parkinsonism PLoS Biol 2004 2 e327 15502868
Olanow CW Perl DP DeMartino GN McNaught KS Lewy-body formation is an aggresome-related process: A hypothesis Lancet Neurol 2004 3 496 503 15261611
Olzmann JA Brown K Wilkinson KD Rees HD Huai Q Familial Parkinson's disease-associated L166P mutation disrupts DJ-1 protein folding and function J Biol Chem 2004 279 8506 8515 14665635
Petrucelli L O'Farrell C Lockhart PJ Baptista M Kehoe K Parkin protects against the toxicity associated with mutant alpha-synuclein: Proteasome dysfunction selectively affects catecholaminergic neurons Neuron 2002 36 1007 1019 12495618
Shen J Cookson MR Mitochondria and dopamine: New insights into recessive parkinsonism Neuron 2004 43 301 304 15294138
Shendelman S Jonason A Martinat C Leete T Abeliovich A DJ-1 is a redox-dependent molecular chaperone that inhibits α-synuclein aggregation formation PLoS Biol 2004 2 e362 15502874
Shimura H Schlossmacher MG Hattori N Frosch MP Trockenbacher A Ubiquitination of a new form of alpha-synuclein by parkin from human brain: Implications for Parkinson's disease Science 2001 293 263 269 11431533
Singleton AB Farrer M Johnson J Singleton A Hague S Alpha-synuclein locus triplication causes Parkinson's disease Science 2003 302 841 14593171
Valente EM Abou-Sleiman PM Caputo V Muqit MM Harvey K Hereditary early-onset Parkinson's disease caused by mutations in PINK1 Science 2004 304 1158 1160 15087508
Volles MJ Lansbury PT Zeroing in on the pathogenic form of alpha-synuclein and its mechanism of neurotoxicity in Parkinson's disease Biochemistry 2003 42 7871 7878 12834338
Yang Y Nishimura I Imai Y Takahashi R Lu B Parkin suppresses dopaminergic neuron-selective neurotoxicity induced by Pael-R in Drosophila
Neuron 2003 37 911 924 12670421
Yokota T Sugawara K Ito K Takahashi R Ariga H Down regulation of DJ-1 enhances cell death by oxidative stress, ER stress, and proteasome inhibition Biochem Biophys Res Commun 2003 312 1342 1348 14652021
Zhang Y Gao J Chung KK Huang H Dawson VL Parkin functions as an E2-dependent ubiquitin-protein ligase and promotes the degradation of the synaptic vesicle-associated protein, CDCrel-1 Proc Natl Acad Sci U S A 2000 97 13354 13359 11078524
| 15547643 | PMC526782 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Nov 16; 2(11):e401 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020401 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1554764410.1371/journal.pbio.0020404FeatureNeuroscienceAnesthesiologyGeneral MedicineHomo (Human)Nicotine as Therapy FeaturePowledge Tabitha M 11 2004 16 11 2004 16 11 2004 2 11 e404Copyright: © 2004 Tabitha M. Powledge.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.In daily use for centuries by hundreds of millions of people, nicotine has only lately been investigated for its therapeutic potential in a long list of common ills
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There's a cheap, common, and mostly safe drug, in daily use for centuries by hundreds of millions of people, that only lately has been investigated for its therapeutic potential for a long list of common ills. The list includes Alzheimer disease, Parkinson disease, depression and anxiety, schizophrenia, attention deficit hyperactivity disorder (ADHD), and even pain and obesity. Why has interest in this potential cure-all been slow to develop? One reason: in its current forms the drug offers pharmaceutical companies no possibility of substantial profit. Another, perhaps more important: the drug is reviled as the world's most addictive. The drug, of course, is nicotine.
Nicotine is an alkaloid in the tobacco plant Nicotiana tabacum, which was smoked or chewed in the Americas for thousands of years before European invaders also succumbed to its pleasures and shipped it back to the Old World. Nicotine has always been regarded as medicinal and enjoyable at its usual low doses. Native Americans chewed tobacco to treat intestinal symptoms, and in 1560, Jean Nicot de Villemain sent tobacco seeds to the French court, claiming tobacco had medicinal properties and describing it as a panacea for many ailments. Higher doses are toxic, even lethal—which is why nicotine is used around the world as an insecticide. Yet few of the horrendous health effects of smoking are traceable to nicotine itself—cigarettes contain nearly 4,000 other compounds that play a role. Until recently, nicotine research has been driven primarily by nicotine's unparalleled power to keep people smoking, rather than its potential therapeutic uses.
Nicotine locks on to one group of receptors that are normally targeted by the neurotransmitter acetylcholine. Nicotinic acetylcholine receptors (nAChRs) are ion channels threaded through cell membranes. When activated, either by acetylcholine or by nicotine, they allow selected ions to flow across the cell membrane. In vertebrates nAChRs are all over the autonomic and central nervous sytems and the neuromuscular junction. A nAChR is composed of five polypeptide subunits (Figure 1), but there are many nAChR subtypes made of different subunit combinations, a diversity that helps explain why nicotine can have so many different physiological and cognitive effects.
Figure 1 Schematic Illustration of an Acetylcholine Receptor (Illustration: Giovanni Maki)
It is now conventional wisdom that acetylcholine and nicotine act at these receptors to alter electrochemical properties at a variety of synapses, which can in turn affect the release of several other neurotransmitters. This wisdom exists thanks in part to work by Lorna Role and her colleagues at Columbia University in New York City. “In 1995, we turned people's attention to how nicotine works as a modulator, tuning synapses and increasing the gain on transmitter release,” Role recalls. Although all nAChRs are activated by nicotine, other drugs could be found or designed that affect only a subset of these receptor types. “If you can dissect out the important players with respect to which nicotine receptors are tuning [a] particular set of synapses, then that provides another way to potentially target the therapeutics.”
Nicotine and the Brain
People with depressive-spectrum disorders, schizophrenia, and adult ADHD tend to smoke heavily, which suggested to researchers that nicotine may soothe their symptoms. Common to all these disorders is a failure of attention, an inability to concentrate on particular stimuli and screen out the rest. Nicotine helps. Researchers at the National Institute on Drug Abuse have shown via functional magnetic resonance imaging that nicotine activates specific brain areas during tasks that demand attention (Box 1). This may be because of its effects, shared with many other addictive drugs, on the release of the neurotransmitter dopamine. “Schizophrenia is a disorder largely of the dopamine system,” says John Dani of the Baylor College of Medicine in Houston, Texas. Dopamine signals in the brain occur in two modes—a kind of background trickle, punctuated by brief bursts. “It's thought that schizophrenics have a hard time separating that background information from important bursts. We've shown that nicotine helps to normalize that signaling by depressing the background but letting the bursts through well,” he says. “I'll be surprised if there's not a co-therapy [to help schizophrenics] that takes advantage of nicotine systems in less than a decade.”
Box 1. Nicotine's Effect on Attention
Using functional magnetic resonance imaging, scientists at the National Institute on Drug Abuse provided the first evidence that nicotine-induced enhancement of parietal cortex activation is associated with improved attention. They compared brain activity during a task demanding sustained attention—rapid visual information processing (RVIP)—with that during an undemanding sensorimotor control task (Figure 2). Group results from 15 smokers (right) illustrate the effects of nicotine and placebo patches in left and right parietal cortex (1 and 2) and left and right occipital cortex (3 and 4). Nicotine significantly increased activation in occipital cortex during both the control and rapid visual information processing tasks, suggesting a general modulation of attention. In contrast, nicotine increased activity in the parietal cortex only during rapid visual information processing, suggesting a specific modulation on task performance.
Nicotine may be the link between two genes that appear to figure in schizophrenia. Sherry Leonard and Robert Freedman of the University of Colorado in Denver, Colorado, have shown that expression of the gene for the alpha 7 neuronal nicotinic receptor is reduced in schizophrenics, and have argued that alpha 7 abnormalities lead to attention problems. Researchers in Iceland and elsewhere have shown that a different gene, for the growth factor neuregulin, also appears to figure in the disease. Neuregulin, Role and her colleagues have shown, governs the expression of nAChRs in neurons and helps to stabilize the synapses where they are found. The researchers are currently studying interactions between neuregulin and alpha 7, which Role thinks will prove important.
Smokers also have lower rates of neurodegenerative disorders, and nicotine improves cognitive and motor functioning in people with Alzheimer disease and Parkinson disease. The prevailing hypothesis is that nicotine increases release of neurotransmitters depleted in those diseases. Dani and his colleagues have recently shown that acetylcholinesterase inhibitors—which block the degradation of acetylcholine and hence prolong its action—used to treat Alzheimer disease also stimulate dopamine release. They suspect that malfunctioning of the dopamine system may be affecting noncognitive aspects of dementia such as depressed mood, and that this might be alleviated by nicotine.
Paul Newhouse and his colleagues at the University of Vermont in Burlington, Vermont, are studying nicotine drugs as potential therapeutic agents for cognitive dysfunction. Newhouse, a long-time nicotine researcher, is heading the first study ever to examine the efficacy and safety of nicotine patches for treating mild cognitive impairment, thought to be a precursor of Alzheimer disease. The researchers hope to see a positive effect on attention and learning. Newhouse also heads two studies of nicotinic stimulation in ADHD, using the patch, nicotine blockers, and some novel drugs that activate nicotine receptors.
Nicotine and Pain
Nicotine's salutary effects in patients with neurodegenerative and mental disorders have been studied a lot and are fairly well known. Two much newer topics of academic research are nicotine's potential for pain relief and for treating obesity.Nicotine itself has provided modest pain relief in animal studies. Although the analgesic effect of drugs that mimic acetylcholine were originally attributed to a different class of receptors, it is now clear that nAChRs play an important role in the control of pain. For instance, epibatidine, a drug that is extracted from the skin of an Ecuadorian frog and that acts at nAChRs, has been shown to be 200 times more potent than morphine at blocking pain in animals. Current animal research is aimed at discovering just where, how, and which classes of nAChRs work against pain, with the aim of developing more selective drugs.
Meanwhile, nicotine is also being investigated as an analgesic in humans. For example, Pamela Flood, an anesthesiologist at Columbia, is investigating nicotine's future as a postoperative analgesic. She recently completed a pilot study of 20 women undergoing gynecological surgery. All the women had access to unlimited morphine and also got either a single 3-mg dose of nicotine nasal spray or a placebo. The placebo group had peak pain scores of eight out of a possible ten in the first hour after surgery. Women who got nicotine averaged a pain score of five. Despite the small sample size, Flood says, the results were highly significant. “As far as I know this is the first clinical study to use nicotine for analgesia, and it was much more successful than I ever would have imagined.”
“The nice thing about nicotine and drugs like nicotine is that they have opposite side effects to anesthetics. Instead of being respiratory depressants, they are respiratory stimulants. Instead of being sedating, they increase alertness. So theoretically this class of drugs is actually the perfect thing to add to an opioid regimen. The fact that they're synergistic was a fortuitous thing that we had never looked at, and neither had anybody else.”
Nicotine and Weight Gain
Nicotine may be the most effective drug around for weight control. As ex-smokers know, to their rue, one of the worst things about quitting cigarettes is putting on pounds—as much as 10% of body weight. “Something about being addicted to nicotine and then going off it causes massive increase in weight,” Role points out.
Young-Hwan Jo in Role's lab is looking at a particular brain circuit involved in motivational behavior, especially feeding behavior. It is lodged primarily in the lateral hypothalamus but has projections all over the cortex, especially the nucleus accumbens, which is the center of reinforcement. “This is where information that has come in to the thalamus and the hypothalamus is relayed to cortical areas with some sense of salience or remembrance. It presumably is involved in changing perception and motivation for eating. It's not, ‘I have to eat this,’ it's, ‘I want to eat this,’” says Role.
Jo has been comparing the synaptic effects of nicotine, which reduces appetite, to those of cannabinoids, which stimulate it. “Control of these projection neurons seems to be oppositely regulated by these two,” Role notes. “It doesn't necessarily mean we've found the root of the munchies, but it at least points to pathways that these things have in common.” Jo is also examining how nicotine and cannabinoids modulate these pathways in genetically obese mice, and also their interactions with leptins. Role says tuning these pathways up or down might be a reasonable aim. “If that could be done in a selective fashion, maybe that could be introduced in appetite control. Certainly I see…antagonism of some of these pathways that nicotine activates or the complementary activation of the cannabinoid pathways as very important targets for therapeutics with respect to the anorexia that's associated with chemotherapy.”
Ming Li and his colleagues at the University of Texas in San Antonio, Texas, are studying nicotine's effects on weight and on expression of genes that nicotine upregulates orexin and neuropeptide Y and, more recently, that it also regulates leptin signaling. All three molecules regulate feeding behavior controlled by the hypothalamus. In the weight study, nicotine-treated rats not only lost weight, they lost about 20% of their body fat compared to saline-treated controls. The researchers suggest that, among its other effects, nicotine alters fat storage.
The University of Texas researchers have scoured the literature for genes related to nicotine, and they are developing microarrays to study the expression of these genes (Figure 3). While nicotine seems to affect all the molecules known to influence weight, Li says it's clear the story is even more complex. “That's the reason we keep looking at different molecules, to find key targets involved in this regulation.” The ultimate hope is to develop new drug applications.
Figure 3 Microarray Showing Patterns of Gene Expression Influenced by Nicotine
(Image: Ming Li, University of Texas Health Science Center at San Antonio)
Dani predicts that weight control is likely to be one of the earliest nicotine-based therapies. “There's a very good chance that the first drug is unlikely to be…nicotine itself, but will take advantage of nicotinic receptors in the therapy,” he says. “I know there are drugs now being tested by drug companies just for that purpose.”
Nicotine's Future
Developing new drugs that selectively target specific subtypes of nicotine receptors is an expensive, albeit potentially lucrative, proposition. And therein lies a question. Will nicotine-based therapy consist mostly of costly new drugs from the pharmaceutical industry? Or can less expensive nicotine products like the patch, chewing gum, and nasal spray—which are generally intended for smoking cessation but widely available, usually without prescription—find their way into the world's medicine cabinets?
“It's a little early to call whether nicotine will be used itself as a therapeutic agent or whether these more specific drugs that are being produced or maybe even used in combination with other drugs may be the most important way to go,” says Dani. But he doesn't see the medicinal use of plain nicotine as very likely. Dani points out that the body's own agent, acetylcholine, acts over milliseconds to activate nicotinic receptors, whereas nicotine itself stimulates these receptors for hours. That lengthy action means that, although nicotine activates the receptors, it then often turns particular receptor subtypes off again, a process called desensitization. “It's hard to predict inside of a body what you're getting. Am I getting an activation or am I turning the receptors off?”
Yet much of the work to date showing nicotine's effectiveness on a huge range of disorders has involved products available at any drugstore and intended to help people quit smoking. Newhouse is using patches for mild cognitive impairment. Flood has demonstrated pain relief with nasal spray and will use patches in her next study. And Role feels that gum hasn't been adequately explored for its therapeutic potential. Nicotine gum, she notes, is a better imitator of smoking than the patch because it delivers brief hits rather than a steady supply. She's also uncertain whether natural nicotine has been studied enough. But Role also points out that nicotine has its serious problems—addictive potential, cardiovascular damage, and (especially when delivered through the mucosa) cancer.
Dani says, “People are probably going to have to find creative ways to understand which subtypes of nicotinic receptors they're turning on and which ones they're desensitizing. Maybe drug delivery methods will matter. Maybe subtype specificity will matter. It's less than a decade that we've known how important nicotinic receptors are. Now we have to move forward from there.”
“We've made an enormous amount of progress on understanding the biology of these receptor systems and how to target them. What has been trickier has been to develop an appropriate pharmacology that allows one to selectively target agents for particular therapeutic purposes with an adequate safety index,” Newhouse says. “But some of the drugs that are coming on in human trials now are very promising. So I'm cautiously optimistic that we're on the road to developing some useful nicotinic therapies.”
Figure 2 The Brain on Nicotine (Image: Elliot Stein, National Institute on Drug Abuse)
Tabitha M. Powledge is a freelance science writer who specializes in neuroscience, genomics, and science policy. E-mail: [email protected]
Abbreviations
ADHDattention deficit hyperactivity disorder
nAChRnicotinic acetylcholine receptor
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Further Reading
Flood P Sonner JM Gong D Coates KM Isoflurane hyperalgesia is modulated by nicotinic inhibition Anesthesiology 2002 97 192 198 12131122
Freedman R Adams CE Adler LE Bickford PC Gault J Inhibitory neurophysiological deficit as a phenotype for genetic investigation of schizophrenia Am J Med Genet 2000 97 58 64 10813805
Li MD Kane JK Effect of nicotine on the expression of leptin and forebrain leptin receptors in the rat Brain Res 2003 991 222 231 14575895
McGehee DS Heath MJ Gelber S Devay P Role LW Nicotine enhancement of fast excitatory synaptic transmission in CNS by presynaptic receptors Science 1995 269 1692 1696 7569895
Newhouse PA Potter A Singh A Effects of nicotinic stimulation on cognitive performance Curr Opin Pharmacol 2004 4 36 46 15018837
Yang X Kuo Y Devay P Yu C Role L A cysteine-rich isoform of neuregulin controls the level of expression of neuronal nicotinic receptor channels during synaptogenesis Neuron 1998 20 255 270 9491987
Zhang L Zhou FM Dani JA Cholinergic drugs for Alzheimer's disease enhance in vitro dopamine release Mol Pharmacol 2004 66 538 544 15322245
| 15547644 | PMC526783 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Nov 16; 2(11):e404 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020404 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1554764510.1371/journal.pbio.0020406EssayEcologyEvolutionHemispheric Asymmetries in Biodiversity—A Serious Matter for Ecology EssayChown Steven L [email protected] Brent J Leinaas Hans P Gaston Kevin J 11 2004 16 11 2004 16 11 2004 2 11 e406Copyright: © 2004 Chown et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Although the poles are less diverse than the tropics, this decline shows substantial asymmetries between the hemispheres, suggesting that responses to environmental change may differ substantially in the north and the south.
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Penguins have been receiving a lot of bad press lately. They are considered somehow counter, spare, strange. Unlike most plant and animal groups, they do not show a peak of species richness towards the equator and a decline towards the poles. This more conventional spatial pattern is conveniently known as the latitudinal diversity gradient because of the strong covariance of richness and other measures of biodiversity that it describes. It is one of the most venerable, well-documented, and controversial large-scale patterns in macroecology (Willig et al. 2003). Equatorial peaks in species richness have characterised the planet since the Devonian (408–362 million years ago) (Crame 2001) and are typical of a wide range of both terrestrial and marine plants and animals (Gaston 1996; Willig et al. 2003). Despite the fact that this pattern has been documented since the late 1700s, sustained interest in both the regularity of the pattern and its likely underlying mechanisms is relatively modern. The realisation that human activity is posing substantial threats to biodiversity has quickened the pace of this interest (Willig et al. 2003). Where the peaks in richness lie (biodiversity hotspots), how these peaks relate to centres of endemism (areas that support large numbers of species that occur nowhere else), and how these patterns are likely to change through time, especially in the face of major environmental change, are major concerns. Without such knowledge, conservation is unlikely to succeed.
Although spatial patterns in biodiversity, and particularly the latitudinal gradient, are increasingly well documented for a range of taxa, the proposed mechanisms underlying these gradients remain controversial. In essence, the multitude of mechanisms proposed to explain diversity gradients can be reduced to three categories: historical, ecological, or null. Most significant in raising the temperature of recent discussions is the question of the relative importance of each of these major categories. Historical mechanisms are those that suggest that earth history (e.g., the opening of the Drake Passage and the cooling of Antarctica) and phylogenetic history have played major roles in generating current patterns in diversity, and tend to emphasise regional (and especially longitudinal) differences therein (Qian and Ricklefs 2004; Ricklefs 2004). Explanations involving ecological mechanisms often downplay the significance of such regional differences and give most attention to covariation between current diversity and variables such as energy and water availability, and to the ultimate mechanisms underlying this covariation (Hawkins et al. 2003; Currie and Francis 2004). By contrast, null models, and specifically the geometric constraints model, argue that the expected pattern of latitudinal variation in richness is not a uniform one, but rather a mid-domain peak, which is almost inevitably the outcome of the random placement of a set of variable species ranges within a bounded domain (Colwell et al. 2004; but see also Zapata et al. 2003). It is deviation from the mid-domain expectation that is then argued to be of most interest. In many cases the historical and ecological mechanisms might be difficult to disentangle, such as the historical effects of the establishment of the Antarctic Circumpolar Current, and its consequences for energy availability in the region today (Clarke 2003).
Nonetheless, juxtaposing these three major mechanisms raises several questions that could substantially inform the debate in many ways, but have enjoyed far less attention than debating the relative merits of each of them. The geometric constraints model suggests that, to the extent that there is symmetry in the continuity of land (or water) about the equator, declines in richness from the tropical peak should also be symmetrical, with any asymmetries in the latter matching those in the former. Indeed, most texts and reviews dealing with latitudinal diversity gradients only briefly mention hemisphere-related differences and focus instead on the general decline of diversity away from the tropics in both directions (e.g., Brown and Lomolino 1998; Willig et al. 2003). However, that diversity gradients in the two hemispheres might in many cases be highly asymmetric has long been appreciated (Gaston 1996). Although several historical hypotheses suggest reasons why this asymmetry should exist (reviewed in Brown and Lomolino 1998), differences in present ecological factors, such as temperature gradients and rainfall variation, might also explain such asymmetry. If ecological factors are important, then these asymmetries should show up not only in diversity patterns, but also at other levels in the ecological and genealogical hierarchies. From the perspective of ecological explanations for such spatial variation, the questions, then, are how common and strong are such asymmetries, how common are they in patterns of diversity, and what, if any, might be the ecological, rather than null or historical, mechanisms responsible for them?
Continents and Climates
The last 100 million years have seen both a substantial steepening in latitudinal diversity gradients and the fragmentation of continental land masses (Crame 2001). By 15 million years ago the continents had largely assumed their current positions and a latitudinal temperature gradient very similar to the present one had been established. Today, 70% of all land is in the northern hemisphere, and between latitudes 30° and 60° north, the ratio of water to land is about 1:1, whereas between 30° and 60° south, it is approximately 16:1. The continentality of the north and oceanicity of the south have considerable effects on the climates of the hemispheres, as has long been appreciated (Bonan 2002). Although there is obviously much local and mesoscale variation, terrestrial temperatures in the south (excluding Antarctica) are usually warmer, and much less extreme in terms of their absolute range, than those in the north (Figure 1A), especially over the winter months. Southern sites between 30° and 60° typically have mean July temperatures between 0 and 10 °C, whereas at similar latitudes in the north, mean January temperatures vary from −40 to 0 °C. In winter the smaller range of variation in the south is around a physically and biologically significant threshold: the freezing point of water. In the north, winter temperatures are more variable, but generally well below this point. Ocean water temperatures are much less variable than those on land, although variability in the ocean surrounding Antarctica is much reduced compared with that of the Arctic (Figure 1B).
Figure 1 Temperature Variation with Latitude
(A) Mean and absolute minimum and maximum temperatures across the New World.(B) Mean and absolute range in sea surface temperatures across the Pacific at 165° W.
Mean annual precipitation is spatially more complex. Overall, precipitation is slightly higher in the south than in the north, at least below 60° latitude. However, much of this precipitation falls over the ocean in the south, leaving the more temperate parts of the southern continents as dry as their northern counterparts (Bonan 2002). Spatial patterns in rainfall variability are also complex, but variability tends to be higher and predictability lower in southern areas. From a biological perspective the significant factor is not necessarily just the magnitude of the variance, but also the mean about which it occurs (Guernier et al. 2004).
Clearly, the spatial complexity of climatic variation is much greater than the present overview would suggest. However, these broad brush strokes capture the hemisphere-related variation that might be most significant from a biological perspective.
Ecological Consequences
If differences in climates do cascade upwards to influence individuals, species, and broader scale patterns in diversity, their influence should be readily detectable at the level of species' life histories and distributions. In birds, large-scale geographic variation in life history variables, such as the incidence of cooperative breeding, extent of parental care, survival, and the timing of reproduction, has been studied for at least the past 50 years, and the mechanisms underlying this variation have been much debated. Taking phylogeny and the idiosyncrasies of the Australian avifauna into account, southern species typically lay small clutches and have long fledging periods, and it is often difficult to predict their date of first laying or, indeed, whether they will lay in a particular year at all (Covas et al. 1999; Russell et al. 2004). By contrast, northern species lay larger clutches and have shorter fledging periods, and laying date is more readily predicted, making investigations of phenological shifts associated with modern climatic change more straightforward (e.g., Crick et al. 1997).
These kinds of differences extend to other taxa. Thus, although the variation of metabolic rate with latitude is becoming increasingly well known for a variety of groups, Lovegrove (2000) has recently suggested, based on comparative work taking both species body mass and phylogeny into account, that unpredictability of resources associated with considerable inter-annual unpredictability in rainfall (in turn partly a consequence of El Niño–associated variability) has been responsible for the evolution of generally low metabolic rates in terrestrial mammals of most of the southern continents. Although El Niño effects are by no means restricted to these regions, it is perhaps low resource availability to start off with, associated with considerable unpredictability, that is of most significance (see also Guernier et al. 2004).
Insect life histories also show hemisphere-related variation. Low-temperature-related diapause is virtually absent in southern species (e.g., Convey 1996), and metabolic rate–temperature relationships are much shallower in the south than the north (Addo-Bediako et al. 2002). The latter is a consequence of relatively cool growing seasons and lack of pronounced seasonality in the south. However, the clearest example of a hemispheric asymmetry is that of cold hardiness strategies (Sinclair et al. 2003). Insects can survive sub-zero temperatures either by tolerating internal ice formation or by reducing their freezing points to avoid ice formation altogether. Although there is further variation within each of these strategies, in general, freeze-avoiding species need to undergo substantial preparation for winter cold and consequently can take some time to emerge from the cold hardy state. This also seems to be true of strongly freeze-tolerant species that can survive freezing far below the point at which they actually freeze. By contrast, moderately freeze-tolerant species—those that can survive only a few degrees of freezing—appear to need little preparation for a freezing event and seem perfectly prepared to continue with their routine activities immediately after thawing. In northern cold climate areas, with the exception of the Arctic, where extremely low temperatures constrain insects to being strongly freeze tolerant, most cold hardy species avoid freezing, whereas in the south most are moderately freeze tolerant (Figure 2). Microclimates reveal why this is the case. As might be expected from macroclimatic variation, southern temperate insects are faced with regular freeze–thaw cycles (i.e., variation about 0 °C), including pronounced summer cold snaps, whereas the continental climates of many areas in the north mean that once temperatures decline below freezing for winter, they stay below this threshold.
Figure 2 Latitudinal Variation in Cold Tolerance Strategies in Insects
The proportion of insects, as a function of latitude, that are moderately freeze tolerant down to relatively high sub-zero temperatures (moderate FT), that are freeze tolerant down to low sub-zero temperatures (strong FT), that are freeze tolerant but that cannot be classified (other FT), and that are freeze avoiding.
North–south asymmetries also show up in snowlines, treelines, the frost tolerance of trees, and the proportion of winter deciduous species (Woodward 1987; Körner 1998; Körner and Paulsen 2004). Indeed, such differences have long been appreciated for vegetation. In marine systems, one of the best-known asymmetries is the low upper thermal limit to performance and survival in Antarctic compared with Arctic ectotherms. This difference in limits to survival and performance is characteristic of fish, invertebrates, and macroalgae (e.g., Wiencke et al. 1994) (Figure 3). Asymmetries are also apparent in the geographic ranges of a wide variety of animals and plants. Rapoport's rule proposes that species ranges will be larger at high than at low latitudes (Stevens 1989). The pattern is thought to be a consequence of considerably greater temporal climatic (and especially temperature) variation at high latitudes, and the resulting need for broader physiological tolerances of individuals. These broad tolerances enable the species to which these individuals belong to occur across a wider range of sites than species at lower latitudes. However, if there is much less temporal temperature variation in the south than in the north, evidence for the rule should be less forthcoming in the southern hemisphere. This is indeed the case. Consistent increases in latitudinal extents with latitude are uncommon in the south, and Rapoport's rule is now largely considered to be a northern phenomenon (Gaston et al. 1998).
Figure 3 Variation in Upper Survival Temperatures of Macroalgae from across the Planet
Mean and standard error of upper survival temperatures of macroalgae (open symbols, macrothalli; closed symbols, microthalli) from cold areas across the planet. Ant., Antarctic; Arct., Arctic; CT, cool temperate; End., endemic; N, northern hemisphere only; S, southern hemisphere only; N+S, occurrence in both hemispheres. Redrawn from Wiencke et al. (1994).
Large-Scale Asymmetries in Biodiversity
In the years since Platnick (1992) suggested that the world is pear-shaped from a biodiversity perspective, with more rapid declines in richness from the equator in the northern than in the southern hemisphere, evidence that there are large-scale asymmetries in the latitudinal diversity gradient has been accumulating. Seed plant and mammalian family richness per unit area declines more steeply in the northern hemisphere than in the south (Woodward 1987; Gaston et al. 1995), and similar asymmetries, mostly at the species level, have been noted for other groups such as New World birds, several groups of insects, spiders, foraminiferans, and a variety of benthic marine taxa (Platnick 1992; Rex et al. 1993; Eggleton 1994; Blackburn and Gaston 1996; Culver and Buzas 2000; Rodriguero and Gorla 2004). Nonetheless, not all groups show these trends, and a recent meta-analysis, albeit one on a relatively coarse scale, failed to find consistent north–south differences in latitudinal gradients (Hillebrand 2004). Recent reviews, particularly of marine diversity, have pointed out the difficulty of making comparisons of this kind owing to sampling constraints (Clarke and Johnston 2003). However, it remains remarkable that even simple exercises—such as plotting richness values for different latitudes or latitudinal bands against each other for the hemispheres and examining the resulting relationship, or overlaying them on the same range of latitudes—rarely appear in the literature. Thus, it is not yet clear how common or strong hemisphere-related asymmetry is.
By contrast, it appears that proximate ecological correlates of diversity gradients differ considerably between north and south. Although both historical and ecological factors have led to variation in the numbers and identity of species across the globe (Ricklefs 2004), climate, and particularly energy and water availability, is a strong predictor of broad-scale patterns in species richness for both plants and animals. However, the extent to which energy and water availability constrain species richness varies. In a recent comparative analysis, Hawkins et al. (2003) showed that water availability is the key limiting component of richness for the southern hemisphere, but for temperate regions of the north, energy availability is more important (Figure 4). They ascribe this difference to the warmer and less thermally variable conditions of the southern hemisphere, which, as we have already noted, have considerable effects on species life histories.
Figure 4 Latitudinal Variation in the Energy–Water Correlates for Species Richness
Latitudinal distribution of energy–water correlates for species richness in which spatial variation in pure energy variables (closed bars), typically measured as temperature or potential evapotranspiration, or spatial variation in pure water availability variables (open bars), typically measured as rainfall or precipitation, best explains richness variation through space. Redrawn from Hawkins et al. (2003).
Of course, biodiversity is not just species richness, but also encompasses the ecological complexes of which species are a part. Although potential north–south asymmetries in interactions have not been widely explored, recent work is providing tantalising glimpses of such variation. Thus, it appears that on the basis of a straightforward (not phylogenetically corrected) comparative analysis, specialisation in plant–pollinator relationships is much greater in the south than in the north. European and North American orchids are typically visited by five species of insects, whereas in southern Africa the median is a single pollinator species per species of orchid (Johnson and Steiner 2003) (Figure 5). Insect–plant interactions might also vary in other ways between the hemispheres, as the rarity of showy autumn colours and the paucity of aphid species—which are thought by some to be a driver of these displays (Archetti and Brown 2004)—in south temperate areas suggests. Asymmetries in patterns of human disease point to similar hemisphere-related variation in interactions between organisms (Guernier et al. 2004).
Figure 5 Number of Insect Species Pollinating Orchid Species in the Northern and Southern Hemispheres
Europe and North America, closed bars, n = 41; southern Africa, open bars, n = 73. Redrawn from Johnson and Steiner (2003).
A World in Flux
Despite considerable spatial complexity, there do seem to be regular north–south differences in species life histories and patterns of range size variation that are consistent with disparities in the climates of the two hemispheres (Figure 6). These differences extend to the proximate ecological mechanisms underlying spatial variation in species richness, and, in some cases, apparently to ecological interactions. However, what is less clear is the regularity and strength of north–south differences in spatial diversity patterns, and especially the latitudinal gradient in diversity, as well as the ways in which abiotic variation between the hemispheres might extend through the genealogical and ecological hierarchies to effect such differences. Indeed, if the extent to which abiotic differences between the hemispheres influence biodiversity patterns is to be better comprehended, several key issues deserve attention.
Figure 6 Biological Diversity in the Northern and Southern Hemispheres
Regular differences between the northern and southern hemispheres in patterns of diversity show up in various groups such as the birds (A) (Adelie Penguin, Pygoscelis adeliae) and seed plant families (B) (King Protea, Protea cynaroides). North–south differences in life histories are also apparent in a diverse array of groups ranging from seaweeds (C) (Bull Kelp, Durvillaea antarctica) and insects (D) (the sub-Antarctic, flightless tineid moth Pringleophaga marioni) to birds (E) (Cape Sugarbird, Promerops cafer) and mammals (F) (Sloggett's Rat, Otomoys sloggetti, from the high Drakensberg in South Africa). (Photos: [A, C, and F] Brent J. Sinclair; [B and D] Steven L. Chown; [E] Mhairi L. McFarlane)
First, both phylogenetically independent and non-independent comparisons of life history traits and physiological variables across a variety of groups are required. Contrasting these approaches will provide considerable insight into how much of the signal is based on phylogenetic patterns, and how much on current ecological responses. Whilst in some cases data may be obtained from the literature, it is likely that new work will have to be undertaken, especially in the southern hemisphere, where the number of past investigations of such traits is generally much lower than in the north. Moreover, replicated studies using similar methods might substantially improve the signal-to-noise ratio, which can be weakened in “macrophysiological” or large-scale life history and physiological comparisons by the fact that different methods often lead to different outcomes.
Second, there is much to be said for the application of similar methods to investigations of large-scale, hemisphere-related patterns of species interactions. Differences like those in plant–pollinator systems discussed here might extend to other interactions in marine and terrestrial systems. Contrasting phylogenetically independent and non-independent comparisons are likely to provide much insight into the reasons for those asymmetries that are found.
Finally, comparisons of latitudinal gradients and their underlying correlates in the two hemispheres for the same taxon, sampled using similar methods, and investigated with methods that take cognisance of likely confounding effects are required. This approach will provide a means of determining whether asymmetries in the climates of the two hemispheres really do translate into differences in biodiversity patterns. Such an approach goes to the heart of the question of the processes underlying the latitudinal gradient in species richness, and could go a considerable way to teasing apart the importance of historical, ecological, and null explanations, and identifying the mechanisms that underlie them.
In our view, clarifying these issues is of considerable importance. What is at stake is not a set of arcane ecological questions, but rather questions that are central to determining whether ecological and conservation lessons learnt in one area can be applied more broadly. For example, it has been suggested that climate change will cause substantial extinctions in the near future (Thomas et al. 2004). Indeed, responses by species to such change, via phenological shifts and northward movement of species range margins, are well documented for northern hemisphere species (Parmesan and Yohe 2003). However, if there are substantial differences in abiotic environments such that patterns in diversity and their responses to change differ between hemispheres, then such shifts may not be of similar consequence in the south. To date, southern hemisphere studies represent less than 1% of the total in this field (Root et al. 2003), suggesting that it is not at all clear how the considerable biodiversity in the south will respond to future change. We find such a situation extraordinary. Thus, whilst penguins might at first appear counter, spare, and strange, they serve as a reminder that differences between the north and south might not be so much strange, as remarkable and worthy of closer attention.
We thank Sue Jackson, Melodie McGeoch, and three anonymous reviewers for comments on the manuscript. SLC is supported by the Department of Science and Technology Centre of Excellence for Invasion Biology, HPL by a Norway–South Africa Bilateral Grant, BJS by a National Geographic Committee for Research and Exploration grant, and KJG by Natural Environment Research Council grant NER/O/S/2001/01257.
Steven L. Chown has a joint position in the Centre for Invasion Biology and Department of Botany and Zoology, University of Stellenbosch, Stellenbosch, South Africa. Brent J. Sinclair is a post-doctoral associate funded by the Foundation for Research Science and Technology in the Department of Botany and Zoology, University of Stellenbosch, Stellenbosch, South Africa. Hans P. Leinaas is in the Program for Experimental Behavioural and Population Ecological Research, Department of Biology, University of Oslo, Oslo, Norway. Kevin J. Gaston is professor of biodiversity and conservation in the Department of Animal and Plant Sciences, University of Sheffield, United Kingdom.
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| 15547645 | PMC526784 | CC BY | 2021-01-05 08:21:17 | no | PLoS Biol. 2004 Nov 16; 2(11):e406 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020406 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-341548814010.1186/1479-5876-2-34ResearchMelanoma-restricted genes Wang Ena [email protected] Monica C [email protected] Katia [email protected] Susanna [email protected] Nan [email protected] Phil R [email protected] Barbara [email protected] Paola [email protected] Ralph S [email protected] Francesco M [email protected] Immunogenetics Section, Department of Transfusion Medicine, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892, USA2 Department of Oncology and Surgical Sciences, Oncology Section, University of Padova, Padova, Italy3 Cancer Prevention Studies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA4 Institute of Medical Immunology, Martin Luther University Halle-Wittenberg, 06112 Halle, Germany5 Department of Gynecologic Oncology, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA2004 15 10 2004 2 34 34 16 9 2004 15 10 2004 Copyright © 2004 Wang et al; licensee BioMed Central Ltd.2004Wang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Human metastatic cutaneous melanoma has gained a well deserved reputation for its immune responsiveness. The reason(s) remain(s) unknown. We attempted previously to characterize several variables that may affect the relationship between tumor and host immune cells but, taken one at the time, none yielded a convincing explanation. With explorative purposes, high-throughput technology was applied here to portray transcriptional characteristics unique to metastatic cutaneous melanoma that may or may not be relevant to its immunogenic potential. Several functional signatures could be identified descriptive of immune or other biological functions. In addition, the transcriptional profile of metastatic melanoma was compared with that of primary renal cell cancers (RCC) identifying several genes co-coordinately expressed by the two tumor types. Since RCC is another immune responsive tumor, commonalities between RCC and melanoma may help untangle the enigma of their potential immune responsiveness. This purely descriptive study provides, therefore, a map for the investigation of metastatic melanoma in future clinical trials and at the same time may invite consideration of novel therapeutic targets.
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Background
Human metastatic cutaneous melanoma relative to other common solid tumors shares with renal cell cancer (RCC) the well deserved reputation of being responsive to immune manipulation [1,2]. However, the reason(s) for this phenomenon remain(s) largely unknown [3]. Possibly, metastatic cutaneous melanoma is endowed compared to other tumors with a wealth of "tumor rejection" antigens of unique immunogenic potential. Indeed, the ease in which tumor infiltrating lymphocytes recognizing autologous tumor cells can be isolated from melanoma metastases suggests an extraordinary ability of melanoma cells to elicit cognitive T cell responses [4]. In addition, the broad repertoire of melanoma-associated antigens so far discovered largely outnumbers that of other tumors suggesting a stronger immunogenicity of this cancer [5-7]. This explanation, however, contrasts with the paucity of RCC-specific antigens described and the relative difficulty of expanding tumor infiltrating lymphocytes from RCC that can recognize autologous cancer cells. Yet, RCC is somehow also responsive to immune therapy [2,8]. suggesting that explanations other than solely the identity of T cell epitopes should be considered.
We have previously shown that the microenvironment of a subgroup of melanoma metastases expresses at the transcriptional level an array of biologically active factors that may influence both the innate and the adaptive arm of the immune system [9]. We have also observed that subcutaneous melanoma metastases likely to respond to immunotherapy have a different genetic profile than those unlikely to respond to therapy [10]. This genetic profile differs particularly in expression of immunologically relevant genes suggesting that melanoma metastases that respond to therapy are conditioned to respond even before therapy by an immunologically active environment. These pilot studies encouraged us to collect a large series of melanoma metastases and analyze their genetic profile to search for molecular signatures specific for this tumor entity compared with other less immunogenic cancers. The lack of clinical information limited this study to a descriptive analysis of the molecular signatures characteristic of melanoma that could serve as a map for future studies on this subject. In addition, the application of high-throughput technology to identify transcriptional characteristics unique to metastatic cutaneous melanoma may define novel targets which can be employed for further analysis. . Several signatures were identified descriptive of immune or other biological functions that might be relevant to immune responsiveness. Furthermore, a comparison of the transcriptional profile of metastatic melanoma with that of a library of available primary RCC identified several genes co-coordinately expressed by the two tumor types. Since RCC represents another immune responsive human tumor it is possible that commonalities with melanoma may reveal, in the future, the secret of immune responsiveness. This purely descriptive study provides, therefore, a map of markers for the investigation of metastatic melanoma in novel clinical trials and may invite consideration of novel therapeutic targets.
Results and Discussion
Differences between the transcriptional profile of melanoma metastases and other solid tumors
We first identified genes differentially expressed between 69 melanoma samples and 87 samples obtained from available primary or metastatic solid tumors (Table I). RCC samples were excluded from the statistical comparison because this tumors share immune responsiveness with metastatic melanoma and, therefore, were considered separately from non-immunogenic tumors. Differential expression was defined significant at a p2-value ≤ 0.001 (unpaired two-tailed Student t test). This test identified 4,658 cDNA clones differentially expressed between melanoma metastases and tumors of other histology (see Additional file 1). Non parametric Wilcoxon test yielded comparable results in terms of number and identity of differentially expressed genes (data not shown). Permutation analysis strongly supported the significance of these findings. Approximately half of the differentially expressed clones (2,044) were up-regulated in melanoma metastases relative to other tumors and the remaining 2,614 clones were down-regulated. Up-regulation was defined as a positive value after subtracting the average ratio of other tumors from that of melanoma lesions (Figure 1). Down-regulation was considered a negative value resulting from the same formula. A large proportion of the genes down-regulated in melanoma relative to other tumor were lineage specific and reflected its unique ontogeny from the neuroectoderm while the tumors studied were mostly of epithelial origin. We have previously described the weight that ontogeny may play in balancing the transcriptional profile of RCC [11]. Unfortunately, for this type of analysis to be conclusive availability of matched normal tissues is required which is not as readily achievable in the case of melanoma due to the scattered distribution of normal epithelial melanocytes within the skin layers. The complete list of the 4,658 genes differentially expressed by melanomas is available at
Table 1 Samples used for the analysis presented in the same ordered displayed in the supervised analyses.
Histology Location # of Specimens Source
RCC Primary 14 Mainz University, Germany
Melanoma Primary 1 Padua University, Italy
Melanoma In Transit Metastases 3 Padua University, Italy
Melanoma Cutaneous Metastases 7 Padua University, Italy
Melanoma Lymph Node Metastasis 35 Padua University, Italy
Melanoma Visceral Metastases 2 Padua University, Italy
Melanoma Cutaneous Metastases (FNA) 21 NCI, NIH, Bethesda, USA
EOC Primary 15 MD Anderson CC, Houston, TX, USA
Soft Tissue Sarcoma Primary 3 Tissue Network, Philadelphia, PA, USA
Endometrial Cancer Primary 1 Tissue Network, Philadelphia, PA, USA
Laryngeal Cancer Primary 1 Tissue Network, Philadelphia, PA, USA
Breast Cancer Primary 2 Tissue Network, Philadelphia, PA, USA
Colon Adeno-Carcinoma Primary 1 Tissue Network, Philadelphia, PA, USA
Esophageal Carcinoma Primary 12 NCI, NIH, Bethesda, USA,
Colorectal Carcinoma Primary 35 University of Pisa, Italy
Colorectal Carcinoma Lymph Node Metastasis 16 University of Pisa, Italy
Colorectal Carcinoma Hepatic Metastasis 1 University of Pisa, Italy
Total Specimens 180
RCC = Renal Cell Carcinoma; FNA = Fine Needle Aspirates; EOC = Epithelial Ovarian Cancer;
Figure 1 Eisen's clustering based on 2,044 genes up-regulated in metastatic melanoma lesions compared with all other tumors. Signatures include growth regulation (maroon vertical bar); a signature of genes similarly expressed by melanoma and RCC (blue vertical bar) including a sub-cluster of genes predominantly expressed by RCC (double vertical blue bar and blue arrow); an immunological signature (orange vertical bar); a signature specific for genes predominantly expressed by cutaneous and subcutaneous melanoma metastases (green vertical bar); gene related to blood contamination in fine needle aspirates (FNA; red arrow) and a signature specific for melanoma differentiation antigens (MDA; blue arrow). Genes were identified by a two-tailed Student's t test comparing all melanoma lesions with other tumors (with the exception of RCC) applying as cut off of significance a p2-value < 0.001. Up-regulation was defined as a positive value after subtracting the average of other tumor samples Cy5/Cy3 ratios from that of melanoma samples.
Signature-specific genes
Several signatures representing genes preferentially expressed by melanomas were identified that could be partially linked to specific gene functions. Those signatures were segregated according to unsupervised gene rearrangement based on the Eisen's clustering method. The first cluster (cluster a, Figure 1) included 76 clones of which 63 were named corresponding to 53 genes. A subset of genes in this cluster were commonly up-regulated in melanoma and RCC including enolase 2 (neuronal γ-enolase) which is a previously described serum marker of RCC also associated with renal carcinogenesis [11-13]. Differential expression of enolase-2 between melanoma and other cancers with the exception of RCC reached a significance of 5 × 10-7 and 1 × 10-6 for two clones representing this gene (Student's t-test p2 value). Overall, this cluster was enriched of genes associated with active cellular metabolism and included only few genes of previous known relevance to melanoma with the exception of a member of the melanoma antigen family D (MAGED2, Table 2 and Figure 2). Cluster b included 91 clones (72 named representing 65 distinct genes) predominantly associated with growth regulation and apoptosis. Among the genes included in this cluster was BNIP3L (BCL2/Adenovirus E1B interacting protein like-3, t-test p2-value = 2 × 10-7 for both cDNA clones representing this gene) that we have previously reported to be associated with the immune responsiveness of melanoma metastases [10]. Two large and related clusters (cluster c and d) included 262 and 613 clones, respectively (112 and 296 named corresponding respectively to 110 and 289 genes). These clusters were characterized by a high density of unnamed clones and by relatively low Cy5/Cy3 ratios. However, it should be noted that these clusters may be of particular interest because the gene expression profile was similar between melanoma and RCC tumors suggesting that some of these genes may conceal the enigma of immune responsiveness. In particular, a relatively sizable sub-cluster was noted with genes predominantly up-regulated in RCC but also expressed by melanoma lesions compared with other tumors (Blue arrow and double vertical bar, Figure 1). Genes concomitantly up-regulated in melanoma and RCC will be separately discussed later, however, it is important to note that this cluster included JAK-1 (t-test p2-value = 2 × 10-5) that was previously also reported in association with melanoma immune responsiveness to interleukin-2-based immunotherapy [10]. JAK-1 was recently linked to the apoptotic role that interleukin-24 (melanoma differentiation associated gene-7: MDA 7) may exert on melanoma cells [14]. The following cluster (cluster e) included 208 clones (151 named representing 143 different genes) predominantly associated with immune function. This immune signature was underlined by the high prevalence of expression of these genes in samples obtained from lymph node metastases whether from melanoma or colorectal primaries. Cluster f integrated 129 clones (91 named representing 87 distinct genes) including a mixture of genes with disparate functions difficult to categorize into a predominant pattern. This group also included APPBP1 (amyloid β precursor protein; t-test p2-value = 1 × 10-4) which was previously reported in association with melanoma immune responsiveness [10]. APPBP1 is a recently discovered epidermal growth factor that regulates dendrite motility and melanin release in epidermal melanocytes and melanoma cells [15]. It is possible that some of its functions may have an indirect effect in modulating the immunological profile of subcutaneous metastases. Cluster g included genes preferentially up-regulated in subcutaneous melanoma metastases known to be more responsive to immunotherapy with interleukin-2 [16]. This cluster included 201 clones (142 named representing 132 genes). Among the genes representative of this cluster were two classic melanoma associated genes (PRAME and tyrosine-related protein-1; TRP-1). In a small proportion, this cluster included a group of genes only over-expressed in fine needle aspirates (FNA) and generally expressed by circulating cells revealing blood contamination of FNA material (red arrow, Figure 1). Cluster h included 131 clones (102 named representing 99 genes). Cluster I included 222 clones (171 named identifying 155 genes) with most of the melanoma differentiation antigens (MDA) clustering in close proximity with the exception of the TRP-1 already discussed in cluster g. Interestingly, this cluster was also highly enriched of genes associated with ribosomal function and active translation. Furthermore, it included the melanocyte master regulator MITF (t-test p2-value for two respective cDNA clones = 2 × 10-15 and 8 × 10-14) which has been shown to modulate lineage survival and melanoma cell viability through interaction with the anti-apoptotic protein BCL2 [17]. MITF was coordinately expressed with several genes associated with calcium and other solute metabolism including cytochrome p450 (t-test p2-value for two respective cDNA clones = 3 × 10-12 and 7 × 10-13) solute carrier family 7 (t-test p2-value for two respective cDNA clones = 2 × 10-10 and 2 × 10-6), G protein coupled receptor 56 (t-test p2-value = 2 × 10-7) and calpain 3 (t-test p2-value = 6 × 10-15), a calcium-regulated gene found to be highly expressed in melanoma cells [18]. Finally, cluster J included 88 clones of which the 62 named identified 58 genes.
Table 2 Genes of known association with melanoma
Clone ID Chromosomal Location Name AVERAGE t-test (p2-value)
RCC MEL Other RCC vs MEL MEL vs Other
Cluster a
2569910 Xp11.2 MAGED2 -0.22 0.4 -0.28 7.10E-03 8.00E-07
316397 Xp11.2 MAGED2 -0.24 0.41 -0.29 5.60E-04 3.00E-07
P24478 Xp11.2 MAGED2 -0.21 0.31 -0.22 5.10E-03 1.00E-06
Cluster d
1735474 Xq26 MAGEC1 0.03 0.25 -0.26 3.40E-02 6.80E-06
131595 Xq28 MAGEA10 -0.03 0.28 -0.24 6.20E-02 1.80E-04
1505360 Xq28 MAGEA2 -0.88 0.94 -0.76 6.00E-12 2.00E-10
Cluster e
781233 2p23.3 POMC -0.03 0.17 -0.15 2.20E-01 1.40E-05
Cluster g
897956 22q11.22 PRAME -0.62 1.33 -1.05 1.60E-06 1.00E-18
853789 9p23 TYRP1 -0.82 0.59 -0.3 7.70E-06 8.00E-04
768344 9p23 TYRP1 -0.7 0.8 -0.61 1.60E-07 1.80E-06
40056 15q23 CSPG4 -0.32 0.69 -0.53 2.20E-04 2.70E-09
P07338 n.a. CSPG4 -0.63 0.78 -0.61 8.50E-05 5.70E-09
2447688 11q23.3 MCAM 0.21 0.69 -0.6 8.70E-02 3.40E-09
1585510 3q28-q29 MFI2 (p97) -0.48 0.59 -0.44 1.10E-03 5.70E-07
Cluster i
P30563 n.a. CD63 -0.66 0.7 -0.44 8.20E-08 1.90E-09
1631546 Xq28 MAGEA6 -0.55 0.35 -0.22 1.30E-08 5.60E-04
291448 12q13-q1 SILV (gp100) -1.31 1.55 -1.16 4.90E-16 5.00E-15
271985 11q14-q2 || TYR Tyrosinase -1.37 1.73 -1.38 6.30E-18 2.90E-18
272327 9p24.1 Melan-A -0.76 1.19 -0.95 2.40E-08 1.80E-16
269124 9p24.1 Melan-A -0.65 1.21 -0.99 5.30E-09 2.70E-16
Figure 2 Eisen's clustering of genes already reported to be preferentially expressed by melanomas. The analysis was performed on 180 cancer samples as described in the Results section and ordered according to Table 1. In particular, renal cell cancer (RCC, orange), melanoma (blue), Epithelial Ovarian Cancer (EOC, yellow), Esophageal Cancer (green), Primary Colorectal Cancer (CRC, dark brown) and lymph nodal metastases of CRC (light brown) are shown. Melanoma samples are further subdivided in cutaneous metastases (CM, light blue) from frozen sections (continuous line) or fine needle aspirates (FNA, dashed line) and lymph nodal metastases (LN, darker blue line). Below is the distance among the various genes based on Eisen's clustering.
Genes previously recognized to be associated with melanoma
Genes previously described to be preferentially expressed by melanoma lesions were confirmed to be so at a very high level of significance (Figure 2). Exceptions included AIM-1, CXCL-1 (GRO-α), D2S448 and MAGEA1 which are all significantly more expressed by tumors other than melanomas. Interestingly, different types of melanoma associated genes displayed a different pattern of expression with MDA (tyrosinase, gp100/PMel17 and MART-1/MelanA) being co-coordinately expressed in close proximity to each other in cluster I and MAGE family genes preferentially expressed in cluster d (Table II). Cluster g included a number of genes whose expression had been previously associated with melanoma including preferentially expressed antigen in melanoma (PRAME) and the tyrosine-related protein-1 (TRP-1). When the melanoma associated genes were studied alone, PRAME clustered close to the other MDA believe to be involved in the pigmentation process (tyrosinase, MART-1/Melan and gp100/PMel17). This is of particular interest because PRAME has been also reported to be highly expressed in other cancers of ectodermic origin such as medulloblastoma and neuroblastoma suggesting a link between ectoderm and pigmentation [19,20]. The coordinated expression of MDA suggests that their down-regulation or loss of expression during melanoma progression may be related to a central regulatory pathway not as yet identified. Indeed in previous studies [21-25], we noted that loss of expression of MART-1/Melan A paralleled that of gp100/PMel17 (SILV) in melanoma metastases while genes of the MAGE family manifested an independent behavior [25]. This finding may have important repercussions in the design of antigen-specific immunization protocols and at the same time may complicate the interpretation of tumor antigen loss variant analysis by broadening loss of expression to antigens other than those targeted by a given therapy.
Immunological Signature
The large majority of genes associated with immune function were included in cluster e. These genes appeared up-regulated in lymph node metastases of melanoma as well as those from colorectal primaries suggesting that their expression results from lymphoid cell infiltration (Figure 3). The same genes were up-regulated in a significant proportion of subcutaneous melanoma metastases suggesting that a strong and active infiltrate of immune cells is present in these tissues. In fact, most of the genes included in cluster e were significantly up-regulated in 10 cutaneous/subcutaneous melanoma lesions compared to 70 primary cancers of other histology (Table III shows a selection of the most significantly up-regulated genes in cutaneous/subcutaneous melanomas). Of interest is the observation that several of the genes up-regulated in these lesions are clustered in specific chromosomal locations with a high predominance of genes located in position 6p21.3, 11p11.2, 19p13 and 19q13. Among the immunologically-related genes specifically up-regulated in subcutaneous melanoma metastases, some are of particular interest because of their known relationship with effector T cell function. In particular, we find interesting that NK4, an anti-angiogenic factor released by natural killer cells [26], was constitutively expressed by cutaneous melanomas. We found this gene to be associated with regression of a melanoma metastasis during interleukin-2 therapy [27] and to be one of the genes most frequently up-regulated during activation of antigen-specific T cells in vitro [28,29]. Of interest was also the constitutive expression of CD27 a co-stimulatory member of the TNF receptor family strongly associated with cell activation [30,31]. The expression of CX3CR1 a gene constitutively expressed by natural killer cells that makes them sensitive to chemo-attraction by CXCL12 and CXC3L1 [32] may be an explanation for a preferential localization of these effector cells in melanoma lesions. In particular, this finding suggests that the microenvironment of melanoma metastases is rich of fractalkine (CX3CL1) which is a potent chemo-attractant released by endothelial cells stimulated by interferons [33]. Overall, the presence of these and other (KLRG1; killer cell lectin like receptor subfamily G, member 1 and KLRK1; killer cell lectin like receptor subfamily K, member 1 and the interleukin-21 receptor) natural killer cell-related genes suggests potent chemo-attraction toward natural killer cells by the tumor micro-environment of subcutaneous and cutaneous melanoma metastases. This is also emphasized by the high expression of interleukin-21 receptor which is usually expressed by natural killer cells and stimulates their cytolytic activity upon ligation with interleukin-21 produced by activated T cells [34,35]. The constitutive expression of interferon regulatory factor (IRF)-7 implicated in the amplification of the innate immune response [36] through interactions with the NF-κB pathway [37] may lead to the activation of various types of type I interferons [38]. More puzzling is the constitutive expression of interleukin-16, a pleiotropic cytokine with predominant chemo-attractant activity for CD4+ T cells [39] and CD4+ eosinophils [40]; a relationship between this cytokine and melanoma metastases has never been observed before. In summary, the immunological signature portrayed by subcutaneous melanoma metastases is that of an active innate immune response centered on natural killer cells. More broadly, the preferential expression of genes with immune function in melanoma lesions compared with other tumors suggests that this cancer is constitutively immunologically active and this status may predispose metastatic melanoma to respond to general or antigen-specific immune manipulation.
Figure 3 Eisen's clustering of immunologically relevant genes selected from clusters e and f-j. To the right the identity of the genes included in cluster e is shown.
Table 3 Immune-relevant genes specifically up-regulated by sub-cutaneous melanomas
Clone ID Location Gene AVERAGE t-test (p2-value)
SQ Other SQ vs Oth Me vs Oth
295868 1p34 LAPTM5 0.43 -0.68 2.00E-04 3.00E-04
P37265 1p34.3 LCK 0.55 -0.58 1.00E-04 6.00E-06
2563224 1p36.2 PIK3CD 0.8 -0.88 3.00E-07 5.00E-15
842871 1q12 PDE4DIP 0.58 -0.21 5.00E-04 9.00E-05
773509 1q21.3 SNX27 1.05 -0.88 9.00E-11 7.00E-16
701332 1q22 IFI16 0.25 -0.39 2.00E-04 1.00E-05
472009 1q42.1 DISC1 0.35 -0.26 1.00E-07 9.00E-08
746229 2q11.2-q MAP4K4 0.15 -0.24 7.00E-04 6.00E-05
840466 2q12-q13 MARCO 0.34 -0.28 3.00E-05 2.00E-04
328542 2q24-q3 GALNT3 0.51 -0.4 4.00E-04 1.00E-03
825715 2q37.1 SP110 0.71 -0.59 9.00E-07 3.00E-09
283023 3p21 CX3CR1 0.31 -0.34 2.00E-05 4.00E-10
1605539 4p16.3 IDUA 0.27 -0.29 4.00E-06 6.00E-06
724932 5q35 GRK6 0.43 -0.19 4.00E-05 3.00E-05
753587 6p21.3 BTN3A3 0.49 -0.43 2.00E-05 5.00E-06
753236 6p21.3 TAP2 0.31 -0.42 2.00E-04 1.00E-07
752557 6p21.3 GPSM3 0.42 -0.42 1.00E-04 3.00E-06
2549448 6q21 FYN 0.6 -0.45 1.00E-07 2.00E-07
2306953 8q13.3 LY96 1.01 -0.32 3.00E-06 2.00E-08
645332 10p12 NEBL 0.23 -0.23 6.00E-04 2.00E-04
1631391 11p11.2 BHC80 0.35 -0.29 8.00E-04 4.00E-04
686164 11p11.2 DGKZ 0.44 -0.21 2.00E-04 4.00E-04
487115 11p11.2 PTPRJ 0.78 -0.4 3.00E-09 5.00E-07
151430 11p13 CD44 0.71 -0.22 1.00E-03 2.00E-05
740117 11p15.5 IRF-7 0.53 -0.3 4.00E-05 3.00E-04
P33303 11p15.5 LSP1 0.55 -0.4 1.00E-03 2.00E-05
1850690 11q23.3 BLR1 0.36 -0.36 6.00E-05 2.00E-04
2120815 12p12-p1 KLRG1 0.51 -0.44 1.00E-04 5.00E-04
34637 12p13 CD27 0.72 -0.54 4.00E-04 1.00E-04
1517162 12p13.2- KLRK1 0.55 -0.53 5.00E-04 6.00E-04
1569551 12q13.11 CSAD 0.37 -0.45 2.00E-04 6.00E-06
429186 13q21.33 LMO7 0.41 -0.37 5.00E-06 4.00E-19
P41256 15q26.3 IL-16 0.64 -0.58 4.00E-05 2.00E-06
P14913 16p11 IL-21R1 0.48 -0.43 6.00E-06 6.00E-04
P07382 16p11.2 ITGAL 0.55 -0.48 2.00E-05 3.00E-06
P12753 16p13.3 NK4; 0.32 -0.3 4.00E-05 3.00E-06
206795 17p ASGR2 0.57 -0.53 8.00E-05 6.00E-06
488575 17p11.2 ULK2 0.35 -0.17 3.00E-06 1.00E-04
155717 17q23 CD79B 0.44 -0.41 3.00E-05 2.00E-11
156343 17q24.2 MAP3K3 0.62 -0.46 4.00E-06 7.00E-11
P38436 17q25 CARD14 0.54 -0.33 4.00E-08 2.00E-08
1551273 19p12 MEF2B 0.19 -0.21 7.00E-05 1.00E-09
814377 19p13.1 BRD4 0.9 -0.7 6.00E-06 2.00E-16
2010562 19p13.3 MYO1F 0.55 -0.62 9.00E-04 7.00E-05
824384 19p13-q1 CD37 0.64 -0.74 1.00E-03 7.00E-04
788272 19q13.1 CLC 0.61 -0.64 7.00E-06 3.00E-06
815239 19q13.13 ARHGEF1 0.38 -0.42 3.00E-05 1.00E-04
683276 19q13.33 CARD8 0.86 -0.64 1.00E-08 3.00E-05
277906 19q13.4 LILRB1 0.89 -0.51 4.00E-04 5.00E-04
202897 19q13.4 LILRB2 0.72 -0.61 4.00E-04 1.00E-06
2072768 20q12 NCOA3 0.69 -0.36 4.00E-06 2.00E-05
SQ = Average Cy5/Cy3 ratios of10 frozen samples from cutaneous and subcutaneous metastases as described in Table I. Oth = Sample from 80 primary tumors other than melanoma and RCC with the exclusion, in this table of lymph nodal metastases from colorectal cancer (see Table I). Complete and extended gene name is available at
A second and smaller group of immunologically-related genes was identified and included genes that segregated separately in clusters f to j. These genes had an expression profile opposite to the immune-related genes seen included in cluster e and appeared over-expressed in subcutaneous compared to lymph node metastases. Two granzyme-related genes were found strongly up-regulated in cluster e including granzyme A and M. This observation contrasted with the increased expression of cathepsin F and L in cluster f to j suggesting an opposite regulation of these genes involved in cell death or survival.
Subcutaneous metastases-associated genes
It has been reported that subcutaneous metastases of melanoma are more responsive to immunotherapy with interleukin-2 than lymph nodal and visceral metastases [16]. Therefore, we identified genes differentially expressed in the former compared with the latter. Since no material from visceral metastases was available, we limited the comparison to subcutaneous versus lymph nodal metastases. Overall, clusters g - j appeared to demonstrate a preferential expression of genes in subcutaneous metastases independent of the technique used for biopsy (excision versus FNA). In particular, cluster g contained a small node of 47 clones highly expressed in subcutaneous metastases that included PRAME and TRP-1. This cluster also included the renal tumor antigen RAGE which has been previously shown to be highly expressed by melanomas [41] and melanophilin and the s100 protein often associated with clinical parameters in melanoma [42]. Interestingly, closely linked to PRAME was the pattern of expression of the serine/threonine-specific protein kinase B-RAF. This gene is mutated in approximately 70 % of melanomas and it is often over-expressed [43]. Although several of these genes had been associated with melanoma their co-ordinate expression has never been previously appreciated. Overall, the identity of the genes over-expressed in subcutaneous metastases did not offer an obvious explanation for the increased immune responsiveness of these lesions and more extensive understanding of their relationship will be necessary in the future.
Genes differential expressed by melanoma and RCC samples compared with other solid tumors
We then pooled together melanoma and RCC samples data to identify genes commonly expressed by these tumors and not by tumors of other histology. Significance was assessed by a two-tailed unpaired student's t test and identified 4,221 genes at a cut off p2-value of ≤ 0.001. The data set was then filtered using the Cluster Program (Stanford, CA) selecting genes that were expressed in at least 80 % of the experiments and for which a Cy5/Cy3 log2 ratio ≥ 2 was present in at least one experiment. Two-thousand eight hundred and forty-three genes resulted from this filter. Because of the predominance of melanoma lesions (69 melanoma lesions compared to 14 RCC) the genes identified strongly represent differences between melanomas and other tumors. Therefore, we identified among them those that were not differentially expressed between melanoma and RCC samples to identify those genes that are truly uniquely expressed by the two immune responsive cancers. Two-thousand three hundred and fifty-eight genes were expressed similarly between the two types of cancer (at a t test p2-value > 0.05). A significant number of genes commonly expressed by melanoma and RCC and not by other tumors had no known function (681 genes). The remaining 1, 677 genes were further analyzed by separating those up-regulated from those down-regulated in melanoma and RCC compared with other tumors. The genes up-regulated in RCC and melanoma were considered those with a median LogRatio above 0.3 in either RCC or melanomas (a selection of these genes is shown in Table IV). This analysis selected 199 genes. A proportion of genes appeared to be specifically related to lymph nodal and immune infiltration as they were particularly up-regulated in melanoma metastases to lymph nodes and in lymph node metastases obtained from patients with CRC (orange vertical bar, Figure 4). These genes included annotations related to immunological function. A set of genes was specifically expressed in cutaneous metastases of melanoma and in RCC and not other tumor samples (dark blue vertical bar, Figure 4). These genes included microphtalmia transcription factor MITF [17,44]. that has been shown previously to exert a central role in the regulation of transcriptional activity of melanoma cells. Similarly, enolase-2 (previously known to be up-regulated in RCC) [11] was found to be over-expressed in common between the two histologies. This is somewhat surprising since immunohistochemical analysis has used lack of staining for enolase-2 as a reliable method to differentiate malignant melanoma (enolase-2 negative) from Merkel cell carcinoma [45]. It is possible, that although identifiable at the transcriptional level, enolase-2 is not processed into a protein in melanomas. On the other hand, enolase-2 has been shown to be expressed in approximately 90 % of canine oral melanomas [46]. In addition, the macrophage migration inhibiting factor (MIF) which is a modulator of cell cycle progression and angiogenesis in melanoma [47,48]. was found co-expressed by melanoma and RCC lesions. MIF has modulatory properties on natural killer cell mediated lysis of cancer cells contributing, therefore, to an immune privileged microenvironment in uveal melanoma [49]. Two genes coding for adhesion molecules; L1 cell adhesion molecule (LCAM) and melanoma cell adhesion molecule (MCAM) were also up-regulated in both lesions and may play an important role in mediating migration of immune cells to the tumor deposits [50]. Finally, it is remarkable that serologically defined colon cancer antigen 8 was specifically expressed by melanoma and RCC while was completely absent in colon cancers underlying the need for a better nomenclature of newly identified genes.
Table 4 Selected genes constitutively expressed by RCC and melanoma metastases.
UNIQID NAME Extended Name Median log2Cy5/Cy3 Averagelog2Cy5/Cy3 t-test*
RCC MEL OTH RCC MEL OTH p2-value
274276 IFIT2 interferon-induced protein with tetratricopeptide repeats 2 0.74 0.30 -0.28 0.54 0.18 -0.23 0.06
191173 ITGB7 integrin, beta 7 0.17 0.33 -0.26 0.14 0.30 -0.26 0.51
191169 FLT3LG fms-related tyrosine kinase 3 ligand 0.18 0.32 -0.27 0.31 0.19 -0.20 0.53
187264 CORO1A coronin-like protein p57=actin binding protein p57 0.38 0.13 -0.19 0.47 0.12 -0.17 0.16
189684 SP110 SP110 nuclear body protein 0.22 0.60 -0.56 0.26 0.44 -0.42 0.27
279561 TNFRSF7 CD27 -0.06 0.65 -0.39 -0.09 0.45 -0.35 0.07
279871 CD37 CD37 antigen 0.09 0.56 -0.56 0.33 0.47 -0.38 0.72
276143 TAP2 transporter 2 0.23 0.33 -0.21 0.27 0.31 -0.24 0.86
281103 sialic acid binding Ig-like lectin 7=D-siglec=expressed in dendritic cells 0.33 0.26 -0.34 0.27 0.24 -0.24 0.86
279699 BTK btk = Bruton agammaglobulinemia tyrosine kinase || -0.04 0.45 -0.28 -0.09 0.34 -0.26 0.05
274604 CST7 cystatin F (leukocystatin) || -0.14 0.37 -0.29 -0.07 0.45 -0.34 0.06
281440 ITGB7 CD103 beta=Integrin beta 7 || 0.36 0.39 -0.25 0.31 0.38 -0.35 0.67
191157 KLRG1 killer cell lectin-like receptor subfamily G, member 1 || 0.32 0.40 -0.17 0.35 0.23 -0.25 0.39
274016 RASGRP1 RAS guanyl releasing protein 1 (calcium and DAG-regulated) || 0.48 0.38 -0.29 0.36 0.30 -0.29 0.78
274444 ITGAL integrin, alpha L (antigen CD11A (p180)| 0.34 0.57 -0.43 0.30 0.33 -0.31 0.88
282504 CX3CR1 chemokine (C-X3-C motif) receptor 1 0.84 0.66 -0.53 0.62 0.51 -0.50 0.76
274267 KLRK1 killer cell lectin-like receptor subfamily K, member 1 0.80 0.33 -0.20 0.71 0.34 -0.38 0.14
187290 LILRB1 LIR-7=PIR homologue| 0.11 0.67 -0.15 0.12 0.49 -0.41 0.20
186380 SLC2A3 solute carrier family 2 (facilitated glucose transporter), member 3 0.14 0.34 -0.21 0.34 0.36 -0.34 0.94
188111 CD3Z CD3Z antigen, zeta polypeptide (TiT3 complex) 0.09 0.42 -0.30 -0.01 0.35 -0.27 0.10
186528 SLA SLAP=src-like adapter protein 0.38 0.31 -0.24 0.32 0.33 -0.31 0.98
185279 ASGR2 asialoglycoprotein receptor 2| 0.28 0.61 -0.33 0.17 0.45 -0.40 0.16
187450 LILRB2 leukocyte immunoglobulin-like receptor, subfamily B, member 2 0.10 0.65 -0.45 0.13 0.54 -0.44 0.07
184382 FGR Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homolog| 0.57 0.23 -0.29 0.40 0.27 -0.28 0.49
190623 MYO1F myosin IF| 0.18 0.50 -0.22 0.16 0.46 -0.38 0.24
188800 PILRA paired immunoglobin-like type 2 receptor alpha 0.16 0.56 -0.08 0.07 0.31 -0.27 0.17
188004 CLC Charcot-Leyden crystal protein| 0.08 0.51 -0.33 0.08 0.53 -0.44 0.08
186399 PPP3CC protein phosphatase 3, catalytic subunit, gamma isoform (calcineurin A gamma)| 0.07 0.36 -0.15 0.05 0.21 -0.18 0.17
278997 XLHSRF-1 heat shock regulated 1 -0.12 0.32 -0.09 0.10 0.28 -0.20 0.33
281827 LLT1 lectin-like NK cell receptor 0.46 0.26 -0.33 0.53 0.22 -0.26 0.08
282466 LLT1 lectin-like NK cell receptor 0.22 0.38 -0.23 0.23 0.32 -0.32 0.66
189527 FMNL1 formin-like 1 0.46 0.43 -0.25 0.44 0.24 -0.26 0.32
282550 natural killer cell transcript 4 -0.20 0.41 -0.29 0.00 0.30 -0.24 0.15
282534 B-cell CLL/lymphoma 2 0.05 0.38 -0.38 -0.05 0.33 -0.34 0.05
282477 ICOS inducible T-cell co-stimulator| -0.07 0.37 -0.18 -0.07 0.28 -0.26 0.07
282624 granzyme A granzyme A (granzyme 1, cytotoxic T-lymphocyte-associated serine esterase 3)| -0.10 0.58 -0.33 -0.16 0.43 -0.31 0.06
* Two-tailed un-paired t test between RCC and MEL samples. RCC = Renal cell carcinoma; Mel = melanoma lesions; Oth = all other tumors in the study (see Table I).
Figure 4 Eisen's clustering of genes similarly expressed by RCC and melanoma lesion. To the right the identity of genes most prominently expressed by RCC lesions and cutaneous or subcutaneous melanoma lesions is shown.
This is a descriptive study where genes specifically expressed by melanoma metastases were identified comparing a large collection of samples from patients with metastatic cutaneous melanoma with other primary tumors and lymph nodal metastases. A limitation of the study is the lack of other samples including visceral metastases of melanoma and metastases from tumors of other histology. Nevertheless, we considered useful to compile a list of genes characteristically expressed by subcutaneous and lymph nodal lesions of melanoma for reference purposes and we are willing to provide full information about these genes upon request. In spite of the limitations of this study, few general conclusions could be drawn.
Materials and Methods
Tissue procurement
Fourteen primary renal cell carcinoma (RCC) specimens were collected at the Department of Urology of The Johannes Gutenberg-University, Mainz, Germany; one primary melanoma, three in transit metastases, seven cutaneous metastases, thirty-five lymph nodal metastases and two visceral metastases of cutaneous melanoma were collected at the Department of Surgical Sciences, University of Padua, Italy; twenty-one fine needle aspirates of cutaneous melanoma metastases were obtained at the Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; seventeen primary epithelial ovarian cancer (EOC) specimens were obtained at the Department of Gynecologic Oncology, MD Anderson Cancer Center, TX; three primary sarcoma, one primary endometrial cancer, one primary laryngeal cancer, two primary breast cancers and one primary colon adeno-carcinoma were obtained from the Tissue Network (Philadelphia, PA); twelve primary carcinomas of the esophageal junction were obtained from the NCI (Division of Cancer Treatment and Diagnosis); thirty-five primary, 16 lymph node metastases and one hepatic metastasis from colorectal adeno-carcinomas were obtained from the Department of Pathology of the University of Pisa, Italy. Specimens were collected as the result of routine operative procedures and portions were frozen for subsequent analysis while the remnant tissue was used for pathological confirmation. Tissue procurement followed standard ethical procedure according to institutional policy. A summary of the specimens studied is presented in Table 1 with their order reflecting their distribution in figures where supervised analyses are shown.
RNA preparation, amplification and labeling
Total RNA was extracted from frozen material using Trizol reagent according to manufacturer's instructions (Invitrogen, CA) and amplified into anti-sense RNA (aRNA) as previously described [10,27,51,52]. Although the quantity of starting total RNA was in most cases sufficient for cDNA array hybridization, we have shown repeatedly that the fidelity of aRNA hybridization is at least equal and likely superior to total RNA for transcriptional profiling due to lack of contaminant ribosomal and transfer RNA [51,53]. Therefore, we used aRNA to increase consistency of results particularly when low quality total RNA was documented by Agilent Bioanalyzer 2000 (Agilent Technologies, Palo Alto, CA). After amplification the quality of aRNA was tested with the Agilent Bioanalyzer as previously described [52].
Total RNA from peripheral blood mononuclear cells pooled from six normal donor was extracted and amplified to serve as constant reference as previously described [10,27,51,52]. Test and reference RNA were labeled with Cy5 (red) and Cy3 (green) and co-hybridized to a costum-made17.5 K cDNA micro-array . Micro-arrays were printed at the Immunogenetics Section, DTM, CC, NIH with a configuration of 32 × 24 × 23 and contained 17,500 elements. Clones used for printing included a combination of the Research Genetics RG_HsKG_031901 8 k clone set and 9,000 clones selected from the RG_Hs_seq_ver_070700 40 k clone set. The 17,500 spots included 12,072 uniquely named genes, 875 duplicated genes and about 4,000 expression sequence tags.
Data analysis
All statistical analyses were performed using the log2-based ratios normalizing the medial log2 ratio value across the array equal to zero. Validation and reproducibility were performed using our internal reference concordance system as previously described [54]. Unsupervised clustering was performed according to the Eisen's Pearson correlation method [55] and visualized with Tree-View software (Stanford University, CA). Genomic portraits were depicted according to the central method for display using a normalization factor as suggested by Ross et al. [56]. Details about different tests are discussed in the respective results section. Identification of tumor-specific genes was performed using un-paired 2-tailed Student's t test. The same analyses were performed using un-paired Wilcoxon's non-parametric assessment and provided the same conclusions (not shown). Details of each analysis are presented in the results section.
Supplementary Material
Additional file 1
AVE Ratio = average Log2 CY5/Cy3 ratio between test and reference sample. The t test p2-value refers to a two-tailed unpaired analysis between the samples mentioned below. RCC = renal cell cancer; MEL = melanoma; Other = tumors other than RCC and melanoma.
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| 15488140 | PMC527872 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Oct 15; 2:34 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-34 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-341549150310.1186/1477-7819-2-34Case ReportGastric T-cell lymphoma associated with hemophagocytic syndrome Fukui Rika [email protected] Fumitake [email protected] Takahiro [email protected] Ryuuichi [email protected] Minoru [email protected] Kiyoshi [email protected] Masaaki [email protected] Toshio [email protected] Keisuke [email protected] Yoshiyuki [email protected] Katsuya [email protected] Hidefumi [email protected] Koichi [email protected] First Department of Surgery, Sapporo Medical University School of Medicine, South-1, West-16, Chuo-ku, Sapporo 060-8543, Japan2 First Department of Pathology, Sapporo Medical University School of Medicine, South-1, West-16, Chuo-ku, Sapporo 060-8543, Japan3 Department of Clinical Pathology, Sapporo Medical University School of Medicine, South-1, West-16, Chuo-ku, Sapporo 060-8543, Japan4 Department of Surgery, Shinsapporo Keiaikai Hospital, East-5, Ooyachi higashi, Atsubetsu-ku, Sapporo 004-0041, Japan2004 19 10 2004 2 34 34 1 6 2004 19 10 2004 Copyright © 2004 Fukui et al; licensee BioMed Central Ltd.2004Fukui et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Lymphoma-associated hemophagocytic syndrome (LAHS) occurs in mostly extra nodal non-Hodgkin's lymphoma. LAHS arising from gastrointestinal lymphoma has never been reported. Here we report a case of gastric T-cell lymphoma-associated hemophagocytic syndrome.
Case presentation
A 51-year-old woman presented with pain, redness of breasts, fever and hematemesis. Hematological examination revealed anemia. Gastroscopy revealed small bleeding ulcers in the stomach and the computed tomography scan showed liver tumor. She underwent total gastrectomy for gastrointestinal bleeding and the histopathology revealed gastric T-cell lymphoma. She continued to bleed from the anastomosis and died on the 8th postoperative day. Autopsy revealed it to be a LAHS.
Conclusions
If Hemophagocytic syndrome (HPS) occurs in lymphoma of the gastrointestinal tract, bleeding from the primary lesion might be uncontrollable. Early diagnosis and appropriate treatment are needed for long-term survival.
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Background
Hemophagocytic syndrome (HPS) in adults is characterized by reactive and systemic proliferation of benign histiocytes that phagocytose blood cells [1]. It is often associated with infections, malignant neoplasms, autoimmune diseases and various immunodeficiencies. Lymphoma-associated hemophagocytic syndrome (LAHS) mostly occurs from extra nodal lymphoma and is known to have a poor prognosis. Here we report a case of LAHS arising from gastric lymphoma with a fulminant clinical course and difficult diagnosis until the time of autopsy.
Case presentation
A 51-year-old female was admitted on May 9, 1995, because of severe hematemesis. The patient had been treated elsewhere for one month for pain and redness of both breasts and fever (≥ 38°C). There was no generalized lymphadenopathy. On gastroscopic examination multiple small ulcers were observed in the stomach. An abdominal computed tomographic (CT) scan showed liver tumor and a normal spleen. Hematological and biochemical examination at admission showed the following results: RBC 352 × 104/mm3, hemoglobin 10.3 g/dl (post transfusion), WBC 4,900/mm3, Platelets 51,000/mm3, serum albumin 1.5 g/dl, total bilirubin 0.6 mg/dl, AST 691 IU/l, ALT 187 IU/l, LDH 2976 IU/l, fibrinogen 134 mg/dl, FDP 10 μg/ml, and AT-III 40%. Bleeding from the stomach continued and did not stop with conservative treatment; therefore, two days later the patient underwent total gastrectomy and a partial liver resection. Histopathology of the resected specimen showed it to be a gastric lymphoma (pleomorphic medium-large cell type, non-Hodgkin's T-cell lymphoma) with liver metastasis (Fig. 1). From first postoperative day (POD), bleeding from the esophagojejunostomy continued; the patient developed disseminated intravascular coagulopathy and died on 8th postoperative day.
Figure 1 Photomicrograph showing medium-large sized atypical lymphoid cells with pleomorphic features in the stomach suggesting a gastric lymphoma (Hematoxylin and Eosin, ×170).
On autopsy, malignant lymphoid cell infiltration and hemophagocytosis were observed in the liver, spleen, heart, small bowel, lung, both breasts, kidney, pancreas, uterus, and gastroduodenal lymph nodes (Fig. 2). The bone marrow presented hyperplasia and hemophagocytic macrophages but no infiltration by lymphoma cells. Immunohistochemically the neoplastic cells were positive for T-cell marker UCHL1 (CD45RO) and EBV by EBER in situ hybridization. The final diagnosis was EBV-related T-cell LAHS.
Figure 2 Photomicrograph of the lymph node at autopsy illustrating histiocytes that show hemophagocytosis of normoblast in a lymph node (Hematoxylin and Eosin, ×200).
Discussion
HPS is a clinicopathological entity characterized by systemic proliferation of benign hemophagocytic histiocytes, fever, cytopenia, liver dysfunction, hepatosplenomegaly, and coagulopathy [1]. This syndrome has been observed during the clinical course of a wide variety of disorders, including viral infections and malignant neoplasms. Diagnostic guidelines of Henter et al, [2] are widely used for the diagnosis of HPS. However, these guidelines are not satisfactory in diagnosing HPS in adults; therefore, a number of studies on adult HPS have used their own criteria [1,3,4]. On the other hand for the diagnosis of LAHS, in addition to the clinical features, it is also important to confirm the presence of malignant lymphoid cells histopathologically. Takahashi et al, [5] has proposed a set of new diagnostic criteria for adult LAHS that has been detailed in Table 1.
Table 1 Diagnostic criteria for adult lymphoma associates hemophagocytic syndrome (LAHS)
1 High fever for more than a week (peak 38.5°C)
2 Anemia (Hb < 9 g/dl) or thrombocytopenia (platelet < 100,000 μ/l)
3 a) LDH ≥ 2 × upper limit
b) Hyperferritinemia (≥ 1,000 ng/dl)
c) Hepatosplenomegaly on CT, US or MRI
d) FDP ≥ 10 μg/ml
4 Hemophagocytosis in bone marrow, spleen or liver
5 No evidence of infection
6 Histopathologically confirmed malignant lymphoma
A diagnosis of LAHS requires that all of the above conditions are fulfilled.
Of the item 3, at least two of the four sub-items (a~d) should be fulfilled.
When item 1 to item 5 are present for 2 weeks and glucocorticoid or γ-globulin therapy is not effective, a diagnosis of probable LAHS can be made and chemotherapy against malignant lymphoma can be started.
In Japan, T-cell LAHS accounts for 48.5% of all adult LAHS [5]. T-cell LAHS mostly occurs in extra nodal, especially nasal, cutaneous, or malignant lymphoma involving liver and spleen. There have been no reports on LAHS from gastric lymphoma. As the diagnosis in the present case was made at autopsy it is not clear as to when the HPS occurred initially. One possibility is the setting of disseminated T-cell lymphoma. This is supported by the patient's fever, which continued for one month, liver dysfunction, and coagulopathy, which existed from the initial stage of the disease, however the bone marrow did not show any lymphoma infiltration. It could also be considered that the hemophagocytic syndrome occurred as a result of the surgery as pancytopenia and hepatosplenomegaly were not observed before the operation and hemophagocytosis was not recognized on histopathological examination in the resected stomach. In T-cell lymphoma, the hemophagocytic syndrome is assumed to be caused by cytokines, especially, tumor necrosis factor-α, and interferon-γ released from neoplastic T-cells [4,6]. Uncontrolled secretion of cytokines may stimulate the proliferation and phagocytic activity of macrophages. It seems likely that hypercytokinemia due to surgical resection might have contribute to the development of HPS in the present case. In our opinion the former is more likely however based on the findings of this case the second hypothesis too cannot be rejected.
The poor prognosis of LAHS, especially T-LAHS, is well known. The median survival time from the diagnosis is reported to be 143 and 69 days respectively in Japan [5]. For LAHS prompt initiation of treatment with multi agent chemotherapy is required to improve the symptoms and survival [7]. Bone marrow transplantation is considered to be a treatment for chemotherapy-resistant LAHS [8]. The median survival time of LAHS patients without chemotherapy is only 11 days [5].
In this case, the initial presentation was mastalgia and hence it took a considerable amount of time to reach a diagnosis. Furthermore, bleeding from the anastomosis continued leading to a rapidly progressive fatal clinical course.
In HPS occurring in lymphoma of the gastrointestinal tract uncontrollable bleeding from the primary lesion might occur. Therefore, an earlier diagnosis of HPS should be made by bone marrow aspirates, and appropriate treatments should be started as soon as possible. Surgery if performed, must be performed with utmost caution.
Conclusions
LAHS could also occur from lymphoma of the gastrointestinal tract. For long-term survival; early diagnosis and appropriate treatment are needed. Surgery if performed without a proper diagnosis could prove fatal.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
RF, FH, TY, RD, KO and KH were gastrointestinal surgeons.
MO referred this patient to us.
KK and MS performed pathological examination and the autopsy.
KS was a member of the intensive care team.
TH, YY, HN gave us helpful comments about the manuscript
Acknowledgements
Permission of patient's relatives was obtained for publication of her case records
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Henter JI Elinder G Ost A and the FHL study group of the histiocyte society Diagnostic guidelines for hemophagocytic lymphohistiocytosis Semin Oncol 1991 18 29 33 1992521
Yao M Cheng AL Su IJ Lin MT Uen WC Tien HF Wang CH Chen YC Clinicopathological spectrum of haemophagocytic syndrome in Epstein-Barr virus-associated peripheral T-cell lymphoma Br J Haematol 1994 87 535 543 7993793
Tsuda H Hemophagocytic syndrome in children and adults Int J Hematol 1997 65 215 226 9114593 10.1016/S0925-5710(96)00560-9
Takahashi N Chubachi A Miura I Nakamura S Miura BA Lymphoma associated hemophagocytic syndrome in Japan [Japanese] Jpn J Clin Hematol 1999 40 542 549
Lay JD Tsao CJ Chen JY Kadin ME Su IJ Upregulation of tumor necrosis factor-α gene by Epstein-Barr Virus and activation of macrophages in Epstein-Barr Virus-infected T cells in the pathogenesis of hemophagocytic syndrome J Clin Invest 1997 100 1969 1979 9329960
Imasyuku S Differential diagnosis of hemophagocytic syndrome: underlying disorders and selection of the most effective treatment Int J Hematol 1997 66 135 151 9277044 10.1016/S0925-5710(97)00584-7
Hasegawa D Sano K Kosaka Y Hayakawa A Nakamura H A case of hemophagocytic lymphohistiocytosis with prolonged remission after syngeneic bone marrow transplantation Bone Marrow Transplant 1999 24 425 427 10467334 10.1038/sj.bmt.1701917
| 15491503 | PMC527873 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Oct 19; 2:34 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-34 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-351549407010.1186/1477-7819-2-35ReviewPerivascular epithelioid cell tumor (PEComa) of the uterine cervix associated with intraabdominal "PEComatosis": A clinicopathological study with comparative genomic hybridization analysis Fadare Oluwole [email protected] Vinita [email protected] Yesim [email protected] M Rajan [email protected] Linglei [email protected] Denise [email protected] Mazin B [email protected] Pei [email protected] Department of Pathology, Yale University School of Medicine, New Haven, CT, USA2 Department of Laboratory Medicine Yale University School of Medicine, New Haven, CT, USA3 Department of Genetics, Yale University School of Medicine, New Haven, CT, USA4 Department of Pathology, Hospital of St Raphael, New Haven, CT, USA5 Department of Pediatrics, Louisiana State University Health Sciences Center, New Orleans, LA, USA6 Department of Pathology, Stanford University, Stanford, CA, USA7 Department of Pathology, New York University, New York, NY, USA2004 19 10 2004 2 35 35 10 9 2004 19 10 2004 Copyright © 2004 Fadare et al; licensee BioMed Central Ltd.2004Fadare et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The World Health Organization recently recognized a family of neoplasms showing at least partial morphological or immunohistochemical evidence of a putative perivascular epithelioid cell (PEC) differentiation. These tumors include angiomyolipoma (AML), clear cell "sugar" tumors of the lung (CCST), lymphangioleiomyomatosis (LAM), clear cell myomelanocytic tumors of the falciform ligament and distinctive clear cell tumors at various other anatomic sites.
Case presentation & methods
A 41-year old gravida-1 para-1 with tuberous sclerosis presented with an incidentally identified 2.2 cm mass. The morphology and immunohistochemical profile was consistent with PEComa. Distinct aggregates of HMB-45 epithelioid cells were present in an occasionally distinctive perivascular distribution in the myometrium, small bowel lamina propria and ovarian hila. These distinctive aggregates, for which we propose the designation "PEComatosis" based on their intraabdominal distribution, did not display cytological atypia, mitotic activity or necrosis. CGH and DNA ploidy analysis showed a balanced chromosomal profile and diploid nuclei, respectively. There was no recurrence or metastases at 35 months' follow-up. Fifty-one previously reported cases of non-AML, LAM and CCST PEComas [perivascular epithelioid cell tumors- not otherwise specified (PEComa-NOS)] are reviewed.
Conclusions
The lesions may be a reflection of tumor multicentricity, in which each may be a potential nidus for the development of future more well-developed tumors. Alternatively, they may be a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout the abdomen. Finally, and least likely in our opinion, they may represent tumor spread from its primary site.
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Background
Perivascular epithelioid cell tumors (PEComa) have been the subject of abundant discussion in the medical literature over the past decade [1-47]. Morphologic and immunophenotypic similarities between some constituent cells of renal angiomyolipomas (AML) and those of a case of clear cell "sugar" tumor of the lung (CCST) were initially noted in 1991 [33]. One year later, Bonetti et al [4], formally proposed the concept of "perivascular epithelioid cell" (PEC), a then provisional term meant to describe the epithelioid cells that characterize, at least in part, the aforementioned lesions. Characteristics of PEC (which does not have a normal anatomic homologue) include co-expression for melanocytic and muscle markers, epithelioid to spindle cellular shapes with ample clear to eosinophilic cytoplasm, and at least in some cases, arrangement around blood vessels [2]. Ultrastructurally, structures interpreted as melanosomes and premelanosomes have been demonstrated in some tumors composed of PECs [14,18,31,38], but not in others [12,19,20,28,41]; an additional case showed macroscopic pigmentation [1]. In 1994, based on the morphologic and immunophenotypic distinctiveness of PECs, in addition to the fact that similar cells had been described in some other tumors, Bonetti et al proposed the concept of a family of lesions sharing this cellular phenotype, including CCST, AML, and lymphangioleiomyomatosis (LAM) [5]. The term "PEComa" was introduced by Zamboni et al [42] in 1996 as synonym for this family of tumors.
Over the past decade, PEC and tumors composed of them have engendered significant discussions and controversies with respect to their very existence as a clinico-pathological entity, their histogenesis, pathogenesis, and nomenclature [2-6,16,17,25,26,32,33,35,39,40]. Nonetheless, in 2002 and 2003, two monographs published under the auspices of the World Health Organization (WHO) recognized a family of neoplasms with perivascular epithelioid cell differentiation and accepted the designation "PEComa" [13,21]. In the WHO soft tissue volume, PEComas are defined as "mesenchymal tumors composed of histologically and immunohistochemically distinctive perivascular epithelioid cells" [13]. Members of the PEComa family that were recognized include AML, CCST, lymphangioleiomyomatosis (LAM), clear cell myomelanocytic tumor (CCMMT) of the falciform ligament/ ligamentum teres and a heterogeneous group of other "unusual clear cell tumors" at various anatomic sites [13]. The latter group includes tumors that have been reported under varying designations, such as abdominopelvic sarcoma of perivascular epithelioid cells [6], primary extrapulmonary sugar tumor (PEST) [38], clear cell myomelanocytic tumors of the skin [7] and thigh [15], and simply PEComa of various anatomic sites [1,9,12,19,24,27,28,31,40,41,45,46]; these, in addition to CCMMT of the falciform ligament [14] will henceforth be referred to as PEComa not otherwise specified (PEComa NOS). This descriptive designation, as used in this report, excludes the well-established entities LAM, CCST of the lungs and all variants of AML. Most of the reported cases of PEComa NOS have been tumors located in the uterine corpus (21/51; 41%); however, consequent to the publication of the WHO monographs, there has been a recent noticeable increase in the number of reported cases of PEComa NOS, with almost 70% of all cases reported between 2001 and 2004 [1,6-8,10-12,15,19,20,22,24,27,28,31,38,40,41,43-46](additional file 1). Concurrently, there has been an increase in the diversity of anatomic sites from which they reportedly arose, such that, it now appears that these tumors may potentially arise from any anatomic location. The morphologic and immunophenotypic spectrum as well as exclusion/inclusion criteria of PEComa NOS are not well-defined, and pathologic parameters with prognostic value have yet to be elucidated. In addition, there is a striking scarcity of information on the molecular and cytogenetic data of these lesions, probably due to their rarity. In this report, we present an example of a PEComa NOS of the uterine cervix, with the following objectives: 1) to document the uterine cervix as another potential site for a PEComa NOS, 2) based on the presence of tumorlets outside of their primary site, to analyze morphologic criteria predictive of clinical behavior of PEComa NOS, 3) to present CGH and ploidy analysis of PEComa NOS, and 4) to present another example of a PEComa NOS occurring in a tuberous sclerosis patient, an association which has been documented only twice previously [6,40]).
Case presentation
In October 2001, a left pelvic adnexal mass was palpated during a routine physical examination of a 41-year-old gravida-1 para-1 Caucasian female. The patient's past medical history was significant for tuberous sclerosis (diagnosed previously based on seizures, radiographic evidence suggestive of lymphangioleiomyomatosis, cutaneous hypopigmented macules and bilateral cystic renal disease that culminated in end-stage renal disease in 1989). A transvaginal ultrasound showed a heterogeneous complex adnexal mass whose size was estimated at 7.0 × 4.8 cm. Also noted was a well-defined lesion (<1.0 cm) in the uterine cervix (figure 1); the clinical impression of the latter lesion was a leiomyoma. CA-125 (13 U/ml) and CA19-9 (21 U/ml) levels were within normal limits. The decision was made to resect the adnexal mass and in November 2001, the patient underwent a total abdominal hysterectomy with bilateral salpingo-oophorectomy. The surgical procedure was complicated by severe intra abdominal adhesions (secondary to long-term peritoneal dialysis), the lyses of which resulted in two inadvertent nicks to small bowel segments that necessitated the excision of those segments. The patient did not receive any adjuvant or neoadjuvant therapy, and she remains alive with no evidence of recurrent or metastatic disease after 35 months of close surveillance.
Figure 1 Ultrasonographic appearance of the cervical mass showing it to be deceptively circumscribed
Sample preparation
Standard representative sections, including the entirety of the uterine cervix were processed routinely for microscopic examination. Sections were fixed in 10% neutral buffered formalin, embedded in paraffin and stained with hematoxylin and eosin. Selected sections were stained for Periodic Acid Schiff (PAS) with and without diastase pre-digestion. The immunohistochemical profile of the tumor was evaluated on 4μ thick, formalin-fixed, deparaffinized sections using a DAKO Autostainer (Carpinteria, CA, USA) based on the avidin-biotin-peroxidase complex. Specifications for the various immunohistochemical stains that were utilized are listed in table 1. The extent and intensity of the immunoreactivity for each antibody was scored semi-quantitatively on a 1+(+) to 4+(++++) scale representing increasing staining extent and intensity. Labeling index for Ki-67 was calculated by assessing at least 4000 cells and determining the percentage showing unequivocal nuclear staining. In our literature review, the significance of the differences between the group means of two continuous variables (patient age and sizes of lesions) was determined using the student's t-test (Excel®, Microsoft Inc, Redmond, WA). DNA ploidy analysis was performed on isolated tumor nuclei according to standard procedures. Comparative genomic hybridization [48] was performed on tumor tissue samples from the cervical mass as previously described [49].
Table 1 Immunohistochemistry: antibody specifications and results
Antibody Clone Dilution Antigen retrieval method Vendor Results*
Spindle cells Epithelioid cells
HMB-45 HMB-45 1:200 None DakoCytomation Carpinteria, CA, USA +++ +++
Vimentin V9 1:5 None Ventana, Tucson AZ, USA +++ +
Desmin D33 1:250 Trypsin Ventana +++ +
Progesterone receptor (PR) PGR 636 1:2 Steam¶ DakoCytomation ++ ++
Melan-A A103 1:40 Steam DakoCytomation +++ +++
Estrogen receptor (ER) 1D5 1:2 Steam DakoCytomation - -
Caldesmon H-CD 1:100 Steam DakoCytomation + -
Smooth muscle actin (SMA) 1A4 Neat None Sigma, St Louis, MO, USA +++ ++
Keratin AE1/AE3 1:1200 Trypsin Chemicon Int, Temecula, CA - -
S100 Polyclonal 1:2 Pronase DakoCytomation + -
Ki-67 (LI)¶ MIB-1 1:300 Steam DakoCytomation +(<1%) +(<1%)
CD68 PG-M1 1:4 Steam DakoCytomation - -
CD117 Polyclonal 1:200 Steam DakoCytomation - -
EMA E29 1:1000 None DakoCytomation - -
p53 D07 1:3200 Steam DakoCytomation - -
¶ Heat-induced epitope retrieval, 20 minutes in 10 mM citrate buffer in steam chamber, 20 minutes cooling down period.
¶ LI: Labeling Index
*Semi-quantitative scoring of combined extent and intensity of staining (+ to ++++)
Pathological findings
Gross and microscopic assessment of the left adnexal mass showed it to be a 7 cm hemorrhagic cyst devoid of any specific lining and involving the ovarian parenchyma. For the uterine cervical mass, a distinct lesion was not grossly appreciated. The ectocervical and endocervical surfaces and the endometrial cavity were described as unremarkable. Microscopically, the cervical mass was unencapsulated but possessed a deceptively circumscribed appearance at scanning magnification, attributable to the architectural homogeneity of its "core" (Figure 2). However, the peripheral regions of the tumor showed a significant degree of infiltration. The tumor's maximal dimension was estimated at 2.2 cm, extending from just below the ectocervical basement membrane (Figure 3) and extending proximally to the lower uterine segment and attaining 2 cm in depth (the peripheral limits of the tumor were at least 1 cm from the parametrial margins). The aforementioned central "core" (1 cm) was probably responsible for its radiographic appearance and consisted of fascicles of spindle cells with a smooth muscle appearance (Figure 4a). The spindle cells displayed bland nuclei with regularly distributed chromatin and only rarely conspicuous nucleoli. Towards the periphery, the spindle cells displayed increasingly PAS+, diastase sensitive cytoplasm (Figure 4b), although occasional cells displayed dense eosinophilia. At its most peripheral regions, the tumor was composed predominantly of solid sheets of large epithelioid cells with bland nuclear features, abundant clear cytoplasm, and well-defined cytoplasmic membranes. Although predominantly solid in the epithelioid regions, a pseudo-alveolar pattern was also evident (Figure 4c). At the most proximal regions near the lower uterine segment, the tumor appeared to be "invading" in single cells in a hyalinized stroma. The nuclei of the epithelioid cells showed a mild to moderate degree of nuclear atypia, manifested mostly as nucleomegaly and irregularity of nuclear membranes in the absence of hyperchromasia. Rare cells displayed bizarrely enlarged nuclei and multinucleation with a "smudged" chromatin pattern consistent with degenerative atypia (Figure 5). Also identified in these regions were CD68+ foamy histiocytes mostly in single cells but occasionally in aggregates especially around the endocervical glands. No tumor necrosis was identified and mitotic figures were extremely sparse (<1/50 HPF). Small arching sinusoidal vessels were prominent throughout the tumor, but no large malformed vascular profiles were present. Pigment-laden cells and adipocytes were not present in the cervical mass. Small bowel segments measuring 21 cm in total length were also processed. Grossly, irregular areas of transmural thickening were noted. Microscopically, aggregates of epithelioid cells with more eosinophillic cytoplasm and vacuolated cytoplasm were noted in the lamina propria in two out of twelve sections (Figure 6). In the both ovaries, similar aggregates of cells were present in a distinctive perivascular location in the hilar regions. These aggregates were either subendothelial (predominantly), adventitially attached to affected vessels, or present as free aggregates in the perihilar fat (Figure 7). Each measured less than 1 mm in maximum dimension. No intraluminal tumor cells were seen. CGH and DNA ploidy analysis of the cervical mass showed a balanced chromosomal profile (figure 8) and diploid nuclei, respectively. Immunohistochemically, both the epithelioid cells and spindle cells stained diffusely with HMB-45 (Figure 9) and Melan-A in a cytoplasmic pattern at all sites (cervix, ovary, bowel). Scattered spindle cells showed unequivocal immunoreactivity for S100 while the epithelioid cells were negative. Both components showed at least focal immunoreactivity for muscle markers: smooth muscle actin and desmin with the spindle cells predominating both in the quantity of cell stained and the intensity of staining where positive. The complete immunohistochemical profile of the tumor is shown in table 1.
Figure 2 Photomicrograph of panoramic view of the cervical mass showing a central circular "core"
Figure 3 At the tumor's advancing edge, it merges almost imperceptibly with the sub-ectocervical stroma
Figure 4 The cervical mass displayed a morphologic spectrum from purely spindle, smooth-muscle-like areas (figure 4a) to transitional areas composed of spindle cells with more clear cytoplasm (figure 4b) to overtly epithelioid areas with abundant, clear cytoplasm, which occasionally displayed a pseudo-alveolar appearance (figure 4c)
Figure 5 Occasional cells showed degenerative multinucleation with a "smudged" chromatin nuclear pattern consistent with degenerative atypia
Figure 6 Aggregates of spindle to polygonal cells with eosinophillic to clear cytoplasm was present in the lamina propria of the excised small bowel segments. These aggregates were HMB-45-positive.
Figure 7 In the ovarian hila, similar tumor aggregates were associated with vascular structures in a subendothelial (figure 7A), intramedial (Figure 7B), or para-adventitial (Figure figrure 7C ) pattern, but were also present freely in the hilar fat (Figure 7D).
Figure 8 Comparative genomic hybridization showing a balanced chromosomal profile
Figure 9 All components of the tumor were HMB-45-positive
Result of literature review
Of the 51 cases of PEComa NOS that have been documented in the literature [1,6-8,10-12,14,15,19,20,22-25,27,28,31,32,34,37,38,40-46], 90% (46/51) developed in females and 41% (21/51) were described in the uterine corpus. Follow-up information was unavailable (n = 7) or too recent at the time of the report (n = 4) in 22% of cases [7,10,14,15,34,40,43,46]. One patient whose tumor was primary in the left atrium died postoperatively of "cardiac failure thought to be due to nontumorous coronary artery thromoboemboli" [37]. Two cases were excluded from the following analysis (44, 45) based on insufficient morphologic information (44) or inconsistence with our analytic paradigm (45). Of the remaining 37 cases (additional file 2), the associated tumors behaved in a benign fashion in 25 cases (68%). These tumors were confined to their respective primary sites and showed no evidence of recurrence or metastases with follow-up periods that ranged from 6 weeks to 22 years, and will hereafter be referred to as the "benign cases" [1,6,8,14,20,23,25,27,32,37,38,40,42,44]. However in twelve cases, the tumors recurred after apparently complete surgical resections, were either metastatic at presentation or metastasized after long periods. Four of these cases were ultimately fatal [6,19,22,28], and all 12 will hereafter be referred to as the "non-benign cases" [6,11,12,14,19,22,24,28,31,41].
Clinical parameters were not particularly discriminatory between the two groups: the average patient age of the benign cases (37 years) was not significantly different from that of the non-benign cases (43 years) [p = 0.56] and there was a diversity of clinical presentations related primarily to the sites of origin. The non-benign cases (mean diameter of 11 cases: 7.5 cm) tended to be larger than the benign cases (mean diameter of 24 cases: 4.54 cm); the statistical significance of the difference (p = 0.022) is maintained even when the outlier effect of the 1 case with a 20 cm diameter tumor reported by Folpe et al [14] in the non-benign group is removed. Morphologic features in the benign and non-benign cases are compared below.
Discussion
We have presented herein the first documented case of a PEComa NOS of the uterine cervix. In addition to the primary tumor, aggregates of HMB-45+ clear cells were present at several other intraabdominal sites, including the small bowel lamina propria, ovarian hila and myometrium. Based on this pattern of distribution of tumor cells, we propose the designation "PEComatosis" to describe such aggregates. The morphogenetic basis for the intraabdominal lesions remains unclear. The similarity in morphologic features and immunophenotype between the cervical and extracervical lesions suggests that either a) both lesions arise from the same site or progenitor, or b) they both represent tissue responses of different degrees to the same stimulus. Several possibilities were considered, all of which are necessarily speculative. The lesions may be a reflection of tumor multicentricity, in which each may be a potential nidus for the development of future more well-developed tumors. Alternatively, they may be a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout the abdomen. Finally, and least likely in our opinion, they represented the tumor spread from its primary site.
The diffuse occurrence of apparently heterologous tissue is a well-known phenomenon in the peritoneal cavity. These include leiomyomatosis peritonealis disseminata (LPD), gliomatosis peritonei and the recently described diffuse cartilaginous metaplasia of the peritoneum [50,52]. In LPD, for example, diffusely distributed peritoneal nodules of benign smooth muscle may proliferate or regress based on the hormonal milieu of pregnancy [52]. In this instance, circulating hormone levels are thought to represent the main stimuli underlying the proliferation of the peritoneal lesions. Even though a uterine smooth muscle tumor is associated with most cases, they are not thought to be the origin of LPD [52]. In contrast, gliomatosis peritonei is believed to represent an overgrowth of glial implants from ovarian teratomas [52]. In either instance, the presence of diffuse intraperitoneal lesions associated with these benign lesions (an ovarian teratoma and a uterine leiomyoma) is deemed insufficient to assign them a malignancy status [52]. These examples are directly relevant to our case, in which a malignant potential has to be assigned to a pathologically benign tumor that is showing similar appearing cells remote from its primary site. In the current case, the distinctive tropism of tumor cells for vascular structures without true intraluminal foci, argues against a hematogenous spread of tumor and argues for a de novo proliferation of PEC at those sites. However, whether these are foci that would have undergone involution or continued proliferating into tumor masses remains unclear. Do these lesions represent a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout abdomen? The most obvious underlying condition in this particular patient is tuberous sclerosis. A potentially comparable condition may exist in the lungs. In a few patients with and without tuberous sclerosis, a distinctive diffuse pulmonary interstitial proliferation has been described [53-56]. These proliferations are composed of clear HMB-45+ cells and are inconstantly associated with LAM [50-53]. Whether a similar mechanism is operational here is not clear. Although we do not believe the extracervical lesions represent metastases, it should be noted that contrary to the conventional paradigm, molecular evidence is accumulating regarding the "metastasis of benign tumor cells" in patients with tuberous sclerosis, although this is currently limited to the renal angiomyolipoma to pulmonary lymphangioleiomyomatosis model [57].
The issues raised by this case highlight the absence of well-established morphologic criteria predicting aggressiveness or malignancy in PEComas. Based on an analysis of 31 of the 51 reported cases detailed in Additional file 1, the following information is stated in the WHO monograph regarding the aforementioned criteria [13]: "it appears that PEComas displaying any combination of infiltrative growth, marked hypercellularity, nuclear enlargement and hyperchromasia, high mitotic activity, atypical mitotic figures, and coagulative necrosis should be regarded as malignant". However, while the presence of all the mentioned features would probably assign malignancy to any tumor, it is unclear what significance there is of the presence or absence of individual features or small combinations thereof.
Since the relevance of any set of pathologic criteria is ultimately dependent on their correlation to clinical behavior based on published experience, we analyzed in greater detail the clinicopathologic features of those 12 cases in which aggressive behavior was already manifest and compared them to those of the 25 cases with benign outcomes. It is well recognized by the authors that this separation is artificial, the definitional threshold for 'aggressiveness" is low, and that for example the recurrence of a tumor is by no means necessarily indicative of its malignancy. Nonetheless, this separation allows comparative analysis of groups of cases whose clinical behaviors have been shown to be different.
With regard to morphologic appearance, some features appeared to distinguish the two groups. Nuclear atypia (nucleomegaly, multinucleation, pleomorphism etc) was more likely to be present in the non-benign group, with this feature described in 8 of the 10 cases in which a comment was rendered. However, some degree of nuclear atypia was also described in 9 of the 25 cases in the benign group; the atypia in all of the latter cases were described as "minimal" or mild to moderate. Two cases that, in our opinion, showed the highest degree of nuclear atypia (in addition to high mitotic activity and necrosis) were unfortunately reported without follow-up information [10,34].
Mitotic activity was uniformly low in the benign group, with no mitotic figures identified in 13/25 (52%) cases and rare (<1/20HPF) mitotic figures found in the remainder with information. However, for the purposes of answering the more clinically relevant question, i.e. segregation of the non-benign group, mitotic activity was not useful. Only 3 of the 10 non-benign cases (in which mitotic activity information was given) showed significant mitotic activity (≥ 6/10HPF). In the remaining 7 cases, mitotic figures were described as "rare" (n = 3), "low" (n = 2) and <1/20hpF (n = 2).
Necrosis was a common feature of cases in the non-benign group, being present in 7 of 11 cases (64%), with an 8th case described as showing "gelatinous-appearing material" macroscopically [11]. In contrast, of the benign group, necrosis was present in only 4 of the 18 (22%) cases in which such information was stated. Additionally, the necrosis in one of those 4 cases was described as "infarct-type" (non-coagulative) [40]. However, one of the benign cases was described as showing "scattered foci of coagulative necrosis and hemorrhage" [8]. As can be anticipated, lymphovascular invasion (LVI) by tumor was more characteristic of tumors in the non-benign group as compared to their benign counterparts: LVI was present in 4 out of 8 (50%) cases in the non-benign group and in only one case in the benign group. The latter is Case 3 of the "abdominopelvic sarcomas" described by Bonetti et al [6]; this tumor was a 2.5 cm well-circumscribed pelvic nodule, the patient showed no evidence of tumor recurrence or metastases at 6 months follow-up. For the purposes of this analysis, we placed this case in the benign group, definitionally based on the benign follow-up.
We also analyzed the degree and types of tumor infiltration as another potential discriminator between the benign and non-benign groups. In what remains the largest series of PEComa NOS reported to date, Vang and Kempson [40] divided 8 uterine PEComas into 2 groups (A and B) based on, in part, the degree of tumor infiltration. Of the group A, 75% of tumors showed a tongue-like myometrial infiltration reminiscent of low-grade endometrial stromal sarcoma while this type of infiltration was only focal in 75% of their group B tumors; all cases were limited to the uterus. Due to the absence of follow-up in 75% of their group A cases, the prognostic significance of this classification is unclear. Two of the four "hyalinized uterine mesenchymal neoplasms with HMB-45-positive epithelioid cells" reported by Michal and Zamecnik [25] showed a similar "tongue-like" infiltration and had a benign follow-up. All of the 7 cases of CCMMT reported by Folpe et al [14] "appeared circumscribed but displayed an infiltrative pattern microscopically at the periphery", a pattern remarkably similar to our case. One of their 6 cases with follow-up showed evidence of pulmonary metastases at 3 months, while follow-up was unremarkable in others. This patient reportedly died of other causes. Analysis of the usefulness of infiltration was hampered somewhat by the absence of a comment on this subject in some of the non-benign cases; however, it is unlikely to be a criterion of significant use in isolation. Only in 3 of 10 malignant cases infiltration was prominent. In 4 of the 7 remaining cases in the non-benign group, infiltration was not specifically noted; the latter includes a well-circumscribed and encapsulated 9 cm mass involving the terminal ileum and cecum [6] which metastasized to the liver and the patient died in 28 months. The 7th and 8th cases showed only local infiltration [11,14]. Another case reported by Ruco et al [34], which we excluded from our analysis of the non-benign cases due to an absence of outcome information, consisted of a partially necrotic 5 cm mass showing high mitotic activity (11 m/10hpF) and was described as "poorly circumscribed". One of the cases described by Fukunaga [8], which we have placed in the benign group showed focal infiltrative growth. For the rest of the benign group, significant infiltration was not described in any case.
Overall, the experience with PEComas NOS is currently too limited to make a definitive assessment of prognostic features. In addition, the above analysis presumes a biologic homogeneity to tumors arising from various anatomic sites (table 2). Nonetheless, from our analysis of the reported cases with clinical outcomes as end-points, necrosis and large tumor size (both of which were more characteristic of tumors in the non-benign group) appears to have the greatest discriminatory value. However, it is likely that when more cases are described, combinations of features will provide the greatest prognostic information. In the present case, as previously noted, there was no necrosis, mitotic activity or significant pleomorphism, but there were tumor aggregates in the ovaries (perivascular) myometrium, and small bowel. The fact that close surveillance of our patient for 29 months has revealed no evidence of tumor recurrence or metastases is suggestive of tumor benignancy, although even that statement is tempered by the cases of metastases developing after long periods, up to 7 years in one case ([11], Additional file 1).
Table 2 Summary of pathologic features in reported cases of PEComa NOS
Pathologic feature* Non-Benign cases (n = 12) Benign cases (n = 25)
Cytologic atypia 8/10 (80%) 9/25 (36%)
Necrosis 7/11 (64%) 4/18 (22%)
Lymphovascular invasion 4/8 (50%) 1/11 (9.1%)
Size (average diameters) 7.5 cm (n = 11) 4.54 cm (n = 24)
Significant mitotic activity (6 mitoses per 10 high power fields or greater 3/10 (30%) 0/24 (0%)
* For each histological feature, the denominator represents the number of cases in which the information assessed was available in the published reports. See text for caveats, exceptions and more detailed analysis
An important differential consideration for PEComas arising in the uterus is epithelioid smooth muscle tumors. Vang and Kempson [40] expressed the opinion that PEComas and epithelioid smooth muscle tumors "exist on a morphologic spectrum" and recommended that HMB-45 staining be performed on all epithelioid tumors of the uterus. The contrary view was expressed by Silva et al [58] who demonstrated immunoreactivity for HMB-45 in 4 (80%) out of 5 "unequivocal uterine leiomyosarcomas" with epithelioid features. They concluded that HMB-45 immunoreactivity is insufficient to designate these tumors as PEComas and separate them from epithelioid smooth muscle tumors. Zamecnik and Michal [59] found immunoreactivity for HMB-45 in four distinctively hyalinized epithelioid mesenchymal tumors of the uterus, but all four cases were negative for the other three melanogenesis markers tested (Melan-A, tyrosinase and micropthalmia transcription factor). The authors concluded that their cases were closer linked to epithelioid smooth muscle tumors than PEComas. In the report by Ruco et al [34], twelve uterine leiomyomas, two of which were epithelioid, were negative for HMB-45. In contrast, at least focal HMB-45 positivity was demonstrated in 43 of 79 (54%) typical (non-epithelioid) smooth muscle tumors of the uterus in 2 combined series [60,61]. These somewhat contradictory findings illustrate that at this time, the relationship between epithelioid smooth muscle tumors and PEComas (outside of their shared co-expression of muscular markers) is unclear. However, since both tumors are rare, we agree with Vang and Kempson [40] that HMB-45 immunostaining should be performed on all epithelioid uterine tumors, not only to better delineate the features of both epithelioid smooth muscle tumors and PEComas, but due to the possibility of an association between the latter and the tuberous sclerosis complex (TSC).
Although the association between some members of the PEComa family (AML and LAM) and the tuberous sclerosis complex is well-known, this case represents only the third case of a PEComa NOS reported to occur in a patient with stigmata of this complex. Both of the previous cases were primary in the uterus [6,40]. Even this seemingly low rate of association (6%; 3/50) is almost certainly higher than that associated with most tumors, and may thus warrant an investigation for features of TSC in patients in whom these tumors are diagnosed.
The validity of segregating a tumor group based almost entirely on the clear appearance of constituent cells and immunoreactivity for melanogenesis markers may be proven if recurrent molecular or cytogenetic abnormalities are identified in this group. However, remarkably sparse information exists on the cytogenetic or molecular pathogenesis of PEComa NOS. Using conventional cytogenetics, Folpe et al [14] identified loss of X chromosome and a t(3;10) in 1 of 5 metaphases examined from a case of CCMMT. RT-PCR analysis of one perivascular epithelioid cell tumor has failed to show the EWS/ATF-1 fusion transcript from the t(12;22) characteristic of clear cell sarcoma of soft parts – another differential consideration [41]. No other cytogenetic analyses of PEComa NOS have been reported to our knowledge. p53 does not appear to be involved in the pathogenesis of these tumors, as neither p53 mutations as determined by single-stranded conformational pleomorphism analysis, nor protein overexpression as determined by immunohistochemistry have been identified [33]. Our case represents the first PEComa NOS that has been studied by CGH. Although this needs to be confirmed with more cases, the absence of chromosomal gain or loss detectable by this method, in additional to a diploid DNA content of our case, suggests that karyotypical changes may not be features of PEComa NOS.
In summary, we have documented herein the first case of a PEComa NOS of the uterine cervix occurring in a tuberous sclerosis patient. With the description of additional cases, more insight into their behavior and predictive morphologic parameters may be achieved.
Competing Interests
The authors declare that they have no competing interests.
Authors' contributions
OF wrote the original version of the manuscript.
PH and VP diagnosed the case and supervised the entire project.
DH and MRM collected clinical and pathologic data and participated in manuscript preparation.
DH also contributed statistical analysis.
LM, YY, PH and MBQ performed and/or analyzed and interpreted the CGH. All authors have read and approved the final manuscript.
Additional files
Additional file 1: PEComa additional file 1. doc : All reported cases of PEComa NOS
Additional file 2: PEComa additional file 2. doc: Morphologic analysis of the 37 cases of PEComa NOS with adequate follow-up information, classified by outcome
Supplementary Material
Additional file 1
Click here for file
Additional file 2
Click here for file
Acknowledgement
Patient consent was obtained for the presentation of her records.
A preliminary version of this study was presented at the annual meeting of the American Society of Clinical Pathologists (ASCP), San Antonio, TX, Oct 7–10, 2004.
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| 15494070 | PMC527874 | CC BY | 2021-01-04 16:38:39 | no | World J Surg Oncol. 2004 Oct 19; 2:35 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-35 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-371551128910.1186/1479-5876-2-37CommentaryStatistical design considerations for pilot studies transitioning therapies from the bench to the bedside Carter Rickey E [email protected] Robert F [email protected] Department of Biostatistics, Bioinformatics and Epidemiology, 135 Cannon Street, Suite 303, Medical University of South Carolina, Charleston, SC 29425, USA2004 28 10 2004 2 37 37 7 10 2004 28 10 2004 Copyright © 2004 Carter and Woolson; licensee BioMed Central Ltd.2004Carter and Woolson; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Pilot studies are often used to transition therapies developed using animal models to a clinical setting. Frequently, the focus of such trials is on estimating the safety in terms of the occurrence of certain adverse events. With relatively small sample sizes, the probability of observing even relatively common events is low; however, inference on the true underlying event rate is still necessary even when no events of interest are observed. The exact upper limit to the event rate is derived and illustrated graphically. In addition, the simple algebraic expression for the confidence bound is seen to be useful in the context of planning studies.
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Introduction
In the translational research setting, statisticians often assist in the planning and analysis of pilot studies. While pilot studies may vary in the fundamental objectives, many are designed to explore the safety profile of a drug or a procedure [1,2]. Often before applying a new therapy to large groups of patients, a small, non-comparative study is used to estimate the safety profile of the therapy using relatively few patients. This type of investigation is typically encountered in the authors' experiences as collaborating biostatisticians at our General Clinical Research Center as well as developing applications addressing the National Institutes on Health Roadmap Initiative .
In the context of pilot studies, traditional levels of α (the Type I error rate) and β (the Type II error rate) may be inappropriate since the objective of the research is not to provide definitive support for one treatment over another [3]. For example, the null hypothesis in a single arm pilot study might be that the tested intervention produces a safety profile equal to a known standard therapy. A Type I error (rejecting the null hypothesis when it is false) in the context of this preliminary investigation would encourage additional examination of the treatment in a new clinical trial. This is in contrast to a Type I error in a Phase III/IV clinical trial in which the error could result in widespread exposure of an ineffective treatment. Allowing for a less stringent Type I error rate is critical when trying to transition therapies from the animal models to clinical practice since it identifies a greater pool of potential therapies that could undergo additional research in humans.
Similarly, power (1 - β) is of less practical importance in a single arm, non-comparative (or historically controlled) pilot study since the results would almost always require confirmation in a controlled trial setting. Shih et al [4] extend the deviations from traditional hypothesis-driven analyses to suggest preliminary investigations should focus on observing responses at the subject level rather than testing a treatment's estimated mean response. In the section that follows, we will relate these notions under the context of safety data analysis and provide interpretations that can be used for sample size considerations.
Methods
For ease of presentation, assume the pilot study will involve n independent patients for which the probability of the adverse event of interest is π, where 0 <π < 1. A 100 × (1 - α)% confidence interval is to be generated for π and an estimate of the sample size, n, is desired. Denote X as the number of patients sampled who experience the adverse event of interest. Then, the probability of observing x events in n subjects follows the usual binomial distribution. Namely,
Denote πu as the upper limit of the exact one-sided 100 × (1 - α)% confidence interval for the unknown proportion, π [5]. Then πu is the value such that
A special case of the binomial distribution occurs when zero events of interest are observed. In pilot studies with relatively few patients, this is of practical concern and warrants particular attention. When zero events are realized (i.e., x = 0), equation (1) reduces to
(1 - πu)n = α.
Accordingly, the upper limit of a one-sided 100 × (1 - α)% confidence interval for π is
πu = 1 - α1/n. (2)
The resulting 100 × (1 - α)% one-sided confidence interval is (0, 1 - α1/n).
Graphically, one can represent this interval on a plot of π against n as illustrated in Figure 1 for α = 0.05, 0.10 and 0.25. As the figure illustrates, for relatively small sample sizes, there is a large amount of uncertainty in the true value of π. It is critical to convey this uncertainty in the findings and to guard against inferring a potential treatment is harmless when no adverse effects of interest are observed with limited data. Louis [6] also cautioned the clinical observation of zero false negatives in the context of diagnostic testing stating that zero false negatives may generate unreasonable optimism regarding the rate, particularly for smaller sample sizes.
Figure 1 Upper limit of the 100 × (1 - α)% one-sided confidence interval for the true underlying adverse event rate, π, for increasing sample sizes when zero events of interests are observed
Furthermore, one can consider using (2) in other clinically important manners. For instance, an investigator may be planning a pilot study and want to know how large it would need to be to infer with 100 × (1 - α)% confidence that the true rate did not exceed a pre-specified π, say π0, given that zero adverse events were observed. Using (2), it follows that:
To illustrate the utility of this solution, consider the following example. Ototoxicity is well documented with increasing doses of cisplatin, a platinum-containing antitumoral drug that is known to be effective against a variety of solid tumors. It is of clinical interest to identify augmentative therapies that can alleviate some of the cell death since up to 31% of patients receiving initial doses of 50 mg/m2 cisplatin are expected to have irreversible hearing loss [7,8]. Therefore, it is desirable to rule out potential treatments not consistent with this rate of hearing loss before considering more conclusive testing. Using equation (3), we would conclude that the augmentative therapy has a hearing loss rate less than 0.31, at the 90% confidence level, if a total of 7 patients are recruited and all 7 do not experience ototoxicity. Therefore, an initial sample size of 7 patients would be sufficient to identify augmentative therapies, such as heat shock or antioxidant supplements, that demonstrate preliminary efficacy in humans. In the event one or more ototoxic events are observed, then the results in relationship to the historical rate (31% in this example) may not be statistically different. The results of several of these pilot studies could then be used to rank-order potential therapies thereby proving an empirically justified approach to therapy development.
Conclusions
In translational research, it is common to explore the adverse event profile of a new regimen. In this note, we illustrate how a simple expression has utility for the generation of confidence intervals when zero events are observed. A more comprehensive and methodological treatment of inference with zero events can be found in Carter and Woolson [9], and Winkler et al [10], which treats the issue from a Bayesian statistical viewpoint. This commentary and related works have implications as a practical finding for the interpretation of clinical trial safety data and offer clinicians advice on the range of adverse event rates that can be thought to be consistent with the observation of zero events. The presented formula offers more flexibility than the "rule of 3" approximation [11] since it allows for the specification of significance levels other than α = 0.05. The ability to choose the significance level might be important when designing or interpreting preliminary data obtained from a pilot study. In summary, small sample sizes and a focus on safety are often associated with translational research, and the statistical approaches to these studies may need to deviate from traditional, hypothesis-driven designs.
Competing interests
The author(s) declare that they have no competing interests.
Authors' Contributions
RC and RW contributed to the conceptualization, writing and editing of this manuscript.
Acknowledegments
This work was partially supported by the National Institute of Health grants DA013727 and RR01070.
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| 15511289 | PMC527875 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Oct 28; 2:37 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-37 | oa_comm |
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J NeuroinflammationJournal of Neuroinflammation1742-2094BioMed Central London 1742-2094-1-201549809810.1186/1742-2094-1-20ReviewNeuronal oxidative damage and dendritic degeneration following activation of CD14-dependent innate immune response in vivo Milatovic Dejan [email protected] Snjezana [email protected] Kathleen S [email protected] Feng-Shiun [email protected] Thomas J [email protected] Department of Pathology, University of Washington, Harborview Medical Center, Seattle Washington 98104, USA2004 21 10 2004 1 20 20 6 10 2004 21 10 2004 Copyright © 2004 Milatovic et al; licensee BioMed Central Ltd.2004Milatovic et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The cause-and-effect relationship between innate immune activation and neurodegeneration has been difficult to prove in complex animal models and patients. Here we review findings from a model of direct innate immune activation via CD14 stimulation using intracerebroventricular injection of lipopolysaccharide. These data show that CD14-dependent innate immune activation in cerebrum leads to the closely linked outcomes of neuronal membrane oxidative damage and dendritic degeneration. Both forms of neuronal damage could be blocked by ibuprofen and alpha-tocopherol, but not naproxen or gamma-tocopherol, at pharmacologically relevant concentrations. This model provides a convenient method to determine effective agents and their appropriate dose ranges for protecting neurons from CD14-activated innate immunity-mediated damage, and can guide drug development for diseases, such as Alzheimer disease, that are thought to derive in part from CD14-activated innate immune response.
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Introduction
Activated innate immunity is associated with several degenerative and destructive brain diseases including Alzheimer disease (AD), HIV-associated dementia (HAD), ischemia, head trauma, stroke, cerebral palsy, and axonal degeneration in multiple sclerosis [1]. In this complex response, some aspects are proposed to be neurotrophic, others neurotoxic, and each potentially a consequence rather than a contributor to neurodegeneration. Indeed, a severe limitation to understanding the precise role of innate immunity in these diseases and their corresponding animal models is that innate immunity is activated simultaneously with multiple other stressors and responses to injury, thereby greatly confounding any clear conclusion about cause-and-effect relationships. For these reasons we have adopted a simple but highly specific model of isolated innate immune activation: intracerebroventricular (ICV) injection of low dose lipopolysaccharide (LPS).
LPS specifically activates innate immunity in peripheral organs through a well-described Toll-like receptor (TLR)-dependent signaling pathway [2,3]. There are 9 known human plasma membrane-spanning TLRs expressed in many cell types throughout the body that have been discovered in the context of innate immune response to micro-organisms. TLR-mediated innate immune response can be considered in three phases: initial signal transduction cascade, secondary signaling cascades, and effectors. The initial signaling cascade starts with ligand activating one of the 9 plasma membrane TLRs. All of these receptors require the adaptor protein MyD88 for immediate response to LPS and initiate a bifurcated signal transduction cascade that culminates in altered gene transcription, primarily via NF-κB activation but also through c-Fos/c-Jun-dependent pathways. Some of the activated gene transcripts encode directly for receptor ligands while others are enzymes that catalyze the formation of receptor ligands that in turn activate secondary autocrine and paracrine signaling cascades. These signaling events culminate in the generation of effector molecules including bacteriocidal molecules, primarily free radicals generated by NADPH oxidase and myeloperoxidase (MPO), as well as cytokines and chemokines that can attract an adaptive immune response. Although originally identified as part of the response to exogenous antigens from micro-organisms, a broader pathophysiologic role for TLR-dependent signaling in response to endogenous ligands in now clear. Indeed, from this perspective, the effectors at the culmination of these signaling pathways are more appropriately viewed as cytocidal rather than specifically bacteriocidal. The precise agents responsible for cytocidal activity are not clearly established but likely include free radicals generated principally by NADPH oxidase, MPO, and inducible nitric oxide synthase (iNOS) in combination with cytokines and chemokines.
TLR-4 is the receptor for LPS in peripheral organs [2,3]. However, another protein, CD14 is critical to LPS activation of TLR-4. Membrane-anchored CD14 is now thought to act a co-receptor for LPS but not to initiate intracellular signaling cascades. It is important to note that CD14 serves a similar function with TLR-2, although the activating agents here are bacterial products other than LPS [4]. Within minutes to hours of exposure to LPS, there is increased gene transcription and subsequent translation of cytokines and chemokines, prominently including tumor necrosis factor, interleukin-1, and interferons, as well as several enzymes; important among these are iNOS and cyclooxygenase 2 (COX-2) that catalyze the formation of NO and prostaglandin (PG) H2, respectively [4]. While NO is a potent cell signaling molecule, PGH2 has relatively low receptor binding affinity but is rapidly and efficiently converted to multiple PGs or thromboxane A2, each of which are potent activators of a large family of G protein-coupled receptors [5]. The combination of these initial and secondary signaling cascades produces a robust innate immune response. This same response can occur in response to endogenous ligands that also activate the CD14/TLR-4 pathway [2,3]. Indeed, several endogenous CD14/TLR ligands have received increasing attention for their potential roles in human diseases [6], and polymorphisms in TLR-4 are associated with risk for atherosclerosis and asthma, as well as other human diseases [7]. With respect to AD, amyloid beta (A) fibrils have been shown to activate the microglial innate immune response through CD14-dependent mechanisms [8]. Relevant to a broader range of neurodegenerative diseases, novel peptides and neoantigens exposed by apoptotic cells [9] also activate CD14-dependent innate immune response in macrophages. While none of these data point to CD14 or innate immune response as etiological in neurodegenerative disorders, these findings from in vitro and cell culture experiments raise the possibility that CD14-dependent signaling may be a common process shared in the pathogenesis of neurodegenerative diseases, especially AD.
Here we present our results from studies that have identified the molecular and pharmacologic determinants of ICV LPS-initiated cerebral neuronal damage in vivo. It is important to stress that several laboratories have shown that glia, predominantly microglia, are activated by LPS but that neurons do not respond to LPS because they lack the appropriate receptors [10,11]. We measured two main endpoints; one biochemical and one structural. Since free radicals are a primary mechanism of cytocidal activity from innate immune response, we used a stable isotope dilution method with gas chromatography and negative ion chemical ionization mass spectrometry to quantify compounds formed by free radical attack on the neuronal membrane-enriched fatty acid, docosohexaenoic acid (DHA); we have termed these molecules F4-neuroprostanes (F4-NeuroPs) [12]. In addition to this biochemical marker of neuronal oxidative damage, we directly quantified neuron number as well as dendrite length and spine density in pyramidal neurons of hippocampal sector CA1 using the Golgi impregnation technique followed by quantitative morphometry with Neurolucida (MicroBrightField, VT) [13].
Lack of adaptive immune response, fever, or structural damage to brain following ICV LPS
Despite the expectation that LPS would produce a febrile response with widespread damage to brain and an acute encephalitis, we observe that ICV LPS does not yield any of these outcomes (Figure 1) [14]. Indeed, others who injected similar amounts of LPS directly into brain parenchyma also do not observe behavioral changes, tissue damage, or acute inflammatory infiltrate in young wild type (wt) mice [14-18]. We pursued this further by stereological counting of hippocampal CA1 pyramidal neurons 24 and 72 hr following ICV LPS and observed no change in neuron number from untreated controls [14]. These data show that, at least over 3 days following ICV LPS, there is no gross structural damage to brain, no detectable adaptive immune response, and no loss of pyramidal neurons from hippocampal sector CA1.
Figure 1 NeuN immunohistochemistry of mouse hippocampus. Photomicrograph (× 40) of NeuN immunoreactivity in mouse hippocampus and adjacent structures 24 hr after ipsilateral ICV LPS injection. Note normal density and distribution of neurons without a cellular infiltrate.
Neuronal oxidative damage
Numerous methods exist to determine free radical-mediated damage to cells. While most of these function well in vitro, important limitations arise in living systems where extensive, highly active enzymatic pathways have evolved to metabolize many of the commonly measured products, such as 4-hydroxynonenal [19]. One method that has been highly replicated as a robust quantitative means of measuring free radical damage in vivo is measuring F2-isoprostanes (F2-IsoPs) [20], products generated from free radical damage to arachidonic acid (AA), that are not extensively metabolized in situ (Figure 2). Since AA is present throughout brain and in different cells in brain at roughly equal concentrations, measurement of cerebral F2-IsoPs, like all other measures of oxidative damage, reflects damage to brain tissue but not necessarily to neurons. For these reasons, we developed an assay to measure the analogous products generated from DHA, F4-NeuroPs [12]. Since DHA is highly concentrated in neuronal membranes, F4-NeuroPs offer a unique window into free radical damage to neuronal membranes in vivo [21].
Figure 2 Diagram showing the formation of F2-IsoPs and F4-NeuroPs.
We first determined the time course of F4-NeuroP accumulation in cerebrum of wt mice exposed to ICV LPS and observed a delayed, transient elevation that peaks at approximately 24 hr after exposure and then returns to baseline by 72 hr post exposure [14]. It is important to note that while detectable neuronal oxidative damage is delayed several hours following ICV LPS, others have shown that altered gene transcription and increased cytokine secretion occur rapidly and peak within a few hours of LPS exposure. As with oxidation of lipoproteins, it is likely that this delay in neuronal oxidative damage is related, at least in part, to the time required to deplete anti-oxidant defenses. Thus, despite the lack of tissue damage, adaptive immune cell infiltrate, or detectable neuron loss, there is significant, reversible free radical damage to neuronal membranes following ICV LPS.
We next used a series of mice, all on the C57Bl/6 genetic background, lacking specific genes to establish the determinants of neuronal oxidative damage in this model. Our results showed that genetic ablation of one co-receptor (CD14), the required adaptor (MyD88), or one arm of the initial signal cascade (the p50 subunit of NF-κB) each completely blocks an LPS-induced increase in cerebral F4-NeuroPs (Table 1). Further investigation of mice lacking iNOS, an element of secondary signaling pathways, also completely blocks ICV LPS-induced neuronal oxidative damage. Finally, mice lacking prostaglandin E2 receptor subtype 2 (EP2), one of four prostaglandin E2 (PGE2) receptors expressed in brain and one of the two PGE2 receptors expressed by microglia, have no neuronal oxidative damage in response to ICV LPS [16]. There are some important points to consider when interpreting these data. First, not only glia but neurons also will be exposed to LPS in this model. However, we and others have repeatedly shown that primary neurons enriched in cell culture do not respond to LPS [10,11,22-24]; indeed, neurons do not express CD14 and TLR-4 in vivo [25,26]. Second, genetic ablation was not specific to cell type. While this limits interpretation of data from some mice, such as p50 -/- and EP2-/- mice because these proteins are expressed by both neurons and glia [27-32], it does not influence interpretation of data from CD14 -/- mice because CD14 expression in vivo is restricted to microglia among parencymal cells in brain [25,26]. Thus, these data strongly imply that LPS-activated microglial-mediated paracrine oxidative damage to neurons in vivo is dependent on CD14, MyD88, p50 of NF-κB, iNOS, and EP2.
Table 1 Neuronal oxidative damage and dendritic degeneration in various knockout mice. Effects of ICV LPS treatment determined at 24 hr in mice homozygous deficient (knockout) for different genes or wildtype (wt) mice all on the C57Bl/6 genetic background (*P < 0.001 by Bonferroni-corrected repeated pair comparisons with ICV saline-exposed mice).
Knockout Function Endpoints*
F4-NeuroPs Dendrite Length Spine Density
None (wt) N/A 352 + 53* 32 + 4* 37 + 6*
CD14 Receptor 87 + 14 101 + 8 92 + 11
TLR-2 Receptor ---- 37 + 5* 51 + 8*
MyD88 Adaptor 98 + 10 96 + 9 102 + 7
p50 Initial Signal Cascade 108 + 11 105 + 7 106 + 10
iNOS Secondary Signaling 92 + 12 103 + 8 97 + 6
EP2 Secondary Signaling 89 + 9 102 + 12 109 + 5
*% ICV saline-exposed; n > 5 in each group
Dendritic degeneration
These data left us with an apparent conflict. We have clearly demonstrated neuronal oxidative damage to mouse cerebrum following ICV LPS that is of a magnitude comparable to diseased regions of AD brain [33]. However, there is no apparent structural damage to brain in our study or in others' following ICV or intraparenchymal LPS. We viewed this as a serious potential challenge to the significance of oxidative damage in neurodegeneration. There are differences, of course, between the acute stress of ICV LPS stress and the presumably chronic stress of AD; nevertheless, these data force at least consideration of the question: could oxidative damage to neurons occur in vivo to the extent that is observed in AD brain without any neurodegeneration?
To address this question, we decided to examine directly the dendritic compartment of neurons, which is largely transparent to the standard histological techniques used so far to investigate ICV LPS-induced damage. Using Golgi impregnation and Neurolucida-assisted morphometry of hippocampal CA1 pyramidal neurons [13], we first determined the time course of dendritic structural changes following ICV LPS in wt mice. Our results show a time course similar to neuronal oxidative damage with maximal reduction in both dendrite length and dendritic spine density at approximately 24 hr post LPS and, remarkably, a return to near baseline levels by 72 hr [14] (Figure 3).
Figure 3 Dendritic degeneration of CA1 pyramidal neurons in mouse hippocampus. Neurolucida renderings of CA1 pyramidal neurons stained by Golgi method; blue is soma and first order dendrites, red is second order dendrites, green is third order dendrites, yellow is fourth order dendrites, brown is fifth order dendrites, and pink is sixth order dendrites. A. Typical pyramidal neuron 24 hr after ipsilateral ICV Saline injection. B and C. Pyramidal neurons following ipsilateral ICV LPS injection showing moderate (B) to severe (C) dendrite shortening and spine loss.
We next pursued the molecular determinants of ICV LPS-induced dendritic degeneration using the same genetically altered mice that we used above (Table 1). We observed perfect concordance between these results in that lack of a gene that protected cerebrum from neuronal oxidative damage also protected hippocampal CA1 pyramidal neurons from dendritic degeneration and vice versa [14]. Importantly, we had the opportunity to add TLR-2 knockout mice to our analysis. TLR-2, like TLR-4, is one of the plasma membrane TLRs that may be activated by LPS and that also uses CD14 as a co-receptor. Our results show that lack of TLR-2 does not protect hippocampal CA1 pyramidal neurons from ICV LPS-induced neurodegeneration, while lack of CD14 completely protects the dendritic tree of these neurons. Further, it is interesting to note that in mice receiving ICV saline, pyramidal neuron dendrite length (Figure 4), but not spine density, is significantly greater in CD14-/- mice than in wt or MyB88-/- mice, suggesting that even in the absence of specific stimuli like ICV LPS, lack of CD14 perhaps has a net neuroprotective or neurotrophic effect.
Figure 4 Dendritic arbor in CA1 pyramidal neurons of hippocampus from knockout mice. Adult (6 to 8 week old) wt C57Bl/6, CD14-/-, or MyD88-/- mice received ICV saline 24 hr prior to sacrifice. Tissue sections of hippocampus and surrounding structures were processed for Golgi stain and then evaluated by Neurolucida. Data are dendrite length for CA1 hippocampal pyramidal neurons (n > 15 neurons for each group). One-way ANOVA had P < 0.0001 with Bonferroni-corrected repeated pair comparisons having *P < 0.001 for wt vs. CD14-/- and CD14-/- vs. MyD88-/-.
Pharmacologic interventions
Considerable controversy surrounds the effective in vivo neuroprotective doses of nonsteroidal anti-inflammatory drugs and anti-oxidants that are being evaluated as potenital protectants from AD. Indeed, a major criticism leveled against nonsteroidal anti-inflammatory drugs (NSAIDs) is that the concentrations that appear to be neuroprotective in epidemiologic studies are lower than those that classically considered anti-inflammatory doses. Moreover, there is some data suggesting that some NSAIDs, such as ibuprofen and naproxen, that may differ in their effectiveness as AD protectants despite being equivalent anti-inflammatory agents in peripheral assays of inflammation suggesting alternative mechanisms of action in AD [34]. Therefore, we determined the dose-response relationship for ibuprofen and naproxen in our ICV LPS model utilizing a two-week pre-treatment with each NSAID in drinking water (with concentration expressed as μg/ml drinking water) followed by ICV LPS injection [14]. Neither NSAID alone alters basal levels of cerebral F4-NeuroPs. For ibuprofen, the EC50 for suppressing ICV LPS-induced F4-NeuroPs is between 0.1 and 0.5 μg/ml and the maximal effect is reached by 1.4 μg/ml, considerably lower than the classic anti-inflammatory dose. In contrast, naproxen is without effect up to 1.4 μg/ml and thus an EC50 cannot be calculated from these data. As with F4-NeuroPs, ibuprofen completely protects both dendrite length and spine density (Figure 5) from the degenerative consequences of ICV LPS; in contrast, naproxen is not significantly protective even at the highest dose. These results are intriguing because some have suggested that ibuprofen may be more effective than naproxen in lowering the risk for AD [34]. The basis for the differing results with these NSAIDs in our experiments are not entirely clear but may derive from pharmacokinetic differences or pharmacodynamic differences in actions other than COX inhibition.
Figure 5 Pharmacologic suppression of dendritic degeneration in CA1 pyramidal neurons of mouse hippocampus. Adult (6 to 8 week old) wt C57Bl/6 mice received ICV saline or ICV LPS 24 hr prior to sacrifice. Tissue sections of hippocampus and surrounding structures were processed for Golgi stain and then evaluated by Neurolucida. Data are dendritic spine density for CA1 hippocampal pyramidal neurons (n > 6 neurons for each group). Two-way ANOVA had P < 0.001 for ICV saline vs. ICV LPS, effect of drugs, and interaction. Post hoc one-way ANOVA showed that no effect of drugs in ICV saline exposed mice. Ibuprofen and α-tocopherol completely protected spine density from ICV LPS exposure (P < 0.01 compared to vehicle treated mice) while naproxen and γ-tocopherol did not significantly protect (P > 0.05).
Next, we extended our studies to tocopherols, natural antioxidant products with a number of proposed actions [35] including both anti-oxidant and anti-inflammatory activities [36]. As with NSAIDs, α-tocopherol (AT) or γ-tocopherol (GT) alone does not alter basal F4-NeuroP levels or dendritie arbor (not shown). AT partially suppresses ICV LPS-induced F4-NeuroPs at 10 mg/kg and completely suppresses F4-NeuroP formation and both reduction in dendrite length and reduction in spine density at 100 mg/kg (Figure 5). GT, an isomer of AT that has one-tenth its anti-oxidant activity in vitro and lacks a specific transporter in vivo, does not, as expected, protect from neuronal oxidative damage or dendritic degeneration at the same dose.
Conclusions
Our data show that CD14-dependent activation of cerebral innate immunity leads to an acute, transient increase in oxidative damage to neuronal membranes that coincides with reversible dendritic degeneration. Although we did not directly test TLR-4 deficient mice in our studies, given what is know about LPS receptor activation and the fact that TLR-2-/- mice were not protected from neuronal damage caused by ICV LPS, these data argue strongly for CD14/TLR-4-dependent neuronal damage in our model. Moreover, using a wide array of genetically altered mice, we observed complete concordance between dendritic degeneration and neuronal membrane oxidative damage. In combination, these data suggest that these two events are mechanistically related, perhaps with neuronal membrane oxidative damage being a proximate contributor to dendritic degeneration in the context of innate immune activation.
One obvious, commonly voiced criticism of the model described here is that it produces an acute stress that does not correspond to chronic neurodegenerative diseases. However, it has yet to be shown whether the stress to individual neurons in these protracted diseases truly is chronic or instead the integration of innumerable microscopic acute stresses over many years. Finally, to the extent that CD14-dependent innate immunity activation contributes to neurodegenerative diseases, such as AD and HAD, the model described here provides a convenient means to screen experimental therapeutics and rapidly optimize dosing and timing parameters before moving to more complex animal models or clinical trials.
List of abbreviations used
AA: arachidonic acid; AD: Alzheimer disease; AT: α-tocopherol; Aβ: amyloid beta; COX-2: cyclooxygenase 2; DHA: docosohexaenoic acid; EP2: prostaglandin E2 receptor subtype 2; F2-IsoPs: F2-isoprostanes; F4-NeuroPs: F4-neuroprostanes; GT: γ-tocopherol; HAD: HIV-associated dementia; ICV: intracerbroventricular; iNOS: inducible nitric oxide synthase; LPS: lipopolysaccharide; MPO: myeloperoxidase; NSAIDs: nonsteroidal anti-inflammatory drugs; PG: prostaglandin; PGE2: prostaglandin E2; TLR: Toll-like receptor; wt: wild type.
Competing Interests
The authors declare that they have no competing interests.
Acknowledgements
This work was supported by the Alvord Endowed Chair in Neuropathology as well as grants from the NIH including AG05144, AG05136, and AG24011.
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| 15498098 | PMC527876 | CC BY | 2021-01-04 16:38:19 | no | J Neuroinflammation. 2004 Oct 21; 1:20 | utf-8 | J Neuroinflammation | 2,004 | 10.1186/1742-2094-1-20 | oa_comm |
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J NeuroinflammationJournal of Neuroinflammation1742-2094BioMed Central London 1742-2094-1-211550068410.1186/1742-2094-1-21ResearchBrain inflammation and oxidative stress in a transgenic mouse model of Alzheimer-like brain amyloidosis Yao Yuemang [email protected] Cinzia [email protected] Hanguan [email protected] John Q [email protected] Virginia MY [email protected]ò Domenico [email protected] Center for Experimental Therapeutics and Department of Pharmacology; University of Pennsylvania, School of Medicine, Philadelphia, PA 19104 USA2 Center for Neurodegenerative Disease Research; University of Pennsylvania, School of Medicine, Philadelphia, PA 19104 USA3 Institute on Aging; University of Pennsylvania, School of Medicine, Philadelphia, PA 19104 USA2004 22 10 2004 1 21 21 22 9 2004 22 10 2004 Copyright © 2004 Yao et al; licensee BioMed Central Ltd.2004Yao et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
An increasing body of evidence implicates both brain inflammation and oxidative stress in the pathogenesis of Alzheimer's disease (AD). The relevance of their interaction in vivo, however, is unknown. Previously, we have shown that separate pharmacological targeting of these two components results in amelioration of the amyloidogenic phenotype of a transgenic mouse model of AD-like brain amyloidosis (Tg2576).
Methods
In the present study, we investigated the therapeutic effects of a combination of an anti-inflammatory agent, indomethacin, and a natural anti-oxidant, vitamin E, in the Tg2576 mice. For this reason, animals were treated continuously from 8 (prior to Aβ deposition) through 15 (when Aβ deposits are abundant) months of age.
Results
At the end of the study, these therapeutic interventions suppressed brain inflammatory and oxidative stress responses in the mice. This effect was accompanied by significant reductions of soluble and insoluble Aβ1-40 and Aβ1-42 in neocortex and hippocampus, wherein the burden of Aβ deposits also was significantly decreased.
Conclusions
The results of the present study support the concept that brain oxidative stress and inflammation coexist in this animal model of AD-like brain amyloidosis, but they represent two distinct therapeutic targets in the disease pathogenesis. We propose that a combination of anti-inflammatory and anti-oxidant drugs may be a useful strategy for treating AD.
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Introduction
Alzheimer's disease (AD) is the most common, complex and challenging form of neurodegenerative disease associated with dementia in the elderly. Neuropathological examination of the AD brain shows extensive neuronal loss, accumulation of fibrillar proteins as extra-cellular amyloid β (Aβ) plaques, and as neurofibrillary tangles (NFTs) inside neurons [1]. However, besides these pathological hallmarks, AD brains exhibit clear evidence of chronic inflammation and oxidative damage [2,3]. Currently, data from human studies as well as animal models strongly support the concept that oxidative imbalance and subsequent oxidative stress are among the earliest events in the pathogenesis of AD [4,5]. Thus, an increase in lipid peroxidation, protein oxidation and DNA oxidation has been reported not only in AD patients, but also in subjects with mild cognitive impairment (MCI) [6,7]. Similarly, immunohistochemical and biochemical evidence for these signatures of oxidative stress have been shown in animal models of AD-like brain amyloidosis, i.e. the Tg2576 transgenic mouse model thereof [8-10]. Chronic neuroinflammation is another constant feature of AD, and this also is thought to play a significant role in the onset and progression of AD. Support for this hypothesis comes from epidemiological studies showing that prolonged use of nonsteroidal anti-inflammatory drugs (NSAIDs) decreases the risk of developing AD as well as delaying the onset of this disorder [11], while many mediators of inflammation have been detected in the AD brain [12]. Further, recent studies in AD mouse models have shown that chronic treatment with a subset of NSAIDs (e.g. ibuprofen, flurbiprofen, indomethacin) reduced brain inflammation and Aβ levels in addition to the deposition of Aβ in brain [13,14]. Despite this evidence, and the considerable theoretical and therapeutic interest, the relationship between brain inflammation responses and oxidative stress has not yet been clearly delineated in AD. For example, its is possible to consider these two events as elements of the same response mechanism, or they can be envisaged as two separate events. Alternatively, they also could work in concert to contribute synergistically to the pathogenesis of AD.
In the present study, we examined whether the simultaneous administration of an anti-oxidant, vitamin E, with an anti-inflammatory drug, indomethacin, would exert an additive anti-amyloidogenic effect in the Tg2576 mouse model of AD-like Aβ brain amyloidosis, one of the most extensively studied mouse models of AD [15]. Significantly, we found for the first time that coincidental suppression of brain oxidative stress further augments the anti-amylodogenic effect of indomethacin.
Materials and Methods
Animals
The genotype and phenotype features of the heterozygote Tg2576 mice that we studied here have been described in earlier reports on these mice from our group [9]. Mice were weaned at 4 weeks, kept on a chow diet, and males were always separated from females for the entire study. Eight-month-old Tg animals were divided in two groups (n = 10 each), and randomized to receive placebo, or simultaneously indomethacin (10 mg/liter) in their drinking water, and vitamin E (α-tocopherol) in their diet (2 I.U./mg diet) for seven months before being sacrificed. The detailed dosing of the animals receiving indomethacin or vitamin E alone (at the same concentration used in the present study) were described in two previously published studies which also included data on numerous non-transgenenic littermate controls of the Tg2576 mice [16,17]. Fresh drinking water and diet were always replaced every other day. Preliminary experiments demonstrated that the selected dose of indomethacin suppressed total cylcooxygenase-1 activity in vivo and significantly reduced brain inflammation [16]. The high dose of vitamin E was selected based on a previous study, which indicated that at this concentration it significantly reduced brain oxidative stress response [17]. During the study, all mice gained weight regularly, and no significant difference was detected between the two groups.
Tissue preparation
Animals were anesthetized and euthanized following procedures recommended by the Panel on Euthanasia of the American Veterinary Medical Association. They were always perfused intra-cardially for 30 min with ice-cold 0.9% phosphate buffer saline (PBS), containing EDTA (2 mM/L) and BHT (2 mM/L), pH7.4. Brains were removed and one hemisphere was fixed by immersion in 4% paraformaldehyde in 0.1 M PBS (pH7.4) at 4°C overnight, blocked in the coronal plane, and embedded in paraffin as previously described for immunohistochemistry [9,16,17], The other hemisphere was gently rinsed in cold 0.9% PBS, then immediately dissected in three anatomical regions (total cerebral cortex, hippocampus, and cerebellum) for biochemistry.
Biochemical analysis
Tissue samples were minced and homogenized, and total lipid extracted with ice-cold Folch solution (chloroform: methanol; 2:1, vol/vol). Lipids were subjected to base hydrolysis by adding aqueous 15% KOH and then incubated at 45°C for 1 hr for measurement of total iPF2α-VI by ion chemical ionization gas chromatography/mass spectrometry assay, as previously described [9,16,17]. In brief, a known amount of the internal standard is added to each sample, after solid phase extraction samples are derivatized and purified by thin layer chromatography, and finally analyzed. An aliquot of these extracts was assayed for total levels of PGE2 and TxB2 by a standardized ELISA kit following the manufacturer's instructions (Cayman Chem. Com.). Briefly, extracts were diluted with acetate buffer and purified through an affinity column. The purified samples were evaporated, re-dissolved in the assay buffer and applied to 96-well plates pre-coated with goat anti-serum IgG and incubated with PGE2 or TxB2 monoclonal antibodies. The plates were rinsed with washing buffer and developed using Ellman's reagent for 60–90 min at room temperature with gentle shaking. Specific concentrations were determined spectrophotometrically and expressed as pg/mg tissue.
IL-1β levels were measured by a standardized sandwich ELISA kit following the manufacturer's instructions (Endogen Pierce). Briefly, equal amounts of sample were loaded onto 96-well plate pre-coated with monoclonal antibody against mouse IL-1β overnight at 4°C. The plates were rinsed three times with washing buffer and developed with streptavidin-horseradish peroxidase (HRP) [13]. Specific concentrations were determined spectrophotometrically and expressed as pg/mg protein.
Total protein carbonyls in tissue were determined by using the Zenith PC test kit according to the manufacturer's instructions (Zenith Tech.) [18]. Briefly, aliquots of the tissue homogenates were first reacted with dinitrophenylhydrazine (DNP), transferred to a multi-well plate, incubated with blocking reagent, washed and probed with anti-DNP-biotin solution. After washing, samples were incubated with streptavidin-HRP, washed again, and then developed. After 15 min, the reaction was stopped and absorbance immediately read at 450 nm. Oxidized protein standards, internal controls and blanks were always assayed at the same time and in the same way. All samples were always determined in triplicate and in a blind fashion.
Immunoblot analysis
An aliquot of brain homogenates was electrophoresed on a 10% acrylamide gel under reducing conditions. Protein were transferred to a polyvinylidene membrane before blocking in 10% nonfat dry milk for 2 hr. Blots were incubated with monoclonal antiboby against glial fibrillary acidic protein (GFAP) (2.2B10) (1:1,000), or an anti-beta actin (1:5,000) antibody overnight at 4°C. After three rinses, blots were incubated with HRP-conjugated goat anti-mouse for 45 min before development with chemiluminescent detection system using ECL (Amersham). Bands were quantified using densitometric software (Molecular Analyst). The anti-GFAP is a monoclonal antibody, and its characterization has previously been published [19] (Zymed Lab. Inc.). The anti-beta actin is from commercial sources (Novus Biological).
Brain Aβ1-40 and Aβ1-42 levels
Sequential extraction of brain samples was performed with high-salt buffer and formic acid, respectively to measure soluble and insoluble Aβ1-40 and Aβ1-42 levels, as previously described [9,16,17]. Briefly, cerebral cortex, hippocampus and cerebellum were serially extracted in high-salt Re-assembly buffer (0.1 M Tris, 1 mM EGTA, 0.5 nM Mgso4, 0.75 M NaCl, and 0.02 M NaF, pH 7.0) containing protease inhibitor mixture (pepstatin A, leupeptin, N-tosyl-L-phenylalanine chloromethyl ketone, soybean trypsin inhibitor, each at l μg/ml in 5 mM EDTA). Homogenates were centrifuged at 100,000 × g for 1 hr at 4°C. Supernatants were removed, pellets were re-suspended in 70% formic acid and sonicated and centrifuged at 100,000 × g for 1 hr at 4°C. Supernatants were diluted 1:20 with 1 M Tris base. Samples were mixed with buffer EC [0.02 M sodium Phosphate, 0.2 mM EDTA, 0.4 M NaCl, 0.2% BSA, 0.05% CHAPS, 0.4% Block-ace (Dainippon, Suita, Osaka, Japan), 0.05% sodium azide, pH7.0] and analyzed directly using Ban 50/BA27 for Aβ1-40 or Ban50/BC-05 for Aβ1-42/43 sandwich ELISA system as previously described [16,17]. Results were expressed as pmol/g tissue. The values were calculated by comparison with a standard curve of synthetic Aβ1-40 and Aβ1-42. Analyses were always performed in duplicates and in a coded fashion.
Burden of brain Aβ deposits
Serial 6-μm-thick paraffin sections were cut throughout each brain, and mounted on APES-coated slides. Sections were deparaffmized, hydrated, rinsed with PBS and pre-treated with formic acid (88%) for 10 min to antigen retrieval, and with 3% H202 in methanol for 30 min to eliminate endogenous peroxidase activity in the tissue and with the blocking solution (5% normal horse serum in Tris buffer, pH 7.6). Subsequently, sections were incubated with a biotinylated antibody against Aβ (4G8) (1:10,000 dilution), at 4°C overnight [16,17]. Sections were then incubated with secondary antibody for 1 hr (dilution 1:1,000), then reacted with horse-peroxidase-avidin-biotin complex (Vector Lab.), and immuncomplexes visualized by using 3,3'-diaminobenzidine as the chromogen. Finally, they were dehydrated with ethanol, cleared with xylene and coversliped with Cytoseal. As control, sections from the same group of animals were treated in the same manner, except for the primary antibody. Light microscopic images from the somatosensory cortex, perihippocampal cortex, and hippocampus were captured from eight series of sections using a Nikon Microphot-FXA microscope with 4 × objective lens. The area occupied by Aβ-immunoreactive products in the region of interest were identified, and the total area occupied by the outlined structures was measured to calculate: 1) the total area with selected immunoreactive products, 2) the percentage of the area occupied by immunoreactive products over the outlined anatomical area in the image, as previously described [9,16,17]. Analyses were always performed in a coded fashion.
Statistical analysis
Data are expressed as mean ± standard error of mean (S.E.M.), analyzed by analysis of variance (ANOVA), and subsequently by student unpaired 2-tailed t test corrected for multiple comparisons. Significance was set at p < 0.05.
Results
Starting at eight months of age, Tg2576 mice were randomized to receive placebo or vitamin E (2 I.U./mg diet) added to their diet, plus indomethacin (l0 mg/liter) in their water, and they were treated until they were 15 months old. Notably, at 8 months of age, the Tg2576 mice show elevated brain levels of soluble and insoluble Aβ as well as isoprostanes, relative to their non-transgenic littermates, but they show no evidence of any brain Aβ deposits, while following the initial onset of mature plaque-like brain deposits at about 11–12 months of age, the Tg2576 mice show abundant Aβ deposits and higher levels of isoprostanes in neocortex and hippocampus a 15 months of age [9,15,20]. Assuming that each mouse eats 4–5 mg chow/day, the estimated average vitamin E intake for each animal was ~8–10 I.U./day. Assuming that each mouse drinks 3 to 4 mL water/day, the estimated daily intake of indomethacin was calculated around 30–40 ng. At the end of the study, body weight, total plasma cholesterol, triglycerides and peripheral blood cell count were not different between placebo and active treatment (not shown). Compared with placebo, Tg2576 mice receiving indomethacin plus vitamin E at the same time had a significant reduction in PGE2 and suppression of TxB2 levels in tissue homogenates from total cortex and hippocampus (Table 1). Further, the presence of vitamin E significantly reduced two independent markers of oxidative stress injury in both brain regions. Thus, neocortex and hippocampus levels of iPF2α-VI (a reliable biomarker of lipid peroxidation), as well as protein carbonyls (known biomarkers of protein oxidation) were both significantly decreased (Figure 1). Compliance with the diet was evident from the rise in brain levels of vitamin E (+57%) in the mice receiving the supplemented chow.
Table 1 Effects of indomethacin plus vitamin E on total brain cortex levels of PGE2, TxB2 and IL-1β in Tg2576 mice. Mice were treated starting at 8-months of age until they were 15-month-old (n = 10 animals per group).
Placebo Indomethacin Vitamin E P
PGE2 (pg/mg tissue) 92 ± 8 39 ± 5* <0.01
TxB2 (pg/mg tissue) 148 ± 10 15 ± 4* <0.001
IL-1β (pg/mg protein) 75 ± 12 33 ± 8* <0.01
Values are expressed as means ± S.E.M.
Figure 1 Effect of indomethacin plus vitamin E supplementation on markers of brain oxidative stress. Total cerebral cortex homogenates from Tg2576 receiving placebo (open bars) or the combination therapy (closed bars) were assayed for levels of iPF2α-VI (upper panel) and protein carbonyls (lower panel) (*p < 0.01, n = 10 per group).
Western blot analysis was used to determine the effect of the drug treatment on GFAP levels, a marker of astrocytosis [13]. These levels were significantly lower in the treated than in the placebo group (Figure 2). Another marker of brain inflammation was also assessed, i.e. IL-1β, which has been reported to be increased in these mice [13]. Compared with placebo, we found that IL-1β levels were significantly reduced by 55% in homogenates from neocortex (Table 1), and 61% in hippocampus (not shown) of the mice receiving the combination therapy.
Figure 2 Effect of indomethacin plus vitamin E supplementation on GFAP levels. GFAP and actin levels were detected by immunoblots in homogenates from total cortex of Tg2576 administered with placebo (open bars) or indomethacin plus vitamin E (closed bars) (*p < 0.02, n = 8 per group).
Next, we assessed the effect of indomethacin and vitamin E on brain levels of soluble and insoluble Aβ1-40 and Aβ1-42. As expected for their age, Tg2576 mice on placebo showed elevated levels of both forms of these peptides in their cortex and hippocampus (Figure 3), whereas cerebellum had much lower levels (not shown). Soluble Aβ1-40 and Aβ1-42 were reduced by ~65% in both neocortex and hippocampus homogenates from treated mice (Figure 3). Further, we found that the combination of vitamin E with indomethacin resulted in a significant reduction (55% and 59%) of the insoluble fraction of these peptides in both brain regions (Figure 4). The same treatment had no effect on both forms of Aβ in the cerebellum of Tg2576 compared with placebo (not shown). Amyloid deposits were widely present in the cerebral cortex and hippocampus of Tg2576 mice at 15 months of age, as previously reported [15,20]. To determine the effect of this treatment on amyloid deposition, the areas occupied by 4G8-immunopositive reactions were analyzed in three different brain regions: the somatosensory cortex (SSC), perihippocampal cortex (PHC), and hippocampus (HIP) areas. Comparison of the burden of Aβ positive deposits between placebo and combination therapy groups revealed a significant reduction for the amyloid burden in all three regions considered (Figure 5, 6).
Figure 3 Effect of indomethacin plus vitamin E supplementation on soluble Aβ levels. Levels of high salt soluble Aβ1-40 and Aβ1-42 in total cortex and hippocampus of Tg2576 on placebo (open bars), or indomethacin plus vitamin E (closed bars) (*p < 0.01, n = 8 per group).
Figure 4 Effect of indomethacin plus vitamin E supplementation on insoluble Aβ levels. Levels of formic acid soluble Aβ1-40 and Aβ1-42 in total cortex and hippocampus homogenates from Tg2576 receiving placebo (open bars) or indomethacin plus vitamin E (closed bars) (*p < 0.001, n = 8 per group).
Figure 5 Effect of indomethacin plus vitamin E supplementation on amyloid deposition. Percentage area of somatosensory cortex (SSC), hippocampus (HIP) and parahippocampal cortex (PHC) occupied by Aβ immunoreactive deposits in Tg2576 receiving placebo (open bars), or indomethacin plus vitamin E (closed bars) for seven months (*p < 0.001; n = 8 per group).
Figure 6 Representative pictures of brain sections from mice on placebo or receiving indomethacin plus vitamin E.
Discussion
There is substantial evidence implicating both oxidative stress and inflammatory mechanisms in AD pathogenesis. Evidence for oxidative stress derives from both human (post-mortem and living patients) studies, and transgenic mouse models of the disease. There is a long list of surrogate markers of reactive oxygen species-mediated injury that have been found increased in the brain and cerebrospinal fluid of AD patients. It includes, just to mention a few, malondialdehyde, 4-hydroxynonenal, F2-isoprostanes (lipid peroxidation); protein carbonyls, nitrotyrosine (protein oxdidation); 8-hydroxy-2'-deoxyguanosine (DNA oxidation) [3-5]. Transgenic animals show the same type of oxidative damage that is found in AD, and it directly correlates with the presence of Aβ deposits [8,10,21]. Oxidative stress also precedes amyloid deposition in human AD, the Tg2576 and a transgenic Caernorhabditis elegans model, which over-expresses Aβ1-42 [9,22,23]. Furthermore, dietary or genetic perturbation of the anti-oxidant defense system causes exacerbation of the amyloid pathology characteristic of Tg models [24,25]. Taken together, the data accumulated so far clearly indicate that oxidative imbalance and subsequent chronic oxidative stress are not only early events, but they also play a functional role in AD pathogenesis. Based on this evidence we started the treatment at an early stage before the amyloid deposition occurs.
Inflammatory mechanisms are also operative in the AD brain and significantly contribute to the pathophysiology of the disease. Although classical defined inflammation, including such features as edema and neutrophil invasion, is not seen in the AD brain, hallmark of innate immune response are constant elements of the neuropathology associated with brain degeneration in AD [12]. Further, evidence that inflammation contributes to the AD pathogenesis stems out from several retrospective epidemiological studies showing a significant reduction in the risk of AD associated with a prolonged usage of NSAIDs [11]. Tg2576 mice display age-related neocortical and hippocampal amyloid deposits, which correlate with microglia activation, reactive astrocytes with increased GFAP, IL-1β levels, and dystrophic neuritis [13,26]. Furthermore, plaque-associated reactive microglia in these animals show enhanced staining for TNFα and IL-1β [27].
Lim et al. first reported that chronic administration of the NSAID ibuprofen to Tg2576 reduces total Aβ levels, amyloid burden and brain inflammation [13]. More recently, we showed that a high dose of indomethacin, another NSAIDs member, which fully suppresses total cyclooxygenase (COX)-l activity, by modulating brain inflammation response reduces soluble Aβ1-40 and Aβ1-42, and insoluble Aβ1-42 but not Aβ1-40 levels in the same model. This effect was accompanied by a significant reduction of the amyloid burden in the hippocampus of these mice [16]. However, recent studies indicate that a subset of NSAIDs, including indomethacin, also possesses a direct, COX-independent Aβ-lowering capacity in cell cultures as well as transgenic models [28]. Further, we showed that vitamin E alone at the same high dose used in this study decreased soluble and insoluble Aβ1-40 and Aβ1-42 levels by ~28% and ~35%, respectively. This effect was associated with a significant reduction in amyloid deposition in the somatosensory cortex, but not in the hippocampus or parahippocampal areas [17].
In the present study, we extended these previous observations by examining whether administration of indomethacin in combination with vitamin E would result in a better anti-amyloidotic effect. Our findings show that soluble Aβ1-40 and Aβ1-42 levels were reduced by ~65%, while the insoluble fractions were decreased by ~55%. Consistently, we observed a better and more diffuse effect also on the amount of amyloid deposited in the brain at the end of the study. Finally, the two drugs together produced an additive affect on brain inflammation and oxidative stress [16,17].
Our results confirm previous observation where low-dose curcumin, a drug with reported both anti-oxidant and anti-inflammatory activities, reduced total Aβ and plaque burden [29]. However, several other mechanisms of action, unrelated to inflammation or oxidation, could underlie the effect of this compound in vivo, and the relative importance of each of them for the anti-amyloid effect observed is still unclear [30].
In our study, we used two different drugs with a more restricted therapeutic target to provide further evidence that both oxidative stress and inflammation are indeed functionally relevant in the development of the phenotype of these animals. However, we also provide new information on the critical issue of the in vivo relationship between these two events. Thus, our results suggest that brain inflammation and oxidative stress are two separate events, which work in concert to modulate the development of this AD-like brain Aβ amyloidosis model. Previously, we have shown that a full dose of indomethacin alone despite a significant reduction in brain inflammation had only a marginal effect on brain oxidative stress in the Tg2576 mice [16]. This finding suggests that lipid peroxidation products contribute minimally to brain inflammation in this model, and raise the possibility that vitamin E alone might have influenced amyloidosis by other mechanisms related to its anti-oxidant effect, such as inflammation. Thus, we observed that this antioxidant further suppressed both amyloidosis and brain inflammation when combined with indomethacin.
In summary, our findings support the hypothesis that oxidative stress and inflammation represent important but distinct therapeutic targets in AD-like amyloidosis. We conclude that a combination of therapeutic agents targeting these different disease-modulating mechanisms might be rationally evaluated in the prevention or therapy of AD in humans.
List of abbreviations
AD: Alzheimer's disease
Aβ: Amyloid β peptide
Tg: Transgenic mouse model
NSAIDs: Non-steroidal anti-inflammatory drugs
PGE2: Prostaglandin E2
TxB2: Thromboxane A2
GFAP: Glial fibrillary acidic protein
IL-1β: Interleukin 1-β
IPF2α-VI: Isoprostane F2α-VI
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
Yuemang Yao and Cinzia Chinnici have made substantial contribution to the acquisition of data and biochemical analyses. Hanguan Tang contributed to the immunohistochemical analyses. John Q. Trojanowski and Virginia M-Y Lee have been involved in the interpretation of data, and the critical revision of the manuscript for intellectual content. Domenico Praticò has been involved in the conception and design of the studies, interpretation of data, drafting and critical revision of the manuscript.
Acknowledgements
This work was supported by grants form the National Institute of Health (AG-11542, AG-22512), the Alzheimer's Association (IIRG-02-4010), and the CART Fund. We thank Dr. Karen Hsiao (now Dr. Karen Ashe) for the generous gift of the Tg2576 line of mice, and Ms. Susan Leight for assistance with the ELISAs.
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| 15500684 | PMC527877 | CC BY | 2021-01-04 16:38:19 | no | J Neuroinflammation. 2004 Oct 22; 1:21 | utf-8 | J Neuroinflammation | 2,004 | 10.1186/1742-2094-1-21 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-151548813910.1186/1471-2369-5-15Research ArticleAtherosclerotic renal artery stenosis: one year outcome of total and separate kidney function following stenting Coen Giorgio [email protected] Eleonora [email protected] Carlo [email protected] Raffaella [email protected] Italo [email protected] Giuseppe [email protected] Daniela [email protected] Alvaro [email protected] Rosario [email protected] Renal Pathophysiology and Hypertension Unit, Dept of Medical Sciences, La Sapienza University, Rome, Italy2 Dept. of Radiology, La Sapienza University, Rome Italy3 6th Medical Clinic, La Sapienza University, Rome, Italy4 Dept.Experimental Medicine and Pathology, La Sapienza University, Rome, Italy5 Dept. Of Vascular Surgery, La Sapienza University, Rome, Italy2004 15 10 2004 5 15 15 14 6 2004 15 10 2004 Copyright © 2004 Coen et al; licensee BioMed Central Ltd.2004Coen et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Renal artery stenosis (RAS) is a known cause of hypertension and ischemic nephropathy. Stenting of the artery is a valid approach, in spite of cases of unexpected adverse evolution of renal function.
Methods
In this study, 27 patients with unilateral RAS were subjected to stenting and followed for a period of one year, while 19 patients were observed while on medical treatment only. The group of 27 patients, 67.33 ± 6.8 years of age, creatinine of 2.15 ± 0.9 mg/dl, following stenting, were followed at intervals with biochemical tests, renal scintigraphy and doppler ultrasonography. The control group (70.0 ± 6.1 years, creatinine 1.99 ± 0.7 mg/dl) was also followed for one year.
Result
One year after stenting mean creatinine clearance (Ccr) increased from 36.07 ± 17.2 to 40.4 ± 21.6 ml/min (NS). Arterial BP, decreased after 1,3,6, and 12 months (p < 0.05). The number of antihypertensive drugs also decreased (p < 0.05). A significant increase in proteinuria was also observed. In the control group both Ccr, BP and proteinuria did not show significant changes. Based on renal scintigraphy and Ccr at subsequent times, it was possibile to evaluate the timecourse of renal function in both kidneys of the stented patients. In the stented kidneys Ccr increased significantly. On the controlateral kidney a decrease of renal function (p < 0.05) was observed. Resistance index appeared to be a risk factor of the functional outcome.
Conclusions
Stenting of RAS due to atherosclerosis is followed by stabilization or improvement of Ccr, mainly at the stented kidney, while contralateral renal function showed a decrease.
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Background
Renal artery stenosis due to atherosclerotic changes of the renal arteries has become a serious concern as a cause of hypertension and renal ischemia, resulting frequently in end-stage renal failure [1,2]. Several epidemiologic studies have shown the elevated prevalence of ischemic nephropathy, with special regard to atherosclerotic renal artery stenosis, in elderly patients [3,4]. Instead of the classical surgical approach, percutaneous balloon angioplasty or endovascular stenting have recently become accepted procedures in the attempt to revascularize the stenotic kidney and prevent chronic renal insufficiency. However, in spite of the arterial dilatation obtained with these procedures, there is still some doubt that the long-term outcome is in general satisfactory [5]. There is currently no clear evidence that such interventions prevent further progressive decline of renal function. However the results have been somewhat different in different case series [6,7]. It is known that there are patients with satisfactory results in terms of improvement or stabilization of renal function, while some cases may deteriorate renal function in spite of the dilating procedure [8,9]. As for the results of stenotic artery dilatation procedure on blood pressure, most of reports have confirmed a significant fall in systolic and diastolic blood pressure [10,11], an important finding which however cannot justify the stenting procedure if not accompanied by a consensual improvement in kidney perfusion and stabilization or improvement of renal function. Therefore the purpose of many researchers has been to identify the risk factors which might exclude patients from the revascularizing procedure, due to predictable poor outcome. Radermacher et al. [12] have identified the resistance index (RI) as an important factor predicting the outcome of the stenting. In addition, a limited number of studies [9,13,14] have evaluated not only the overall renal glomerular filtration following the dilating procedure, but also the individual function of the stented and contralateral kidneys. The results are interesting since the behaviour of the two kidneys after the one-sided dilating procedure was found to be divergent. This study provides further data on the evaluation of the two kidneys with a follow-up of one year.
Methods
The study has been carried out prospectively on 46 patients affected by hemodynamically significant atherosclerotic renal artery stenosis, detected by Magnetic Resonance Angiography or Selective Digital Angiography. All the patients had a unilateral stenosis. 27 patients (diabetes mellitus in 8 cases) were subjected to stenting of the stenotic renal artery while 19 patients (diabetes mellitus in 9 cases) were kept on medical treatment only. Clinical data of the two groups are reported in table 1. Patients were allotted to the control group in case of refusal of the invasive procedure. All patients had a stenosis judged by ultrasonography to be above 70%.
Table 1 Clinical and biochemical data of the stented and control groups
Stented Control
Patient, n° 27 19
age, years 67,3 ± 6,8 70.0 ± 6,1 n.s
M/F, n° 17/10 15/4 n.s.
Systolic BP, mmHg 169,1 ± 23 165,8 ± 24,7 n.s.
Diastolic BP, mmHg 89,1 ± 14,8 87,4 ± 13,4 n.s.
Creatinine, mg/dl 2,15 ± 0,9 1,99 ± 0,7 n.s.
Cr. Clearance, ml/min 36,1 ± 17,3 34,6 ± 15,6 n.s.
Urea, mg/dl 73,2 ± 36,7 75,5 ± 29,2 n.s.
Tot. Cholesterol, mg/dl 236,9 ± 33,8 241,1 ± 41,2 n.s.
Tryglicerides, mg/dl 181,2 ± 87,6 166,1 ± 82,8 n.s.
Sodium, mEq/L 139,7 ± 5,2 142,0 ± 3,3 n.s.
Potassium, mEq/L 4,34 ± 0,5 4,7 ± 0,6 n.s.
Proteinuria, mg/24 h 308 ± 323 545 ± 572 n.s.
Uric acid, mg/dl 6,8 ± 1,4 6,6 ± 2,3 n.s.
Resistance Index (DDS) 0,76± 0,11 (25) 0,79± 0,04 n.s.
Severity of stenosis (%) 78,8 ± 8,66 (25) 79,06 ± 9,0 n.s.
% %
Smoker 14/27 51,9 12/19 63,2 n.s.
Hypertension 25/27 92,6 19/19 100 n.s.
Diabetes Mellitus 8/27 29,6 9/19 47,4 n.s.
Dyslipidemia 17/27 63 15/19 78,9 n.s.
Peripheral arterial insuff. 17/27 63 13/19 76,5 n.s.
In all these patients the renal artery was approached through the femoral artery. French 6 guiding catheter (type "Cobra"or "Bates") was used for selective renal artery angiography and for positioning the stent. All stenotic lesions were repaired with stainless stent Express Vascular SD Monorail 5.5-6-15/20 mm.premounted on a balloon catheter on Choice extra support 014" guide. In these cases primary stenting was performed. The procedure requires, usually, an injection containing 30 ml of 50–50 mixture of isotonic contrast and normal saline.
The patients were followed at the outpatient clinic of the Nephrology unit. Duplex-doppler sonography and renal scintigraphy were carried out basally and following 1,3,6 and 12 months after the stenting procedure. At the same times, biochemical parameters, like serum creatinine and proteinuria were measured. Creatinine clearance was evaluated with the Cockcroft and Gault formula [15].
Doppler ultrasonography was carried out after fasting, following a three days of a low fibre diet and without smoking for a minimum of six hours before the procedure. Patients were studied with an Acuson 120 XP/4 (Acuson Corp., Mountain View, CA), equipped with a 3.5 MHz transducer, with longitudinal anterior, lateral and oblique approach, with at least threefold sampling of parameters along the artery. The standard criteria for the diagnosis of significant renal artery stenosis have been previously reported [4].
Renal radionuclide scintigraphy was performed with a gamma camera (Starcam 4000, General Electric, USA) with 99mTc-DTPA or with 99mTc-MAG3 (mercaptoacetyltriglycine). MAG3 was chosen in patients with creatinine clearance <25 ml/min. Diuretics and/or ACE inhibitors were discontinued at least three days before, if treatment was underway. The criteria of positivity have previously been reported [4].
Evaluation of single kidney renal creatinine clearance was performed by measurement of creatinine clearance and simultaneous renal scintigraphy with MAG3, with percentage-wise function of each kidney, enabling to calculate the creatinine clearance pertaining to each kidney. The entire set of data for this evaluation was available in 21/27 patients.
Statistical analysis. A descriptive univariate analysis, consisting in evaluation of percentages, means and standard deviations has been carried out as first step. To evaluate the dependence among the nominal variables, a Parson's Chi square test was also carried out. In case of not applicable Chi square test due to low theoretical frequencies (<5), Fischer exact test for tables 2 × 2 was employed. Comparison of means of the two groups was made. Since the data did not show a normal distribution, non parametric tests, as Wilcoxon rank-sum test for paired data and Mann-Whitney test, were employed. The significance level was <0.05, as usual. The more interesting significant and not significant data were represented graphically. The data were evaluated with the statistical package BMDP Release 7 (Cork, Ireland,1997).
Results
Clinical and biochemical data of the treated and control groups are reported in Table 1. There was no significant difference between experimental and control groups.
During 12 months observation period one patient of the stented group began dialysis treatment, while in the control group 4 patients died of cardiovascular events and one patient started the dialysis treatment.
A significant drop in systolic and diastolic blood pressure at all control times compared to basal values, was found in the stented patients, while no significant blood pressure drop was found in the patients not undergoing the PTA stenting procedure (Table 2). A significant fall in number of antihypertensive drugs was also found at 3 and 6 months after the stenting.
Table 2 Timecourse of blood pressure (mm Hg), number of antihypertensive drugs and proteinuria (mg/24 h) in the stented and control groups
STENTED GROUP
Systolic BP Diastolic BP n° antihypertensive drugs Proteinuria
Basal 169 ± 23 89 ± 14,8 2,07 ± 1,1 309 ± 323
1 m 155 ± 12,9* 81,9 ± 6,77* 1,93 ± 1,07 764 ± 690*
3 m 157 ± 17,7* 82,0 ± 6,31* 1,6 ± 1,05* 1381 ± 2160
6 m 148 ± 12,2* 81,4 ± 5,39* 1,42 ± 0,93* 1743 ± 2884
12 m 152 ± 14,4* 81,2 ± 8,87* 1,7 ± 1,17 1377 ± 1643*
CONTROL GROUP
Systolic BP Diastolic BP n° antihypertensive drugs Proteinuria
Basal 165,8 ± 24,6 87,4 ± 13,4 2,37 ± 0,83 545 ± 572
3 m 163,7 ± 16,5 83,0 ± 10,2 2,26 ± 0,93 534 ± 404
6 m 155,6 ± 20,4 88,1 ± 8,45 1,82 ± 0,73 812 ± 536
12 m 154,4 ± 20,8 87,1 ± 8,21 2,0 ± 0,94 225 ± 368
(*) = Significant difference compared to basal value (p < 0,05)
A significant increase in proteinuria following the stenting was found at 1 and at 12 months, while the increment in proteinuria observed at the other control times was only borderline significant. There was no diference in the increase of proteinuria between patients with and without diabetes mellitus. No changes in proteinuria was observed in the control group.
An increase in creatinine clearance and a slight fall in serum creatinine, however not reaching a significance level, was observed in the stented group, while no increment in creatinine clearance was found in the control group (Table 3). This observation is however limited by the fall in the number of the control group due to death and beginning of dialysis in a total of 5 patients. However the analysis of the separate renal function in the stented and non stented kidneys of the experimental group showed differences in behaviour at the two sides. An increment in the percentage of total glomerular filtration in the stented kidney as a group was found, while a significant fall in percentage of filtration was found on the controlateral side (Fig. 1).
Table 3 Evolution of creatinine clearance (ml/min) and serum creatinine (mg/dl).
STENTED GROUP CONTROL ROUP
Global Ccr Serum Cr Global Ccr Serum Cr
Basal 36,07 ± 17,2 (27) 2,15 ± 0,94 34,78 ± 15,5 (19) 1,99 ± 0,72
1 m 35,29 ± 16,39 2,32 ± 1,33 - -
3 m 34,78 ± 14,38 2,18 ± 0,76 29,99 ± 12,4 2,13 ± 0,73
6 m 37,03 ± 18,0 2,15 ± 0,94 33,84 ± 17,9 2,01± 0,66
12 m 40,42 ± 21,63 (26) 2,03 ± 0,73 34,78 ± 14,3 (14) 1,98 ± 0,56
Figure 1 Evolution of percent GFR in the stented and controlateral kidneys.
In addition, patients with the stent were divided in those cases with a RI above 0.80 and cases with this parameter below 0.80. The patients with lower RI improved, on average, renal function while the patients with elevated RI had a worse outcome (Fig. 2). RI values correlated negatively with changes in creatinine clearance from baselines (r = -0.6712, p < 0.01)(Fig. 3).
Figure 2 Timecourse of creatinine clearance in stented patients with Resistance index < and > of 0.80. Significance of the difference is indicated (*)
Figure 3 Inverse correlation between Resistance index and changes in creatinine clearance after stenting
Discussion
The advantage deriving from positioning a stent in a significantly stenotic renal artery has been debated in recent years. Generally favorable results have been reported by Dorros et al [10] on a wide cohort of patients, with special regard to patients with preserved renal function. Lederman et al [8] have found either improvement or stabilization of renal function in 73 % of 300 patients with atherosclerotic renal artery stenosis, bilateral in 48% of cases. Beutler et al. [16] have found similar results on patients with atherosclerotic ostial renal artery stenosis. Perkovi et al [17] consider, as risk factors for an unfavorable outcome, diabetes mellitus, advanced age and renal failure, while the use of ACE inhibitors following the stenting procedure was protective toward death or deterioration of renal failure. Airoldi et al [9] have given a message of caution in extending the dilating procedure to all the patients with renal artery stenosis, due to the low rate of renal improvements and of fall in blood pressure, in their experience, with the finding of at least 20% of restenosis. On the contrary, renal function improvement or stabilization was found in 94% of cases by Rocha-Sing et al. [18], in patients who had a progressive decline of renal function prior to stent implantation.
In our experience, the fall in blood pressure and of the number of antihypertensive drugs was confirmed. Our results on the overall outcome of renal function, over a one year observation period, in patients with one sided renal artery stenosis of atherosclerotic origin, have been satisfactory. As a risk factor of worse outcome, our data have confirmed that RI above 0.80, results in a less satisfactory outcome compared to patients with RI lower than 0.80, as already reported by Radermacher et al. [12]. The stenting procedure, in our experience, was not followed by restenosis or other ischemic complications. Stabilization of renal function observed in the control group should take into account the unfavorable outcome of 5 cases, four deaths and one starting dialysis during the observation period. Anyhow a rational selection of patients who might get benefit from the procedure is advocated. The differences in the percentage of complications following the stenting procedure, as reported in the literature [19], might suggest at least in part the possibility of differences in the individual surgeon's skill in positioning the stent.
As for the increment in proteinuria observed following stenting of renal artery, this finding has not been reported previously, while in basal conditions significant proteinuria in patients with renal artery stenosis has been already reported in the literature [20]. Therefore proteinuria does not exclude the diagnosis of renal ischemia as a cause of renal failure. Proteinuria is probably connected with the type of renal lesions due to chronic ischemia, like focal and segmental glomerulosclerosis, or ischemic glomerular damage. Glomerular lesions resembling focal glomerulosclerosis have been reported in patients with renal artery stenosis [21]. The increase in proteinuria following stenting should probably be attributed to increased perfusion pressure in damaged sclerotic glomeruli.
Less is known of the renal function in the kidney affected by the arterial stenosis, following the stenting procedure, compared to the contralateral kidney. In all our patients renal artery stenosis was of atherosclerotic origin, without cases of fibromuscular dysplasia. In general, no adverse events were found following the stenting in the patients closely followed for one year. In our experience, there were no apparent cases of cholesterol embolization, of thrombosis of the artery, of occlusion of the stent, or of dissecation of the renal artery. The function of the stented kidney improved in most of patients while a reduction of renal function was observed in the controlateral kidney. The overall renal function was stable. Similar findings have been published by Airoldi et al., Leertouwer et al., and La Batide-Alanore et al. [9,13,14]. However their patient cohorts were rather different. One third of the cases of Airoldi et al. [9] were affected by fibromuscular hyperplasia. In only seven of the 27 patients a Palmaz stent was inserted. The increment in glomerular filtration rate of the stenotic kidney was more evident in the cases with fibromuscular dysplasia. Also in the Leertouwer et al. experience [14], renal artery dilatation was carried out in atherosclerotic renal artery stenosis, while the average age of the patients was younger than in our experimental group. Dilatation of the artery was able to induce an improvement of glomerular filtration rate of the treated kidney, although the overall glomerular filtration rate did not change. In La Batide et al. experience [13], 14/32 patients had renal artery stenosis due to fibromuscular hyperplasia and also the average age was decisively younger than our experimental group. Therefore, in our cases, with selective atherosclerotic renal artery disease and an older age, an improvement in function of the stenotic kidney following the stenting procedure was also observed and deserves to be underlined.
The reduction in contralateral kidney function has been attributed to ultrafiltration in the non stenotic kidney, declining after stenting of the stenotic contralateral renal artery. In addition, hemodynamic factors consequent to a decrease in renin-angiotensin activity could be considered as a factor. The involvement of the renin angiotensin system in renal artery stenosis should be suspected due to the significant fall in systolic and diastolic blood pressure following the procedure, an occurrence not found in the control group. Actually, a fall in plasma renin activity or concentration following dilatation of the stenotic artery has been reported by Airoldi et al [9] and by Leertouwer et al. [14].
Conclusions
In conclusion, the stenting procedure of a stenotic renal artery does not seem to carry important risks, and is accompanied by a definite improvement of the stented kidney, with some reduction of the filtration rate of the controlateral kidney. This event cannot be considered unfavorable, since it denounces a condition of hyperfiltration of the kidney, probably, if left unchanged, able to induce a deterioration of renal function with time. Therefore, also in case of overall stabilization of renal function following the stenting procedure, improvement of the stented side and reduction of hyperfiltration on the contralateral side are both favorable evolutions for long-term success of the revascularization procedure. The results are less satisfactory in patients with RI >0.80. They should probably be excluded from the stenting procedure.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
GC, conceived the protocol and cohordinated the study
EM, participated in the design of the study and in the cohordination
CC, was encharged of the magnetic resonance image analysis
RL, collaborated in the duplex doppler ultrasonography examination
IN, was responsible of the statistical examination of data
GR, responsible of renal scintigraphic investigation
DS, performed all the biochemical tests
AZ, performed arteriography and stenting of renal arteries
RC, was active in the ultrasonography investigation and screening of patients with renal artery stenosis
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15488139 | PMC527878 | CC BY | 2021-01-04 16:32:50 | no | BMC Nephrol. 2004 Oct 15; 5:15 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-15 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020379Research ArticleCell BiologyGenetics/Genomics/Gene TherapySystems BiologyDrosophilaParallel Chemical Genetic and Genome-Wide RNAi Screens Identify Cytokinesis Inhibitors and Targets Parallel Chemical and RNAi ScreensEggert Ulrike S [email protected]
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Kiger Amy A
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Richter Constance
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Perlman Zachary E
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Perrimon Norbert
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Mitchison Timothy J
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Field Christine M
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1Department of Systems Biology, Harvard Medical SchoolBoston, MassachusettsUnited States of America2Institute of Chemistry and Cell Biology, Harvard Medical SchoolBoston, MassachusettsUnited States of America3Department of Genetics, Harvard Medical SchoolBoston, MassachusettsUnited States of America4Howard Hughes Medical Institute, Harvard Medical SchoolBoston, MassachusettsUnited States of America12 2004 5 10 2004 5 10 2004 2 12 e37917 5 2004 7 9 2004 Copyright: © 2004 Eggert et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Chemical and Genetic Screens Hit the Target in Cytokinesis
Cytokinesis involves temporally and spatially coordinated action of the cell cycle and cytoskeletal and membrane systems to achieve separation of daughter cells. To dissect cytokinesis mechanisms it would be useful to have a complete catalog of the proteins involved, and small molecule tools for specifically inhibiting them with tight temporal control. Finding active small molecules by cell-based screening entails the difficult step of identifying their targets. We performed parallel chemical genetic and genome-wide RNA interference screens in Drosophila cells, identifying 50 small molecule inhibitors of cytokinesis and 214 genes important for cytokinesis, including a new protein in the Aurora B pathway (Borr). By comparing small molecule and RNAi phenotypes, we identified a small molecule that inhibits the Aurora B kinase pathway. Our protein list provides a starting point for systematic dissection of cytokinesis, a direction that will be greatly facilitated by also having diverse small molecule inhibitors, which we have identified. Dissection of the Aurora B pathway, where we found a new gene and a specific small molecule inhibitor, should benefit particularly. Our study shows that parallel RNA interference and small molecule screening is a generally useful approach to identifying active small molecules and their target pathways.
Parallel genetic and chemical screens are used to find small molecules that interfere with cell division and to identify the specific targets for these molecules
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Introduction
Small molecule inhibitors are useful tools for studying dynamic biological processes. Compared to mutations and RNA interference (RNAi), cell-permeable small molecules allow inhibition of protein function with precise temporal control, and may also spur development of new therapeutics. One approach to finding useful small molecules is phenotypic screening, in which cells are treated with small molecules from a library and scored for inhibition of the process of interest. The rate-limiting step in this approach is identifying the cellular targets of active small molecules. Traditionally, the targets of small molecules have been identified by methods based on physical affinity, for example, affinity chromatography (Harding et al. 1989). These require chemical modification of the small molecule and suffer the limitation that irrelevant proteins will bind in addition to the authentic target. A complementary method is to use information on the biological activity of the small molecule to identify the cellular pathway it perturbs. In some cases an educated guess can be successful (Mayer et al. 1999), but to be generally useful, the biological activity of a small molecule would need to be systematically compared to the effect of perturbing different cellular pathways. Currently, the most general method for systematically perturbing pathways and collecting phenotypic information is RNAi, which can be used to inhibit protein function on a genome-wide basis. Here, we develop a parallel screening strategy for finding small molecules that inhibit the biological process of cytokinesis, the genes required for this process, and, by cross-comparison of phenotypes, information on the protein targets of the small molecules.
Cytokinesis is the final step of cell division, when the daughter cells are physically separated by constriction of a cleavage furrow. Complex spatiotemporal coordination of several cell systems, including the microtubule and actin cytoskeletons, the cell cycle engine, and vesicle trafficking, is required for furrow positioning, assembly, ingression, and eventual cell separation. Some important components of the cleavage furrow, for example, actin (Schroeder 1973), Myosin (Mabuchi and Okuno 1977), and Anillin (Oegema et al. 2000), have been identified, as well as signaling systems that position and regulate the furrow, such as Aurora B (Carmena and Earnshaw 2003) and Polo (Carmena et al. 1998) kinases and Rho family GTPases (Prokopenko et al. 2000). Since many of these proteins also play roles in additional cellular processes, analysis of their function in cytokinesis by genetic methods can be difficult, and small molecule tools would be useful. To date, only three cytokinesis proteins, actin, Myosin II, and Aurora B kinase, have been targeted with small molecules, but even this limited set has been very useful. For example, the actin inhibitor cytochalasin was used to discover the central role of actin in cytokinesis (reviewed in Peterson and Mitchison 2002), and the Myosin II inhibitor, blebbistatin, provided insight into the coordination of different processes in cytokinesis (Straight et al. 2003). Small molecule inhibitors of Aurora B kinase have recently been reported, but their effect on cytokinesis has yet to be investigated in detail (Ditchfield et al. 2003; Hauf et al. 2003).
Although several key cytokinesis proteins are known, we lack a complete list of proteins required for cytokinesis in any organism. In a genome-wide study, 98 proteins were reported to localize to the bud neck, the site of cytokinesis in Saccharomyces cerevisiae (Huh et al. 2003), but their functional roles have not yet been systematically investigated. A proteomic screen that identified many components of mammalian midbodies, organelle-like remnants of the cleavage furrow, was reported recently (Skop et al. 2004) and several small-scale RNAi studies have been conducted in Drosophila (Somma et al. 2002; Goshima and Vale 2003; Kiger et al. 2003; Rogers et al. 2003). These screens identified genes required for cytokinesis, but did not assay an entire genome. Genome-wide RNAi screens have been carried out in Caenorhabditis elegans and in Drosophila cells, but they did not focus on genes required for cytokinesis (Kamath et al. 2003; Boutros et al. 2004).
Results/Discussion
Parallel Screening Protocols
To identify all genes required for cytokinesis, and small molecules that target their products, we developed an assay for both comprehensive functional genomic and large-scale chemical genetic screens in cultured Drosophila cells. We chose this system because of both the availability of genome-wide RNAi resources (Boutros et al. 2004) and the ease of RNAi in Drosophila cells. Drosophila cells can take up long pieces of double-stranded RNA (dsRNA) from the culture medium and process them into small interfering RNAs without triggering an interferon response, in contrast to mammalian cells (Clemens et al. 2000; Elbashir et al. 2001). Furthermore, use of a single targeting dsRNA is efficient for the knockdown of a specific gene in each experiment. Therefore, we were able to screen a library of existing dsRNAs with an average length of 408 bp (Hild et al. 2003) to functionally test nearly all Drosophila genes for roles in cytokinesis.
Cells that undergo mitosis normally, but fail cytokinesis, acquire two nuclei. This phenotype is a specific and irreversible consequence of cytokinesis failure, and can be scored by automated fluorescence microscopy. Drosophila Kc167 cells were cultured together with either gene-specific dsRNAs or discrete small molecules in optical-bottom 384-well plates. In total, we screened 19,470 dsRNAs covering more than 90% of the annotated genome in triplicate at the Drosophila RNAi Screening Center (http://www.flyrnai.org) and over 51,000 small molecules at the Institute of Chemistry and Cell Biology (http://iccb.med.harvard.edu). The cells were incubated for 4 d in the presence of dsRNAs to allow for depletion and turnover of targeted gene products, or 2 d for small molecules to permit each cell to complete at least one cell cycle. After fixation, cells were stained with amine-reactive tetramethylrhodamine-NHS ester to visualize total cytoplasm and Hoechst dye to visualize DNA. In the RNAi screen, microtubules were visualized by immunofluorescence. Cells were imaged by automated fluorescence microscopy, and assay wells containing a high frequency of binucleate cells were identified by a combination of automated image analysis and visual inspection.
Small Molecule Screening Results
From approximately 51,000 small molecules that included a mixture of commercial “drug-like” molecules, natural product extracts, and natural-product-like libraries synthesized at the Institute of Chemistry and Cell Biology (http://iccb.med.harvard.edu, we identified 50 small molecule inhibitors of cytokinesis, and selected 25 of the most potent and readily available for further analysis (Table S1). This structurally diverse group, which we named binucleines 1–25, included 12 small molecules from commercial libraries, ten known bioactives, and three natural product extracts. We screened at a nominal concentration of 12.5 μg/ml (approximately 25 μM) and retested the effect of our 25 small molecule inhibitors on Drosophila tissue culture cells at three different concentrations: 100 μM, 30 μM, and 10 μM (Table S1). To determine cross-reactivity with other species, we also assayed cytokinesis inhibition in HeLa (64%, 16/25 active) and BSC-1 (52%, 13/25 active) tissue culture cells as well as growth inhibition in drug-sensitive S. cerevisiae (48%, 12/25 active) (see Table S1).
Since most compounds currently known to inhibit cytokinesis are natural product actin binders, we tested if the small molecule inhibitors affected actin polymerization. Binucleines 4, 6, 24, and 25 inhibited pyrene–actin polymerization in a pure protein assay (data not shown). Binucleines 24 and 25 are the actin binders cytochalasin D and jasplakinolide, which were included in our small molecule collection as control compounds. Binucleine 4 is a natural product extract from Ircinia ramosa, which contains swinholide A, a known actin binder, as its active ingredient (F. C. Schroeder and J. Clardy, personal communication). To learn more about the cellular targets of the remaining compounds, we proceeded with our plan to systematically compare small molecule and RNAi phenotypes.
Genome-Wide RNAi Screening Results
We identified dsRNAs corresponding to 214 genes with phenotypes important for cytokinesis (Table S2). Only dsRNAs that resulted in a binucleate phenotype in at least two of the three replicate screens were summarized in our final results (Table S3). These genes resulted in either a strong, medium, or weak increase in frequency of binucleate cells (Figure 1) and represented a diverse range of predicted cellular functions (Figure 2; Table S4), reflecting the complexity of cytokinesis. Of the RNAi phenotypes, 20% identified genes previously directly implicated (Table S5 and references therein) or involved in processes associated with cytokinesis. Eleven of the strong phenotypes identified such genes, including two copies of actin (Act57B and Act5C), Myosin heavy chain (zipper), Anillin (scraps), a formin (diaphanous), Rho GTPase (Rho1) and its known guanine nucleotide exchange factor (pebble) and GTPase-activating protein (RacGAP50C), a kinesin (pavarotti), Citron kinase (CG10522), Aurora B kinase (ial), and a PRC1 homolog (fascetto). We discovered one new gene essential for cytokinesis (CG4454, see discussion below), increasing the number of specific essential proteins confirmed by RNAi to thirteen (the twelve genes listed above and INCENP; see Table S5). Although required for cytokinesis (Adams et al. 2001) and successfully resynthesized for later experiments, INCENP was not identified in our screen because of failure in INCENP dsRNA synthesis.
Figure 1 Distribution of Active Small Molecules and Genes Targeted by RNAi Identified by Penetrance of Binucleate Phenotype and by Phenotypic Classes
(A and B) Penetrance of binucleate phenotype for small molecules (A) and RNAi hits (B). (A) For the small molecules, 24% (6/25) were strong (s), 44% (11/25) medium (m), and 32% (8/25) weak (w). (B) For the RNAi hits 6% (13/2114) were strong (s), 20% (43/214) medium (m), and 74% (158/214) weak (w). In a weakly penetrant phenotype, the binucleate level was increased by more than 1.25-fold relative to the two neighboring wells in at least two experiments. In a medium penetrance phenotype, the binucleate level was above 4%, or four times as high as the neighboring wells. In a strongly penetrant phenotype, the binucleate level was above 15%. The average binucleate level in controls was approximately 1%.
(C and D) Phenotypic classes for small molecules (C) and genes targeted by dsRNAs (D). (C) For the small molecules, 40% (10/25) were binucleate (b; Figure 2A), 8% (2/25) binucleate with large, diffuse DNA (d; Figure 2B), 28% (7/25) binucleate with low cell count (lc; Figure 2C), and 24% (6/25) binucleate with microtubule extensions (MT; Figure 2D). (D) For the RNAi hits, 51% (109/214) were binucleate (b; Figure 2A), 2% (5/214) binucleate with large, diffuse DNA (d; Figure 2B), 29% (62/214) binucleate with low cell count (lc; Figure 2C), and 12% (25/214) binucleate with microtubule extensions (MT; Figure 2D). In addition, 5% (10/214) were binucleate with low cell count and microtubule extensions, and 1% (3/214) were binucleate with low cell count and large, diffuse DNA.
Figure 2 Predicted Functional Annotations of 214 Genes Associated with RNAi Binucleate Phenotypes
Functional groups were assigned using Gene Ontology information presented in FlyBase or the literature (see Table S4). Genes involved in processes associated with cytokinesis are shown in shades of yellow, nucleic acid and protein synthesis and degradation in shades of red, and uncharacterized genes in shades of blue. The uncharacterized genes encode protein sequences that predict recognizable domains (“putative domain”), no recognizable domains (“no recognized domain”), or new gene predictions from the reannotation of the Drosophila genome used as the basis of the dsRNA library (“new annotation”).
Cytokinesis is a complex, multistep process, unlikely to be regulated and executed by only thirteen proteins, suggesting that many other proteins with less stringent requirements are also involved. Our screen identified 201 genes with a loss-of-function phenotype of a medium or weakly penetrant cytokinesis failure. Of these genes, 54 were predicted to encode proteins with unknown function, including 25 that were targeted with dsRNA on the basis of new gene model predictions (Hild et al. 2003). The remaining genes had a variety of predicted functions, including cell cycle regulation and vesicle transport. Cytokinesis is known to require insertion of new plasma membrane (Finger and White 2002), consistent with our identification of genes involved in vesicle transport (12 genes). We were surprised, however, that this group included most of the components of the coatomer complex COPI (5/7 COPI subunits). The COPI complex is thought to be involved in retrograde transport from Golgi apparatus to endoplasmic reticulum, and its role in cytokinesis remains to be elucidated. An unexpected functional group, identified mostly with weak phenotypes, included genes involved in nucleic acid and protein synthesis and degradation, including a large number of ribosomal proteins. We subjected nine of these genes to further analysis. In seven of nine cases, filamentous actin staining was very weak, while other proteins such as tubulin and Myosin were of normal abundance, suggesting that the phenotype may result from low-level synthesis of actin or other cortical components (data not shown). Since a library of a single long dsRNA per gene was used in this screen, it is conceivable that some phenotypes are due to off-target effects. The approximately 400-nt dsRNAs are processed into smaller small interfering RNAs, and if appropriately processed, could cross-hybridize partially or completely with identical sequences in mRNA corresponding to other genes (Bartel 2004; Tijsterman and Plasterk 2004). Of 214 dsRNAs identified in our screen, 57 had a potential 21-nt overlap with other genes (see Table S3). In the majority of these cases (39/57), full-length dsRNA corresponding to the potential cross-match gene did not itself score. Some related genes, for example, the five copies of actin identified in our screen, show high homology and are therefore expected to contain overlapping dsRNA sequences.
Comparison of RNAi Screen to Other Screens
Since one of our goals was to create an inventory of all genes required for cytokinesis, it is important to evaluate the success rate of the genome-wide RNAi screen with respect to other published screens and to the cytokinesis literature in general. Four small-scale screens have examined the role of specific genes in cytokinesis in Drosophila cells (Somma et al. 2002; Goshima and Vale 2003; Kiger et al. 2003; Rogers et al. 2003). Results from our genome-wide screen correlate well with data from the four smaller screens and other experiments, indicating that the field is converging on a consensus of genes absolutely required for cytokinesis (see Table S5). Ten genes, reported elsewhere with RNAi binucleate phenotypes in Drosophila cells, did not score in our screen (INCENP [Adams et al. 2001]; syx1A [Somma et al. 2002]; profilin, aip1 [CG10724], and capt [Rogers et al. 2003]; and kst, Toll, Toll-4, bazooka, and kekkon [Kiger et al. 2003]). The dsRNA targeting these genes, apart from INCENP, passed quality control, suggesting alternative explanations for the differences between various RNAi experiments. Three of the smaller screens were carried out in Drosophila S2 cells (Somma et al. 2002; Goshima and Vale 2003; Rogers et al. 2003), which may differentially express or require certain proteins. The timing of RNAi experiments may also contribute to differences that were observed. We exposed cells to dsRNAs for 4 d, balancing sufficient depletion with potentially detrimental effects of prolonged culture and exposure of cells to dsRNA. With an average cell cycle of 24 h, 4 d may be too short to completely deplete very stable proteins. For example, depletion of the Myosin II regulatory light chain (spaghetti squash) resulted in a weak phenotype, whereas depletion of its complex partner encoded by zipper, the Myosin II heavy chain, resulted in a much higher frequency of binucleates. This illustrates the importance of identifying medium and weak phenotypes. Genes with weaker binucleate phenotypes may also be significant because depletion of cytokinesis proteins with multiple functions during the cell cycle can cause arrest prior to cytokinesis, diminishing the likelihood of detecting the phenotype in unsynchronized cells. Overlap between our screen and a recently published proteomic analysis of the midbody (Skop et al. 2004) highlights the importance of identifying genes with weaker binucleate phenotypes. For example, the Arp2/3 complex was not thought to play a role in cytokinesis in metazoans, but components of this complex were identified in both approaches. Eventually, a combination of different methods will result in a definitive list of all proteins involved in cytokinesis.
Systematic Comparisons between RNAi and Small Molecule Phenotypes
Following our strategy to systematically compare chemical genetic and functional genomic data, we classified the data from both screens into four phenotypic groups (summarized in Figure 1C and 1D): (1) binucleate cell phenotype only (Figure 3A), or a combination of binucleate cells with an additional phenotype of (2) large, diffuse DNA (Figure 3B), (3) low total cell count (Figure 3C), or (4) microtubule extensions (Figure 3D). The initial classification into four phenotypic groups was based on the raw screening data, where our parameters were whole cell, DNA, or tubulin staining. While these phenotypic classes are useful for global analysis and preliminary characterization, there were too many genes in each group to allow meaningful comparisons between small molecule and RNAi phenotypes. Therefore, we selected 40 genes and 25 small molecules for more detailed analysis. To ascertain specific defects in cytokinesis, we determined by immunolocalization the behavior of 15 proteins involved in cytokinesis. Our bank of reagents included antibodies to proteins that are normally found in the cleavage furrow such as actin (phalloidin), Anillin, Myosin II, and the septin protein Peanut; proteins involved in the regulation of cytokinesis such as Aurora B (Giet and Glover 2001), RhoA, Pebble (Prokopenko et al. 1999), and Polo kinase (Tavares et al. 1996); proteins involved in other aspects of cytokinesis like Diaphanous, Lava-lamp (Sisson et al. 2000), and Pavarotti; and proteins that report on the stage of cytokinesis or the state of the cell cycle such as Lamin (Risau et al. 1981), phospho-Histone H3, and tubulin. As a specific example of this detailed analysis, the phenotypes for Aurora B kinase and CG4454 are discussed below.
Figure 3 Phenotypic Classes
The phenotypic classes are (A) binucleate (CG10522 RNAi) and binucleate with (B) large, diffuse DNA (aurora B RNAi), (C) low cell count (RpS18 RNAi), or (D) microtubule extensions (Act5C RNAi). In (A), (B), and (C), the cytoplasm (tetramethylrhodamine stain) of Kc167 cells is shown in red and DNA in green. In (D), tubulin is shown in red and DNA in green. See Table S2 for full classification.
Small Molecules Can Result in Additional Phenotypes
We identified phenotypes common to both datasets, but the detailed phenotypic analyses did not match exactly, with more phenotypic subclasses distinguished with the small molecules. Two considerations may account for the existence of additional phenotypic categories for small molecules. One is timing. During our detailed secondary analysis, we added small molecules to cells for variable amounts of time (3 h to 48 h). When cells were exposed to a drug for a short time, we were able to analyze localization of furrow components immediately after cytokinesis failure. These phenotypes became less apparent upon longer exposure because the long delay gave cells the opportunity to disassemble residual furrow structures. When cells were exposed to dsRNAs for days, long, variable delays between cytokinesis failure and fixation may have obscured interesting phenotypes, which could be revealed by subsequent real-time imaging experiments (Goshima and Vale 2003). The other consideration is potential gain-of-function effects of small molecules. For example, a natural product extract from Cowania mexicana containing a cucurbitacin (M. Fujita and J. Clardy, personal communication) caused clusters of filamentous actin to accumulate in interphase cells, in addition to completely blocking cytokinesis. Since no dsRNA caused this phenotype, we suspect it is a gain-of-function effect of the small molecule, whose mechanism we will pursue.
Two Sub-Phenotypes Correlate in Both Small Molecule and RNAi Datasets
Systematic comparison between the phenotypic categories based on detailed immunofluorescence analysis of both screens did, however, allow us to connect small molecules to two specific pathways involved in cytokinesis, namely actin cortex integrity and the Aurora B pathway. Both dsRNAs and small molecules that weakened the actin cortex caused microtubule-rich extensions to protrude from interphase cells as well as failure of cytokinesis (Figure 4). These included dsRNAs targeted against several actin genes (see Table S2) and three natural product small molecules known to target actin that were present in our small molecule collection (cytochalasin D, jasplakinolide, and swinholide A [from Ircinia ramosa extract]). This sub-phenotype represents a portion of the genes identified as “binucleate with microtubule extensions” shown in Figure 3D.
Figure 4 Kc167 Cells Exposed to dsRNA Targeting Act5C or to Cytochalasin D
The cells were exposed to dsRNA targeting Act5C for 4 d (A) or to cytochalasin D at 5 μM for 48 h (B). Tubulin is shown in red, DNA in green.
A second phenotypic class exhibited a high incidence of both mitosis and cytokinesis defects, a sub-phenotype of the category “binucleate with diffuse DNA” (see Figure 3B). Mitosis was abnormal, with malformed spindles and misaligned chromosomes, resulting in large, diffuse arrangements of DNA in binucleate cells (Figure 5). Individual depletion of any of three proteins encoded by aurora B, INCENP, and CG4454, or addition of one small molecule N′-[1-(3-chloro-4-fluorophenyl)-4-cyano-1H-pyrazol-5-yl]-N,N-dimethyliminoformamide (binucleine 2; Figure 5), caused this phenotype.
Figure 5 Kc167 Cells Untreated or Exposed to aurora B dsRNA, borr (CG4454) dsRNA, or Binucleine 2
TMR-stained cells were untreated, or treated with dsRNA for 4 d or binucleine 2 (50 μM) for 2 d. TMR is shown in red, DNA in green. The chemical structure of binucleine 2 is also shown.
CG4454 RNAi Phenotype and Localization Matches Chromosomal Passenger Proteins
Aurora B, INCENP (Adams et al. 2001), and Survivin (Wheatley et al. 2001) form the chromosomal passenger complex, which also includes CSC-1 in C. elegans (Romano et al. 2003) and Borealin/Dasra B in humans (Gassmann et al. 2004; Sampath et al. 2004). Aurora B kinase plays a number of roles during mitosis (Carmena and Earnshaw 2003), including phosphorylating Histone H3 on Ser-10 (Giet and Glover 2001) and detecting errors in chromosome attachment in mitosis (Lampson et al. 2004), and performs an essential, but poorly understood, function in cytokinesis. Chromosomal passenger proteins localize to the inner centromere during mitosis and move to the interzonal microtubules, the cleavage furrow, and eventually the midbody during cytokinesis. Because the sequences that targeted CG4454 and aurora B both had 21-bp overlaps with other genes in the dsRNA collection we screened (see Table S3), we remade dsRNA targeting different areas of these two genes and observed no change in phenotype. Since RNAi depletion of the new gene we discovered in our screen, CG4454, resulted in the same phenotype as depletion of aurora B and INCENP, we hypothesized that it could be a new member of the chromosomal passenger complex. We constructed green fluorescent protein (GFP) fusion proteins to both C- and N-termini of CG4454. CG4454-GFP exhibited the signature localization of a passenger protein and co-localized with Aurora B throughout mitosis and cytokinesis (Figure 6), suggesting that it might be complexed to Aurora B. RNAi depletion of CG4454 or aurora B resulted in an absence of phosphorylated Histone H3 on mitotic chromosomes (Figure 7, bottom row), further supporting the participation of CG4454 in the chromosomal passenger complex. Although CG4454 amino acid sequence reveals a remote similarity with Borealin/Dasra B (Gassmann et al. 2004), it is unclear at this point whether CG4454 is its Drosophila homolog. Unlike CG4454, RNAi depletion of Borealin does not significantly reduce Histone H3 phosphorylation (Gassmann et al. 2004). It might not be possible to confirm whether CG4454 and Borealin are related until structural information becomes available. However, to prevent further confusion in naming conventions, we have decided to tentatively name CG4454 Borealin-related (Borr).
Figure 6 Kc167 Cells Transfected with Borr-GFP
In the top row, cells in metaphase, anaphase, and cytokinesis are shown. Borr-GFP is shown in green, tubulin in red, and DNA in blue. The bottom row shows cells in metaphase and cytokinesis. Borr-GFP is shown in green, Aurora B in red, and DNA in blue.
Figure 7 Kc167 Cells Untreated or Exposed to aurora B dsRNA, borr (CG4454) dsRNA, or Binucleine 2
INCENP-stained cells in the top row were untreated or treated with aurora B dsRNA for 5 d, borr (CG4454) dsRNA for 3 d, or binucleine 2 (20 μM) for 4 h. Phospho-Histone H3 stained cells in the bottom row were untreated or treated with dsRNA for 4 d or binucleine 2 (20 μM) for 4 h. White arrows indicate absence of phospho-Histone H3 staining in the failed mitotic figures.
Detailed Comparison of Binucleine 2 and Aurora B Complex Phenotypes
We compared the phenotypes caused by RNAi depletion of aurora B and borr to treatment of cells with binucleine 2 using immunofluorescence. The phenotypes were very similar, as judged by perturbation of localization or expression of 14 of the 15 markers used in our detailed analysis (Figure S1), suggesting that the two genes and binucleine 2 perturb a similar step in cytokinesis. The only difference we observed was localization of the chromosomal passenger protein INCENP (Figure 7, top row). No INCENP staining at any cell site was detected in borr-depleted cells, suggesting that Borr is required for INCENP localization. This phenotype was also observed in Borealin-depleted cells (Gassmann et al. 2004). In contrast, we observed INCENP accumulations in binucleine 2–treated and aurora B–depleted cells. INCENP localizes to the chromosome arms during prometaphase in aurora B–depleted cells, which is consistent with reported observations (Adams et al. 2001). In cells exposed to binucleine 2, INCENP aggregated (Figure 7, top row), but did not appear to co-localize with Aurora B or DNA. Given its effect on INCENP localization, binucleine 2 might be a useful tool to study the localization and movement of the Aurora B complex during mitosis and cytokinesis, since the factors that regulate these processes remain obscure. In total, binucleine 2 shares phenotypes with aurora B RNAi and affects localization of INCENP, a member of the Aurora B complex, suggesting that binucleine 2 targets the Aurora pathway. Small molecules can target and inhibit protein activity directly, whereas dsRNAs target destruction of mRNA. This difference in mechanism between small molecule inhibition and RNAi could account for the variation in INCENP localization we observed. The specific activity of binucleine 2, however, is very highly related to its structure. We assessed the effect of several similar compounds and found that none were more active than binucleine 2, while most had very little activity (Figure S2).
To test whether binucleine 2 inhibits Aurora B kinase function, we monitored Histone H3 phosphorylation on Ser-10 in mitotic cells (Giet and Glover 2001). When cells were exposed to binucleine 2, phospho-Histone H3 was absent on chromosomes in mitotic cells (Figure 7, bottom row). To get a quantitative measure of both the concentration of binucleine 2 required and the speed of its action, we assayed about 10,000 cells per time point and concentration for phospho-Histone H3 staining by immunofluorescence (Figure 8). We were unable to detect phospho-Histone H3 in cells treated with 25 μM or 100 μM binucleine 2 for only 30 min, while the percentage of cells exhibiting phospho-Histone H3 staining decreased over time in cells treated with binucleine 2 at 1 μM and 5 μM (Figure 8). While binucleine 2 inhibits Aurora B–dependent phosphorylation, it is not a general kinase inhibitor. Binucleine 2 did not inhibit cyclin-dependent-kinase-dependent entry into mitosis and had no effect on bulk phosphorylation activity in a Drosophila cell extract (data not shown). Altogether, phenotypic similarities between loss-of-function for Aurora B and binucleine 2 strongly suggest that binucleine 2 targets a protein involved in the Aurora B pathway. Several small molecule inhibitors of Aurora kinases have been reported, although their chemical scaffolds are different from binucleine 2. These small molecules are not active in fly cells, while binucleine 2 is inactive in mammalian systems (data not shown). Aurora kinase levels are elevated in some tumors, making these proteins a potential target for cancer therapy. Interestingly, lower binucleine 2 concentration or shorter Aurora B RNAi treatment favors binucleate formation, while higher drug concentration and longer incubation for RNAi results in a relative increase in large cells with diffuse DNA. Thus, the cytokinesis function of the Aurora pathway may be more sensitive to inhibition than its mitosis function. This observation may be important for understanding the response of cancer cells to Aurora inhibitors now entering clinical trials (Harrington et al. 2004).
Figure 8 Time- and Concentration-Dependence of Binucleine 2
Kc167 cells were treated with 1 μM, 5 μM, 25 μM, or 100 μM binucleine 2. Phospho-Histone H3 staining was assessed at different time points. Binucleine 2 at 100 nM and 300 nM was also tested and showed no effect (data not shown).
Conclusion
In summary, our parallel screening approach succeeded in identifying new proteins involved in cytokinesis, and new small molecules that inhibit it. We identified 214 proteins important for cytokinesis, including 25 previously uncharacterized predicted proteins. Depletion of one new gene, borr, had a profound effect on cytokinesis. Borr exhibits the signature localization of a chromosomal passenger protein and co-localizes with Aurora B kinase throughout the cell cycle. We also uncovered a potential role of the COPI coatomer complex in cytokinesis. By comparative phenotypic analysis we were able to show that one class of small molecules targets actin cortex integrity, and another the Aurora B pathway. A third class of small molecules, whose phenotype has no RNAi counterpart, presumably causes gain-of-function effects. Traditional methods like affinity chromatography and enzyme inhibition assays will be required to describe the precise biochemical mechanisms of these new cytokinesis inhibitors, but the information already gained from comparative screening will focus this work and allow rapid confirmation or invalidation of candidate biochemical targets.
The problem of target identification has been one of the main barriers to more widespread use of phenotype-based screening in drug discovery. As functional genomic data and systematic RNAi resources become widely available for human cells, parallel screening approaches like the one we describe could be used to discover leads for therapeutic drugs as well as research reagents.
Materials and Methods
Small molecule screen
20,000 Drosophila Kc167 cells in 40 μl of medium (Schneider's Drosophila Medium [GIBCO, San Diego, California, United States] supplemented with 10% heat-inactivated fetal bovine serum [HyClone, South Logan, Utah, United States] and penicillin/streptomycin [Cellgro, Mediatech, Herdon, Virginia, United States]) were added to each well using a MultiDrop 384 (Thermo Electron, Waltham, Massachusetts, United States) liquid dispenser and incubated at 24 °C overnight. Then, 100 nl of compound stocks dissolved in DMSO at approximately 10 mg/ml was added using the pin transfer robot at the Institute of Chemistry and Cell Biology at Harvard Medical School (http://iccb.med.harvard.edu). Cells were incubated at 24 °C for 48 h. All fixation, staining, and washing steps were carried out using a MultiDrop liquid dispenser and 24-channel wand (V&P Scientific, San Diego, California, United States) for liquid removal. Cells were fixed and permeabilized in 40 μl of 100 mM Pipes/KOH (pH 6.8), 10 mM EGTA, 1 mM MgCl2, 3.7% formaldehyde, and 0.2% TritonX-100 for 15 min and washed in 50 μl of PBS. The cytoplasm was stained with 40 μl of 0.5 μg/ml NHS-tetramethylrhodamine (TMR, 5-[and-6]-carboxytetramethylrhodamine, succinimidyl ester C-1171, Molecular Probes, Eugene, Oregon, United States) in PBS for 15 min. Subsequently, 40 μl of 5 μg/ml Hoechst 33342 (Sigma, St. Louis, Missouri, United States) in TBST (TBS with 1% TritonX-100) was added for 30 min. This step stains the DNA and quenches excess NHS ester to ensure uniform TMR staining. Cells were washed twice with 40 μl of TBST and sealed with aluminum seals (Costar 6570, Corning, Corning, New York, United States) for image acquisition.
Pyrene–actin assay
Pyrene-labeled actin (2 μM, final concentration; 80 μl, final volume) was added to 10 mM HEPES (pH 7.7), 2 mM MgCl2, 100 μM CaCl2, 100 mM KCl, 5 mM EGTA, 200 μM ATP, and 10 μM small molecule. Pyrene–actin polymerization was followed by fluorescence spectroscopy over 45 min. An increase in fluorescence indicates actin polymerization. Adapted from Peterson et al. (2001).
RNAi screen
dsRNAs were aliquoted into black, clear-bottom 384-well plates (Costar 3712, Corning) at the Drosophila RNAi Screening Center at Harvard Medical School (http://www.flyrnai.org). Each well contained 5 μl of approximately 0.05 μg/μl dsRNA in water. 10,000 Drosophila Kc167 cells in 10 μl of serum-free Schneiders's Drosophila Medium were added to each well containing dsRNA using a MultiDrop liquid dispenser. After 1 h of incubation at room temperature, 30 μl of medium (Schneider's Drosophila Medium supplemented with 10% heat-inactivated fetal bovine serum and penicillin/streptomycin) was added. The plates were sealed or placed in a humidified chamber and incubated for 4 d at 24 °C. Fixation, TMR, and Hoechst staining were carried out as described above for the small molecule screen. Cells were then blocked in 40 μl of AbDil (TBST with 2% BSA) for 30 min and stained overnight at 4 °C with 20 μl of 1:250 monoclonal anti-tubulin (DM1α, Sigma) and 2 μg/ml Alexa 488 goat anti-mouse antibody (Molecular Probes) in AbDil. Cells were washed twice with 40 μl of TBST and sealed with aluminum seals (Costar 6570) for image acquisition. In order to identify weak hits reliably, the RNAi screen was carried out in triplicate on three separate occasions.
Image acquisition
Plates from the RNAi and small molecule screens were imaged using a Universal Imaging (Downingtown, Pennsylvania, Unites States) AutoScope or a Universal Imaging Discovery-1. The AutoScope is a Nikon (Tokyo, Japan) TE300 inverted fluorescence microscope with filter wheel (Lamda10-2, Sutter Instruments, Novato, California, United Stats), x-y stage (Prior H107N300), piezoelectric-motorized objective holder (P-723.10, Physik Instruments, Downingtown, Pennsylvania, United States), and a CCD camera (OrcaER, Hamamatsu, Hamamatsu City, Japan). MetaMorph software (Universal Imaging) running the Screen Acquisition drop-in allowed coordination of software-based autofocusing, movement between wells, imaging, and image evaluation. Two images per well were acquired in each of two (small molecule screen) or three (RNAi screen) channels using a 20x objective with 2 × 2 binning.
Scoring of images
Given the need for greater accuracy in an annotation screen, the RNAi images were initially scored by visual inspection. We looked at two images per well from two independent datasets (datasets 2 and 3, approximately 85,000 images) and noted wells with elevated binucleate levels. To determine the percentage of binucleate cells per image, we used the Integrated Morphometry feature in the MetaMorph software to count the number of nuclei per image and then manually counted the number of binucleate cells. We collected two images per assay well and report the level of binucleates per well as an average of both images. Because the level of binucleates can vary depending on the location of the well in the assay plate or the position of the assay plate in a stack of plates, we decided to compare each proposed hit well to its two neighboring wells to prevent false positives or negatives due to local variations. If a neighboring well also exhibited a phenotype, we chose the next neighbor for our analysis. Although we performed the screen in triplicate, we were only able to apply this analysis to two datasets because the cells in the third screen were too clustered to use automated cell counting. It was possible, however, to estimate the binucleate level in the third dataset by visual inspection. We only scored a phenotype if it repeated in at least two experiments, and the vast majority of phenotypes repeated in all three datasets. In a weakly penetrant phenotype the binucleate level was increased by more than 1.25-fold relative to the average of both neighboring wells. In a medium penetrance phenotype the binucleate level was above 4%, or four times as high as the neighboring wells. In a strongly penetrant phenotype the binucleate level was above 15%.
Borr-GFP cloning and transfection
For the C-terminal fusion protein, Borr (CG4454) cDNA (LD36125) was cloned into the EcoRI and KpnI sites of pEGFP-C1 (Clontech, Palo Alto, California, United States), cut with NheI and KpnI, and ligated into pPacPL (Krasnow et al. 1989). For the N-terminal fusion, CG4454 digested with SpeI and HindIII, pEGFP-N1 (Clontech) digested with NotI and HindIII, and pPacPL digested with SpeI and NotI were ligated in a triple ligation reaction. Kc167 cells were transfected with these constructs using Insect GeneJuice transfection reagent (Novagen, Madison, Wisconsin, United States) according to the manufacturer's instructions and were used for live cell imaging and immunofluorescence 6–7 d after transfection.
Immunofluorescence analysis
Cells were exposed to dsRNA or small molecules, fixed, and stained as described in the screening protocols. Cells were stained with TRITC-labeled phalloidin (Sigma) to visualize actin or antibodies to the following proteins (data not shown): Anillin, Aurora B (a gift from D. Glover), Diaphanous, INCENP (a gift from W. Earnshaw), Lamin (a gift from H. Saumweber), Lava-lamp, Myosin II, Pavarotti, Peanut, Pebble (a gift from H. Bellen), phospho-Histone H3 (Upstate Biotechnology, Lake Placid, New York, United States), Polo (a gift from D. Glover), Rho1 (from the Developmental Studies Hybridoma Bank) and tubulin (DM1α, Sigma).
Synthesis of N′-[1-(3-chloro-4-fluorophenyl)-4-cyano-1H-pyrazol-5-yl]-N,N-dimethyl iminoformamide (binucleine 2)
Since binucleine 2 is no longer available commercially, we resynthesized it (see Figure S3): 3-chloro-4-fluorophenylhydrazine hydrochloride (compound 1 in Figure S3) (500 mg, 2.5 mmol, Alfa Aesar, Karlsruhe, Germany) and ethoxymethylenemalononitrile (compound 2 in Figure S3) (305 mg, 2.5 mmol, Sigma-Aldrich, St. Louis, Missouri, United States) were refluxed in 3 ml of ethanol for 4 h. The resulting pyrazol (compound 3 in Figure S3) was partially purified by recrystallization from ethanol. Pyrazol (140 mg, 0.5 mmol) and N,N-dimethylformamide dimethyl acetal (150 μl, 1 mmol, Aldrich) were refluxed in ethanol for 1 h. The product (binucleine 2) (compound 4 in Figure S3) was recrystallized from ethanol. 1H NMR (500 MHz, (CD3)2SO) δ 8.29 (s, 1 H), 8.06 (dd, J1 = 2.7 Hz, J2 = 6.8 Hz, 1 H), 8.03 (s, 1 H), 7.84–7.81 (m, 1 H), 7.55 (t, J = 9.2 Hz, 1 H), 3.14 (s, 3 H), 3.00 (s, 3 H). ESI-MS calculated for C13H11ClFN5 291, [M + H]+ found 292.
Dose response of binucleine 2.
Kc167 cells were treated with 100 nM, 300 nM, 1μM, 5μM, 25μM, or 100 μM of binucleine 2 and fixed after 15 min, 30 min, 45 min, 1 h, 1.5 h, 2 h, 2.5 h, 3 h, 3.5 h, or 4h. After staining with phospho-Histone H3 antibody (Upstate), tubulin (DM1α, Sigma), and DNA and then imaging, cells with phospho-Histone H3 staining were counted. The total number of cells was counted using the Integrated Morphometry feature in the MetaMorph software, and the percentage of cells with H3 staining was calculated and is plotted in Figure 8. Approximately 4,000–5,000 cells per experiment were assessed in two separate experiments for each time point.
Supporting Information
Figure S1 Examples of Detailed Secondary Analysis Using Immunofluorescence
Cells were untreated or treated with binucleine 2 (100 μM, 48 h) or dsRNA corresponding to aurora B or borr (CG4454). Cells were stained with TRITC-labeled phalloidin to visualize actin, or antibodies against Anillin, tubulin, Lava-lamp, or Lamin. Lamin-stained cells were treated with binucleine 2 (100 μM) for 24 h.
(4.5 MB TIF).
Click here for additional data file.
Figure S2 Structure Activity Relationships for Binucleine 2
(49KB DOC).
Click here for additional data file.
Figure S3 Synthesis of Binucleine 2
(32 KB DOC).
Click here for additional data file.
Table S1 Small Molecule Phenotypes and Structures
Kc167 cells were exposed to small molecules at 100 μM, 30 μM, or 10 μM for 48 h. In a weakly penetrant phenotype (w), the binucleate level was increased by at least 1.25-fold above background. In a medium penetrance phenotype (m), the binucleate level was above 4%, and in a strongly penetrant phenotype (s), the binucleate level was above 15%, while the average binucleate level was approximately 1%. In the binucleate phenotype column, “binucleate” indicates binucleate cells only, “diffuse DNA,” binucleate cells with large, diffuse DNA, “lc,” binucleate cells with low cell count, and “MT ext,” binucleate cells with microtubule extensions. HeLa and BSC-1 cells were exposed to small molecules at 30 μM for 24 h. Growth inhibition in drug-sensitive S. cerevisiae RDY98 (Mat a, erg6ΔTRP1cg, pdr1ΔKAN, pdr3ΔHIS5+, ade2, trp1, his3, leu2, ura3, can1) was measured at a small molecule concentration of 250 μM after an overnight exposure.
(159 KB DOC).
Click here for additional data file.
Table S2 List of Targeted Genes Identified by Binucleate Cells in the RNAi Screen
The “DRSC dsRNA ID” is an internal dsRNA ID number. In the potency column, “s” represents strong, “m,” medium and “w,” weak penetrance of the binucleate cell phenotype. In the phenotypic classification column, “binucleate” indicates binucleate cells only, “diffuse DNA,” binucleate cells with large, diffuse DNA, “lc,” binucleate cells with low cell count, and “MT ext,” binucleate cells with microtubule extensions. Six genes were independently identified in multiple wells, either scored twice—CG10522, cycA, Pp4-19C, RpL32, Tra1, and Ubi-63E—or three times—crn.
(141 KB DOC).
Click here for additional data file.
Table S3 Information about Genes with Binucleate Phenotypes and Quantitative Analysis of Binucleate Phenotypes
Gene names, FlyBase IDs, Gene Ontology annotations, forward and reverse primers, and amplicon lengths are shown. “HFA amplicon” and “DRSC dsRNA ID” are internal dsRNA identifiers. The number of potential secondary targets based on 21 nucleotide fragments is the number of genes that have at least one length of 21 bp or more with matching sequence of 21 bp or more of this amplicon. The criteria used in this analysis are such that it may be prone to false positives for secondary targets. The binucleate percentage per well, the relative increase in binucleates relative to the neighboring wells (1.25 = 25% increase), cell number per well, and relative increase or decrease in cell number relative to the neighboring wells are shown for datasets 1 and 2. Annotations for dataset 3 are only shown when they help to define a particular phenotype.
(97KB XLS).
Click here for additional data file.
Table S4 Genes That Scored in the RNAi Screen Sorted by Assigned Functional Groups
Functional groups are based on the predicted function as reported by FlyBase (new annotation excluded).
(90 KB DOC).
Click here for additional data file.
Table S5 Genes Reported to Be Involved in Cytokinesis and Genes That Resulted in Strong and Medium RNAi Phenotypes
(181 KB DOC).
Click here for additional data file.
We thank R. Ohi for many helpful discussions, S. Miller for help in designing the synthesis of binucleine 2, I. Ivanovska for helpful comments on the manuscript, and the staff at the Institute of Chemistry and Cell Biology and the Drosophila RNAi Screening Center for their assistance. We thank R. Dorer for the gift of S. cerevisiae strain RDY98 and J. Peterson for the gift of pyrene–actin. The antibodies used were kindly provided by M. Carmena and W. Earnshaw (INCENP), D. Glover (Aurora B, Polo), H. Saumweber (Lamin), and H. Bellen (Pebble). UE is a Merck-sponsored fellow of the Helen Hay Whitney Foundation, AAK was supported by the Jane Coffin Child's Memorial Fund for Medical Research, and ZEP was supported by a Howard Hughes Medical Institute Predoctoral Fellowship. NP is an investigator of the Howard Hughes Medical Institute. This research was supported by National Institutes of Health grant R01 GM023928–25 awarded to TJM.
Conflicts of interest. The authors have declared that no conflicts of interest exist.
Author contributions. USE, AAK, NP, TJM, and CMF conceived and designed the experiments. USE, AAK, and CR performed the experiments. USE, AAK, and CR analyzed the data. USE, AAK, CR, ZEP, and CMF contributed reagents/materials/ analysis tools. USE, AAK, TJM, and CMF wrote the paper.
Academic Editor: Gerald Joyce, Scripps Research Institute
Citation: Eggert US, Kiger AA, Richter C, Perlman ZE, Perrimon N, et al. (2004) Parallel chemical and genome-wide RNAi screens identify cytokinesis inhibitors and targets. PLoS Biol 2(12): e379.
Abbreviations
BorrBorealin-related
dsRNAdouble-stranded RNA
GFPgreen fluorescent protein
RNAiRNA interference
==== Refs
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| 15547975 | PMC528723 | CC BY | 2021-01-05 08:21:16 | no | PLoS Biol. 2004 Dec 5; 2(12):e379 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020379 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020386SynopsisGenetics/Genomics/Gene TherapySystems BiologyDrosophilaChemical and Genetic Screens Hit the Target in Cytokinesis Synopsis12 2004 5 10 2004 5 10 2004 2 12 e386Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Parallel Chemical and Genome-Wide RNAi Screens Identify Cytokinesis Inhibitors and Targets
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Cytokinesis, in which newly formed daughter cells separate, is the culmination of the cell cycle. It is necessary for normal growth and development, and it is also a sine qua non in the pathogenesis of cancers—cells that can't divide can't form tumors, can't metastasize, and can't kill. Therefore, understanding the full range of proteins involved in cytokinesis has both deep theoretical and immediate practical applications. In this issue, Ulrike Eggert and colleagues report results from two complementary screening approaches to identify those proteins and to discover molecules that inhibit them.
A cell cannot divide if it lacks a protein vital for cytokinesis or if that protein is inhibited. When that occurs, the cell retains both nuclei, and can be quickly identified by an automated process. Working with Drosophila cells, the first screen used almost 20,000 double-stranded RNAs, representing virtually the entire Drosophila genome. A double-stranded RNA pairs with, and causes the destruction of, a matching messenger RNA, thus preventing the encoded protein from being formed, a process called RNA interference (RNAi). The authors identified 214 proteins whose absence prevented cytokinesis. While some of these, including actin and Myosin, were already known to be essential for the process, others were not. The latter included a new discovery, CG4454 (named Borealin-related or Borr), which was found to be one of the handful of proteins deemed most critical to cytokinesis.
Drosophila cells that have failed to divide
The second screen also treated Drosophila cells, but this time used over 50,000 “small molecules,” a catchall term for molecules small enough to pass easily into cells. The vast majority of drugs currently in clinical use are small molecules. This screen revealed 50 cytokinesis inhibitors, of which 25, dubbed binucleines, were selected for further characterization. Not surprisingly, several inhibited actin, whose role in cytokinesis is key in contraction of the cytokinesis furrow.
For the purposes of this study, however, binucleines affecting other proteins were even more interesting. By comparing the appearances of binucleate cells from the small-molecule screen with those from the RNAi screen, Eggert et al. identified one molecule and three proteins that caused a similar phenotype, suggesting that the three proteins acted within a single pathway, which the molecule could disrupt. One of the three proteins was CG4454/Borr, and the researchers' results indicated it interacts with Aurora B, an essential but still poorly understood protein that is needed for proper division of the chromosomes. The identified binucleine will be a valuable reagent for exploring the details of the Aurora B pathway.
While these results are from Drosophila, the insights they provide into the cell cycle are likely to be applicable to humans as well. Equally importantly, they provide the proof of principle for a new drug discovery method. A major bottleneck in drug development is target identification—determining which of the cell's thousands of proteins is the right one to inhibit with a drug. The unique aspect of this study is the parallel use of the two approaches—small molecules and RNAi—to provide a “stereoscopic” view of cytokinesis and its inhibition. Working together, they provide a set of proteins and a matched set of inhibitors, the target and the bullet at the same time.
| 0 | PMC528724 | CC BY | 2021-01-05 08:21:16 | no | PLoS Biol. 2004 Dec 5; 2(12):e386 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0020386 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1511548814910.1186/1471-2105-5-151SoftwareCaryoscope: An Open Source Java application for viewing microarray data in a genomic context Awad Ihab AB [email protected] Christian A [email protected] Tina [email protected] Catherine A [email protected] Gavin [email protected] Dept. of Genetics, 300 Pasteur Drive, Stanford University Medical School, Stanford, CA 94305-5120, USA2 Dept. of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305-5307, USA2004 15 10 2004 5 151 151 12 5 2004 15 10 2004 Copyright © 2004 Awad et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context.
Results
We have developed Caryoscope, which is an open source Java application for visualizing microarray data from array comparative genome hybridization experiments in a genomic context. Caryoscope can read General Feature Format files (GFF files), as well as comma- and tab-delimited files, that define the genomic positions of the microarray reporters for which data are obtained. The microarray data can be browsed using an interactive, zoomable interface, which helps users identify regions of chromosomal deletion or amplification. The graphical representation of the data can be exported in a number of graphic formats, including publication-quality formats such as PostScript.
Conclusion
Caryoscope is a useful tool that can aid in the visualization, exploration and interpretation of microarray data in a genomic context.
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Background
The application of high-throughput technologies (such as DNA microarrays) to biomedical experimentation generates large quantities of data that can be difficult to browse and interpret in the absence of a graphical representation. Eisen et al. have previously displayed clustered microarray data using a false color representation that greatly aids in the intuitive interpretation of the data ([1]). However, when these data are from array comparative genome hybridization (arrayCGH) experiments (e.g., see [2]), the genomic locations of the reporters (the molecules in each spot on a microarray) that were used to generate the data are important for interpretation. A relative increase or decrease in the ratios for a group of reporters that report on adjacent genomic locations may indicate amplification or deletion of that genomic region, respectively. Additionally, even the analysis of expression data in the context of genomic position can also identify regions of amplification or deletion, or even cases of aneuploidy ([3,4]).
In addition to being able to view and browse arrayCGH data, it is also important that the data be readily connected to annotation sources, such that a user can easily determine the identity and attributes of the gene represented by a reporter that was present on a microarray, which for instance may show evidence of amplification or deletion. For example, in arrayCGH experiments using tumor cells as the DNA source, there is an obvious value in rapidly determining whether a deleted region contains a tumor suppressor gene.
Finally, researchers frequently need to create figures, for publications, communication with co-workers, supplemental websites, or presentations. Thus researchers should be able to produce the visual representation of their data in a variety of graphic formats.
Caryoscope was originally implemented as a Web form, generating either a bitmap or a clickable PDF output. When this became an important day-to-day tool for our users ([5-9]), we created an improved, interactive version, consisting of a standalone application for analyzing arrayCGH data and an open architecture of re-usable classes that may be embedded by other developers in their own applications. In this paper, we focus on Caryoscope as an application.
Some other software packages were developed while this work was in progress, and can perform some of these functions. For instance, Genome2D ([10]) is designed to display bacterial transcriptome data on linear chromosome maps, while SeeCGH ([11]) was designed for viewing arrayCGH data (only for 2-channel arrays). However, both of these programs are designed to run solely on the Windows operating system, whereas Caryoscope is a Java application that can be used on Macintosh OS X, Linux and various UNIX operating systems, as well as Windows. Greshock et al. ([12]) have built similar functionality, called CGHAnalyzer, on TIGR's Multiple Experiment Viewer (MeV) platform, but with a different (circular) whole-genome view. Furthermore, Caryoscope can be run in a command line mode, making it easy to embed within a CGI or a processing pipeline.
Implementation
We implemented Caryoscope in Java ([13]) and deployed it as a Java Web Start ([14]) application, so a user may run it directly from our website ([15]) by clicking on a link. One can also install Caryoscope directly on a computer, but we recommend launching via the website in order to obtain the most current version of the software.
Caryoscope accepts data as text input files in simple formats so as to maximize interoperability with other systems.
Results
Application features
As input, Caryoscope accepts a single file in either the General Feature Format (GFF, [16]) or a tab-delimited (TXT) or comma-delimited (CSV) spreadsheet-compatible format. This file describes the chromosomes to be displayed, and a set of loci on the chromosomes annotated by a number of associated microarray datasets and other descriptive information. The structure of a Caryoscope input file is illustrated in Figure 1.
Once the user opens a file, Caryoscope automatically displays one of the datasets contained therein (Figure 2). Caryoscope displays each feature as a rectangle on the chromosome axis; the size of the rectangle on the horizontal axis, perpendicular to the chromosome, represents the magnitude of the associated data value, while the size of the rectangle in the vertical direction, along the chromosome axis, represents the size of the represented feature, based on its genomic coordinates. Pursuant to convention, the default display of Caryoscope represents positive values in red bars, which are drawn to the right of the chromosome, and negative values in green bars (though these colors can be changed), which are drawn to the left of the chromosome. Thus, based on color, size, and location, researchers can easily intuit the meaning of the graphical representation of their data.
Caryoscope provides several modes in which the user may view the data; these are controlled by the View modes toolbar (Figure 2). In the various panning and zooming modes, the user may change the view of the data to drill down to specific regions of interest. In Navigate mode, the user sees tooltips (small informational pop-up windows) that appear immediately when mousing over the features, and can navigate to related URLs by clicking on each feature. Typically, users at Stanford University (our primary source of testers and users) link GenBank accessions, associated with the cDNA clones that are on their microarrays, to SOURCE Gene Pages ([17]).
The zooming paradigm in Caryoscope is somewhat novel in that it permits independent control of the zoom scales in the X and Y directions (Figure 3). It allows users to select the best scaling to see detail along the chromosome axis, and the data values perpendicular to the chromosome axis, for their specific data. The Reset viewpoint button on the View modes toolbar (Figure 2) allows the user to return quickly to the default scaling.
The behavior of Caryoscope in Navigate mode is shown in Figure 4. The tooltip and URL text are computed for each feature by substituting the value of its annotations into the Feature tooltip expression and Feature URL expression settings, as illustrated in the figure. These features allow users to have immediate access to information about each feature as they browse the data.
The user can enable two built-in computations on the data values: a user can compute the logarithm of the values (to any base specified by the user), and a user can compute a moving average of the values. Both these computations can be controlled from the Settings dialog (see Figure 2b). Users can perform other computations outside Caryoscope; this is facilitated by the fact that we support common spreadsheet-compatible file formats (TXT and CSV).
To prepare diagrams, the user can export the Caryoscope display to a variety of graphics formats via the Export dialog. Specifically, Caryoscope supports vector (e.g., PostScript and PDF) output for scalable publication-quality results, and raster (e.g., JPEG and PNG) output for ease of viewing, posting on supplemental websites, and inclusion in presentations.
A user may export graphics from Caryoscope via the command line mode, without having to invoke the interactive user interface. For example, to export a view of a dataset as a PDF file, the user could invoke Caryoscope as follows:
java -jar caryoscope-run.jar
-inputFile 3395-2004-04-04.csv
-visibleDataset RAT2_MEAN
-outputFile 3395.pdf
-outputFormat pdf
All settings may be modified via the command line. The -listFormats option provides a list of available graphics output formats, and the -help option prints a brief summary of the options.
A comparison of the features of Caryoscope with other, similar software is presented in Table 1.
Obtaining Caryoscope
In addition to immediately executable copies, the complete source code for Caryoscope is available without limitations from our website ([15]), and is covered by a very liberal Open Source ([18]) license (the MIT License, [19]). All external components used by Caryoscope are also Open Source.
We update Caryoscope frequently (approximately once every three weeks) and post news items on the website. We also send e-mail announcements to people who have requested them.
Discussion
Biological context
Caryoscope is useful for viewing both arrayCGH and expression data in the context of genomic position. It helps a biologist gain insight by providing a high-level view of a large amount of data at once, where patterns can be perceived at a glance.
A biologist studying amplifications or deletions in tumor cells may create and export graphics representing arrayCGH and expression data for the same cells using Caryoscope, and visually compare the two side-by-side. For instance, co-located regions that are amplified at the DNA level and over-expressed at the RNA level would provide excellent confirmation of the results. Using the zooming and panning features of Caryoscope, the biologist could focus on specific regions of interest.
To identify regions of aneuploidy, the biologist can again simply examine the data visually. In this case, however, one would look for a large-scale pattern. One might specify a Minimum feature width of, say, 2 pixels (Figure 5c), to ensure that any deletion or amplification, no matter how small in genomic coordinates, is easily visible. Rather than zooming in on specific regions, a researcher would tend to compare overall views of the entire genome. If it seems like practically all of one chromosome is amplified or deleted, the biologist would have strong evidence for aneuploidy.
In a gene expression study, a biologist may suspect that some expression patterns are correlated with genomic position. Caryoscope allows one to view expression data, either for the whole genome or on a region of interest, to help confirm or refute a hypothesis.
Finally, in all this work, the biologist may want to have quick and easy access to information about the genomic features displayed. As long as the information needed is available from the annotations that were saved in the input data file (or available at a URL that can be built based on the annotations), one can use the Feature URL expression and Feature tooltip expression (Figure 4) settings to provide immediate mouse-over feedback with this information – almost as if the application were customized for a specific field of interest.
Software context
We intend Caryoscope to be a bench-top visualization tool that biologists can use immediately to get day-to-day work done, with a very low "cost of entry" for getting started. This led us to a number of design choices.
Caryoscope can be launched from our web site without a prior installation step. Since it is Open Source, anyone, including any for-profit organization, can use it without restrictions and without having to obtain a license or register for access.
The input file formats and output graphical formats we chose are all in common usage. In particular, the TXT and CSV input formats can be generated using any popular spreadsheet or database software, or even with a plain text editor, without having to do any programming.
Finally, we built Caryoscope to be content-neutral, with no hard-coded specificity to any research field. Thus, users of Caryoscope may control how annotations are treated as numerical data, and can "program" data-driven interactive behavior of the display (i.e., the tooltips and hyperlinks).
Future work
From the outset of this project, our biggest challenge has been how to accurately represent the huge amounts of data in a typical gene expression or arrayCGH experiment using the limited number of pixels available on the screen or on a printed page. If we skew our display algorithms too much towards producing a "sharp", high-contrast plot, we risk obscuring detail in the data and leading biologists to the wrong conclusions. On the other hand, since the size of the data elements, properly scaled from genomic coordinates to the display device, can be far less than the size of one pixel, we need a supportable way to "summarize" the data within each pixel and represent that summary as a single value: the color (including the brightness) of that pixel.
Modern computer graphics systems use a technique called "anti-aliasing" ([20] and Figure 5a) to render sub-pixel details with the illusion of smoothness. The Java subsystems we use in Caryoscope do this automatically and, in the current version of the software, we simply rely on them (Figure 5b). However, the anti-aliasing in Java is designed to display visually appealing text, lines and arcs, but not to ensure the most accurate possible on-screen rendering of scientific data. Specifically, at low magnification, the data almost disappear unless we force a minimum pixel size for each locus (Figure 5c).
We will develop our display methods further to ensure that we can provide an easy-to-read display while retaining the subtle variations in the data. Following the spirit of medical diagnostic imaging (DICOM, [21]), whereby incorrect details in a few pixels could lead to an incorrect conclusion, we must ensure that our displays, which are used for important research decisions, are never misleading.
One solution, suggested by [12], is to display the data elements, not aligned to the position of the loci along the chromosome, but rather in strict sequential order with a fixed width. While this solves the anti-aliasing problems, it does eliminate consistent chromosomal positions and alignments of the data. Furthermore, it causes the appearance of the display to be dependent on the specific choice of clones – which can be another source of subtle variation when comparing multiple datasets. Another idea is to display dots, rather than horizontal bars, so that the "spread" of the data is more visible even if data points are super-imposed. We will investigate this for a future release of Caryoscope.
We are particularly concerned about the use of Caryoscope (or similar) graphics in vector formats (such as PostScript, PDF and SVG) that are subsequently rendered on diverse display devices and printers. Since we have no control over the rendering at the destination, it is likely that the same vector output could look very different on different devices. Once we have studied this problem in more detail, we intend to provide practical usage guidelines for researchers.
Our experience with the application, and how it is used, leads us to believe that the current zooming paradigm should be revisited. While the model of a continuously zoomable 2D space provides users with the features they need, it can lead to displays whereby the data in specific regions of adjacent chromosomes are juxtaposed, even though their being next to each other is not intrinsically meaningful (see Figure 6). (An exception to this might occur in telomere amplification, or perhaps special behavior around the centromeres. In this case, the ability to align the chromosomes at either end, or at the centromeres, would be helpful.)
In the future, we will modify our display so that the user can turn "on" or "off" the display of the available chromosomes. Within that display, and with the help of our users, we will review the role of the X-axis scaling: perhaps it should change the scaling of the data, or perhaps it does not belong in Caryoscope-like applications at all.
A common user request is to display two or more arrayCGH and/or expression datasets side by side, either to determine regions of recurrent deletion or amplification, or to discern visually the impact that changes in chromosome copy number may have on transcript levels (e.g., see [5]). Another frequent request is to show the cytoband information for each chromosome. We intend to add these features as part of our future development, in the course of which we would perhaps redefine, or extend, the manner in which our input data are defined (i.e., we would accept the definition of the chromosome names, lengths and cytobands in a separate file that would be re-used by different datasets).
Summary
Caryoscope currently provides a flexible method to visualize, explore and create images of microarray data in a genomic context. With such a tool, microarray researchers will be able to answer questions about how genome copy number or genome position plays a role in biological processes or human diseases.
Conclusions
Caryoscope is a useful, flexible Java application for the visualization of microarray data in a genomic context. It is available as Open Source under the permissive MIT License, allowing anyone to use or modify it.
Availability and requirements
Project name: Caryoscope
Project home page:
Operating system(s): Platform independent
Programming language: Java
Other requirements: Java 1.4 or higher
License: MIT License
Any restrictions to use by non-academics: None
Authors' contributions
IABA compiled the functional requirements for the Java version of Caryoscope and designed and implemented the software. CAR conceived and wrote the original Perl version of Caryoscope. THB aided and participated in the design of the study, dealt with user support and worked with users to get feedback and suggestions for further development. CAB aided and participated in the design and evaluation of the study and provided feedback and suggestions for future development. GS supervised and participated in the design of the study. All authors read and approved the final version of the manuscript.
Acknowledgements
The authors would like to thank members of the Pollack, Brown and Botstein laboratories, particularly Jon Pollack, for their feedback on Caryoscope, and for providing datasets for testing, and Ash Alizadeh for suggesting the Caryoscope name. Thanks also go to all the members of the Stanford Microarray Database group for stimulating discussions. This work was funded by a grant from the NHGRI, R01HG002732, to GS.
Figures and Tables
Figure 1 The Caryoscope input file format. An illustration of the input file format for Caryoscope. A tab-delimited (TXT) file is shown as an example, but the CSV and GFF formats are similar. Each column of data represents an annotation, with the name of the annotation at the top. Note how, at this level, there is no distinction between expression data and other information: for maximum flexibility, everything is an annotation.
Figure 2 The Caryoscope main window. (a) Caryoscope displays all numerical annotations from the data file as datasets. The datasets are available for viewing via the dataset selector at the top of the Caryoscope window. (The data shown are provided by Jon Pollack, from a breast tumor sample. Note the significant regions of amplification and deletion.) The toolbar at the top allows the user to select a variety of modes for viewing the data. The panning and zooming modes allow the user to navigate the data, while the Reset viewpoint button returns to display all of the data in the current dataset, scaled to the current window size. The Navigate mode enables hyperlinking and tooltips. The zoom level indicators improve the user's situational awareness while viewing the data. (b) The Settings window allows the user to configure the application.
Figure 3 Zooming and panning modes. The zooming and panning modes are selected from the View modes toolbar. (a) In Dynamic zoom mode, the user clicks then drags the mouse; based on the mouse motion, the view zooms continuously with independent zoom speeds in the X and Y directions. (b) In Dynamic pan mode, the user clicks then drags the mouse; the direction and distance of the mouse motion determines the speed and direction of the panning motion through the data. (c) In Zoom in mode, the user selects a region (denoted by a "marquee"). The display is zoomed so that the selected region is fit to the current window. The user may also click anywhere within the current window, which zooms in by a fixed scaling, centered on the click point. (d) In Zoom out mode, the user clicks anywhere, which zooms out by a fixed scaling, centered on the click point.
Figure 4 Navigate mode. (a) In this mode, Caryoscope shows a tooltip (small informational pop-up window) whenever the user moves the mouse over a feature. When the user clicks on the feature, Caryoscope navigates to a URL related to the feature. Both the tooltip text and the URL are generated dynamically from the annotations on the feature under the mouse pointer, via the Feature tooltip expression and Feature URL expression settings. (b) Illustration of how the tooltips and URLs are computed from the Feature tooltip expression and Feature URL expression settings and the annotations in the input file.
Figure 5 Example of anti-aliasing. A demonstration of the effect of anti-aliasing in Caryoscope. (a) Anti-aliased text displayed by Adobe Acrobat 6.0 on an Apple computer running Macintosh OS 10.3.3. Note that the text appears as if drawn by smooth black ink but, upon magnification, it becomes clear that this smoothness is a visual illusion created by careful manipulation of the color of each pixel. (b) The analogous situation in Caryoscope, where the scaled sizes of the feature loci are smaller than one pixel on the display device. In this example, we applied a form of anti-aliasing by prefiltering, where we computed the color of each pixel based on the area of red rectangles it contains. See [20]. (c) Actual displays from Caryoscope showing how the Minimum feature width setting highlights different aspects of the data. The three deletions d on chromosome 1 are clearly distinct at a setting of zero, but are lost in the midst of other artificially expanded features when the setting is increased. On the other hand, the two amplification "spikes" a on chromosomes 3 and 5 are invisible at a setting of zero, but become visible when the setting is increased.
Figure 6 Zooming paradigm. An example of a partially zoomed-in display, which highlights adjacent regions of four chromosomes. The model of continuous 2-dimensional zooming breaks down in this case, since the adjacency of these regions is not biologically relevant.
Table 1 Comparison to other tools Itemized comparison of the features of Caryoscope ([15]), CGHAnalyzer ([12],[22]) and SeeCGH ([11],[23]).
Caryoscope CGHAnalyzer SeeGH
Platform Java (runs on any popular platform) Java (runs on any popular platform) Microsoft Windows
Application availability Unlimited use; no signup Unlimited use with signup Free with signup for academics
Installation process Install Java; run application directly from Web Install Java; install application Install MySQL; install application; configure database tables
Input file formats Single input file; spreadsheet compatible text or General Feature Format (GFF) Multi-component input; text-based input formats Text-based input formats
Internal data format Text file Text file or database MySQL database
Source code availability Free download Download with signup Not available
Source code license Open Source Unknown N/A
Internal code dependencies All Open Source Depends on some commercial software N/A
Superimposed karyotype plot No Yes Yes
Informational summary for each feature Yes, configurable in application settings Yes, fixed tabular format Yes, fixed tabular format
Web link from each feature Yes, configurable in application settings Yes, fixed Ensembl or GoldenPath links No
Data points in genome coordinates Yes Yes Yes
Data points in linear order No Yes No
Simultaneous display of multiple experiments No Yes No
Zoomable display Yes No Yes
Part of a more complete analysis framework No Yes (TIGR MeV) No
Built-in data point filtering No No Yes
Whole-genome view Linear chromosomes Concentric circles representing chromosomes Linear chromosomes
Generate graphics from command line Yes No No
==== Refs
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Pollack JR Perou CM Alizadeh AA Eisen MB Pergamenschikov A Williams CF Jeffrey SS Botstein D Brown PO Genome-wide analysis of DNA copy-number changes using cDNA microarrays Nat Genet 1999 23 41 46 10471496 10.1038/14385
Crawley JJ Furge KA Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data Genome Biol 2002 3 RESEARCH0075 12537564 10.1186/gb-2002-3-12-research0075
Hughes TR Roberts CJ Dai H Jones AR Meyer MR Slade D Burchard J Dow S Ward TR Kidd MJ Friend SH Marton MJ Widespread aneuploidy revealed by DNA microarray expression profiling Nat Genet 2000 25 333 337 10888885 10.1038/77116
Pollack JR Sorlie T Perou CM Rees CA Jeffrey SS Lonning PE Tibshirani R Botstein D Borresen-Dale AL Brown PO Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors Proc Natl Acad Sci U S A 2002 99 12963 12968 12297621 10.1073/pnas.162471999
Baldus CD Liyanarachchi S Mrozek K Auer H Tanner SM Guimond M Ruppert AS Mohamed N Davuluri RV Caligiuri MA Bloomfield CD de la Chapelle A Acute myeloid leukemia with complex karyotypes and abnormal chromosome 21: Amplification discloses overexpression of APP, ETS2, and ERG genes Proc Natl Acad Sci U S A 2004 101 3915 3920 15007164 10.1073/pnas.0400272101
Linn SC West RB Pollack JR Zhu S Hernandez-Boussard T Nielsen TO Rubin BP Patel R Goldblum JR Siegmund D Botstein D Brown PO Gilks CB van de Rijn M Gene expression patterns and gene copy number changes in dermatofibrosarcoma protuberans Am J Pathol 2003 163 2383 2395 14633610
Dunham MJ Badrane H Ferea T Adams J Brown PO Rosenzweig F Botstein D Characteristic genome rearrangements in experimental evolution of Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2002 99 16144 16149 12446845 10.1073/pnas.242624799
Lin JY Pollack JR Chou FL Rees CA Christian AT Bedford JS Brown PO Ginsberg MH Physical mapping of genes in somatic cell radiation hybrids by comparative genomic hybridization to cDNA microarrays Genome Biol 2002 3 RESEARCH0026 12093373 10.1186/gb-2002-3-6-research0026
Baerends RJ Smits WK De Jong A Hamoen LW Kok J Kuipers OP Genome2D: a visualization tool for the rapid analysis of bacterial transcriptome data Genome Biol 2004 5 R37 15128451 10.1186/gb-2004-5-5-r37
Chi B DeLeeuw RJ Coe BP MacAulay C Lam WL SeeGH - A software tool for visualization of whole genome array comparative genomic hybridization data BMC Bioinformatics 2004 5 13 15040819 10.1186/1471-2105-5-13
Greshock J Naylor TL Margolin A Diskin S Cleaver SH Futreal PA deJong PJ Zhao S Liebman M Weber BL 1-Mb resolution array-based comparative genomic hybridization using a BAC clone set optimized for cancer gene analysis Genome Res 2004 14 179 187 14672980 10.1101/gr.1847304
Java
Java Web Start
Caryoscope
General Feature Format (GFF)
Diehn M Sherlock G Binkley G Jin H Matese JC Hernandez-Boussard T Rees CA Cherry JM Botstein D Brown PO Alizadeh AA SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data Nucleic Acids Res 2003 31 219 223 12519986 10.1093/nar/gkg014
Open Source Initiative (OSI)
The MIT License
Aliasing Problems and Anti-Aliasing Techniques
Digital Imaging and Communications in Medicine (DICOM)
CGHAnalyzer
SeeGH
| 15488149 | PMC528725 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Oct 15; 5:151 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-151 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1591550423910.1186/1471-2105-5-159Research ArticleCombining gene expression data from different generations of oligonucleotide arrays Hwang Kyu-Baek [email protected] Sek Won [email protected] Steve A [email protected] Peter J [email protected] School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea2 Molecular Medicine, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215, USA3 Bauer Center for Genomics Research, Harvard University, 7 Divinity Ave, Cambridge, MA 02138, USA4 Department of Neurology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA5 Children's Hospital Informatics Program, 300 Longwood Ave, Boston, MA 02115, USA6 Harvard-Partners Center for Genetics and Genomics, 77 Avenue Louis Pasteur, Boston, MA 02115, USA2004 25 10 2004 5 159 159 6 7 2004 25 10 2004 Copyright © 2004 Hwang et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.
Results
We first describe the extent of discordance between the results derived from two generations of Affymetrix oligonucleotide arrays, as revealed in cluster analysis and in identification of differentially expressed genes. We then propose a method for increasing comparability. The dataset we use consists of a set of 14 human muscle biopsy samples from patients with inflammatory myopathies that were hybridized on both HG-U95Av2 and HG-U133A human arrays. We find that the use of the probe set matching table for comparative analysis provided by Affymetrix produces better results than matching by UniGene or LocusLink identifiers but still remains inadequate. Rescaling of expression values for each gene across samples and data filtering by expression values enhance comparability but only for few specific analyses. As a generic method for improving comparability, we select a subset of probes with overlapping sequence segments in the two array types and recalculate expression values based only on the selected probes. We show that this filtering of probes significantly improves the comparability while retaining a sufficient number of probe sets for further analysis.
Conclusions
Compatibility between high-density oligonucleotide arrays is significantly affected by probe-level sequence information. With a careful filtering of the probes based on their sequence overlaps, data from different generations of microarrays can be combined more effectively.
==== Body
Background
By providing a genome-wide view of gene expression, microarrays have become a common exploratory tool in many areas of biological and clinical studies [1-3]. While there are several different microarray platforms, photolithographically synthesized oligonucleotide arrays from Affymetrix have become one of the principal technologies. These arrays feature multiple 25-mer probes (a "probe set") for each gene, with their measurements summarized into a single number for the estimated expression level of that gene. Because of the important role played by this technology, many methodological studies have focused on improving the extraction of information from these arrays, from image analysis and the proper role of perfect and mismatch probes to distributional properties of the measurements and optimal statistical tests for differential expression [4,5].
Large-scale gene expression data often contain a large amount of noise from various experimental factors. Fortunately, in most cases, the technical variability is relatively small compared to the biological one and its effect can be minimized by using a sufficient number of replicates [6-8]. However, the high cost of microarray experiments often prevents gathering of enough samples for a reliable analysis in a single laboratory. In such cases, employing existing microarray datasets from other studies can be an efficient way of improving the reliability of a study. Moreover, as the number of publicly available datasets grows rapidly on public data depositories (e.g., Gene Expression Omnibus [9]; Stanford Microarray Database [10]; ArrayExpress at EBI [11]), it is clear that these datasets should be combined to generate a more comprehensive understanding of underlying biology.
Several issues have made this process difficult so far. First, different datasets have been processed using different procedures due to a lack of uniform standards, e.g., for background correction, normalization, and calculation of expression values. This makes it difficult to compare them directly. Raw data files are generally unavailable and, even if they are, reprocessing them requires substantial effort. Second, we have lacked datasets with enough controls and replicates, performed under a proper experimental design and with adequate annotations, in order to make proper comparisons. Third, possibly the most troublesome, the experiments have been performed on many different platforms, with significant differences among them. Even within a single platform, technological and algorithmic advances as well as the evolving annotations of the genomes have resulted in succeeding generations of arrays with substantial modification from one generation to the next. Until now, several studies have found varying degrees of disagreement between platforms, sometimes with large discrepancies that call into the question the reliability of certain conclusions reached in microarray studies [12-19]. A comparison of two Affymetrix arrays, HuGeneFL and HG-U95A, was made previously, but only with the conclusion that the reproducibility is high when the two probe sets share many exact probes and that it is low when they do not [20].
In this work, we carry out a thorough examination of the comparability between the two generations of Affymetrix human GeneChip arrays, HG-U95Av2 and HG-U133A, both of which have been used extensively for studying human gene expression patterns. We then propose a method for enhancing their comparability. The analysis we carry out is made possible by a dataset consisting of the same tissue samples hybridized on both platforms. The procedure is illustrated in Figure 1. Using our replicate dataset, we first examine the effectiveness of three schemes for matching the probe sets across different arrays. We then quantify the surprising amount of difference in analysis results between the platforms, as revealed in correlation analysis, hierarchical clustering, and selection of differentially expressed genes. We find that comparability can be improved by rescaling expression values or data filtering but that these techniques are limited to few specific analyses. As a generic method for comparative analysis, we propose selecting a subset of probes that have sequence overlaps with the probes on the other array and recalculating the expression levels based only on this subset. We demonstrate that this probe filtering significantly improves the reproducibility, without eliminating a significant number of genes from the analysis.
Results
Comparison of the methods for probe set matching
The most common method of matching genes in cross-platform studies is to match the UniGene IDs among genes [12,15-18]. One potential problem with this method is that as the UniGene database is updated, some tags are retired and new ones are created, and these may not be tracked correctly unless the same version of UniGene was used to annotate each platform. LocusLink does not suffer from this problem as much and therefore may be preferable in some cases. We tested three methods for matching probe sets between U95Av2 and U133A: UniGene IDs, LocusLink IDs, and Best Match provided by Affymetrix [21]. As shown in Table 1, there are about 9000 unique IDs shared between U95Av2 and U133A in all three cases, with slightly more for the Best Match. The number of probe sets involved is higher for UniGene and LocusLink matching, since there are multiple probe sets corresponding to the same ID in those cases. For Best Match, the sequence mapping is restricted to many-to-one matching.
As a simple way to assess comparability, the Pearson correlation coefficient between each array pair from the same sample was calculated and the 14 correlation coefficients were averaged. The results are summarized in Table 1. UniGene and LocusLink matching give practically identical results. Best Match, on the other hand, shows somewhat higher reproducibility than other matching methods (.870 vs .831–.832). The main reason for the higher reproducibility in Best Match is most likely that more comparable probes are chosen among multiple matches by considering the sequence information. The overall reproducibility, however, is surprisingly low. It has been observed in many replicate studies that expression values from Affymetrix arrays show high reproducibility, typically in the range of >0.98 [20,22,23]. The low correlation coefficient is already an indication that the cross-generation comparison may not be simple. We use the Best Match in the following sections; UniGene or LocusLink matching performs similarly or slightly worse than Best Match.
In a similar study [24], the authors report the average correlation of .81 ± .01 between two different generations of Affymetrix Arabidopsis arrays. But they conclude that this reproducibility is sufficiently high and that the array generations can be compared without further manipulation of the data. However, in our experience, this number is much too low. In the current data set, for instance, the samples in different disease groups give significantly higher correlation coefficients than that. This is clearly demonstrated later in Figure 2(b), where the arrays in the same generation are shown to be more highly correlated than the arrays in the same disease class.
Exactly matched probes between array generations are highly reproducible
There was a possibility that the lack of high correlation between the two versions was caused by a true inconsistency present in the data, perhaps due to RNA degradation between the times when the hybridizations on the two platforms were performed. To make sure that this was not the case, we investigated the quality of our data by examining the subset of probes which have the exactly same sequences between the array generations.
When we examined about 5% of probes that have the same sequence between U95Av2 and U133A, the mean correlation coefficient of array pairs, calculated by PM intensity, was 0.967 ± 0.007. (A calculation using PM-MM values also gives a very similar result.) This is similar to the conclusion in [20] that the probe sets with exactly the same set of probes have a very high correlation. The high correlation in our dataset confirms that the samples and other experimental factors were nearly identical between the two hybridizations and that any discordant result in comparative analysis is therefore most likely due to the differences in the probe design of the two arrays. When we compare the expression values between Best Match and the exactly matched probes, we can easily see the lack of reproducibility for the Best Match case (See Figure 2 in Additional File 1). It is clear that the probe-level sequence information has a large impact on the relationship between the abundance of transcript and the reported intensity [25] and that the use of probe sequences would be necessary in order to choose a subset of relatively consistent probes between U95Av2 and U133A for enhanced reproducibility.
Standard probe set matching produces discordant results in analyses
To determine the extent to which the analysis results from the two versions of the arrays agree, we employ the two most frequent tools for exploratory analysis: cluster analysis and identification of differentially expressed genes. For evaluating the compatibility in terms of cluster analysis, we combined the datasets from U95Av2 and U133A by Best Match. Then, the 28 samples were clustered by agglomerative hierarchical clustering method with the Pearson correlation coefficient as the distance measure. Figure 2(a) shows the dendrogram of 28 samples. Unexpectedly, instead of each array pair from the same biopsy specimen clustering together, the two array types form the two main clusters. In other words, the most distinguishing feature of the data is the array version, rather than the actual characteristics of the samples. To examine the reason for this incongruent result, correlation coefficients of all the possible sample pairings of the combined dataset were calculated. Figure 2(b) shows the correlation coefficients as a color map. The two red parts of the map (upper left and lower right) represent the high correlation coefficients among samples from the same array version. Compared to these, the correlation coefficients across U95Av2 and U133A are relatively low (lower left and upper right parts of the map).
Next, we identified differentially expressed genes between the DMs and other myopathies from each dataset (5 vs 9 samples), using the two-sample t-test with unequal variances (the Wilcoxon test gives very similar results). If the two generations of arrays were comparable, the lists of differentially expressed genes should contain many overlapping genes. To increase the possibility of overlaps, we filtered out non-expressed genes by deleting those in which more than 75% of the samples received Absent calls in both U95Av2 and U133A arrays. When we examine the list of genes identified in common in the two cases, however, its length is disappointingly small. When we look at the list of length 100 or smaller, the percentage of overlap does not exceed 25%. The plot of the percentage of genes common in both lists as a function of the list size is virtually identical to the dashed line in Figure 7b (A detailed plot is shown in Figure 4 of Supplementary Material). This low overlap indicates that the two array types give highly inconsistent results and brings into question the reliability of the highly ranked genes in either platform. We do note, however, that this result must be interpreted in terms of the sample size and other characteristics of the specific dataset. A low percentage is often partially due to the presence of a large number of genes that are differentially expressed to a similar extent in a particular dataset, in which case a ranking of the genes would be expected to be somewhat unstable.
Gene scaling and data filtering can enhance comparability in specific situations
To understand the reason for the discordance observed in Figure 2(a), we have examined a large number of probes. The underlying problem, we have discovered, is due to a large number of probe sets that exhibit similar relative expression patterns but at different absolute levels. As an illustration, we plot the expression pattern of one such probe set pair, 35828_at of U95Av2 and 208978_at of U133A, in Figure 3(a). Clearly, although the expression patterns of these genes are similar in terms of a correlation coefficient, their scales are very different. This behavior is not simple to explain, but we believe it may be related to a large amount of cross-hybridization by a subset of badly designed probes in a probe set, especially for U95Av2. That would have the effect of amplifying the overall expression values.
A simple solution to this problem is to scale expression values for each gene across samples, for instance, making the mean to be 0 and the standard deviation to be 1. The effect of this gene scaling on the gene pair from Figure 3(a) is illustrated in Figure 3(b). The similarity in the expression pattern is more clearly visible and the measurements for this gene are now more comparable. While the Pearson correlations for the genes are not impacted by this linear scaling for genes, the correlations do change for the arrays. Figures 2(c) and 2(d) show the effect of gene scaling on the clustering result and the correlation coefficient of sample pairs, respectively. In Figure 2(c), the arrays from each platform corresponding to the same sample are now clustered together in every case. In Figure 2(d), the high correlation among the arrays of same type (shown by red colors in Figure 2(b)) is diminished and the correlation between specimen samples across array types is highlighted (shown by dark red diagonal lines in upper right and lower left areas). For comparing datasets in a cluster analysis, gene scaling appears to work very well.
While gene scaling was effective in cluster analysis, it is limited to evening the influence of different genes in a global analysis by focusing on their patterns. It does not enhance the comparability, for instance, in terms of identifying differentially expressed genes in most algorithms. For that case, some simple filtering schemes could enhance reproducibility instead. One way is to consider only the genes that exhibit strong correlations between the two versions. To see the impact of this on the selection of differentially expressed genes, we calculated the overlap for the 1,000 genes whose profiles on the two array versions were highly correlated. The result is plotted in Figure 4(a) (solid line). To make sure that the increase in the overlap percentage is not due to the smaller number of genes, we also calculated the overlap for bootstrap samples of same size and averaged the result in Figure 4(a) (dashed line). As expected, data filtering by correlation coefficients greatly improved the comparability, more than doubling the percentage of genes in common. With more datasets such as the one we examine here, it is in theory possible to catalog a comprehensive list of genes that are reproducible across arrays, and use only these genes in subsequent comparative studies. Instead of choosing highly-correlated gene pairs, we can also filter data by expression values. Figure 4(b) shows the distribution of correlation coefficients for genes between the versions stratified by their average expression values. We first note that the distribution for all genes is very wide, with the Pearson correlation coefficient of .426 ± 390, reflecting the poor concordance for the probe set values on the two platforms. With the stratification, it is clear that highly expressed genes tend to give more reproducible expression patterns across the two versions, although there still is a fraction of genes with low or even negative correlation. The disadvantage of this type of filtering is that, as in the filtering by correlation, it inevitably reduces the number of probe sets for the analysis significantly.
Probe filtering by overlapping length highly improves reproducibility with enough probe sets for comparison
We now describe a more general method for improving comparability by filtering at the probe level, instead of at the probe set level. We have already observed that the probes with exactly the same sequences on the two generations give highly reproducible values (Additional File 1, Figure 2) but that the probe sets do not. This implies that specific probe sequences within the same target region can produce strikingly different results, and suggests that comparability would improve if we select only those probes that have sequence similarities on the two arrays. To carry this out, we mapped the location of all probes using BLAT, as described in Methods. When we select a subset of probes, we mask the rest in the raw data (cel files) and then recompute the expression values using the same algorithm used in MAS 5.0.
An optimal selection scheme requires a balance. On the one hand, we would like to require as large a sequence overlap as possible between the probes to ensure high reproducibility. On the other hand, a stringent restriction means that the number of usable probe sets in an array is reduced and also that each probe set value will be less robust because it is derived from fewer probes. Figure 5 shows the correlation coefficient of array pairs from the same sample according to two criteria: the minimum overlapping length (1 bp ~ 25 bp) and the minimum fraction of used probes per probe set (10% ~ 100%). The latter refers to the fraction for each probe set, e.g., 30% minimum means that at least 4 out of 11 probes for U133A and 5 out of 16 for U95Av2 must satisfy the sequence overlap requirement. If there are too few probes left in a probe set, we discard the probe set as unreliable.
In Figure 5, we plot the average of the correlations for the pairs of U95Av2 and U133 chips on which the same sample is hybridized. We see that the average correlation improves substantially with the greater amount of sequence overlap at all ranges. It also improves with the minimum percentage of probes used but only slightly. Figure 6 shows the number of usable probe set pairs according to the same two criteria. It appears, for example, that we can obtain highly comparable results (correlation coefficient > 0.9) with a large number of probe sets (more than 80%) for comparative analysis. For a given value of minimum overlap length, we can also calculate the average number of probes per probe set (See Figure 5 in Supplementary Material) in addition to the number of retained probe sets. With 20 bp minimum overlap, more than 90% of probe sets can be used, with the expression levels calculated from an average of 30% of the original probes per probe set.
To emphasize the improvement, we again show in Figure 7(a) the increase in the mean correlation coefficient of array pairs, without any criterion on the fraction of used probes per probe set. As a baseline, the mean correlation coefficient of array pairs using Best Match is also represented (dashed line). Enhancement in the mean correlation coefficient of array pairs is roughly proportional to the minimum overlapping length. It appears that the mean correlation coefficient can be worse than in the case of Best Match when the minimum overlapping length is less than 10 bp. It is possibly because such a small overlap constitutes enough dissimilarity as to confer no functional relationship between the probes and instead other good probes that do not have overlaps are thrown away. Based on Figures 5 and 7(a), we suggest that the minimum overlapping length of more than 18 bp is necessary for obtaining significantly improved results in terms of correlation coefficient of array pairs (>0.9).
Next, we show the improvement of comparability in terms of selecting differentially expressed genes. Figure 7(b) shows the percentage of commonly identified differentially expressed genes between U95Av2 data and U133A data when the probes are filtered with minimum overlapping length of 18 bp. The number of usable probe set pairs in this case is more than 9,500. For comparison, the result for the Best Match (10,507 probe set pairs) case is also drawn (dashed line). From Figure 7(b), it is clear that the improvement in comparability is significant, especially when the number of selected genes is small. For example, without the probe filtering, the lists of top 15 genes in the two data sets have no genes in common; with filtering, 30 ~ 50% of the genes are shared. These results demonstrate that the filtered and recomputed data sets are more comparable with only a small reduction in the number of usable probe sets.
Deviation from the original expression profile after probe filtering can be controlled by criterion on the overlapping length
A reduction in the number of usable probes inevitably results in the deviation of the recomputed expression values from the original values calculated using all probes. Figure 8(a) shows the mean Spearman correlation coefficients between the expression values using all probes and those using only the selected probes by our criteria. We use the Spearman correlation here to capture the changes in the ranks of genes. As expected, the correlation decreases, as more stringent criteria are applied and a smaller subset of probes is chosen. Interestingly, the deviation in U95Av2 arrays is much larger than in U133A arrays, although the average fraction of used probes per probe set in each case is similar (see Figure 5 of Supplementary Material). For example, the mean correlation coefficient is greater than 0.9 in U133A when the criterion on the minimum overlapping length is less than 20 bp. For the same criterion, the mean correlation coefficient is about 0.85 in U95Av2. This appears to indicate that, in the process of making the two versions more similar, the larger changes occur to the expression levels in U95Av2 arrays. This result is consistent with the fact that probe design for U133A was performed in a more principled way than for U95Av2 and that U133A values are closer to the true values [25]. In addition to recalculating the expression values, the Affymetrix Present or Absent calls can also be calculated. Figure 8(b) shows the percentage of Present calls for each reduced group of probe sets. The probe filtering appears to reduce the percentage of Present calls, possibly because having fewer probes per probe set increases the likelihood of Absent calls. The usefulness of these calls can be debated; we simply present it here for those who find the calls helpful. In any case, we note that the percentage sharply drops down as the minimum overlapping length increases past 18 bp. Both Figures 8(a) and 8(b) indicate that 18–20 bp may be a reasonable cut-off values for the overlap length. We note that in filtering the probes, our goal is to simply make the expression profiles from U95Av2 and U133A more comparable. In the process, it is possible that this procedure sometimes results in less accurate expression values in absolute terms. By requiring that the probes in U133A have a sequence overlap with the less reliable set in U95Av2, we may be discarding some useful probes and, as a result, may be producing less accurate expression values. This is a trade-off that we make in order to utilize other data sets for a comparative study, but we should be aware of this fact in subsequent analysis.
Conclusions
Comparative analysis of different microarray types has a potential to generate more comprehensive and reliable results by fully exploiting available data. Understanding and resolving both the inter-platform and inter-generation data remain an important and challenging practical issue. So far, attempts at such comparisons have been few, and many were limited to simple observations of low correlations in expression values. In this work, we provided a more quantitative and comprehensive description of the issues and inconsistencies through the analysis of a unique dataset consisting of HG-U95Av2 and HG-U133A hybridizations for each of the sample biopsies, and then we described a general method for resolving some of the problems.
We first observed in cluster analysis that with a standard matching of genes, the dominant feature of the dataset is not the sample characteristics but the array type. But we found that for clustering, this problem can be mitigated by rescaling each gene. We note, however, that this method is effective under certain assumptions, e.g., that there are enough samples for each array type and that each dataset does not contain unrelated experiments. If two groups of patients under study are measured on two different arrays, for example, a gene scaling will simply make the samples more homogeneous and reduce the differences between the groups. We also examined the inconsistencies in the list of differentially expressed genes obtained in the two cases. The overlap was very low, indicating that such a list may be platform-dependent and must be interpreted with caution. Some data filtering steps, either by selecting a subset of genes that are empirically shown to be well-correlated between platforms or by focusing only on highly-expressed genes, can be helpful at times, but they do not resolve the underlying problem.
Our approach based on the probe-level sequence information resulted in a significant improvement in the reproducibility in terms of correlation coefficients and selection of differentially expressed genes. As the probes aligned to multiple regions in the genome are eliminated and the probes that share larger segments are selected, the expression values become more consistent. This result is promising because it does not use data-dependent information such as the empirical correlation for each gene between different versions of arrays, which can only be obtained through special datasets such as ours. We examined the effect of the minimal sequence overlap length and the minimum number of probes per gene on the reproducibility, and found that, when the parameters are chosen properly, higher correlation can be attained while retaining a large number of probes for further analysis. We also examined the deviation from the original data when new expression values are calculated after probe filtering. In general, we recommend the minimum overlapping length of 18 ~ 20 bp and that at least 10 ~ 20% of probes in a probe set be present in the filtering step for a comparative analysis between U95Av2 and U133A.
Combining data across multiple platforms remains a formidable challenge. As a first step, we have studied the issues associated with combining data from multiple generations of a single platform and proposed one method. From our analysis, it is clear that technological issues can have significant effect and that one should be aware of the potential pitfalls in studies involving more than a single array type. In principle, the approach of selecting probes with sequence overlaps can be applied to other array types as well as to different versions of oligonucleotide arrays. For example, to study expression profiles of conserved regions across species using a different array for each species, more accurate results may be obtained by using only a subset of probes with sequence similarity. In each case, appropriate criteria for the length of overlap and the number of probes needed for a robust estimate of a probe set value need to be investigated for different contexts, but the results we provide in this work can serve as a guide.
Methods
Microarray data
Muscle tissue samples of 14 patients with inflammatory myopathies were collected. Among the 14 patients, 5 had dermatomyositis (DM) and 9 had other inflammatory myopathies including necrotizing myopathy, inclusion body myositis, granulomatous myositis, and polymyositis. Because the molecular profile of DM is sufficiently different from those of the rest, we can think of the DMs as one group and the rest as the other group in a two-group comparison [26]. Total RNA was extracted from muscle biopsy tissues and labeled. A portion was hybridized to HG-U95Av2 arrays; the remaining supply was frozen and then later hybridized to HG-U133A arrays at the same facility.
Matching probe sets between U95Av2 and U133A
Although they belong to the same oligonucleotide array platform, the changes from the older version (U95Av2) to the newer one (U133A) were substantial: 1) Main source of probe selection region is different (UniGene Build 95 and 133; for the U133 set, other sequence databases such as dbEST were extensively used for choosing the probe selection region); 2) The number of probe pairs was reduced from 16 to 11 for a single gene; and 3) Probe selection method was improved [25]. The annotation for each probe set in U95Av2 and U133A was obtained from NetAffx Analysis Center (NetAffx annotation files (annotation date: 12/10/2003)) [27]. According to the annotation information, U95Av2 has 12,625 probe sets, which are annotated by 9,091 UniGene and 8,672 LocusLink identifiers. The newer version U133A consists of 22,283 probe sets annotated by 13,624 UniGene and 12,769 LocusLink identifiers. Here, the UniGene identifier was assigned by matching the representative sequence of each probe set to the UniGene database at the time of annotation. The LocusLink identifier was derived from the matched UniGene record (Annotation Methodology, Affymetrix web site).
For considering variations in the probe sets for the same transcript between different array versions, Affymetrix provides the probe set matching tables for comparative analysis. These matching tables were constructed based on the sequence information of probe sets as follows [21]. First, all possible probe set pairs between two array generations were checked by their similarity in the representative sequence for selection. Among the selected probe set pairs, "Good Match" pairs were chosen by the following criteria: 1) Percent identity between the representative sequences >90%; 2) Length of the representative sequence >100 base pairs (bp); 3) At least one perfect match (PM) probe of one array generation should be perfectly aligned to the probe selection region of the other array generation. In addition, "Best Match" is a subset of Good Match selected by more stringent criteria on the similarity of probe set pairs [21]. Best Match is used in the rest of the paper as it performs better than Good Match in all instances. When there is more than one probe set matching on either or both arrays, we take the average of the measurements.
BLAT for the alignment of probes
For improving compatibility between U95Av2 and U133A, those probes whose sequence overlapped with any of the probes for the same gene on the other platform were selected. The extent of overlap necessary is described in the Results section. First, all the perfect match (PM) probes were aligned to the coding regions of the genome. Of commonly used short sequence alignment tools such as SIM4 [28], SPIDEY [29], and BLAT [30], we used BLAT (build version 26, available at as a stand-alone program) because it appears to be more accurate and faster than others for matching short sequences with high sequence identity (more than 90%). BLAT has been used previously for annotating the probe sets of HG-U95Av2 in GeneAnnot system from Weizmann Institute of Science [31]. The alignment was done on the human chromosome sequence Build 34 (July 2003 freeze), available at UCSC Genome Bioinformatics ([32]). We ran BLAT with its default options (-tileSize = 11 -minMatch = 2 -minScore = 30, -minIdentity = 90 -maxGap = 2), without the overused tile file to avoid missing any matches. From the BLAT search result, only the 25-mer perfect alignments were considered for further analysis. All probes aligned to more than two regions in genomic DNA were discarded because of the possibility of cross hybridization. In each matched probe set pair, the overlapping lengths between all the possible PM probe pairings (16 × 11) were calculated.
Filtering probes by overlapping length
The length of the overlap between probe sequences (1 bp ~ 25 bp) was used as a criterion for choosing probes for comparative analysis. The expression values were recomputed each time using only the selected probes by masking out the other probes from the raw (.cel) files. The values were calculated by the Statistical Expression Analysis Algorithm using Microarray Suite version 5.0 (MAS 5.0) (Affymetrix, Santa Clara, CA) without linear scaling to target intensity. MAS 5.0 is a robust estimator of expression index based on one-step biweight estimation algorithm, considering both perfect match (PM) and mismatch (MM) probes. This algorithm alleviates the problem of unstable expression values to some extent when a fraction of the probes is eliminated in our analysis.
Authors' contributions
KBH carried out probe set matching, performed BLAT searches as well as statistical analysis, and drafted the manuscript. SWK carried out the raw data processing, performed statistical analysis, and provided input on drafts of the manuscript. SAG participated in the design of the study as well as providing the microarray data set for this study. PJP conceived the original idea of this study, participated in its design and coordination, and wrote sections of the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Supplementary material for the paper "Combining gene expression data from different generations of oligonucleotide arrays" Supplementary figures for the paper
Click here for file
Acknowledgements
KBH was supported by the Korea Science and Engineering Foundation (KOSEF) and by the Korea Ministry of Science and Technology under the NRL Project; SWK was supported by 5U01HL066582-04 from NIH; PJP was supported by K25-GM67825 from NIH.
Figures and Tables
Figure 1 A schematic view of the procedure. The same RNA was hybridized on both HG-U95Av2 and HG-U133A arrays, for 14 samples. Three methods for matching the probes were considered, but the two datasets gave highly inconsistent results in cluster analysis and identification of differentially expressed genes. To improve the comparability in general, probe-level sequence information was exploited. All 25-mer probes were aligned to human genome sequences by BLAT and then filtered based on the length of their overlap with the probes on the other array. New expression indices were calculated using only the selected probes, and this results in higher reproducibility.
Figure 2 Cluster analysis on the combined dataset from U95Av2 and U133A. (a) The result of hierarchical clustering of 28 samples using the Pearson correlation coefficient as a distance measure. The dendrogram is exactly divided into the two groups representing U95Av2 and U133A, rather than by sample type. (b) The correlation coefficients between every two arrays in the combined dataset. The red (green) color corresponds to a higher (lower) value of correlation coefficient. The bias between U95Av2 and U133A is clearly represented here (upper left and lower right parts of the rectangle). (c) The result of clustering after gene scaling. Each gene expression value was scaled across the samples before combining U95Av2 and U133A datasets. In the dendrogram, the arrays obtained from the same biopsy are now joined together in all cases. (d) The bias between U95Av2 and U133A has clearly disappeared by gene scaling. The dark red diagonals in the upper right and lower left parts denote the high correlation coefficients for the same biopsy hybridized on different arrays.
Figure 3 Expression pattern of probe sets for the same gene: 35828_at in U95Av2 and 208978_at in U133A (matched by Affymetrix "Best Match"). (a) The expression patterns before gene scaling. Even though their shapes are somewhat similar, their scales are very different, (b) The expression patterns after gene scaling. After gene scaling, the similarity in the patterns is more clearly visible and these genes have a comparable effect on the clustering of the samples.
Figure 4 The effect of data filtering on identification of differentially expressed genes and on correlation between array types for the same genes. (a) Percentage of differentially expressed genes common in U95Av2 and U133A datasets. When we considered only the top 1,000 highly correlated genes across U95Av2 and U133A, the overlap between the lists of differentially expressed genes increased dramatically (solid line). For comparison, we show the result without gene selection by correlation (dashed line). For the latter, we subsampled a random gene set of same size repeatedly to eliminate the effect of total size; we also filtered using Present and Absent calls to increase the overlaps. (b) Distribution of the correlation coefficient of probe sets stratified by their mean expression value across U95Av2 and U133A. The density was estimated for upper quartiles using a Gaussian kernel. Filtering by expression values clearly enhances the correlation of probe sets across array types, thus improving the reproducibility in the selection of differentially expressed genes.
Figure 5 Improvement in the correlation coefficients of array pairs for the same biopsy according to the minimum overlapping length (1 ~ 25 bp) and the percentage of used probes per probe set (10 ~ 100%). The correlation coefficients are the average of 14 arrays pairs. The probes are selected based on sequence overlap, and the probe sets with a sufficient number of such probes are used to recalculate the expression profiles. The correlation is enhanced with increasing number of minimum overlap length and, to a lesser extent, with increasing percentage of probes required for probe set.
Figure 6 The number of retained probe set pairs for comparative analysis according to the same two criteria as in Figure 5.
Figure 7 Effect of probe filtering on the comparability between U95Av2 and U133A. (a) We plot the mean correlation coefficient of array pairs from the same biopsy according to the minimum overlapping length used for probe filtering. (standard deviations are drawn as thin dotted line.) Improvement in reproducibility is roughly proportional to the allowed minimum overlapping length. For comparison, the mean correlation coefficient with all 10,507 probe set pairs of Best Match is also drawn (dashed line). For significant enhancement in comparability, the minimum overlapping length should be more than 15 bp. (b) Improvement of reproducibility in the selection of differentially expressed genes (DM vs others). Here, we compare Best Match (10,507 probe sets) with having a minimum overlapping length of 18 bp (9,515 probe sets). The reproducibility was markedly improved by probe filtering, especially for the top ranked genes.
Figure 8 Deviation from the original expression profiles after probe filtering. (a) We compared the modified expression profile of each sample with the original one using the Spearman rank correlation coefficient. The mean correlation coefficient decreases as the minimum overlapping length increases. In addition, the effect of probe filtering is much stronger in U95Av2 than U133A. (b) Percentage of Present calls in the masked dataset compared to the original dataset. It drops down sharply after the minimum overlapping length of around 18 bp.
Table 1 Comparison of the methods for probe set matching. In the case of Best Match, the relation of probe sets between U95Av2 and U133A is many-to-one. The Pearson correlation coefficients of array pairs from the same biopsies were calculated and averaged for the assessment of comparability. The main reason for the high comparability of Best Match is the selection of the most appropriate probe set from the multiple matches using sequence information.
No. of matched probe sets (U95Av2) No. of matched probe sets (U133A) No. of unique IDs shared between U95Av2 and U133A Mean correlation coefficient of array pairs
UniGene IDs 11,596 15,858 8,867 0.832 ± 0.017
LocusLink IDs 11,389 15,666 8,661 0.831 ± 0.017
Best Match 10,507 9,530 9,530 0.870 ± 0.016
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| 15504239 | PMC528726 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 25; 5:159 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-159 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-271549810410.1186/1471-2210-4-27Research ArticleWeight loss dynamics during combined fluoxetine and olanzapine treatment Perrone Jennifer A [email protected] Janet M [email protected] Brian H [email protected] Judith M [email protected] German [email protected] Department of Neuroscience, New York College of Osteopathic Medicine of New York Institute of Technology, Old Westbury New York, 11568 USA2 Department of Psychology, Medaille College, Buffalo New York, 14214 USA2004 21 10 2004 4 27 27 24 6 2004 21 10 2004 Copyright © 2004 Perrone et al; licensee BioMed Central Ltd.2004Perrone et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Fluoxetine and olanzapine combination therapy is rapidly becoming an effective strategy for managing symptoms of treatment-resistant depression. Determining drug-drug interactions, drug metabolism and pharmacokinetics is of particular interest for revealing potential liabilities associated with drug augmentation in special patient populations. In the current studies, we chronically administered fluoxetine and olanzapine in non-stressed rats to extend our previous findings regarding body weight dynamics.
Results
Chronic fluoxetine (10 mg/kg) and olanzapine (5 mg/kg and 0.5 mg/kg) treatment decreased weight gain irrespective of olanzapine dosing. At the 10 mg/kg and 5 mg/kg dose, respectively, fluoxetine and olanzapine also significantly reduced food and water consumption. This pharmacodynamic event-related effect, however, was not observed at the 10 mg/kg and 0.5 mg/kg dosing paradigm suggesting differences in tolerability rates as a function of olanzapine dose. The decrease in weight gain was not associated with apparent changes in glucose metabolism as vehicle- and drug-treated rats showed undistinguishable serum glucose levels. The combination of fluoxetine and olanzapine in rats yielded drug plasma concentrations that fell within an expected therapeutic range for these drugs in psychiatric patients.
Conclusions
These data suggest that fluoxetine and olanzapine treatment decreases weight gain in rats; a pharmacodynamic event-related effect that differs considerably from what is observed in the clinical condition. The possibility of mismatched models regarding body weight changes during drug augmentation therapy should be seriously considered.
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Background
Treatment-resistant depression is a serious issue in psychiatry as a significant number of affected individuals show an inadequate response to single antidepressant therapy. An emerging strategy to achieve maximum mood stabilization for treatment-resistant depression, bipolar illness and depression with psychotic features is the augmentation of fluoxetine (Prozac) with novel anti-psychotic agents such as olanzapine (Zyprexa). Indeed, a number of clinical trials have suggested that such an augmentation strategy offers superior efficacy for treating resistant major depression when compared with either fluoxetine or olanzapine alone [1-3]. Despite the apparent clinical benefits of this drug strategy, little is known about the mechanisms by which fluoxetine plus olanzapine actually function to relieve depression. The limited literature on this issue suggests that drug augmentation therapy, at least in the rat brain, is likely to be more complicated and perhaps more indirect than a simplistic version of fluoxetine or olanzapine would imply [4-7]. For instance, whereas fluoxetine and olanzapine alone activate several signaling pathways involved in cell survival and plasticity [8-10], drug augmentation therapy reduces the levels of certain transcription factors (e.g., cAMP response element binding protein and FOS-like proteins) implicated in the chemical circuitry (e.g., prefrontal cortex and hippocampus) underlying emotional behaviors [5]. Consequently, it is conceivable that fluoxeine plus olanzapine treatment is effective against treatment-resistant depression due to their combined actions on numerous brain regions and various interconnected intracellular signaling pathways that ultimately promote some type of prophylactic effect.
We have previously shown that sub-chronic (i.e., 7 days) administration of fluoxetine plus olanzapine results in a significant reduction of weight gain in rats [5]. This finding is of significant interest as fluoxetine and olanzapine alone have distinct and opposite effects on body weight dynamics in both rodents and humans. For example fluoxetine often reduces food intake and thus body weight in rats during sub-chronic and chronic (i.e., 21 days) drug regimens [11], an effect apparently mediated by fluoxetine impact on serotonin (5-HT) signaling pathways [12]. In sharp contrast, treatment with olanzapine is associated with significant weight gain in schizophrenic patients, a serious side effect that may increase the risk for type II diabetes and may also lead to treatment non-compliance [13,14]. In this case it is thought that olanzapine's particular affinity for 5-HT (5-HT2A), dopamine (DA; D2–4), acetylcholine muscarinic (ACh; M1–M5) and histamine (H1) receptors distributed widely in limbic neural circuits may somehow account for the pharmacological basis of olanzapine-induced weight gain [15]. Needless to say, understanding body weight dynamics in relation to drug augmentation therapy is of critical importance if we are going to gain further knowledge on the mechanisms of therapeutic action and side effect profile of anti-depressant medications. In this regard, appetite disturbances are noted in many medicated depressed patients and several peptide transmitters implicated in feeding behavior co-exist in the hypothalamus and may therefore be involved in the onset of affective states [16]. In this study, we have examined in more detail the effects of fluoxetine (10 mg/kg) plus olanzapine treatment on rat body weight during the time course of 18 days under two olanzapine doses: 5 and 0.5 mg/kg. In addition, we have measured blood levels of these two drugs using gas-chromatography-mass spectrometry (GC-MS) to assess their combined pharmacology and their correlation to body weight dynamics.
Results
All rats tolerated the fluoxetine plus olanzapine regimen well. There were no mortalities as a result of 18 days of drug administration in any of the rat groups tested. The only apparent untoward side effect was tissue necrosis in the peritoneum of rats injected with fluoxetine plus 5 or 0.5 mg/kg olanzapine (Fig. 1). Thus, fluoxetine appears to produce focal necrotising vasculitis within the site of injection. The necrotic properties of the above antidepressant have previously been reported [17]. Olanzapine, on the other hand, does not produce tissue necrosis in the peritoneal cavities of rats when administered alone (data not shown).
Figure 1 Tissue necrosis during chronic fluoxetine (fluox) and olanzapine (olanz) treatment. This figure depicts equally excised peritoneal cavities of males injected IP with either a vehicle-solution (cyclodextrin) or the above drug combination pattern for 18 consecutive days. Note the extent of tissue damage (~1 cm wide) at the site of drug administration. Focal necrosis was evident in drug-treated rats irrespective of olanzapine dosing.
All rats showed a steady increase in body weight during the 18 days of cyclodextrin or fluoxetine plus olanzapine treatment. However, fluoxetine in combination with olanzapine significantly retarded this growth rate (P ≤ 0.001) when compared with the vehicle-treated group (Fig. 2). This weight loss, beginning on day 7 of treatment, was observed equally in both the 5 and 0.5 mg/kg olanzapine-treated groups. At this time, rats administered with cyclodextrin showed body weights of 275.6 ± 3.7 g, whereas fluoxetine plus 5 mg/kg olanzapine-treated animals showed weights of 248.1 ± 4.4 g (at the 95% confidence interval for differences between means: 13.8 to 41.2, P ≤ 0.001). Along the same lines on day 7, rats treated with fluoxetine plus 0.5 mg/kg olanzapine showed body weights of 254.1 ± 3.7 g. In contrast, their vehicle-treated cohorts showed weights of 277.8 ± 7.3 g (at the 95% confidence interval for differences between means: 6.90 to 40.41, P ≤ 0.01). The magnitude of this difference in body weight increased further on day 14 and was firmly established by day 18 of drug augmentation therapy (Fig. 3). Thus, fluoxetine treatment, irrespective of olanzapine's ability to cause weight gain, produces a gradual and considerable weight loss in male rats. In general, these findings are consistent with mono-therapy studies where chronic olanzapine treatment invariably leads to weight loss in rodents [18,19].
Figure 2 Body weight changes during chronic fluoxetine (fluox, 10 mg/kg) and olanzapine (olanz, 5 mg/kg) treatment. Rat body weights were recorded before and after drug augmentation therapy. Data represent means ± SEM. N = 5–7 animals per group. *P ≤ 0.05 when compared with drug-treated rats. NS = not significant.
Figure 3 Changes in food and water intake during chronic fluoxetine (fluox, 10 mg/kg) and olanzapine (olanz, 5 mg/kg) treatment. Rats under this combined drug regimen showed a significant reduction in the consumption of nutrients and fluids at day 10 and 12 of drug therapy, respectively. Data represent means ± SEM. N = 5–7 animals per group. *P ≤ 0.05 when compared with drug-treated rats.
The fact that fluoxetine plus olanzapine treatment for 18 days retards the continuous weight gain observed in cyclodextrin-exposed rats suggests at least two testable possibilities. First, rats exposed to drug augmentation therapy might be eating less than their cyclodextrin-treated cohorts. Second, administration of fluoxetine plus olanzapine might be altering glucose metabolism of drug-treated animals. To test the first possibility, we measured average food intake over a 12 hr period of the dark cycle in rats treated with fluoxetine plus 5 mg/kg olanzapine. On day 10 of drug treatment, rats exposed to this drug augmentation regimen ate significantly less (t10 = 5.5, P ≤ 0.001) than vehicle-treated animals (Fig. 4). Interestingly, the same group of rats also showed a significant reduction in water intake (t10 = 6.7, P ≤ 0.01) when compared with cyclodextrin-treated animals (Fig. 4). Thus, rats exposed to fluoxetine plus 5 mg/kg olanzapine are eating (~32%) and drinking (~38%) less at day 10 and 12 of drug treatment, respectively.
Figure 4 Body weight changes during chronic fluoxetine (fluox, 10 mg/kg) and olanzapine (olanz, 0.5 mg/kg) treatment. Rat body weights were recorded before and after drug augmentation therapy. Data represent means ± SEM. N = 5–7 animals per group. *P ≤ 0.05 when compared with drug-treated rats. NS = not significant.
In contrast to the above findings, animals injected first with fluoxetine and then 15 min later with 0.5 mg/kg olanzapine did not show differences in food (t10 = 2.2, P ≥ 0.05) or water (t10 = -1.2, P ≥ 0.05) consumption during the dark cycle when compared with cyclodextrin-treated rats (Fig. 5). Thus, although fluoxetine plus 0.5 mg/kg olanzapine-treated rats show a significant and progressive weight loss at days 7, 14 and 18, this weight loss is not associated with reductions in food or water consumption. This finding suggests that chronic olanzapine treatment is apparently modifying feeding behavior in rats, an effect particularly conspicuous at the 5 mg/kg dose. To test the second possibility, that chronic fluoxetine plus olanzapine is altering the metabolism of drug-treated animals, we measured their blood glucose levels under fasting conditions (Fig. 6). Glucose levels at the time of sacrifice were not significantly different between cyclodextrin- and drug-treated animals at either the 5 mg/kg olanzapine dose (t10 = -0.73, P ≥ 0.05) or the lower 0.5 mg/kg olanzapine dose (t10 = -1.4, P ≥ 0.05). Thus, changes in glucose metabolism are not the proximate cause affecting the differential body weight dynamics, nor the differential consumption of food and water among vehicle- and drug-treated rats. Along the same lines, levels of the hormone leptin did not differ between cyclodextrin- and drug-treated animals (data not shown), thus suggesting that chronic fluoxetine and olanzapine drug therapy does not affect leptin messages under fasted conditions in male rats.
Figure 5 No changes in food consumption or water intake during chronic fluoxetine (fluox, 10 mg/kg) and olanzapine (olanz, 0.5 mg/kg) treatment. Rats under this combined drug regimen did not show an apparent reduction in the consumption of nutrients and fluids at day 10 and 12 of drug therapy, respectively. Data represent means ± SEM. N = 5–7 animals per group. NS = not significant.
Figure 6 No changes in fasting glucose levels after chronic fluoxetine (fluox, 10 mg/kg) and olanzapine (olanz, 5 mg/g or 0.5 mg/kg) dosing. Rats under the depicted drug regimens did not show overt differences in glucose metabolism. Data represent means ± SEM. N = 5–7 animals per group. NS = not significant. Non-fasting glucose levels in rats are typically in the range of 155–242 mg/dL.
We next assessed the pharmacokinetic profile of fluoxetine and olanzapine in rats treated with the above drug combination pattern (Table 1). At doses of 10 mg/kg and 5 mg/kg respectively, fluoxetine plasma levels ranged from 62 ng/mL to 476 ng/mL. In contrast norfluoxetine levels ranged from 292 ng/mL to 1175 ng/mL. Norfluoxetine is the only identified active metabolite of fluoxetine; it is formed through N-demethylation of the parent molecule. Plasma concentrations of olanzapine ranged from 74 ng/mL to 301 ng/mL. At doses of 10 mg/kg fluoxetine and 0.5 mg/kg olanzapine, levels of the antidepressant drug ranged from 276 ng/mL to 576 ng/mL, whereas those for norfluoxetine ranged from 266 ng/mL to 966 ng/mL. Olanzapine levels at this low dose were in the range of 34 ng/mL and 65 ng/mL. When inter-group comparisons of drug plasma concentrations were made between fluoxetine (10 mg/kg) in combination with olanzapine at either the 5 mg/kg or the 0.5 mg/kg dose range, differences in mean values of the two groups did not vary enough (P ≥ 0.05) to reject the possibility of random sampling variability. A similar statistical trend was observed for norfluoxetine levels after 18 consecutive days of drug treatment; there was not a statistically significant difference (P ≥ 0.05) between the two groups. However, differences in the median values between 5 mg/kg and 0.5 mg/kg olanzapine doses were greater than would be expected by chance (P ≤ 0.001). As expected, rats injected IP with cyclodextrin showed no traces of either fluoxetine or olanzapine levels in plasma (≤5 ng/mL). In general, our GC-MS measurements detect pharmacological and relevant levels of both fluoxetine and olanzapine in rats, a finding consistent with previous reports [4].
Table 1 Plasma concentrations of fluoxetine, norfluoxetine and olanzapine after 18 consecutive days of drug augmentation therapy.
Drug Measured Fluoxetine Dose (10 mg/kg) Olanzapine Dose (5 mg/kg)
Fluoxetine 344.1 ± 54.2 (ng/mL)
Norfluoxetine 695.4 ± 118.4 (ng/mL)
Olanzapine 178.5 ± 34 (ng/mL)*
Drug Measured Fluoxetine Dose (10 mg/kg) Olanzapine Dose (0.5 mg/kg)
Fluoxetine 410.0 ± 36.6 (ng/mL)
Norfluoxetine 501.5 ± 114.7 (ng/mL)
Olanzapine 46.7 ± 4.4 (ng/mL)
The fact that regardless of olanzapine dosing both rat groups lost equal amounts of body weight is indicative that these two phenomena (i.e., relative drug levels and body weight dynamics) may not be causally related. No discernible changes in brain structure or integrity were found, as assessed by Nissl staining and stereological cell counts conducted within the rat hypothalamus (data not shown). Values are means ± SEM. N = 7 per dosing group. * P ≤ 0.01 when compared with appropriate olanzapine dose.
Discussion
The present study shows that 18 days of concomitant fluoxetine and olanzapine treatment leads to a significant decrease of weight gain in rats. Given that the above drug combination is particularly effective in treatment-resistant depression, our findings are of interest for revealing potential liabilities associated with its therapeutic use. Here, our data suggest a possible pharmacodynamic event-related effect regarding the action of two psychoactive drugs over time. Indeed, there is a well-established relationship between clinically effective drugs, appetite control and weight changes across diverse patient populations [20]. For instance, weight gain appears to be correlated positively with clinical responses to anti-psychotic medication [21,22]. The combination of fluoxetine and olanzapine in our studies produced weight loss irrespective of anti-psychotic drug dosing. That is, fluoxetine at a fixed dose of 10 mg/kg administered concomitantly with either 5 mg/kg or 0.5 mg/kg olanzapine yielded an approximately 20% mean reduction in body weight for both doses. Therefore, body weight changes associated with the above drug combination are more likely due to the effects of olanzapine or its metabolic pathways (see below). Indeed, this hypothesis is further supported by the fact that although rats treated with 5 mg/kg olanzapine were eating and drinking less than animals injected with a smaller dose (i.e., 0.5 mg/kg), body weight outcome was nevertheless similar for all drug-treated rats. In this regard, it is conceivable that the suppressed consumption of food and water observed in animals injected IP with 5 mg/kg olanzapine might have been the result of malaise, or at least the result of aversion to the hedonic aspects of food and water. However this possibility does not explain, in general, the sustained and consistent decrease in weight gain for all rats treated with fluoxetine and olanzapine. Changes in glucose metabolism were also ruled out as a causal role for the reduction in weight gain and food intake as both vehicle-and drug-treated animals showed undistinguishable serum glucose levels during fasting. Further studies of these questions will yield insight into centrally acting peptides and/or peripherally acting thermogenic mechanisms underlying decreases in weight gain in adult rats.
Placing our results in the framework of clinical situations, decreases in rat weight gain as a result of fluoxetine and olanzapine treatment do not mirror the profile occurring across diverse patient populations. There is evidence that long-term fluoxetine plus olanzapine treatment frequently leads to weight gain in individuals with major depressive disorders with and without treatment-resistant depression [23]. Further, high doses of fluoxetine do not appear to counteract the weight gain often induced by atypical anti-psychotics such as olanzapine [24]. The stark disparity between rat and human studies regarding body weight dynamics raises the possibility of mismatched models for revealing certain liabilities associated with fluoxetine and olanzapine therapy in mood disorders. It is conceivable, for instance, that rats might be more sensitive to the anorectic effects of fluoxetine than humans. Fluoxetine is known to produce anorectic effects that often lead to a decrease in weight gain; a phenomenon observed equally at the experimental and clinical level [11,25,26]. Alternatively, olanzapine metabolism may differ significantly in rats as indirectly suggested by previous reports [27,28]. In this context, olanzapine is metabolized to its 10- and 4'-N-glucuronides, with the 10-N-glucuronide being the most abundant metabolite in humans [15,29]. As the pharmacokinetic and pharmacodynamic profile of olanzapine in rats is relatively obscure [27], it is possible that changes in glucuronidation metabolism in rodents may have impacted the ability of the parent drug to influence heterogeneous population of cells associated with body weight dynamics. From these statements, one might conclude that our findings are not clinically significant and perhaps of limited value for additional investigation. Although the animal data indeed do not support the clinical situation, the above findings could harbor important information as to how species-specific differences limit drug-drug interactions or body weigh regulation, lessons that could influence subsequent studies regarding fluoxetine and olanzapine therapy in more defined experimental settings.
In the present study, measurements of fluoxetine, norfluoxetine and olanzapine plasma concentrations were made to assess their pharmacology after 18 days of combined drug exposure. In general, drug plasma levels fell within the expected therapeutic range typically observed in psychiatric patients. For instance, after 30 days of dosing at 40 mg/day, plasma levels of fluoxetine are in the range of 90–300 ng/mL across diverse patient populations [15]. In our animal studies, at a dose of 10 mg/kg (IP), mean plasma concentrations achieved were in the range of 300–400 ng/mL after 18 days of combined drug treatment. Oral doses of olanzapine at 20 mg/day often yield plasma levels of 20–100 ng/mL in healthy volunteers and in patients with schizophrenia [30]. Concentrations ≥80 ng/mL are considered threshold for the occurrence of adverse effects. In our present study, at a dose of 5 mg/kg olanzapine, mean plasma levels achieved of the anti-psychotic drug were ~178 ng/mL. The relatively high levels of olanzapine may help explain in part the hypophagic and adipsic phenomena experienced by rats at this particular dosing. Interpreted in this way, olanzapine concentrations ≥80 ng/mL (as in our studies) reached a threshold for the onset of malaise or taste aversion effects. In contrast, animals exposed to a 0.5 mg/kg olanzapine dose showed optimal therapeutic range of olanzapine plasma levels (~47 ng/mL) and normal feeding and drinking behaviors. It should be noted that the dosing paradigm implemented in our current studies yielded fluoxetine, norfluoxetine and olanzapine plasma concentrations similar to those reported by Zhang et al [4] under an acute experimental design. Therefore it is possible that little or no significant metabolic interactions between fluoxetine and olanzapine combination treatment occurs in rats as a function of chronic drug exposure. This possibility has merit as no clinically significant metabolic interactions are also reported during combined fluoxetine and olanzapine therapy [29]. Placing the current data in the framework of the growing body of experimental and clinical evidence, it is unlikely that drug-drug interactions modify the pharmacological profile of fluoxetine and olanzapine when the two psychoactive agents are administered concomitantly to experimental animal models.
Conclusions
Combination therapies of anti-depressant and anti-psychotic drugs are increasingly used for treatment-resistant mood disorders. Here, we have provided further evidence that fluoxetine and olanzapine have pharmacodynamic event-related effects on body weight dynamics [5]. In rats, these effects are manifested in the form of anorexia or perhaps anhedonia to food and water. Of interest, anorectic phenomena are also observed in rats chronically treated with valproic acid and lithium [31]; both valproic acid and lithium are widely touted as effective prophylactic agents for manic-depressive illness [32]. It is quite probable therefore that augmentation therapy of several mood stabilizers is associated with weight loss in rats, whereas the same combination drug pattern results in weight gain in special patient populations. This disparity adds a new level of complexity to the issue of body weight changes associated with psychopharmacology [19], and indicates species-specific variations in this phenomenon. In our particular case, adjusting olanzapine dosing to rat studies from 5 mg/kg to 0.5 mg/kg should preclude malaise bouts and/or taste aversion effects. In addition, the above dosage modification should be considered for achieving clinically therapeutic anti-psychotic plasma levels. If such a dosing paradigm is overlooked, it may lead to erroneous conclusions regarding mechanisms of medication action and side effect profile during drug augmentation therapy.
Methods
Animals and drug administration
Adult male Long-Evans rats (Harlan, Indianapolis, IN) were used in all experiments described herein. Prior to any drug treatment, all rats were handled for 5 days to minimize non-specific stress. Rats were then randomly assigned to the various experimental groups and cage mates received the same drug treatment. Animals were group-housed, 2–3 per cage under a 12 hr light:dark cycle (light on 0700) and allowed ad libitum access to food and water, except when noted (see below). For the chronic drug regimen, rats were injected intraperitoneally (IP) first with fluoxetine (10 mg/kg) followed 15 min later by olanzapine (5 or 0.5 mg/kg) to decrease potential pharmacokinetic interactions. Fluoxetine was dissolved in 5% γ-cyclodextrin, whereas olanzapine was dissolved in 12% γ-cyclodextrin (cyclodextrin was used to improve the stability and bioavailability of poorly soluble drugs). Dosages of the two drugs were chosen according to each drug's in vivo potencies for affecting 5-HT, DA, ACh and H systems [15,33,34], and also from pharmacological doses reported in the literature [4,8,11,35,36]. Doses of drugs are expressed as their respective salts. Control animals received 5% and 12% γ-cyclodextrin injections (1 ml/kg) at 15 min intervals so that this group was given the vehicle-solution at the same times as the fluoxetine plus olanzapine experimental group. All injections were administered between 1000 and 1100 hr of the light cycle. All aspects of the following experiments were carried out in accordance with the NIH Guide for the Care and Use of Laboratory Animals and with approval from the NYIT IACUC.
Experimental procedures
Rats were injected with fluoxetine plus olanzapine or their respective vehicle-solutions for 18 consecutive days and body weights recorded before (day 0) and after (day 18) drug treatment. In addition, body weights were also recorded 7 and 14 days after drug treatment to adjust for dosage. To determine average food intake over a 12 hr period, control-vehicle and experimental rats were given pre-measured food pellets (25 g/rat) on day 10 and subsequent consumption was recorded on the next day. To keep track of food spillage, cage bedding was randomly separated to assess degree of unconsumed food. A similar procedure was instituted to assess average water intake over a 12 hr period: both groups were given pre-measured tap water (100 mL/rat) on day 12 and subsequent consumption was recorded on the next day. On the last day of injections (day 18), rats were either decapitated or perfused under deep chemical anesthesia (ketamine/xylazine/acepromazine, 60 mg/kg) with 4% paraformaldehyde. Trunk blood or blood collected from cardiac puncture was collected in centrifuge tubes containing either no EDTA or a 0.3 M EDTA (pH 7.4) solution. Blood samples with 0.3 M EDTA were centrifuged at 10,000 RPM for 10 min and the collected serum frozen at -80°C until determination of glucose levels. Blood collected without EDTA was also frozen at -80°C until determination of drug plasma concentrations by GC-MS.
Glucose measurements
To determine relative glucose levels, all animals were fasted for 12 hr on the last day of injections (i.e., day 18). Glucose serum levels were determined using the Life-Scan One Touch Basic Meter (Johnson & Johnson, New Brunswick, NJ). In brief, 10 μl of serum was pipetted as a free-flowing drop onto each One Touch Test Strip and read in the glucose meter for 45 sec. Fasting glucose levels are presented as means ± SEM in mg/dL.
Analysis for fluoxetine, norfluoxetine and olanzapine by GC-MS
All solvents used for drug analyses were HPLC grade. Chloroform and 1-chlorobutane were obtained from Burdick & Jackson (Muskegon, MI). Ethyl Acetate and acetonitrile were obtained from EMD (Gibbstown, NJ). Ammonium Hydroxide, ACS certified, was obtained from Fisher (Fairlawn, NJ). Ascorbic acid was obtained from Sigma (St. Louis, MO), whereas trifluoroacetic anhydride was obtained from Pierce (Rockford, IL).
Working solutions containing olanzapine, fluoxetine and norfluoxetine at 10 ng/μL, 1.0 ng/μL, and 0.1 ng/μL, respectively were prepared in methanol. The working solutions used to prepare the calibration standards and controls were derived from different sources of reference material (e.g., fluoxetine) or different weighing of the same reference material (e.g., olanzapine). Calibration standards and controls were prepared by adding appropriate amounts of working solutions to clean, separate silanized 16 × 100 mm culture tubes that contained 1 mL of blank bovine blood and 0.2 mL of 2.5% ascorbic acid. The calibration standards ranged from 1 ng/mL to 1000 ng/mL. The controls were prepared at 35 ng/mL, 100 ng/mL, and 650 ng/mL.
A 0.25 mL volume of each blood sample was transferred to clean silanized 16 × 100 mm culture tubes. To bring the sample preparation to a volume of 1 mL, a 0.75 mL volume of Milli Q H2O was added to each tube. The samples were therefore a 4-fold dilution compared with the standards and controls. A 0.2 mL volume of 2.5% ascorbic acid was added to each sample in a preparation tube. Sample, standards, and controls were extracted by a liquid/liquid procedure. Eighty ng of fluoxetine-d6 (80 μL of 1 ng/μL fluoxetine-d6 in Milli Q H2O) and 80 ng of clozapine (80 μL of 1 ng/μL clozapine in methanol) were added to each tube and the tubes were then briefly vortexed. A 0.1 mL volume of the concentrated ammonium hydroxide and a 4 mL volume of 1-chlorobutane: acetonitrile (4:1) was then added to each tube. A clean teflon-lined screw cap was placed on each tube. The tubes were mixed 20 min using a reciprocating shaker and centrifuged at 2000 rpm for 10 min using an IEC centrifuge (Needham, MA). Using clean, separate glass Pasteur pipettes, the upper organic layer from each tube was transferred to clean, separate 13 × 100 mm culture tubes. The organic layer was evaporated to dryness under a stream of air at 40°C using a Turbo Vap evaporator (Zymark Corporation, Hopkinton, MA).
For derivatization, a 0.1 mL volume of chloroform and a 0.1 mL volume of trifluoroacetic anhydride were added to each tube. Clean teflon-lined screw caps were then placed on each tube. The tubes were heated for 20 min at 70°C using a dry block heater. After heating, the tubes were removed from the heater and allowed to cool at room temperature. The caps were then removed and the tubes evaporated using the same conditions as described above. Derivatized extracts were reconstituted with 100 μL of ethyl acetate and were then transferred to clean, separate auto-sampler vials.
The GC-MS system consisted of an Agilent 6890 gas chromatograph and an Agilent 5973 MSD mass spectrometer (Palo Alto, CA). The data system consisted of a Hewlett-Packard X A 6/400 computer and Agilent Chemstation software. For chromatographic separation, a ZB-5, 30 meter × 0.25 mm id, 0.25 μm capillary column (Phenomenex, Torrance, CA) was used. The carrier gas was ultra high purity helium at a flow rate of 1.0 mL/min. The injection port temperature was 260°C and the transfer line temperature was 300°C. The column oven temperature program was 125°C, held at this temperature for 0.2 min, and then increased to 300°C. Positive chemical ionization was used for the mass spectrometry analysis. The ion source temperature was 200°C and ammonia was used as a reagent gas. Selected ion monitoring was used and the following ions (m/z) were analyzed: norfluoxetine: 409, fluoxetine: 423, fluoxetine-d6: 429, olanzapine: 409, clozapine: 423. For fluoxetine, norfluoxetine, and fluoxetine-d6, the protonated ammonia adducts of the molecule ions were monitored. For olanzapine and clozapine, the protonated molecule ions were also monitored. Fluoxetine-d6 was used as the internal standard for fluoxetine and norfluoxetine. Clozapine was used as the internal standard for olanzapine. The limit of quantification for fluoxetine, norfluoxetine and olanzapine was 5 ng/mL. Data are presented as the means ± SEM.
Data analysis
Statistical comparisons in body weight and glucose levels were carried out using one-way ANOVA or two-tailed t tests where appropriate. Plasma levels of fluoxetine and olanzapine were analyzed by a Student's t-test. The probability level interpreted as significant was P ≤ 0.05.
Authors' contributions
JAP and JMC participated in the in vivo studies and in the biochemical assays. BHH and JMH participated in the design of the studies and performed the statistical analysis. GT drafted the manuscript, conceived the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
We thank Eli Lilly for the generous donation of olanzapine used in these animal studies. This work was supported in part by a NYCOM Faculty Resource Award to GT and by an NIH Grant (#1R15MH6413-01A1) to JMH. The authors are indebted to Aaron Miller (New Media Institute, Medaille College) for his excellent technical assistance.
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| 15498104 | PMC528727 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 21; 4:27 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-27 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-281550930610.1186/1471-2210-4-28Research ArticleLong-term age-dependent behavioral changes following a single episode of fetal N-methyl-D-Aspartate (NMDA) receptor blockade Mickley G Andrew [email protected] Cynthia L [email protected] Colleen A [email protected] Alicia [email protected] Anna M [email protected] Deborah [email protected] Elizabeth L [email protected] Bettina [email protected] Jaclyn M [email protected] Department of Psychology and the Neuroscience Program, Baldwin-Wallace College, 275 Eastland Road, Berea, OH 44017-2088, USA2004 28 10 2004 4 28 28 7 6 2004 28 10 2004 Copyright © 2004 Mickley et al; licensee BioMed Central Ltd.2004Mickley et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Administration of the N-methyl-D-aspartate (NMDA) antagonist ketamine during the perinatal period can produce a variety of behavioral and neuroanatomical changes. Our laboratory has reported reliable changes in learning and memory following a single dose of ketamine administered late in gestation. However, the nature of the drug-induced changes depends on the point during embryonic development when ketamine is administered. Embryonic day 18 (E18) rat fetuses pre-treated with ketamine (100 mg/kg, i.p. through the maternal circulation) and taught a conditioned taste aversion (CTA) learn and remember the CTA, whereas E19 fetuses do not. The current study sought to determine if long-term behavioral effects could be detected in animals that received ketamine or a saline control injection on either E18 or E19. Rat behavior was evaluated on two different measures: spontaneous locomotion and water maze learning. Measurements were collected during 2 periods: Juvenile test period [pre-pubertal locomotor test: Postnatal Day 11 (P11); pre-pubertal water maze test: P18] or Young-adult test period [post-pubertal locomotor test: P60; post-pubertal water maze test: P81].
Results
Water maze performance of ketamine-treated rats was similar to that of controls when tested on P18. Likewise, the age of the animal at the time of ketamine/saline treatment did not influence learning of the maze. However, the young-adult water maze test (P81) revealed reliable benefits of prenatal ketamine exposure – especially during the initial re-training trial. On the first trial of the young adult test, rats treated with ketamine on E18 reached the hidden platform faster than any other group – including rats treated with ketamine on E19. Swim speeds of experimental and control rats were not significantly different. Spontaneous horizontal locomotion measured during juvenile testing indicated that ketamine-treated rats were less active than controls. However, later in development, rats treated with ketamine on E18 were more active than rats that received the drug on E19.
Conclusion
These data suggest that both the day in fetal development when ketamine is administered and the timing of post-natal behavioral testing interact to influence behavioral outcomes. The data also indicate that the paradoxical age-dependent effects of early ketamine treatment on learning, previously described in fetuses and neonates, may also be detected later in young adult rats.
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Background
Administration of ketamine or other NMDA receptor blocking drugs [1] may bring with it both beneficial and problematic outcomes. Ketamine's use as a dissociative anesthetic is well established in clinical practice [1] and more recently, it has also been proposed as a neuroprotectant against hypoxic-ischemic brain damage in neonatal rats [2]. However, in adult animals, NMDA receptor blockade is known to produce psychotomimetic side effects [3], impair memory formation [4-7], and may produce neurotoxicity [3,8-11]. This neurotoxicity is evidenced by vacuolization of cortical neurons [3,10] and has also been linked to programmed cell death (apoptosis) during development [12-14].
The toxic effects of NMDA receptor blockade are apparently dependent on the dose of the drug, administration regimen, and the age of the animal treated. For example, vulnerability to MK-801-induced cortical vacuolization is not evident in fetal animals but rather begins at approximately the time of puberty [8]. On the other hand, apoptogenic effects of ketamine have been seen following drug administrations during the last trimester of pregnancy [12]. Further, the selection of an acute or chronic dosing regimen may also modulate the neurobehavioral outcomes and the permanence of the neurological changes that can be expected [9,13,15-18].
Recent experiments from our laboratory have focused on age-dependent effects of a single dose of ketamine on fetal learning and memory. Rat fetuses can learn conditioned taste aversions (CTAs) and exhibit taste-mediated conditioned motor responses (CMRs) [19,20], which can be modulated in complex ways by exposure to ketamine at different times during the perinatal period [20,21]. For example, ketamine will either cause a potentiation or a blockade of memory formation in rats, depending on the specific day during fetal development when the drug is administered. Rat fetuses that receive ketamine on E18 (30-minutes before CS-US pairing) are able to learn and remember CTAs and CMRs quite well. However, rat fetuses that receive ketamine before CTA training just one day later, on E19, exhibit an amnesia for these conditioned responses [21,22]. We have referred to this phenomenon as the "ketamine paradox" [21].
These previous studies have only tested the durability of the ketamine paradox over a period of up to 2 weeks [22] and have looked at a very narrow range of behavioral measures – all with gustatory components. However, there are some indications that early treatment with NMDA receptor blocking drugs can have long-term behavioral implications. For example, neonatal treatment with MK-801 can produce long-lasting behavioral radioprotection in rats with x-ray-induced hippocampal damage [23]. Likewise, Maier et al. [24] have reported that MK801-induced NMDA receptor antagonism in young rats (P7-17) extends the sparing of hindlimb function after spinal transection in older animals. However, treatment with an NMDA receptor antagonist [(+)HA-966] for a longer time, later in neonatal development (P10-20), impaired motor and cognitive behaviors in adult rats [25]. Several questions remain. What is the range of behaviors that can be influenced by ketamine treatment during the perinatal period? How long lasting are the different behavioral effects of ketamine administered late in gestation?
The current study extends our original observations and reports how a single injection of ketamine on either E18 or E19 modulates spontaneous locomotor activity and performance in a water maze. We tested the rats as juveniles and then as young adults. Our data suggest several age-dependent effects of early ketamine treatment – effects that depend on not only the length of time between drug administration and behavioral testing but also on the day in embryonic development when the NMDA receptor antagonist was administered.
Results
Water maze
P18 water maze test
Over the10 trials of the water maze test, subjects significantly reduced their latencies to mount the hidden platform [F(9, 102) = 4.233; p < 0.0001] indicating the gradual learning of the maze. However, as Figure 1A illustrates, the animals never gained real proficiency on this task. The latencies decreased most dramatically within the first 3 trials and therefore we undertook a more in-depth analysis of this portion of the study. We also noticed that, as the animals swam, they would sometimes stop and tread water against the side of the tank. Therefore, we undertook an analysis of the stop time/trial during the first 3 water maze trials (see Figure 2). The time spent treading water decreased significantly over the first 3 trials [F(2, 111) = 18.359; p < 0.001]. There was also a significant drug effect indicating that ketamine-treated rats spent less time treading water than did saline-treated controls [F(1, 153) = 15.972; p < 0.001]. This effect was independent of the age at which the rats received ketamine.
Figure 1 Time to mount a hidden platform during the first (juvenile; panel A) and second (young-adult; panel B) water maze test. Rats were treated (through the maternal circulation) with either 100 mg/kg ketamine HCl (i.p.) or saline on either E18 or E19 and then tested later on P18 and P81. The data presented here illustrate raw latencies without taking into account the time the animals spent treading water (i.e., not making forward progress). Panel A: P18 rats generally decreased their latencies to mount the hidden platform over the 10 trials. This effect was most prominent over the first 3 trials. The behavioral changes induced by drug or age manipulations were not statistically significant. Panel B: P81 rats all readily re-learned the location of the hidden platform as there was a significant reduction in time to mount the platform over the 10 trials. The analysis (see text) also revealed a significant Age X Drug interaction which was most prominent on trial 1 (see also Figure 3). Data were analyzed using a three-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19) X Time] with time blocks as the repeated time factor and compensation for unequal Ns. Variance indicators are the Standard Error of the Mean (SEM).
Figure 2 Time spent treading water (i.e., not making forward progress) during the P18 water maze test. There was a general decline in the time spent treading water as the animals learned the maze. Ketamine-treated rats spent the least time exhibiting this behavior. Rats treated with the NMDA-receptor blocking drug on E18 stopped swimming for the shortest period and made more-constant progress towards the hidden platform. Data were analyzed using a three-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19) X Time] with time blocks as the repeated time factor and compensation for unequal Ns. Variance indicators are the Standard Error of the Mean (SEM).
Time spent treading water at the side of the tank may be interpreted as an alternative, futile, escape strategy and not necessarily as an indicator of learning the position of the hidden platform. Subsequent analyses subtracted out stop-times in order to provide the most accurate portrayal of maze learning during the first 3 trials. With stop-time removed, the declining time-to-platform [F(2, 102) = 3.501; p = 0.034] and swimming distance [F(2, 102) = 4.632; p = 0.012] over the first 3 trials indicated a learning of the maze during this initial exposure to the apparatus. However the behavioral changes induced by drug or age manipulations were not statistically significant. The number of trials in which the rat failed to mount the platform within 90 seconds was not significantly different among the 4 treatment groups. Likewise, swim speed did not decrease significantly over the first 3 maze trials indicating that the animals were not fatiguing as they undertook multiple swims.
Once the rat mounted the hidden platform we timed how long the subject remained there before it was removed (maximum of 30 seconds). An analysis of these data during the first 3 maze trials revealed neither a significant influence of subject age nor drug treatment.
P81 water maze test
An analysis of the second water maze test indicated that there was a significant reduction in time [F(9, 157) = 26.868; p < 0.001] to mount the hidden platform over the 10 trials (see Figure 1B). The analysis also revealed a significant Age X Drug interaction [F(1, 401) = 5.15; p = 0.024] but no significant main effects of drug or age at treatment. Rats had previous experience with the maze (see P18 maze data) and inspection of the data indicated that, after the first trial, latencies in all groups converged and dropped dramatically. As in the P18 water maze analysis, stop times (i.e., time spent treading water) during this first trial were significantly lower in ketamine-treated rats [Drug effect: F(1, 62) = 6.645; p = 0.012]. A subsequent examination of the data excluded the time spent treading water in swim-time, swim-distance and swim-speed analyses. On the first trial (see Figure 3), ketamine-treated rats found the platform significantly faster than saline controls [Drug effect: F(1, 67) = 7.28; p = 0.009] and swam shorter distances to do so [Drug effect: F(1, 67) = 8.89; p = 0.004]. Animals injected on E18 were generally quicker to find the platform [Treatment Age effect: F(1, 67) = 10.55; p = 0.002] and swam more direct routes to the platform [Treatment Age effect: F(1, 67) = 5.89; p = 0.018] than were animals injected on E19.
Figure 3 Time to mount the hidden platform (top panel) and swimming distance to the platform (bottom panel) during the first trial of the second (young-adult) water maze test. Rats were treated (through the maternal circulation) with either 100 mg/kg ketamine HCl (i.p.) or saline on either E18 or E19 and then tested later on P81. The temporal data presented here do not include time spent treading water but rather represent only the time that the rats were making forward progress. Ketamine-treated rats found the platform significantly faster than saline controls and swam shorter distances to do so. Group comparisons indicated that rats treated with ketamine on E18 or E19 reached the hidden platform significantly faster and swam shorter distances than saline-treated controls (* = p ≤ 0.05; NS = non-significant group differences). Further, E18 rats treated with ketamine later exhibited a shorter latency to reach the hidden platform than did E19 rats treated with ketamine. Data were analyzed using a two-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19)]. Individual group comparisons were accomplished by using t-tests employing the Bonferroni compensation for multiple comparisons. Variance indicators are the Standard Error of the Mean (SEM).
Post-hoc analyses indicated that rats treated with ketamine on E18 reached the hidden platform significantly faster and swam shorter distances than saline-treated controls as well as rats treated with ketamine on E19 (see Figure 3). Rats treated with ketamine (on either E18 or E19) also exhibited significantly fewer failures to reach the hidden platform (within the 90-second limit/trial) than did saline control animals [t(66) = 1.86, p = 0.034 (one-tail test)]: ketamine-treated Mean ± SEM = 0.71 ± 0.05 failures/10 trials; saline-treated Mean ± SEM = 0.24 ± 0.07 failures/10 trials.
The short latencies to mount the platform cannot be attributed to faster swimming speeds. On the first, second and third water maze trials (i.e., the only ones analyzed for swim speeds), P81 rats that were treated with ketamine on E18 did not swim significantly faster than any of the animals in the other treatment groups. For example, the swim speeds for trial 1 were: [E18/ketamine: 34.30 ± 3.59 cm/sec; E18/saline: 37.97 ± 1.85 cm/sec; E19/ketamine: 30.47 ± 2.40 cm/sec; E19/saline: 32.92 ± 2.23 cm/sec (Mean ± SEM)]. Fatigue did not seem to play a role in the group differences since swim speed remained stable in all groups over the first 3 water maze trials on P81.
Locomotion
A single ketamine treatment during the perinatal period had long-lasting effects on spontaneous locomotor movements. Ketamine's effects depended on the age of behavioral testing as well as the age of the drug treatment. Horizontal movements (i.e., line crossings) were more prominently influenced by perinatal ketamine than were vertical movements (rearing).
P11 locomotor tests
P11 rats treated prenatally with ketamine showed habituation to the open-field test chamber and exhibited reduced horizontal movements overall (see Figure 4). After being placed in the activity chamber, P11 rats decreased their horizontal movements (i.e., line crossings analyzed in 6, 5-minute blocks) significantly over the 30-minute test session [F(5, 405) = 80.66, p < 0.0001]. In fact, locomotor activity of all treatment groups was reduced to very low levels (typically <5 line breaks/min) after the first 5 minutes (Figure 4B).
Figure 4 Spontaneous horizontal locomotor activity of P11 rats treated (through the maternal circulation) with either 100 mg/kg ketamine HCl (i.p.) or saline on either E18 or E19. Panel A illustrates the entire 30-minute test. In only the initial 5-minute observation period, rats treated with ketamine in utero crossed significantly fewer lines than did the saline control animals. After this initial period of habituation, indicators of horizontal movement declined and group scores converged. Panel B is a minute-by-minute illustration of the first 5 minutes of locomotor activity. In a time-dependent manner, rats treated with ketamine in utero crossed significantly fewer lines than did the control animals. Habituation to the open field is represented by a rapid decline in movement. * = Significantly different from E18/saline group; + = significantly different from E19/saline group. Data were analyzed using a three-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19) X Time] with time blocks as the repeated time factor and compensation for unequal Ns. If the repeated-measure ANOVA revealed a significant trial effect (indicating a change over time) a two-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19)] was run to analyze the group differences during a particular trial. Individual group comparisons were accomplished by using the Tukey-Kramer test for Multiple Comparisons. An α = 0.05 was used throughout these analyses. Variance indicators are the Standard Error of the Mean (SEM).
Ketamine-treated rats were also generally less active than saline-injected controls [F(1, 81) = 7.79, p = 0.007]. However, there was a significant interaction between drug treatment and the block of time in which the locomotor measurement was made [F(5, 405) = 6.26, p = 0.0002]. At the end of 5 minutes, P11 rats reduced their spontaneous locomotion to approximately 20% of its original level. For this reason, we performed a minute-by-minute analysis of the first 5 minutes in the open field apparatus (see Figure 4B). Once again, there was a significant decrease in horizontal movement over the first 5 minutes in the chamber [F(4, 336) = 143.13, p < 0.001]. Generally, ketamine treatment caused a significant decrease in line crossings as compared to saline-injected controls [Drug effect = F(1, 84) = 12.77, p = 0.0006]. A Drug X Time Block interaction [F(4, 336 = 3.21, p = 0.01] revealed that the largest group differences were exhibited within the first 3 minutes (see Figure 4B for individual group comparisons). There was not a significant difference in the spontaneous horizontal locomotor responses of E18- and E19-ketamine treated rats.
P60 locomotor tests
Horizontal movements of ketamine-treated rats tested on P60 varied depending on when, during the fetal period, they had received the drug (see Figure 5). As was the case during the P11 tests, horizontal movements decreased significantly over the 30-minute test [F(5, 340) = 163.12, p < 0.0001]. There was both an overall effect of subject age at time of drug injection [F(1, 68) = 4.37, p = 0.04] and a Treatment Age X Drug interaction [F(1, 68) = 10.37, p = 0.002]. Over the course of this 30-minute test (Figure 5A), E18 fetuses treated with ketamine were generally more active in their horizontal movements than were animals treated with saline on this day of embryonic development. Further, rats exposed to ketamine on E18 exhibited significantly more horizontal movement than did E19 rats treated with either saline or ketamine. Rats injected with saline on E18 or E19 did not exhibit significant differences in line crossings when tested on P60.
Figure 5 Horizontal locomotion of P60 rats treated (through the maternal circulation) with either 100 mg/kg ketamine HCl (i.p.) or saline on either E18 or E19. Panel A illustrates the entire 30-minute test. E18 rats treated with ketamine were significantly more active than were animals in the other treatment groups. These effects were most prominent at particular time periods. * = Significantly different from E18/saline group; # = significantly different from E19/ketamine group. + = significantly different from E19/saline group. Panel B is a minute-by-minute illustration of the first 5 minutes of locomotor activity. In all treatment groups, there is a significant decline in locomotion over the first 5 minutes of testing. There is also a significant interaction between drug treatment and subject age at time of treatment – indicating that rats treated with ketamine on E18 are more active than both E18 saline-control rats and E19 rats that received ketamine. Data were analyzed using a three-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19) X Time] with time blocks as the repeated time factor and compensation for unequal Ns. If the repeated-measure ANOVA revealed a significant trial effect (indicating a change over time) a two-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19)] was run to analyze the group differences during a particular trial. Individual group comparisons were accomplished by using the Tukey-Kramer test for Multiple Comparisons. An α = 0.05 was used throughout these analyses. Variance indicators are the Standard Error of the Mean (SEM).
An analysis that focused on the first 5-minutes of this P60 locomotor test (Figure 5B) revealed a significant interaction between drug treatment and subject age at time of treatment [F(1, 70) = 14.13, p < 0.001]. Multiple comparisons revealed that rats treated with ketamine on E18 were more active than both E18 saline-control rats and E19 rats that received ketamine. These data reveal a very different pattern of horizontal locomotor responses exhibited by ketamine-treated rats depending on the day during fetal development that they received the drug. There were no significant group differences in rearing movements on P60.
Discussion
The data presented here suggest several age-dependent effects of early ketamine treatment – effects that depend on not only the day of behavioral testing but also the day in embryonic development when the NMDA receptor antagonist was administered. During the initial stage of the second water maze test (on P81), rats treated with ketamine on E18 found the hidden platform more quickly than did animals receiving the same treatment on E19. Moreover, they exhibited enhanced maze performance compared to both groups of saline-treated rats. It is important to note that ketamine treatment on E18 did not induce faster swim speeds. Rather, the animals swam more direct routes to the hidden platform.
Effects of ketamine on spontaneous open-field locomotion were also age-dependent. In neonatal animals (P11), ketamine administration in utero reduced subsequent spontaneous movement. This effect was subtle (i.e., only in evidence within the first 3-minutes of testing) and did not depend on the subject's age at the time of the drug's administration. However, when these animals were re-tested on P60, the rats that received ketamine on E18 both moved more than the rats that received ketamine on E19 and also moved more than saline-injected controls.
First, these data reveal long-term behavioral effects of a single dose of ketamine administered in utero. Drug-induced effects on water maze learning were observed over 11 weeks after birth and locomotor effects were documented 9 weeks post partum. These findings are consistent with others indicating that early NMDA receptor blockade may produce behavioral alterations that are detectable in adulthood [23,25,26]. Second, these data are consistent with the hypothesis that the behavioral effects of NMDA receptor blockade depend on the day in embryonic development when the antagonist is administered. In particular, some of the findings reported here extend our previous work indicating that ketamine treatment on E18 may have different effects than administration of the drug on E19 [21]. The same dose of ketamine administered in the current study potentiated conditioned motor responses of neonates if the drug had been given (through the maternal circulation) on E18. However, ketamine impaired acquisition of this learned response if it was administered one day later on E19 [21]. Similar age-dependent effects have been reported using different behavioral indicators of learning [22,27].
The effects of early ketamine treatment on locomotion are apparently not consistent throughout postnatal development. Ketamine reduced locomotor movements in P11 rats but later (P60) selectively enhanced locomotion of animals that received the drug on E18. The reasons for this change in responding are unclear. In order to accommodate the different size of the animals at P11 and P60, there were differences in the dimensions of the open field chambers used at each test. Also, the chamber walls were clear during the P11 test and opaque at P60. But beyond these differences in apparatus, maturation clearly brings with it a variety of capabilities and propensities many of which can modulate motor responding. For example, at the end of 5 minutes, P11 rats reduce their spontaneous locomotion to approximately 20% of its original level. However, P60 rats are 80% as active during this same time period. These data indicate a general tendency for young rats to habituate (or fatigue) more rapidly than older rats. Other behavioral studies have revealed toxin-induced performance impairments that reveal themselves only at certain stages of early postnatal development but not at older ages [28]. More recently, Beninger et al. [29] reported that rats administered MK-801 on P3 and tested pre- (P35) and post-pubertally (P56) exhibited different locomotor responses to amphetamine depending on the time of the behavioral test.
Our measures of swim speed may offer some insights regarding the relative motor capacities and motivation of our animals. Swim speeds did not significantly differ between animals previously treated with ketamine or saline. Likewise, fetal age at the time of the drug treatment did not influence speed of swimming. Instead, rats reduced their latencies to mount the platform by swimming more-direct routes. Thus, the water maze data reported here are less likely a reflection of the animal's capacity or motivation to get to the platform and more likely a reflection of learning ability.
It should be noted that water maze performance may be influenced by a number of factors beyond cognitive ability. For example, drug-induced alteration of visual acuity, motivation or motor capacities can alter performance of this task [30]. The literature suggests that early NMDA receptor blockade may alter development of the visual system [31,32]. But measures of actual visual acuity following a single occurrence of NMDA receptor blockade in the developing brain are lacking. The available data suggest that visual plasticity is more significantly altered by NMDA receptor antagonism than are visual maps [33] or neural activity per se [34]. Our water maze procedures did not necessarily place demands on the rat's visual system. The location of the hidden platform was not changed from trial to trial or between the P18 and P81 tests. Therefore, once the platform was located, our subjects could have adopted motor strategies to reach the goal on subsequent trials. Although water maze performance is typically cited as an indicator of spatial learning, our paradigm does not eliminate the possibility that other types of learning may also be involved.
We used accepted statistical methods to avoid spurious inflation of sample size and to control for litter effects [35,36] (see Methods section below). However, due to constraints of resources, the small number of litters employed may have reduced our power to detect subtle differences in performance. Thus, this report should be viewed as a conservative account of drug- and age-influenced changes in behavior.
It is worth noting that it was initial responding on the water maze and in the locomotor test chamber that was most sensitive to our fetal ketamine treatment. ketamine-treated rats exhibited significantly faster times to reach the hidden platform (at age P81) – but only on the first trial. Likewise, P11 rats treated with ketamine as fetuses, exhibited fewer locomotor movements than did saline controls – but only during the first 5 minutes of our test. Our laboratory [37], as well as other investigators [38] have reported a role for NMDA receptors in the determination of novelty. The current data seem to suggest that these effects may extend to various behavioral testing paradigms. Moreover, since our tests were conducted weeks after fetal ketamine treatment, our data indicate the persistence of ketamine's effects on initial responding.
What neural, or other, mechanisms might subserve the behavioral phenomenon outlined here? NMDA receptor populations and physiology are neither static nor mature during the perinatal period and blockade of these receptors during particular days of development may produce quite different effects [39,40]. For example, Sircar [41] has shown that the binding of [3H] MK-801 (a potent/selective NMDA receptor antagonist) in synaptosomal membranes is differentially altered by glutamate (and other) agonists during various periods of development. These data build on previous findings [42] indicating a dramatic change in the number of PCP-binding sites in fetal rat brain between the ages of E18 and E19. This is the same time frame in which ketamine's effects on memory change so significantly. Could these developmentally linked changes in NMDA receptor populations and functional roles mediate the ketamine paradox as well as the behavioral phenomena presented here?
The identification of several NMDA receptor subtypes with different functional roles and different patterns of expression during the perinatal period may also eventually reveal aspects of the ketamine paradox's physiological substrate [43-54]. Could a drug-induced change in the population and/or distribution of NMDA receptor subtypes mediate the ketamine paradox as well as long-term behavioral effects? The current data do not address this point directly. However, NR2B NMDA receptors (which are known to be involved in learning, in general, and taste memory formation, in particular) [55-57] have been identified as being especially sensitive to upregulation following pharmacological antagonism [58,59]. Further, other laboratories have reported that NMDA exposure can produce a reduction in NMDA receptors within 4 hours of exposure [60]. Our data suggest the potential usefulness of studies aimed at correlating ketamine-induced changes in NMDA receptor subtype populations with behavioral outcomes recorded at several times in development. Such experiments are currently underway in our laboratory.
It should also be noted that ketamine can influence maternal and fetal physiology in ways that go beyond the drug's well-known effects on NMDA glutamate receptors [1]. Pulmonary vasodilator responses have been recorded following ketamine administration [61]. The drug can also alter uterine tone in pregnant ewes by increasing cardiac output and mean arterial pressure [62]. Although these cardiovascular effects were slight and transient, they may have contributed in yet-unknown ways to some of the long-term behavioral changes we report here. Likewise, ketamine not only affects NMDA receptors but may also inhibit non-NMDA glutamate receptors [63], the high-affinity states of the dopamine D2 receptor, and other G-protein-linked receptors [64]. While ketamine's actions on NMDA receptors are certainly prominent, acute changes in vascular tone and the drug's actions on other brain receptors are capable of influencing fetal development in ways not addressed by the current experiments.
If early ketamine exposure influences NMDA receptor populations or functioning, post-synaptic second messenger pathways would also be engaged as mediators of behavioral change [65]. Downstream calcium and calmodulin signaling, calcium-dependent kinases, and ultimately changes in gene expression are known to produce synaptic restructuring [66]. This cascade of NMDA-receptor-initiated biochemical events provides a likely avenue for further investigation as we examine the physiological substrate of the behavioral phenomena described here.
The variables of subject age and ketamine dose interact in complex ways to produce predictions of neurotoxicity. If NMDA antagonists are used to suppress neuronal activity during a critical developmental period of synaptogenesis, the timing and sequence of synaptic connection is disrupted [67]. This causes neurons to receive an internal signal to commit suicide – a form of programmed cell death called apoptosis [68]. Ketamine, and other NMDA receptor blocking drugs, are reported to produce these neurotoxic effects [69] under certain circumstances. In the rat, the period of brain sensitivity is largely confined to the postnatal period (i.e., from P1 to P14) [70]. Our single dose of ketamine was administered on either E18 or E19, i.e., subject ages that, to the best of our knowledge, have not been systematically manipulated in studies aimed at investigating ketamine's ability to produce apoptosis. These studies should be done to confirm the role that apoptosis may/may not play in producing the long-term behavioral changes we report here.
In addition to subject age, the dose of ketamine is another important factor in determining the likelihood of apoptosis induction as well as the generalizability of our data to clinical settings. In order to produce an increase in apoptotic neurons in neonatal rats, ketamine must be administered in multiple injections over a period of 9 hours [13]. Our study used a single dose of ketamine (delivered to a pregnant rat) that was significantly higher (100 mg/kg) that those used previously in neonates [69]. However, from previous biochemical studies we know that our dosing regimen in pregnant rats [27] produced a concentration of fetal brain ketamine roughly comparable to that seen in blood following repeated doses of 20 mg/kg administered to neonatal rats (14 μg/g) [69]. These blood levels were approximately seven-fold greater than anesthetic blood levels in humans [71,72]. Therefore, by extrapolation, we may predict that our dose of ketamine produced tissue levels of the drug that significantly exceeded those typically produced in human patients who encounter the drug in a clinical setting. Of course, this does not eliminate the possibility that the human recreational use of ketamine (street name: "Special K") [68] may produce blood and brain levels that are significantly higher than those encountered in the clinic. Nor does it exclude the possibility of differing drug sensitivities of rats and humans. Both these factors will influence the clinical relevance of the studies reported here.
Conclusions
These studies were aimed at determining the long-term behavioral effects of ketamine administration on E18 and E19 as a means of assessing the durability, intensity and generalizability of the ketamine paradox [21]. Our previous work indicated that ketamine administration enhanced the formation of a conditioned taste aversion in E18 fetuses but not those treated on E19 or later [20-22,29]. The current data reveal several subtle, but consistent, residual behavioral changes produced by of a single large dose of ketamine administered during the rat's late pre-natal period. In terms of ketamine effects on spontaneous locomotion, we found that, irrespective of the day of fetal dosing employed, ketamine reduced horizontal movements when animals were tested on P11. However, when the animals were tested later, on P60, rats that had received ketamine on E18 differentiated themselves from the E19 ketamine-treated animals (and saline-treated controls) by exhibiting an increase in locomotion – especially in the early minutes of behavioral testing in the open field. Ketamine's long-term influence on water maze learning/retention was limited in scope, but palpable, on the first trial of the P81 test.
Despite the reliable enhancement of CTA learning that has been reported in fetuses and neonates treated with ketamine on E18 [29], this same dosing regimen produced limited improvements in learning/retention of a water maze when the animals were tested as young adults. The usefulness of ketamine as a cognitive enhancer, administered in the perinatal period, appears to be limited not only by its known toxic effects at critical stages of development [68] but also by its influence on spontaneous movement and the drug's weak memory-enhancing properties over the long term.
Methods
Subjects
The subjects were Sprague-Dawley rats (male and female) obtained from timed pregnant female rats supplied by Zivic Laboratories (Zelienople, PA). Litters (N = 2/treatment group) were not culled and ranged in size from 9 to 13 pups. [See behavioral testing sections below for details about the number of animals in each treatment group.] The variable number of subjects/group was due to different litter sizes and several logistical constraints that did not always allow the testing of all rats in each litter. Statistical adjustments were made in order to compensate for the unequal Ns in the treatment groups (see below). The date of conception (i.e., the date a vaginal plug was first detected) was designated as "embryonic day 0" (E0). Postnatal day 0 (P0) was the day of birth (typically E21.5). The pregnant animals from which our subjects were derived were individually housed in plastic 'shoe-box' cages (44.45 cm long × 21.59 cm wide × 20.32 cm high). After birth, perinatal rats were housed with the dam until they were sexed and weaned between P21-25 (this is within recommended weaning dates, see [73], at which point the pups were group-housed (separated by sex) in the standard-sized cages described above. Throughout the experiment home cage temperature was maintained at 23–26°C under a 12:12-h light:dark cycle (lights on at 0600 h).
The Baldwin-Wallace College Institutional Animal Care and Use Committee approved these experiments. The animals involved in this study were procured, maintained and used in accordance with the Animal Welfare Act and the Guide for the Care and Use of Laboratory Animals, prepared by the Institute of Laboratory Animal Resources – National Research Council.
Drug treatments
Pregnant dams received ketamine HCl (100 mg/kg, i.p.; Sigma Chemical Company), or an equal volume of physiological saline, (0.9% NaCl, i.p.) on either E18 or E19. Saline injections controlled for the stress of pre-natal manipulation. Thus, there were 4 treatment groups designated hereafter as follows: E18/ketamine, E18/saline, E19/ketamine, or E19/saline. Rats from two litters were used in each of the treatment groups. In order to separate out treatment effects from litter effects special statistical measures were employed (see Statistical Analyses, below) [35,36]. The dose of ketamine employed (100 mg/kg, i.p.) was selected based on previous experiments [21] in which the drug produced very different behavioral effects when administered to E18 or E19 fetuses through the maternal circulation. Using high-pressure liquid chromatography (HPLC) measures we have previously determined the level of ketamine (approximately 14 μg/g tissue) found in the brains of fetuses following a maternal injection of 100 mg/kg ketamine during the late pre-natal period [27].
Behavioral testing
We recorded performance in a water maze and also spontaneous locomotor movements in an open field. Each of these behavioral tests was conducted twice, i.e., once in each of two different time periods: Juvenile Period (pre-pubertal), within the first three weeks after birth and Young-adult Period (post-pubertal), between 2–3 months of age. We selected these two behavioral test periods based on knowledge of the patterns of development of NMDA receptor subtypes. The NR2B receptor subtype has been implicated in learning and memory [55]. These receptors are present during the late pre-natal period and they rise steadily up to P20 when they achieve adult levels [43,74]. Therefore, our behavioral measures sampled times both before and after maturation of this receptor system. The behavioral tests conducted within the juvenile or post-pubertal periods were separated in time (by a minimum of 1 week) to reduce the influence of one on the other.
Most, but not all, animals were tested and then retested. However, statistical analyses indicated that there were no differences between the animals that were tested once or twice. Therefore, these groups were combined for subsequent statistical analyses. Additional behavioral testing (i.e., conditioned taste aversion) was performed on some of these animals but there were some logistical problems with the experiment. These data did not reveal reliable group differences and are not reported here.
Water maze
At age P18, and then again at P81, we evaluated water maze performance by testing the following number of rats per group: P18: E18/ketamine: (N = 12); E18/saline: (N = 16); E19/ketamine: (N = 14); E19/saline: (N = 15); P81: E18/ketamine: (N = 12); E18/saline: (N = 22); E19/ketamine: (N = 17); E19/saline: (N = 20). Our data corroborate other studies indicating that rats younger than P20 are capable of learning a water maze [75,76].
The water maze was an oval stock-watering tank (manufactured by Rubbermaid, Inc.) measuring 94 cm × 74 cm × 60 cm deep. It was sized to shorten swim distances to the hidden platform and reduce the likelihood of fatigue in our young animals. The tank was filled to 39.5 cm (1 cm above a hidden platform) with water that was made opaque and white by adding 710 ml of evaporated milk (Nestle's Carnation® brand). The water temperature was maintained at 26 ± 1°C. The escape platform was a clear plastic disc (12.5 cm diameter × 1.2 cm) mounted on a stand. The platform remained in the same location (approximately 10 cm from the side of the tank) throughout the test session. The edges of the platform had white rubberized tape attached in order to aid the rats as they mounted it. The tank was in a room lighted with fluorescent lights and surrounded by a rich array of laboratory furnishings.
All test sessions were video recorded and the tapes were later used for analysis of latency to escape, stop time, time on platform, path length and swim speed (see Statistical Analyses below). "Escape latency" was defined as the time (in seconds) it took the rat, once in the water, to mount and gain balance on the platform. "Stop time" was the total time/trial that subjects spent treading water (i.e., not making forward progress). "Time on the platform" was defined as the time (up to 30 sec) the animal remained on the platform after initial mounting. "Path length" was the total distance (cm) swum before the subject mounted the platform. "Swim speed" was expressed in cm/sec and reflected the rate of forward progress towards the platform.
At the beginning of each water maze test session, a rat was placed in the water facing the wall of the tank opposite the one near the hidden platform. A swim trial lasted until the rat reached the hidden platform or until 90 seconds had passed. If the animal reached the platform in the allotted 90 seconds, it was allowed to remain on the platform for up to 30 seconds and was then returned to a holding cage (a dry, plastic "shoe-box" cage). The holding cage sat upon a heating pad set at 33.5°C., producing a floor temperature of approximately 28.5 ± 1°C. If the animal did not reach the platform in 90 seconds it was removed from the water and returned to the cage. If the animal jumped off the platform before 30 seconds, it was removed from the water and returned to its holding cage. All rats were given a 60-second rest period before the next trial was initiated. Each rat experienced ten swim trials during each of the two test sessions. At the end of the 10 trials, each rat was thoroughly dried with a towel and a blow dryer and then returned to its home cage. Escape latencies and time on the hidden platform were recorded for each trial.
Spontaneous locomotion
At age P11, we measured the spontaneous locomotor activity of the following number of rat pups in each group: E18/ketamine: (N = 18); E18/saline: (N = 25); E19/ketamine: (N = 21); E19/saline: (N = 24). The test chamber consisted of a plastic 'shoe-box' cage (44.45 cm long × 21.59 cm wide × 20.32 cm high) with transparent walls and open top. This chamber had a grid on its floor composed of 3 × 6 squares (each measuring 7.1 cm × 7.1 cm). Young rats have limited abilities to thermoregulate [77]. Therefore, the activity chamber was placed on a heating pad set at 33.5°C., producing a floor temperature of approximately 28.5 ± 1°C. An individual pup was initially positioned in the center square of the chamber. Locomotor activity was recorded for 30 minutes. Test sessions were video recorded and tapes were later scored (see below). After each session, the animal was weighed and then returned to its home cage. In preparation for the next animal, the activity chamber was cleaned by spraying the cage with 50% ETOH, wiping it clean with paper towels, and allowing it to air-dry for approximately 10 minutes.
At age P60, we again measured the spontaneous locomotor activity of the following number of rats in each group: E18/ketamine: (N = 12); E18/saline: (N = 21); E19/ketamine: (N = 17); E19/saline: (N = 22). For this second test, a larger test chamber (64 cm long × 46 cm wide × 42 cm high) was used. The walls were opaque plastic and the top open. This chamber had a grid on its floor that consisted of 3 × 3 rectangles (each measuring 21 cm × 15 cm). As before, rats were individually placed in the center square of the chamber at the beginning of the 30-minute test session. After each session, the animal was weighed and then returned to its home cage. In preparation for the next animal, the activity chamber was cleaned as described above.
Videotapes of locomotor movements were later viewed and independently scored by observers blind to the experimental condition of the animal. We counted line crossings to assess the amount of horizontal locomotion exhibited by each animal. A "line-cross" was counted when any part of the rat, except the tail, crossed a line. Rearing was operationally defined as any time the rat raised both front paws from the chamber floor. In P11 rats, rearing was very rare and therefore not scored. However, this behavior was recorded during the P60 test.
Statistical analyses
Escape latencies and time on the hidden platform were recorded for each trial in the water maze. Group differences in water maze performance were most evident early in training. After the first few trials, rats in all treatment groups moved promptly to the hidden platform with a latency of less than 20 seconds. For this reason, our statistical analyses focused on the initial trials of each session. Animals removed from the maze after not finding the hidden platform in 90 seconds were, nevertheless, assigned a time of 90 seconds for purposes of data analysis.
Swim distances, swim speeds, and time spent treading water (stop times) were also calculated for the first 3 swim trials. Towards this end, videotapes were viewed and independently evaluated by raters blind to the experimental condition of the animal. Swim paths were hand-drawn on acetate placed on a video screen during playback of the videotape. The paths were digitized using a light pen providing input to NIH Image software (Bethesda, MD). The lengths of the paths were compared with a calibrated length on the video record in order to calculate the swim distance. An evaluation of the inter-rater reliability of our video scoring methods indicated a high correlation [r(10) = 0.924, p < 0.001]. These methods produced swim distances that were not significantly different [t(9) = 1.02, p > 0.05] between observers. For some of our swim speed analyses, we subtracted out any time that the animal stopped swimming mid-trial, and treaded water at the side of the tank. Dividing the swim distance by the adjusted time to mount the hidden platform produced swim speed.
Two observers, blind to the experimental condition of the animals, evaluated each of the tapes of animals locomoting in the open field by counting line crossings and rears. The counts of these 2 observers were then averaged and this data point was used in our statistical analysis. There was high degree of correlation between the ratings of our 2 observers. P11 locomotor test: r(85) = 0.91, p < 0.01; P60 locomotor test: r(72) = 0.90, p < 0.01.
Unless otherwise stated, locomotor data for the two different test periods (P11 and P60) and water maze data for the 2 different test periods (P18 and P81) were analyzed via separate repeated-measures, three-way ANOVAs [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19) X Time] with time blocks as the repeated factor and compensation for unequal Ns.
We used several rats from each litter and employed statistical corrections in order to avoid spurious inflation of sample size [35]. Since all the rats in a particular litter received the same drug treatment, we included litter as an independent and nested factor in the analysis. This approach controls for litter effects and offers a direct statistical test of the significance of such effects [35]. Denenberg [36] has recommended this procedure to allow the partitioning of litter and treatment effects and thereby allowing investigators to make use of the data from multiple animals in a litter. When significant litter effects were detected, we used the Mean Square (MS) associated with the litters as the error term rather than the MS of the subjects. However, if there was not a statistically significant litter effect, the data were subsequently reanalyzed without this component as part of the general linear model (GLM; software provided by SAS™, SAS Institute, Carey, NC; and, SPSS™ Inc., Chicago, IL).
An initial inclusion of subject sex as a factor in our statistical analyses indicated no significant differences between male and female subjects. Therefore, the subsequent analyses reported here were run without this factor.
Platform navigation for juvenile rats might be more challenging than the task presented to older rats. A small water maze was used in these studies and the same start-to-platform distance was used for all tests. Still, the P18 rat may have found it significantly more challenging than the P81 rat to traverse this distance. For this reason, we intentionally avoided comparing the water maze data of our juvenile and young-adult rats. Likewise, we did not make statistical comparisons between the locomotor responses of animals run at the 2 different ages since the use of different-sized apparatuses would presumably influence these data.
If a repeated-measure ANOVA revealed a significant trial effect (indicating a change over time) a two-way ANOVA [Drug (100 mg/kg ketamine HCl, saline control) X Treatment age (E18, E19)] was run to analyze the group differences during a particular trial. Individual group comparisons were accomplished by using either the Tukey-Kramer Multiple Comparisons Test or t-tests [78] using the Bonferroni compensation for multiple comparisons. Our previous studies with ketamine-treated fetuses lead us to a priori hypotheses regarding possible behavioral differences between rats treated with ketamine on E18 versus E19 [21]. When a priori planned comparisons were made, one-tail probabilities were computed. An α = 0.05 was used throughout these analyses presented here.
Authors' contributions
GAM designed the studies, performed some of the behavioral work, conceptualized the statistical analyses and drafted the manuscript. CK helped design the studies, performed most of the behavioral work, assisted with statistical analyses and the drafting of the manuscript. CM supervised most of the behavioral work, assisted with statistical analyses and the drafting of the manuscript. AS supervised and performed much of the behavioral work. AY performed most of the behavioral work. DL-F assisted with the statistical analyses. EV assisted with the behavioral work. BW assisted with the behavioral work. JMB performed aspects of the behavioral work and assisted with the drafting of the manuscript.
Acknowledgements
The authors are very grateful for the excellent technical assistance provided by: C. Dengler, J. Francway, B. Girdler, S. Howsen, N. Hoxha, Z. Hoxha, B. Kudla, D. Lee, A. J. Marcano-Reik, K. Michel, S. Pankuch, D. Revta, E. Sierko, B. Stanton, E. Valentine, C. Voight, J. Wellman, J. Wickham and J. Yocom. The idea for these studies evolved out of some early discussions with Dr. Charles Levin. Portions of these data were presented at the Society for Neuroscience meeting, Orlando, FL, 2002. The National Science Foundation supported the experiments: Award 9514799.
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| 15509306 | PMC528728 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 28; 4:28 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-28 | oa_comm |
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BMC OphthalmolBMC Ophthalmology1471-2415BioMed Central London 1471-2415-4-151551626210.1186/1471-2415-4-15Research ArticleComparative evaluation of diode laser versus argon laser photocoagulation in patients with central serous retinopathy: A pilot, randomized controlled trial [ISRCTN84128484] Verma Lalit [email protected] Rajesh [email protected] Pradeep [email protected] HK [email protected] Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India2004 29 10 2004 4 15 15 11 5 2004 29 10 2004 Copyright © 2004 Verma et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
To evaluate the efficacy of diode laser photocoagulation in patients with central serous retinopathy (CSR) and to compare it with the effects of argon green laser.
Methods
Thirty patients with type 1 unilateral CSR were enrolled and evaluated on parameters like best corrected visual acuity (BCVA), direct and indirect ophthalmoscopy, amsler grid for recording scotoma and metamorphopsia, contrast sensitivity using Cambridge low contrast gratings and fluorescein angiography to determine the site of leakage.
Patients were randomly assigned into 2 groups according to the statistical random table using sequence generation. In Group 1 (n = 15), diode laser (810 nm) photocoagulation was performed at the site of leakage while in Group 2 (n = 15), eyes were treated with argon green laser (514 nm) using the same laser parameters. Patients were followed up at 4, 8 and 12 weeks after laser.
Results
The mean BCVA in group 1 improved from a pre-laser decimal value of 0.29 ± 0.14 to 0.84 ± 0.23 at 4 weeks and 1.06 ± 0.09 at 12 weeks following laser. In group 2, the same improved from 0.32 ± 0.16 to 0.67 ± 0.18 at 4 weeks and 0.98 ± 0.14 at 12 weeks following laser. The improvement in BCVA was significantly better in group 1 (p < 0.0001) at 4 weeks. At 4 weeks following laser, all the patients in group1 were free of scotoma while 6 patients in group 2 had residual scotoma (p < 0.05). The mean contrast sensitivity in group 1 improved from pre-laser value of 98.4 ± 24.77 to 231.33 ± 48.97 at 4 weeks and 306.00 ± 46.57 at 12 weeks following laser. In group 2, the same improved from 130.66 ± 31.95 to 190.66 ± 23.44 at 4 weeks and 215.33 ± 23.25 at 12 weeks. On comparative evaluation, a significantly better (p < 0.001) improvement was noted in group 1.
Conclusion
Diode laser may be a better alternative to argon green laser whenever laser treatment becomes indicated in patients with central serous retinopathy in terms of faster visual rehabilitation and better contrast sensitivity. In addition, diode laser also has the well-recognized ergonomic and economic advantages.
diode lasercentral serous retinopathylaser in CSR
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Background
Central serous retinopathy (CSR) is a retinal disorder affecting young adults, characterized clinically by a well-defined, translucent, circumscribed detachment of neurosensory retina at the posterior pole, usually involving the macula. The detachment results from accumulation of transparent fluid in the potential space between retinal pigment epithelial layer and the neurosensory retina. However, the source of this fluid and the exact etiopathogenesis of this disorder remain ill understood. Maumenee [1] described the concept of leakage site within the retinal pigment epithelial layer following fluorescein angiographic studies in patients with central serous retinopathy. Recent studies [2-4], using indocyanine green angiography, have indicated that the possible site of primary pathology in central serous retinopathy is the choroidal vessels and the involvement of the pigment epithelial layer is only secondary to it.
Although, other treatment modalities have been suggested for central serous retinopathy, argon green (514 nm) laser photocoagulation has been the most widely accepted modality. The 810 nm-diode laser owing to deeper penetration should prove to be more efficacious in the treatment of central serous retinopathy given the current thinking that CSR may primarily be a choroidal disease. The present study was undertaken to test the efficacy of diode laser photocoagulation in central serous retinopathy in comparison to argon green laser. According to the best of our knowledge, this is the first clinical study in which a comparative evaluation between the above two groups has been done in central serous retinopathy.
Methods
Thirty patients of unilateral central serous retinopathy presenting to the retina services at the Rajendra Prasad Centre for Ophthalmic Sciences were selected for the study. They were randomly assigned into 2 groups according to the statistical random table using sequence generation. As it was a pilot study we decided only the convenient sample size i.e. 15 in each group. The allocation of patients into 2 groups was done by a person who was not involved in the study and the sequence was concealed until interventions were assigned to prevent bias. The patient selection was based on the following inclusion criteria: age less than 50 years, type 1 CSR with a single leak on fluorescein angiography that was at least 300 microns away from fovea, presence of an indication for laser treatment (recurrence, occupational, history of poor visual outcome in fellow eye) and no history of any treatment in the past. Patients with multiple leak CSR, type 2 or type 3 CSR or leak at papillomacular bundle or leak within 300 μ from the foveal centre were not included in the study. Patients with other ocular pathology such as choroidal neovascularization, choroidal inflammatory or neoplastic disorder or a congenital optic nerve pit were also excluded from the study.
Informed consent was obtained from all the patients included in the study before initiating treatment and an approval of the institute body ethics committee was obtained to undertake the study. In all the patients the following parameters were evaluated before laser treatment and also during the follow-up period: best corrected visual acuity (BCVA) on Snellen's acuity chart, fundus examination by direct and indirect ophthalmoscopy, amsler grid for determining the degree of scotoma and / or metamorphopsia, contrast sensitivity using Cambridge low contrast gratings and fluorescein angiography (Canon CF-60 UD camera using 400ASA, 35-mm black and white film).
Technique of scoring scotoma and metamorphopsia on the amsler grid is described below:
The amsler grid consists of a 10-cm square divided into smaller 5 mm squares. When it is viewed at one-third of a meter, each small square subtends an angle of 1°. The patient was asked to look directly at the central spot with the uncovered eye and to mark out any black spot, distortion of lines, or blurred area on the grid. The number of small squares within the area delineated by the patient was counted and scotoma and metamorphopsia score was graded numerically as shown in table 1 and 2.
Patients included in the study were randomly assigned to one of the following two groups. In group 1 (n = 15), the site of leakage was treated by diode laser (810 nm). Laser was performed with Volk area centralis contact lens (Keeler Ltd, Clewer Hill Road, Windsor, Berks, SL4 4AA) and the parameters used were spot size of 100 microns, duration of 100 to 200 milliseconds and power just enough to produce minimal greying. In group 2 (n = 15), patients were treated with argon green laser (514 nm) following the same laser parameters. Patients were followed up at intervals of 4, 8 and 12 weeks after laser treatment and the results were analyzed, which included a comparative analysis of the outcome between the two groups. Descriptive statistics i.e. mean and standard deviation was calculated for each variable at each follow-up for the 2 groups. To see the significant difference between the 2 groups at each point we applied "student 't' test" (unpaired). To see the trend within the group we applied "two way analysis of variance (ANOVA)". 'P' value of 0.05 was considered as statistically significant. BMDP 7.0 statistical package was used for the statistical analysis.
Results
The average age of the patients in group 1 was 34.06 ± 2.54 years (range: 30–40 years) and in group 2, it was 34.66 ± 3.23 years (range: 30–42 years). Twelve cases (80%) in group 1 and 13 patients (86.66%) in group 2 were males. All the patients presented to us within eight days (range: 3–8 days) of onset of visual symptoms. The average number of laser spots applied to the leakage site in group 1 was 3.13 ± 0.71 (range: 2–4) and in group 2 it was 3.26 ± 0.77 (range: 2–4). All the patients came regularly for the follow-up as per the protocol during entire period of study. The study was performed during the period between January 1998 and June 2000.
The mean best-corrected visual acuity (BCVA) in decimal at the time of presentation in patients assigned to group 1 was 0.29 ± 0.14. Best corrected visual acuity four weeks after diode laser treatment had improved to 0.84 ± 0.23. The BCVA further improved to 0.97 ± 0.17 and 1.06 ± 0.09 at 8 weeks and 12 weeks respectively. In group 2, the mean BCVA at the time of presentation was 0.32 ± 0.16 and it improved to 0.67 ± 0.18 at 4 weeks follow-up after argon green laser photocoagulation. Further improvement in visual acuity to 0.91 ± 0.19 and 0.98 ± 0.14 at 8 weeks and 12 weeks follow up respectively was observed. Statistical analysis of data pertaining to improvement of BCVA in the two groups by student 't' test, showed a statistically significant difference at 4 weeks with patients treated by diode laser (group 1) having a better improvement (p < 0.001) (Figure 1). This difference decreased and was not found to be statistically significant during later follow-up at 8 and 12 weeks.
All the patients in both the groups had central scotoma at the time of presentation. At the first follow up at 4 weeks following laser treatment, all the patients in group 1 (n = 15) were free of scotoma while 6 patients in group 2 (n = 15) had residual scotoma of grade 1 (involving 1–50 small squares on the amsler grid). On comparative evaluation, this difference was found to be statistically significant (p < 0.05). At 8 weeks and 12 weeks follow up, none of the patients had residual scotoma; however, 3 patients in group 2 developed a paracentral scotoma corresponding to the site of laser scar.
Four patients in group 1 and five in group 2 had complaints of metamorphopsia at the time of presentation. At the first follow up at 4 weeks, 2 cases in group 1 and 4 cases in group 2 were still having grade 1 metamorphopsia. On comparative evaluation, this difference in the two groups was not found to be significant statistically. At 8 weeks and 12 weeks follow up, all patients in both the groups were free of metamorphopsia.
Contrast sensitivity was evaluated using the Cambridge low contrast gratings. The reference normal for our patients as observed in earlier studies at our centre has been 340 (range 310–370) (unpublished data). The mean absolute value of contrast sensitivity at the time of presentation in group 1 was 98.4 ± 24.77. This improved to 231.33 ± 48.97 at the first follow up at 4 weeks following diode laser photocoagulation. It further improved to 286.00 ± 48.52 at 8 weeks follow up and 306.00 ± 46.57 at the third follow up at 12 weeks. In group 2, the mean value of contrast sensitivity at the time of presentation was 130.66 ± 31.95, which improved to 190.66 ± 23.44 at 4 weeks following argon green laser treatment. This value further improved to 210.00 ± 27.25 at 8 weeks and 215.33 ± 23.25 at 12 weeks follow up. Comparative evaluation of the improvement in contrast sensitivity by student 't' test between the two groups showed that there was an initial rapid improvement in contrast sensitivity in both the groups but group I patients had a significantly greater improvement than group 2 patients at all the follow up visits (p < 0.001) (Figure 2).
In both the groups, the commonest site of fluorescein leakage was the area superonasal to fovea. Fluorescein angiography repeated 4 weeks later showed that in both the groups, all the patients had complete resolution of the leakage site. No recurrence was reported till completion of follow up at 12 weeks.
Discussion
Visual prognosis in central serous retinopathy in most cases is good as they resolve spontaneously. Klein M et al. [5] studied and followed up 34 eyes of CSR without giving any treatment and found that final visual acuity was 20/30 or better. The average time for the fluid to resolve was between 3 months to 6 months and there was a recurrence rate of 45%. Sequelae such as macular degeneration and chronic retinal pigment epithelial decompensation are rare after the first episode. However, in a small group of patients visual prognosis has been reported to be less favorable [6,7].
Encouraging results of laser photocoagulation has been found in various studies which conclude that laser photocoagulation could effectively decrease the time for visual recovery in patients with central serous retinopathy [8-10]. However, the ultimate visual acuity despite laser photocoagulation remained no different from patients in whom no treatment was done and that recurrences could not be prevented by laser photocoagulation [10].
Results of treatment of the leakage site in patients with central serous retinopathy with ruby [9], krypton [11,12] and argon lasers [9,10,13,14] and Xenon arc [15] have been reported in the literature. Although, Slusher [11] suggested that the wavelength emitted by krypton laser might be more tissue-selective, argon green laser photocoagulation has been the most widely accepted treatment modality in patients with central serous retinopathy [9,10,12-14].
Studies performed with indocyanine green angiography in cases of central serous retinopathy have reported that hyperpermeability of choroidal vessels could be the primary pathology in CSR and retinal pigment epithelial abnormality is only secondary to it [2-4].
Brancato R et al [16] performed transpupillary chorioretinal photocoagulation in rabbit eyes using a diode laser prototype and a commercially available argon laser and found that they were similar on ophthalmoscopy. However, histopathological examination performed twenty-four hours after the treatment showed that argon irradiation resulted in damage to both the inner and outer retinal layers while the diode laser radiation produced damage to outer retina and choroid. If this were true, energy delivered by argon laser (514 nm) seems inadequate in being able to reach the deeper choroidal layers and treat the primary site of pathology. This inherent limitation of argon laser in view of recent ICG reports on the pathogenesis of central serous retinopathy could also explain the less than ideal outcome in the earlier studies using this laser in the treatment of patients with central serous retinopathy. The 810 nm-diode laser emission is in the near infrared (810 nm) region and this wavelength is able to reach the deeper choroidal layers [16-18]. Hence, we felt that the 810 nm-diode laser may theoretically be more efficacious in the laser treatment of central serous retinopathy.
Our observations showed that following diode laser treatment in patients with central serous retinopathy, the recovery of visual acuity was more rapid in comparison to the argon laser treatment group. Further, all the patients treated with diode laser showed complete resolution of scotoma at the first follow up at 4 weeks and none of them developed a scotoma corresponding to the site of laser treatment. In contrast, in patients subjected to argon green laser treatment, there was a significant residual scotoma at 4 weeks and 20% of the patients developed scotoma corresponding to the laser scar.
Following laser treatment, contrast sensitivity in both the groups improved rapidly in the first four weeks. However, there was significantly greater improvement in contrast sensitivity in eyes subjected to diode laser. This observation is indicative that diode laser is more beneficial with regard to conservation of contrast sensitivity following laser treatment in patients with central serous retinopathy.
Development of subretinal neovascularization following laser treatment for central serous retinopathy has been described earlier in literature [19]. However, in our study none of the patients in any group developed this complication.
The present study suggests that diode laser photocoagulation brings about a faster quantitative and qualitative visual recovery in comparison to argon green laser in eyes with central serous retinopathy. The main aim of performing laser photocoagulation in eyes with CSR is to hasten resolution and this is better fulfilled by diode laser. Further, as argon laser can hit up to the outer retinal layers, it can seal the site of leaking retinal pigment epithelium, but it cannot reduce the fluid overload over the pigment epithelial layer caused by hyperpermeability of choriocapillaries, which can be the cause of recurrence of CSR. As diode laser hits the choroid, which is the exact site of pathology, it should reduce the fluid overload on the retinal pigment epithelium.
Conclusions
In the present study, our observations are suggestive that diode laser may be a better alternative to argon laser whenever laser treatment is indicated in patients with central serous retinopathy. In addition, diode laser also has the well-recognized economic and ergonomic advantages of being less costly, more efficient, portable and easy to maintain [20,21], which is also a matter of concern particularly in third world countries.
Declaration of competing interest
The author(s) declare that they have no competing interests.
Authors' contributions
LV designed the study and performed laser. RS performed the data collection and wrote the manuscript. PV followed up the patients. HKT performed the angiography.
All authors read and approved the final manuscript.
Presented in part in the Annual Meeting of the American Academy of Ophthalmology, 1997 in San Francisco, California.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Best Corrected Visual Acuity – Group 1 (Diode) Vs Group 2 (Argon green)
Figure 2 Contrast Sensitivity – Group 1 (Diode) Vs Group 2 (Argon green)
Table 1 Amsler Grid: Scotoma Score
No of small squares Grade
0 0
1 – 50 I
51 – 100 II
101 – 150 III
151 – 200 IV
Table 2 Amsler Grid: Metamorphopsia Score
No of small squares Grade
0 0
1 – 25 I
26 – 50 II
51 – 75 III
76 – 100 IV
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Maumenee AE Symposium: Macular disease: Clinical manifestation Trans Am Acad Ophthalmol Otolaryngol 1975 69 605 700
Scheider A Nasemann JE Lund OE Fluorescein and indocyanine green angiographies of central serous retinopathy by scanning laser ophthalmoscopy Am J Ophthalmol 1993 115 50 56 8420378
Guyer DR Yannuzzi LA Slakter JS Sorenson JA Allen HO Orlock D Digital indocyanine green videoangiography of central serous retinopathy Retina 1994 112 1057 1062
Spaide RF Hall L Haas A Campeas L Yannuzzi LA Fisher YL Guyer DR Slakter JS Sorenson JA Orlock DA Indocyanine green videoangiography of older patients with central serous chorioretinopathy Retina 1996 16 203 213 8789858
Klein ML Van Buskirk EM Friedman E Gragoudas E Chandra S Experience with non-treatment of central serous retinopathy Arch Ophthalmol 1974 91 247 250 4621147
Levine R Brucker AJ Robinson F Long term follow up of idiopathic central serous chorioretinopathy by fluorescein angiography Ophthalmology 1989 96 854 859 2740080
Yannuzzi LA Shakin JL Fisher YL Altomonte MA Peripheral retinal detachment and retinal pigment epithelial atrophic tracts secondary to central serous pigment epitheliopathy Ophthalmology 1984 91 1554 1572 6084221
Gass JDM Pathogenesis of disciform detachment of the neuroepithelium I: Idiopathic central serous choroidopathy Am J Ophthalmol 1967 63 587 615
Watzke RC Burton TC Leveston DE Ruby laser photocoagulation therapy of central serous retinopathy. I: A controlled clinical trial. II: Factors affecting prognosis Trans Am Acad Ophthalmol Otolaryngol 1974 78 205 211
Robertson DM Ilstrup D Direct, indirect and sham laser photocoagulation in the management of central serous retinopathy Am J Ophthalmol 1983 95 457 466 6682293
Slusher MM Krypton red laser photocoagulation in selected cases of central serous chorioretinopathy Retina 1986 6 81 84 3749625
Novak MA Singerman H Rice TA Krypton and argon laser treatment for central serous retinopathy Retina 1987 7 162 169 3423431
Leaver P Williams C Argon laser photocoagulation in the treatment of central serous retinopathy Br J Ophthalmol 1979 63 674 677 574397
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Spitznas M Ryan SJ Central serous retinopathy Retina 1989 St Louis, CV Mosby Co 217 227
Brancato R Pratesi R Leoni G Trabucchi G Vanni U Histopathology of diode and argon green laser lesions in rabbit retina. A comparative study Invest Ophthalmol Vis Sci 1989 30 1504 1510 2744993
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Yukihirosato Bruce A Berkowitz Blood retinal barrier breakdown caused by diode versus argon laser endophotocoagulation Arch Ophthalmol 1992 110 227 281
Schatz H Yannuzzi LA Gitter KA Subretinal neovascularization following argon laser photocoagulation treatment for central serous chorioretinopathy: complication or misdiagnosis Trans Sect Ophthalmol Am Acad Ophtalmol Otolaryngol 1977 83 893 906 563122
Puliafito CA Deutsch TF Boll J To K Semiconductor laser endophotocoagulation of the retina Arch Ophthalmol 1987 105 424 427 3827722
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| 15516262 | PMC528729 | CC BY | 2021-01-04 16:03:49 | no | BMC Ophthalmol. 2004 Oct 29; 4:15 | utf-8 | BMC Ophthalmol | 2,004 | 10.1186/1471-2415-4-15 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-211548814510.1186/1472-6920-4-21DebateEnhancing the African bioethics initiative Ogundiran Temidayo O [email protected] Division of Oncology, Department of SurgeryCollege of Medicine, University of Ibadan and University College Hospital, PMB 5116 Ibadan, Nigeria2004 15 10 2004 4 21 21 18 5 2004 15 10 2004 Copyright © 2004 Ogundiran; licensee BioMed Central Ltd.2004Ogundiran; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Medical ethics has existed since the time of Hippocrates. However, formal training in bioethics did not become established until a few decades ago. Bioethics has gained a strong foothold in health sciences in the developed world, especially in Europe and North America. The situation is quite different in many developing countries. In most African countries, bioethics – as established and practiced today in the west- is either non-existent or is rudimentary.
Discussion
Though bioethics has come of age in the developed and some developing countries, it is still largely "foreign" to most African countries. In some parts of Africa, some bioethics conferences have been held in the past decade to create research ethics awareness and ensure conformity to international guidelines for research with human participants. This idea has arisen in recognition of the genuine need to develop capacity for reviewing the ethics of research in Africa. It is also a condition required by external sponsors of collaborative research in Africa. The awareness and interest that these conferences have aroused need to be further strengthened and extended beyond research ethics to clinical practice. By and large, bioethics education in schools that train doctors and other health care providers is the hook that anchors both research ethics and clinical ethics.
Summary
This communication reviews the current situation of bioethics in Africa as it applies to research ethics workshops and proposes that in spite of the present efforts to integrate ethics into biomedical research in Africa, much still needs to be done to accomplish this. A more comprehensive approach to bioethics with an all-inclusive benefit is to incorporate formal ethics education into health training institutions in Africa.
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Background
Before the modern discipline of bioethics evolved, ethics had been on the centre stage of medical practice for more than two millennia, since the time of Hippocrates. In 1803, Thomas Percival published his book on Medical Ethics, which became the template on which the code of ethics of the American Medical Association was based in 1847 [1]. Medical ethics then existed as a code of conduct for medical practitioners and was aimed at the physician putting the interests of his patients uppermost at heart. However, the origin of bioethics, as it is known and practiced today, can be traced back to three different but interrelated events: a set of scandals in biomedical research, advancement in medical technology and the civil rights movement [2]. Of the scandals, the most well-known is the infamous Nazi experimentation on war prisoners and the subsequent Nuremberg trials of the 1940s. As a result of this and other scandals that trailed the medical profession subsequent to Nuremberg, various codes, declarations, guidelines, policies and documents came into existence and became widely applied to research with human subjects and to health care practice as a whole. The scandals and the events thereafter led to three developments. First, physicians became more sensitive to the ethics of the profession. Medicine is a moral discipline but the indirect method of achieving moral acculturation would no longer be sufficient to equip the physician to meet the ethical challenges of modern day biomedical practice. The need for formal education in medical ethics was thus acknowledged. Second, society became sensitized to the necessity of becoming involved in the decisions which ultimately affect their health and liberty. This signaled the decline of paternalism and the rise of liberalism, individual rights and autonomy in medical practice. Third, the coming of other professionals from different disciplines like social sciences, humanities and the law into what had hitherto been the exclusive domain of medical professionals led to the "socialization" of biomedical science, which further spurred on the bioethics movement.
The scope of bioethics has continued to expand in response to changes in societal dynamics, medical technology and health care practices. Perhaps more because of its antecedents than for want of content, most of the discourse and writings in the early days of bioethics centred on issues of patient-physician relationship, respect for person, best interest of the patient, and justice in health care delivery. Today, traditional bioethics discussions and literature are changing as new ethical concerns evolve around dilemmas posed by new technology on the subjects of end of life issues, organ donation, human reproduction and human genomics. Moreover, the bioethics agenda has expanded to include the subjects of resource allocation, organizational ethics and public health ethics, among others.
Bioethics in its present form is rooted in and largely dominated by western culture. The tempo and content of bioethics discourse are largely influenced by the technological creations of the developed world. However, ethics is not exclusively the domain of the west. Core ethical values are essentially the same for all human communities leaving aside each community's customs, culture and preferences [3]. According to Potter, bioethics is "the application of ethics to all of life" [4]. In the globalization of bioethics, different cultural, ethnic and religious perspectives are given a voice. Though bioethics has come of age in the developed and some developing countries, it is still largely "foreign" to most African countries. It is time Africa joined the bioethics bandwagon. Its relevance and applications to science and research are vital and should not be overlooked. The call of this paper therefore, is for bioethics to be integrated as a required component of medical education curriculum in Africa.
Discussion
Research ethics
In the bioethics literature, bioethical discourse and arguments have been most prominent and intense concerning research involving human participants. One major achievement in this regard is the creation of an oversight body that sees to the proper design and conduct of research that conforms to generally acceptable and established ethical guidelines. There resides in this body the duty of ensuring that research sponsors and investigators abide by established conventions for carrying out clinical research. They also perform the role of assuring the safety of research participants and ensuring that participants (and/or society) benefit from the outcome of research. The relevance of this body to modern day health care and research is partly exemplified in the absence of any major scandals in the form and magnitude of those already recorded in history. The establishment of research ethics boards has not solved all the ethical problems of biomedical research though. There is still a lot to do to re-structure, re-empower and re-position the board to match the complexities of the present-day technology-driven medical research and practice.
Current efforts in Africa
Various bodies within and outside Africa have pioneered the movement towards ensuring that medical research in Africa conforms to international ethical guidelines. This is the aspect of bioethics that is most visible in Africa and has been anchored partly by the Pan African Bioethics Initiative (PABIN), a pan-African organization that was established in 2001 to foster the development of bioethics in Africa with a particular focus on research ethics (5). This idea has arisen in recognition of the genuine need to develop capacity for reviewing the ethics of research in Africa. It is also a condition required by external sponsors of collaborative research in Africa. Ethics workshops and conferences have been held in different parts of Africa, including Tanzania [6], Zambia [7], South Africa [8], Ethiopia [9], Cameroon [10] and Nigeria [11,12]. Moreover, some institutions and research centres have established research ethics review committees and some members of these committees have attended training workshops on research ethics.
While the present efforts and achievements are commendable, much still needs to be done for the effects to filter through to the grassroots, which is the main arena of research activities and where the burdens of research are most felt. I say this for the following reasons. First, the present efforts are still limited in extent and effect. Hitherto, most of the conferences have taken place in two or three geographical zones of the continent and have been limited to a few days of activities. Of course, the interests and motives of the sponsoring agencies together with the presence on the ground of those who are available to organize the conference locally determine where, when, for how long and the number of participants in the workshop. There is thus restriction on the number of researchers who can attend the conferences from all over Africa and on the amount of knowledge that can be imbibed in those few days. The consequence is that the same few people attend the conferences most of the time. These attendees from a few centres might not be able to change unethical research practices in their countries. Attempts by these few to train their colleagues locally are often constrained by lack of funds.
Second, absence of national directories of research activities in most African countries makes the magnitude of biomedical and social sciences research in Africa to be underestimated. For example in Nigeria, five categories of research and researchers are easily identifiable. Individual or institution-supported research is done by students, clinicians (including resident doctors) and other scientists. This category constitutes a significant proportion of research in tertiary academic and health institutions. Industry-sponsored research is undertaken by researchers for pharmaceutical companies to promote new or old drugs. Such research protocol may be indigenously developed or be a part of multicentre trials. In most instances, these companies do not go through the institutions where the researchers are based, but deal directly with individual researchers, who may or may not subject the research protocol to an ethics board review. Collaborative research with colleagues from the developed countries is often externally funded. It includes hospital and community-based trials and mostly involves experimenting with drugs or vaccines. Of particular ethical concern in collaborative research is the fact that external sponsors may differ in their motives for conducting research and there may be limited applicability of research benefits to the country or local community [13]. Moreover, the clinician/researcher and/or institutions are themselves vulnerable to funding pressures. Another category of research is that which occurs through indigenous government-funded agencies. An example of such an agency is the National Institute for Medical Research, which has been carrying out research in Nigeria for more than thirty years on parasitic, infective and non-infective diseases. Non-Governmental Organizations (NGOs) are also involved to variable extents in both clinic and community-based research.
Third, a majority of Africa's research participants are highly vulnerable given their low level of formal education and the political, social and economic milieu in which they live. The fourth reason is that Africa is a pluralistic society with diverse peoples and cultures. While general guidelines may apply in most cases, in some the peculiarities of each ethnic and cultural group will significantly affect what research is done and how it is done in those communities. Lastly, not every research centre has established an ethics review process. Where already established, most of the ethics review committees are grossly underfunded and unequipped for their duties.
Clinical ethics
This is the branch of bioethics that addresses ethical conflicts that arise in daily clinical practice in health care institutions, through the establishment of hospital ethics committees and ethics consultation services. Fletcher and Siegler define ethics consultation "as a service provided by an individual consultant, team or committee to address the ethical issues involved in a specific clinical case. Its central purpose is to improve the process and outcomes of patents' care by helping to identify, analyze, and resolve ethical problems" [14]. An ethics consultation service also responds to conflicts that arise from technological improvements in medical care and the increasing pressure to meet a perceived standard of care [15]. Clinical ethics is also concerned with organizational ethics and networks and the implications of health care policy at the bedside. Although there are contrasting views about the presence of ethics consultants at the bedside, hospital ethics committees are now available in most hospitals in North America and Europe, providing services to patients, families, staff and the entire hospital organization.
Do we need the services of hospital ethics committees or consultants in African hospitals or at the bedside? That may not be the point presently. However, clinical ethics is neither about committees and consultations, nor about technology and end-of-life issues alone. Common sense and intuition are insufficient to address all ethical issues that arise in patients' care. The well-intentioned clinical decisions and judgments of yesterday may turn out to be unsound in the searchlight of today's ethical scrutiny. Although core moral virtues have generally guided medical practice in Africa as elsewhere, there is the increasing need to apply both cognitive and behavioural ethical values to everyday decision making at the bedside by the physician as well as other health-care professionals [16]. Perhaps ethical concerns have unwittingly been unrecognized, downplayed or overlooked by health care professionals. The management of chronic diseases like HIV/AIDS and cancer, the incidences of which are on the increase, has attendant ethical implications about care, cost and consequences on patients' personality, values and families. Besides, Africa will someday cross the technological divide that will make resolution of ensuing ethical issues urgent, which health care providers will no longer be able to ignore. The societies are becoming more enlightened and it may be sooner than anticipated when physicians and other health care workers begin to grapple with some ethical challenges for which they are ill-prepared.
It is not here suggested that next on the agenda of health care in Africa is to devote attention and resources to training bioethics consultants for the bedside. Most people in Africa still do not have access to qualified health personnel and reasonable health care. The point is that deliberate efforts should be made to train present and future health care providers to be aware of the core moral virtues required of them in their duties to patients; and be sensitive to the ethical values of their patients, their families and the society.
Ethics education
In the developed world education in ethics is no longer a "hidden curriculum" [17] that is passively passed to medical students during their training. It has become an "open" subject that is actively taught, not only in medical schools, but also in institutions that train other categories of health care workers. It has also become a required module in the training of resident doctors in most countries where bioethics is well grounded. Ethics education is aimed at teaching the cognitive and behavioural aspects of ethics for the purpose of improving the quality of care in terms of both the process and outcome of care. It enhances the student's ability to integrate the technical and moral components of the decision making process in clinical practice [16]. It also prepares the recipients to meet with ethical challenges of clinical practice and biomedical research. The pedagogic formats used include didactic teaching, clinical case studies, small-group discussion of ethical issues, and ethics rounds or grand rounds. A survey of medical graduates in the United States who had received ethics teaching while at medical school revealed that they were better suited to confront ethical issues in their practice and favoured continuation and expansion of ethics teaching in medical schools [18]. Other reports from both developed and developing countries that evaluated medical ethics programmes among medical students attest to the positive impact it has on their appreciation of ethical issues in clinical practice and the way they resolve them [19-22].
It is now time for Africa to join the rest of the world by introducing ethics education into the curricula of all medical schools where it is not presently taught. This is where the future of bioethics and health care delivery and research in Africa lies. Apart from some countries in the southern and eastern part of Africa and a handful of universities in other parts of Africa, there is no formal ethics education in most of Africa's medical schools. Ethics education to medical students is a necessary and required commitment to accomplishing an all-round training of the doctor whose decisions are both technical and moral. Much attention has wholly focused on the technical aspect of medical education, leaving the student to develop his or her moral attitudes passively through observation and intuition. Formal ethics teaching aims at equipping students with a common framework on which to reconcile patients' medical needs with their values, perceptions, situations and beliefs [16]; and may be a process towards achieving the hitherto elusive regulation of medical practice in most of these developing countries.
Pertinent to any discussion about teaching bioethics in Africa is the issue of shortage of trained bioethicists to fill the vacancies that would be created in academic institutions in many African countries. Apart from South Africa and a few others, most countries in Africa lack the requisite bioethics manpower that would be needed in the medical schools. Even in institutions where bioethics is already part of the medical curriculum, it is unlikely that there are enough bioethics teachers. It is in this regard that the efforts of international agencies that fund training of developing world bioethicists are noteworthy. Those Africans who have undergone bioethics training in the developed world and have become pioneers in their institutions have an awesome responsibility of establishing credible training agenda for their countries. They are also well positioned to directly seek funding for such home-based programmes from international sponsors. At the beginning, such scholars may encounter some crisis of identity and acceptance within the established academic system. However, such difficulties would fizzle out as they persist in highlighting and proffering solutions to the myriad of contemporary ethical problems within the system and the society. The initial difficulty of publishing their works in established western bioethics literature can be overcome by patronizing local or regional journals that target the primary audience for which their work is meant and open access journals that reach far and wide. Moreover, most bioethics literature consists of commentaries, observations, personal opinions and philosophical reasonings. As African boethicists embark on qualitative research to highlight ethical issues in Africa and provide quantitative data to fill the gap and provide information which are frequently lacking in most western bioethics journals, access to western dominated journals would be enhanced.
In an editorial published in a recent issue of Bioethics, Chadwick and Schuklenk question the altruism behind training developing world bioethicists in the west and warn against bioethics colonialism. They opine that graduates of such programmes are subjected to western ethical views and ideologies and that the developing world is not funded to develop bioethics capacity based on its own thinking [23]. While I agree with and advocate the principle that more funding should be channeled into establishing bioethics training programmes for Africa, in Africa and by Africans, as is being presently done in South Africa, I do not support the notion that those who have received bioethics training abroad are necessarily placed at the mercy of their trainers to the point of becoming their stooges and hangers-on. Though schooled in the western bioethics tradition I am of the opinion that such trainees have been given the necessary training to critically analyze ethical issues and formulate bioethical frameworks from an African perspective. Their immediate post-training thinking which appears to be shaped by western sentiments would become more and more Afro-centric as they begin to identify, appreciate and explore the hitherto unexplored and emerging ethical issues in their jurisdiction. Rather than retreiting and reinforcing western notions, there are enough ethical issues in the developing world to which such trainees could direct their searchlight and scholarship.
Summary
In the light of the foregoing, it is imperative that African bioethics must evolve which should take cognizance of its unique needs and circumstances and which, though amenable to improvement as a result of continuing interactions with other cultures and values, yet is not overshadowed by those influences. The need for the individual clinician/researcher to be committed to upholding high ethical standards and principles that respect the social, cultural, economic, educational and religious values of the people can not be over-emphasized. More efforts are required towards increasing continent-wide awareness about ethical issues in biomedical practice and research through ethics conferences, workshops, national bioethics conferences, the public media and Non-Governmental Organisations (NGOs). Countries where bioethics presence is fairly strong should assist neighboring countries to establish a presence, especially in organizing ethics review committees at research centres and institutions. Within countries, the possibility of joint or regional ethics review committees should be explored.
Continuing and expanded support from the international bioethics community is required now more than before, to develop capacity for training of academic faculty, clinicians, researchers, government health ministry officials, NGOs, and the media in bioethics. The initiatives of the National Institute of Health of the United States to provide training grants for bioethics programmes within and outside Africa and the support of other institutions and bodies like the Wellcome Trust, African Malaria Network Trust (AMANET) and European Forum for Good Clinical Practice (EFGCP) among others, to the course of bioethics in Africa are noteworthy. Their support for bioethics capacity building programmes should not be limited to one or two sub-regions but to the whole continent. Further sponsorship should be provided for academic institutions within Africa to establish more short- and long-term training programmes at sub-regional levels. More importantly, these institutions should support the movement towards formal incorporation of bioethics into the curricula of medical schools and other health training institutions in Africa. The present and future needs for this in Africa are most apparent now. According to an African adage, the best time to plant a tree is twenty years ago, the next best time is now.
Competing Interests
The author was a Fogarty Fellow (2003/2004) at the Joint Centre for Bioethics, University of Toronto 88, College Street, Toronto, Ontario, Canada M5G 1L4.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgement
I am grateful to Drs Douglas Martin and Dena Taylor, both of the University of Toronto, Canada, for critically and helpfully commenting on the manuscript.
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| 15488145 | PMC528730 | CC BY | 2021-01-04 16:30:53 | no | BMC Med Educ. 2004 Oct 15; 4:21 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-21 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1471547390510.1186/1471-2105-5-147Research ArticleContent-rich biological network constructed by mining PubMed abstracts Chen Hao [email protected] Burt M [email protected] Department of Pharmacology, University of Tennessee Health Science Center, Room 115 Crowe Research Building, 874 Union Avenue, Memphis, Tennessee 38163 USA2004 8 10 2004 5 147 147 27 5 2004 8 10 2004 Copyright © 2004 Chen and Sharp; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public.
Results
We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses.
Conclusions
Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot can be accessed free of charge to academic users.
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Background
A comprehensive understanding of the rapidly expanding corpus of information about the genome, transcriptome, and proteome at large scale requires extensive integration with existing knowledge that often pertains to a number of biological disciplines. Despite the existence of specialized databases (e.g. [1,2]), most of this knowledge is still stored in the form of unstructured free-texts. Different approaches have been developed that automatically retrieve information on molecular interactions from the biomedical literature. Some assume that the co-occurrence of gene/protein names in texts corresponds to a biological relationship [3,4]. Others assign relationships based on similarities in the texts of abstracts [5-7]. While computationally efficient, these methods do not characterize each interaction (e.g., inhibition versus stimulation, directionality). Furthermore, relationships are supported by minimal documentation, other than PubMed IDs. Natural language processing (NLP) has also been used as the basis of programs designed to retrieve more detailed information about molecular relationships ([8-11], reviewed in [12,13]). However, many of these programs were built for testing purposes and are not available to the scientific community at large [14].
Herein, we present a text mining approach, Chilibot (chip literature robot), which constructs content-rich relationship networks between genes, proteins, drugs and biological concepts (figure 1) based on linguistic analysis of relevant records stored in the PubMed literature database. The nature of each relationship (e.g. inhibitory versus stimulative) is encoded in the network map. The network map is also annotated by sentences describing the relationships (content of the network). For example, there are an average of 24 sentences describing each relationship and 11 sentences describing each query term when a maximum of 30 abstracts are analyzed for each relationship. Thus, Chilibot provides a flexible tool for integrating the rapidly expanding body of biomedical knowledge with the highly specialized knowledge of the individual user.
Recent analyses of several types of biological networks (e.g. metabolic [15], proteomic [16], and transcriptomic [17] networks) have found that their connectivities followed the power-law distribution, specifying that the probability of any node connecting to "k" other nodes is proportional to 1/kn. These networks are classified as scale-free networks and are in direct contrast to the bell-shaped distributions seen in random networks [15]. Since most nodes in a scale-free network have very few connections, yet a few nodes (i.e., hubs) have a large number of connections, scale-free networks are robust, resisting the random failure of nodes, but vulnerable if hubs fail. To facilitate comparisons to the structure of other biological networks, the connectivity of networks constructed by Chilibot were analyzed and found to follow the power-law distribution characteristic of scale-free topologies.
Results and discussion
Design and implementation
The overall goal of Chilibot is to generate graphical representations of the relationships among user provided terms (e.g. molecules, concepts, etc). This is achieved by automatically querying the PubMed literature database and extracting information using natural language processing (NLP) techniques.
Chilibot is an Internet-based application [18]. The system has been tested on FreeBSD and Red Hat Linux operating systems. Users interact with the Chilibot server from web-browsers (e.g. Mozilla Firefox, Netscape, or Microsoft Internet Explorer). Batch queries can also be conducted, but only from the server side.
Terms that can be queried include gene symbols, UniGene identifications (including human, rat and mouse) and/or free-form keywords (e.g. "ischemia", "apoptosis", "methylation"). Chilibot retrieves the synonyms of the queried terms from an internal database. The synonym table is compiled from 6 genomic or proteomic databases (see table 1). A total of 113,503 unique symbols were collected; amongst these, 62,178 (54.8%) contained at least one alias (figure. 2). The synonyms can be edited by users if necessary. Pair-wise queries incorporating the synonyms then are sent to PubMed using the Esearch utility, followed by retrieving relevant records using the Efetch utility. By default, a maximum of 30 abstracts per query are retrieved for analysis, however options are available to retrieve 20–50 abstracts. Both utilities are available from the National Center for Biotechnology Information (NCBI).
The texts (including each title and abstract) are then parsed into units of one sentence, which has been shown to yield higher performance levels than paragraphs or phrases in the identification of relationships from MEDLINE abstracts [19]. Sentences containing both query terms or their synonyms are subjected to part-of-speech (POS) tagging using the TnT tagger [20], which is followed by shallow parsing using CASS [21]. A set of rules (see Methods) is followed to classify these sentences into one of five categories: stimulatory (interactive), inhibitory (interactive), neutral (interactive), parallel (non-interactive) and abstract co-occurrence only. The overall relationship between each pair of query terms is then specified based on the relationships found in the sentences (see Methods).
Retrieved relationships are visualized using AiSee (AbsInt, Angewandte Informatik GmbH, Germany). Nodes (boxes) are used to represent query terms and lines for relationships. Icons with different shapes and colors are added to the middle of each line to indicate the nature of the relationship, with arrows indicating directionality. Color coding of individual nodes can be used to report the magnitude of change in experimental data, when provided by the user; different shades of green or red represent up- or down-regulation, respectively, and more saturated colors are associated with larger changes. The weight of an interactive relationship, reflecting the number of abstracts obtained from PubMed, is displayed within the icon (figure. 1). The co-ordinates of the graphical elements are used to link the documentation of the relationships and the query terms to the map. Typically, querying a list of 10 terms takes 3–4 minutes, allowing 3 seconds between PubMed connections as requested by NCBI.
Performance evaluation
We used a set of 770 known relationships (see Methods) specified in the Database of Interacting Proteins (DIP) [2] to measure the performance of Chilibot in finding relationships. DIP was chosen for this purpose because it contains a large number of protein interaction relationships that are manually curated. We defined recall as the fraction of relevant relationships retrieved. The effect of the number of documents analyzed on recall is first evaluated by analyzing a maximum of 5, 10, 20, 30, 40, and 50 of the most recent abstracts for each pair of proteins. Figure 3a shows that analyzing 5 or 50 abstracts achieved recalls of 90.1% and 91.2%, respectively. Thus, increasing the number of documents analyzed does not yield an increase in recall. However, analyzing more abstracts increased the average number of statements demonstrating the relationships (figure. 3a), resulting in a steady increase in stimulatory or inhibitory relationships and a decrease in "parallel relationships" (figure. 3b). In addition, we also evaluated the effect of the total number of abstracts available in PubMed on recall. Of the 770 queries conducted by Chilibot, 66 had no reference in PubMed and no relationship was detected. Chilibot also failed to detect a relationship from two queries where each had 1 reference available. Relationships were detected among the 702 remaining queries; the number of references in PubMed ranged from less than 10 (206 queries), between 10 to 99 (299), to more than 100 (197). Thus, the ability of Chilibot to detect relationships depends on the existence of PubMed records, but is not sensitive to the number of references. Chilibot's recall proficiency may be attributable to a large dictionary of synonyms (currently from 6 databases), optimized PubMed query structure and non-alphanumeric character processing method (see Methods), and to the use of both sentences and abstracts as units of analysis. However, we were not able to directly compare the performance of Chilibot with other NLP-based PubMed-mining software because none of these are available to the public [14]. A potential approach to facilitate such performance comparisons would entail coding software according to published algorithms. However, this is not likely to replicate all details of the original software; thus, the comparisons obtained via such an approach would not be valid.
Amongst the 68 DIP relationships that Chilibot did not detect (table 2), the largest number represented a failure to recognize abstracts containing generalized protein names (e.g. PKA in PubMed abstract vs. type II-alpha form of PKA in DIP), a limitation also reported for FlyBase [22]. Recall was also limited by synonym coverage and by the presence of information in the main text, but not in the abstract. Since many of the DIP relationships were originally based on the main text of a single reference [2], the high recall of Chilibot depends on the redundancy of information in the literature.
To estimate precision, defined as the fraction of retrieved relationships that are relevant, we randomly selected 100 relationships from the 702 relationships recovered by Chilibot (86 interactive, 11 parallel, and 3 abstract co-occurrence). We manually confirmed that the documentation retrieved by Chilibot contained information about 96 of the targeted relationships, and the remaining four shared symbols with other genes. In the interactive category, directionality was correctly identified in 79.1% and inhibitory/stimulatory properties in 74.4%. The original data used to perform these analyses are available [see additional file 1 and 2].
User interface features
One of the key features of Chilibot is its capacity to link the relationships represented in the network map directly to their supporting documentation, usually as sentences containing both of the query terms. In addition, each node is linked to its synonym list and to a set of statements demonstrating the use of the term; these statements are selected from abstract texts by an algorithm favoring conclusive statements (see Methods). By providing the literature in a condensed and highlighted form, Chilibot facilitates the rapid comprehension of the relationships by the user.
Chilibot provides several options for customizing the query process and for viewing the identified relationships. Context specific searches restrict the analysis of relationships to a specific subject area, as defined by the user. Internet searches can also be customized (e.g. searching only documents in PDF format) by using Google WebAPI. Specific subsets of relationships contained in an overall relationship map can be reconfigured. For example, the user can customize the relationship map by requesting only those relationships with direct linkage to a specific node, or those that have a requisite number of supporting publications [see additional file 3 and 4 for examples].
Chilibot also identifies key index terms common to the relationship network. To do so, Chilibot uses Medical Subject Headings (MESH) [23], a controlled vocabulary that indexes the subjects of the documents developed by the National Library of Medicine. Chilibot ranks MESH keywords indexed in the literature that supports the relationship network. The ranking is determined by the frequencies of the keywords, as well as whether the keyword is a major or minor topic of the paper (see Methods). The top ranked keywords, reflecting the subject area(s) shared by the query terms, can serve as a guide for further reading and suggest new Chilibot queries.
Chilibot also has the capability of suggesting new hypotheses based on the retrieved network of relationships. Such hypotheses, originally described by Swanson et al. as "undiscovered public knowledge" [24], referred to the inference of an interaction between two items A and C, based on knowledge that A affects B and B affects C. This involves software that generates a large list of "B" terms from titles returned by PubMed queries. The user filters these terms, aided by the titles and abstracts. Variations of this method have been designed and tested by others [25,26]. Taking a similar approach, Chilibot scans the network of retrieved relationships to find pairs of nodes that have no documented relationship, but have connections to a common tertiary node(s). These pairs of nodes are classified as having a "hypothetical relationship". The networks that contain these "hypothetical relationships", including the tertiary node(s), are then provided to the user in graphical format, with links to their documentation.
To test the value of these "hypothetical relationships" in predicting the results of future research, we queried 22 genes known to be involved in long-term potentiation (LTP), an electrophysiological phenomenon closely associated with memory formation. Chilibot identified a direct relationship between LTP and all 22 genes, along with 194 inter relationships amongst the 22 genes. We then performed retrospective studies by limiting the search to literature published before the years 2000, 1995 and 1990 [additional file 5 contains all the original search results]. The LTP-related "hypothetical relationships" identified by Chilibot, using these date-limited reference sets, are listed in table 3. As an example, by 1990, the involvement of calcium calmodulin kinase type II (CaMKII) in the induction of LTP had been established [27]. It was also known that CaMKII phosphorylates synapsin I [28,29]. Based on these and similar relationships (see table 3) that were documented in the literature available by 1990, Chilibot predicted the involvement of synapsin I in LTP, which was subsequently demonstrated empirically by 1995 [30]. Retrospective analyses like these depend on the progression of specific knowledge in scientific fields during a particular time period. Thus, if we were to test a different set of search terms, we would not expect to obtain the same number of suggested hypotheses, nor would we expect the same proportion of such hypotheses to be validated by the current literature.
Based on the literature that is currently available, Chilibot identified new hypothetical relationships, such as those between synaptophysin/CREB and synaptotagmin/CREB. Currently no direct empirical evidence for these relationships is available. However, scanning the 5' untranslated region of the synaptophysin and synaptotagmin genes did show multiple CREB binding sites, providing bioinformatics-based evidence supporting the plausibility of these potential interactions. Although these examples are promising, they are hypothetical relationships. Further review of the scientific literature, such as the sentences provided by Chilibot, is required to clarify the rationale for these hypotheses.
Network topology of relationships retrieved from the literature
Recent large-scale studies of metabolic [15], transcriptomic [17] and proteomic [16,31] networks, based on analyses of experimental data, have found that their topologies belong within the class of scale-free networks.
For comparison to the preceding biological networks, we studied the connectivity of the literature-based networks obtained by applying Chilibot to three groups of randomly selected genes (300 genes per group). The resulting networks contain 224, 116, and 138 nodes and 3018, 962, and 1912 relationships, respectively. Visualization of the network structure of one of the groups is provided [see additional file 6]. The connectivity of the 3 groups was averaged and plotted in figure. 4, showing a power-law distribution. The relatively low value of n = 1.21 (n is approximately 2 in many of these networks [15,32,33]) may reflect the fact that many relationships are yet to be documented. In addition, we also found a positive correlation between the number of abstracts available per node and the number of connections to that node (R2 = 0.76, p < 0.001). This suggests that the discovery of biological relationships attributable to specific nodes might be influenced both by the amount of scientific effort deliberately devoted to understanding that node and the intrinsic connectivity of that node. Although the commitment of greater resources by the scientific community to certain nodes may bias the topology of the scientific literature to some extent, this is likely to be regulated and limited by the strength of the findings, which would be directly related to the intrinsic connectivity of a particular node. Thus, it is reasonable to postulate that the topology of the biomedical literature on gene/protein interactions may reflect that of the interactions per se.
The scale-free topology of gene/protein relationships provides another dimension for comparing and prioritizing research targets after large-scale experiments. Currently, genes or proteins with large-fold changes are generally favored for further study [34]. However, by itself, a large-fold change may be insufficient to predict whether such molecules are pivotal in the regulation of important biological processes. For example, in many biological signaling pathways, a small increase in up-stream events (such as the binding of a peptide or hormone to its receptor(s)) is usually associated with a hundred to thousand-fold increase in down-stream events [35,36] (e.g., activation of mitogen-activated protein kinases or the production of cAMP). Therefore, knowledge of a network's critical nodes (i.e. hubs), which may be predicted by network connectivity [32], is likely to increase the power and efficiency of identifying potential experimental targets capable of modifying network function.
Conclusion
Chilibot graphically summarizes the relationships amongst a large set of user provided terms by analyzing abstracts retrieved from the PubMed literature database. We have found in our benchmark tests that these retrieved relationships are reliable. We believe that the scientific community will benefit from this literature mining capability along with the many features that Chilibot provides, especially in an era of science when insight can be submerged in an overwhelming sea of data and modularized knowledge.
Methods
Constructing the nomenclature dictionary
Flat text file versions of the six databases (HUGO, LocusLink, OMIM, GDB, SwissProt, and SGD) were downloaded from their corresponding ftp sites. Symbol-name pairs were extracted from the corresponding fields using Perl scripts. Names were curated to remove words that are unlikely to be used in texts, such as "partial cDNA", "fragment", etc. In addition, non-alphanumerical characters were converted into spaces. Entries with the same symbol from the six databases were then combined in a case insensitive manner. The final dictionary is stored in the Postgresql relational database.
Optimization of PubMed querying method
The NCBI Eutilies, in particular Esearch and Efetch, are used in conjunction with the Perl LWP module to interact with the server. Optimization was necessary because phrase or adjacency searches are not supported by PubMed. Thus, when searching for names with multiple words, it is possible to retrieve abstracts that contain all the relevant words, however the words are used in different places of the abstract. Further, PubMed has an automatic term mapping feature that converts user input according to the MESH translation table. For our purposes, we considered this an undesirable feature. After small scale testing, the query structure we selected places a title and abstract restriction tag ([tiab]) after the name of the query term. This disables the term translation feature and also treats the term as a phrase when possible, according to PubMed documentation. To test the effectiveness of this strategy, we sampled 510 names with lengths ranging from 1 to 11 words. A total of 4584 abstracts were retrieved. We were able to find the query name from 4487 (97.9%) of the abstracts. We thus constructed the pair-wise PubMed query in the following format:
(Term 1 synonym 1 [tiab] OR Term 1 synonym 2 [tiab] OR ...) AND (Term 2 synonym 1 [tiab] OR Term 2 synonym 2 [tiab] OR ...)
Acronym disambiguity
Many methods [e.g. [37-40]] have been developed to translate acronyms unambiguously into their full length terminology, since acronyms may have multiple meanings and become a source of false positives [3,41]. Chilibot provides an option to verify the meaning of acronyms when they are used as the query term. When a relevant acronym first appears, Chilibot retains a phrase immediately preceding the acronym that contains the same number of words as the number of characters in the acronym. The phrase then is compared to all synonyms of the acronym, which are retrieved from the nomenclature database of Chilibot. The abstract is excluded from analysis if less than 30% of the words in the phrase are found in the synonym list.
Context sensitive search
All the context keywords provided by the user are combined with an "OR" operation. This string is then combined with the pair-wise PubMed queries, using an "AND" operation. The context keywords are not used in subsequent analyses.
Synopsis generation
A synopsis is a collection of sentences used to annotate the query terms. It is generated from the first 100 sentences that contain the specific query term or its synonyms. These sentences are sorted by a weighting mechanism that favors short, conclusive sentences. Words suggesting a conclusion, such as "suggest", "found", "show", "data" etc weights as +9 points. Starting the sentence with the query term and a verb weights as +5 points. The presence of words suggesting a negative result such as "not", "lack", "fail", "without" is weighted as -3 points. Having more than 30 words also reduces the weight by 3 points. Lastly, having keywords specified by the user adds 5 points to the weight. The 15 sentences with the highest weights are displayed.
Natural language processing
Title and abstract texts retrieved via the Efetch utility are first parsed into individual sentences using a Perl script. Only sentences containing both of the query terms or their synonyms are subjected to NLP analysis, which includes POS tagging by the TnT software [20] and shallow parsing by the CASS software [21]. Testing TnT on a small corpus of 10 PubMed abstracts (2646 words), using the supplied WSJ language model, showed 537 (20.29%) unknown words. Manual inspection identified 150 errors in the assigned POS tags. We then trained the TnT software with the GENIA corpus [42] (a collection of 2000 PubMed abstracts annotated with POS and other information). Re-analyzing the same 2646 words, using the customized language model, resulted in only 289 (10.92%) unknown words. Manual inspection identified 31 errors. Thus, the language model based on the GENIA corpus was used for all subsequent analyses. CASS software was used without further adjustment.
Classification of relationships
All sentences containing two query terms (or their synonyms) are classified into one of six categories: stimulatory (interactive), inhibitory (interactive), both stimulatory and inhibitory (interactive), neutral (interactive), parallel (non-interactive) and abstract co-occurrence only. Sentences are classified into interactive or non-interactive relationships based on the presence or absence of a verb phrase between the two query terms. The following exceptions apply: sentences are classified as parallel when the query terms are present in two separate clauses; sentences without a verb phrase between the query terms, but with specific terms indicating interactions such as "interaction", "bind", etc., are classified as interactive; interactive relationships are converted into parallel relationship when there is a negation (such as "not") within the same clause of the verb phrase. The interactive relationship is further classified into stimulatory, inhibitory, or neutral subtypes based on the presence or absence of words describing such relationships, including "activate", "facilitate", "increase", "induce", "stimulate", "enhance", "elevate", "inactivate", "abolish", "attenuate", "block", "decrease", "eliminate", "inhibit", "reduce", "suppress". For interactive relationships, the direction is defined as from the left query term to the right term and is reversed when passive voice is detected. To avoid the influence by spurious mistakes, the overall relationship between two terms is defined as interactive only when more than 20% of the sentences are detected as either stimulatory or inhibitory. Lastly, the co-occurrence type is assigned when the two query terms are located in the same abstract but not the same sentence. We ranked the informativeness of the relationships in the following order: both stimulatory and inhibitory, either stimulatory or inhibitory, neutral interactive, parallel, abstract co-occurrence. The overall relationship between two query terms is classified as the most informative type of relationship.
Visualization of the networks
Network layout is generated using the aiSee software. Each pair of query terms identified as having relationships is specified by nodes and represented by square boxes. The relationships are represented by solid lines. A special node with unique identification (an icon) is inserted into the middle of each line. The icon is either circular or rhomboidal depending on the relationship it represents (see legend of Figure 1). The network map as well as the links from the map to the descriptions of the relationships are obtained by calling the command line interface of aiSee.
"Hypothetical relationship" generation and testing
After the query session is finished, the user can request Chilibot to suggest hypothetical relationships for any node that is within the retrieved network. For each node requested (NR) by the user, Chilibot scans the retrieved network to find those nodes that are not directly linked to NR, but have connections to the same tertiary nodes as NR. Chilibot then produces a new network map for each of these "hypothetical relationships", while maintaining the links to the supporting documentation. To test the usefulness of these "hypothetical relationships" in predicting future research, a total of 22 terms (ACTIN, ACTININ, AMPA, ARC, ATF, CAMKII, CAMKIV, CREB, ERK, KV4.2, NMDA, PI-3K, PKA, PKC, PLC, SYNAPSIN I, SYNAPTOPHYSIN, SYNAPTOTAGMIN, TAU, TRKA, TRKB, AND ZIF268) were queried together with LTP (long-term potentiation). Retrospective studies were performed by querying these terms again while adding the PubMed date limiting tag "&mindate=1960&maxdate=$maxdate", where the $maxdate equals to 1990, 1995, 2000, respectively.
MESH themes
The MESH Keywords of the abstracts represented by the graph are collected and sorted by their weighted percentage. When the keyword is the major topic of the publication, it is weighted as 3. Otherwise, it is weighted as 1. The weights are then divided by the number of abstracts to obtain the weighted percentage.
Web search and content filtering
Google WebAPI is accessed through Perl scripts. Due to the limitation of the WebAPI, the query terms are searched directly without the expanded synonyms. The URIs of the top 10 hits were retrieved from Google and then the content of these pages was obtained from their individual servers. These pages are then converted into texts, and sentences containing either one of the query terms are presented to the user. Sentences containing both of the query terms are highlighted. Links are also provided to restrict the web search to educational institutions or to files in the portable document format (PDF). Google is a trademark of Google Technology, Inc.
Selection of relationships from the Database of Interacting Proteins (DIP)
DIP [2] is a curated protein interaction database. The version of DIP database released on April 18th, 2003 contains 18494 interactions between 7141 proteins. Relationships that originated from large scale genomic or proteomic studies were excluded, reflecting poor reliability of the data [43] and the low probability that such interactions would be described in textual forms. Proteins with no SwissProt annotation or of yeast origin were also excluded to further reduce the number of relationships to a manageable subset. This selection procedure resulted in a total of 770 relationships.
Authors' contributions
HC conceived of the project (together with BMS), coded the Chilibot program, performed the evaluations and drafted the manuscript. BMS conceived of the project (together with HC), participated in its design, coordination and analysis, and edited and revised the manuscript.
Note
All none-graphic files are archived with tar and compressed with bzip2 to reduce file size.
Supplementary Material
Additional File 1
A total of 770 known relationships were used to test the recall and precision of Chilibot. A maximum of 5, 10, 20, 30, 40, or 50 most recent PubMed records for each relationship was specified for analysis. The relationships identified by Chilibot are summarized and provided in Microsoft Excel and OpenOffice format.
Click here for file
Additional File 2
The original results of the above study (non-essential files are deleted to keep the file size under the limit set by BMC bioinformatics).
Click here for file
Additional File 3
Sub-network graph obtained by filtering figure 1 using the number of supporting publications as a threshold criterion.
Click here for file
Additional File 4
Sub-network graph obtained by filtering figure 1 to selectively display a node of interest (i.e. "cocaine") and other nodes that directly connected to it.
Click here for file
Additional File 5
The original Chilibot query results of the term "long-term potentiation (LTP)" and 22 other terms, limiting the latest references analyzed to the years 1990, 1995, 2000, and 2004.
Click here for file
Additional File 6
A graph demonstrating the scale-free topology of relationship networks derived from the biological literature. The network contains 138 nodes and 1912 relationships. A small fraction of the nodes (10 nodes colored in black) accounted for more than 45% of the relationships (solid lines), a characteristic of scale-free topology.
Click here for file
Acknowledgments
This research was supported by PHS DA-03977 (BMS) and by the University of Tennessee Center for the Neurobiology of Brain Disease.
Figures and Tables
Figure 1 The network map of a biological network constructed by Chilibot. Chilibot queried the entire PubMed abstract database to identify a network of relationships amongst a set of genes reported to be regulated by cocaine [44], a biological concept ("plasticity"), and a drug ("cocaine"). Lines connecting rectangular nodes indicate relationships between the genes shown, and each icon in the middle of a line represents the character of the relationship. Interactive relationships (circles) are neutral (gray), stimulatory (green), inhibitory (red) or both stimulatory/inhibitory (yellow). The number within each icon indicates the quantity of abstracts retrieved for documenting that relationship. Icons containing the plus sign ("+") represent "parallel relationships". Gray rhomboidal icons indicate that only co-occurrence was detected. All arrowheads indicate the direction of the interaction, and some are bi-directional. The green or pink colors of rectangular nodes represent up- or down-regulation of the genes identified therein, respectively, based on experimental data provided by the user. More saturated colors are associated with larger changes. Nodes with no expression values (e.g., "cocaine") are in cyan. The terms and icons are linked to documentation when viewed in a web-browser. See supplementary information for subnetwork maps generated by Chilibot.
Figure 2 Distribution of the number of synonyms. A synonym dictionary of gene symbols was compiled from 6 databases with a total of 113,503 unique symbols. Analysis of the number of synonyms for each symbol shows that 62,178 (54.8%) had more than one.
Figure 3 Effects of the number of abstracts obtained on retrieval, recall, and content of relationships. To measure Chilibot's level of recall, a total of 770 known relationships specified in the Database of Interacting Proteins (DIP) was used as a reference set. A. Distribution of the number of sentences describing relationships when a maximum of 5–50 abstracts were selected for retrieval. For each group, the average number of sentences documenting a relationship is reported. Of the 770 known relationships, the histograms show that an increasing number of relationships are documented by a larger number of sentences when a greater number of abstracts are specified for retrieval. B. Increasing the specified number of abstracts for retrieval from 5 to 50 had no affect on the recall of total relationships, although there were changes within relationship categories (e.g., stimulatory/inhibitory).
Figure 4 Scale-free topology of a relationship network derived from the biological literature. Chilibot was used to retrieve the relationships within 3 sets of randomly selected genes (300 genes per group). The resulting networks contain 224, 116, and 138 nodes and 3018, 962, and 1912 relationships, respectively. The distribution of the average connectivity of the 3 groups follows the power-law (P(k) ~k-n, n = 1.21).
Table 1 Chilibot dictionary of gene/protein synonyms.
Database Number of gene symbols collected
SwissProt 84462
LocusLink 23924
GDB 15770
HUGO 15905
OMIM 8291
SGD 4325
Flat text file versions of the six databases were downloaded from their corresponding ftp sites. Synonym pairs were extracted from the corresponding fields and entries with the same symbol from the six databases were then combined in a case insensitive manner. (HUGO: Human Genome Organization; OMIM: Online Mendelian Inheritance in Man; SGD: Saccharomyces Genome Database)
Table 2 Failure to detect known DIP relationships
Reason Undetected relationships (%)
member of protein family (name generalization) 30.8
incomplete synonym list 26.5
no reference at abstract level 22.1
other 20.6
Chilibot was used to retrieve information about 770 pairs of known protein interactions obtained from the Database of Interacting Proteins (DIP). A total of 702 relationships were found (recall = 91.2%). Relationships were undetectable (n = 68) for the following reasons: 21 (30.8%) occurred when a specific member of the protein family (e.g. cdc25a) was recorded in DIP, yet only the general family name (e.g. cdc25) appeared in abstracts; 18 (26.5%) were due to synonyms present in abstracts and not in Chilibot's dictionary of nomenclature; 15 (22.1%) were caused by lack of documentation of the relationships in PubMed abstracts. Miscellaneous reasons accounted for the remainder (20.6%).
Table 3 Retrospective study of the predictive capability of the "hypothetical relationships" generated by Chilibot
Term 1 Tertiary nodes Term 2 References Analyzed Relationship Documented
LTP PI-3K PKA CAMKII ACTIN ERK TAU PKC AMPA KV4.2 1960~2000 2001~2004
LTP PKA ACTIN SYNAPTOPHYSIN PKC NMDA TAU AMPA PLC ERK 1960~1995 1996~2000
LTP PKA ACTIN NMDA TAU AMPA PLC ARC 1960~1995 1996~2000
LTP PKA ACTIN CREB PKC PLC PI-3K 1960~1995 1996~2000
LTP PKA ACTIN SYNAPTOPHYSIN PKC TAU ACTININ 1960~1995 1996~2000
LTP PKA CAMKII CREB PKC TAU ATF 1960~1995 1996~2000
LTP ZIF268 PKC NMDA PLC TRKB 1960~1995 1996~2000
LTP PKA CAMKII CREB CAMKIV 1960~1995 1996~2000
LTP ZIF268 PLC TRKA 1960~1995 1996~2000
LTP NMDA TAU PLC ACTIN ARC 1960~1990 1996~2000
LTP PKC TAU CAMKII ACTIN SYNAPSIN I 1960~1990 1991~1995
LTP PKC NMDA TAU ACTIN PKA 1960~1990 1991~1995
LTP TAU ACTIN SYNAPTOPHYSIN 1960~1990 1991~1995
LTP TAU ACTIN ACTININ 1960~1990 1996~2000
LTP ACTIN ZIF268 1960~1990 1991~1995
A "hypothetical relationship" is defined when two terms have no documented relationship, but share connections to the same tertiary node(s). To test the value of these relationships in predicting the findings of future research, 22 terms (i.e., term 2; see methods) known to be involved in long-term potentiation (LTP) (i.e. term 1) were queried by Chilibot, limiting the latest references analyzed to the years 1990, 1995, 2000, and 2004. The "hypothetical relationships" (i.e. term 1 is related to term 2) and the time periods when these hypothetical relationships were suggested and documented are listed. The original results of these searches including links to the PubMed abstracts are available [see additional file 5].
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| 15473905 | PMC528731 | CC BY | 2021-01-04 16:35:47 | no | BMC Bioinformatics. 2004 Oct 8; 5:147 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-147 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1561549407110.1186/1471-2105-5-156Methodology ArticleModeling of cell signaling pathways in macrophages by semantic networks Hsing Michael [email protected] Joel L [email protected] Conor [email protected] Artem [email protected] CIHR/MSFHR Strategic Training Program in Bioinformatics, Genetics Graduate Program, Faculty of Graduate Studies, University of British Columbia, Vancouver, British Columbia, V5Z 3J5, Canada2 Upstream Biosciences, Inc., Vancouver, British Columbia, V6H 1H2, Canada3 Visual Knowledge, Inc., Vancouver, British Columbia, V6H 1H2, Canada4 Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, V5Z 3J5, Canada2004 19 10 2004 5 156 156 13 4 2004 19 10 2004 Copyright © 2004 Hsing et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.
Results
We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages.
Conclusions
We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.
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Background
Interactions among genes, gene products and small molecules regulate all cellular processes involving cell survival, cell proliferation, and cell differentiation among others. Such interactions are organized into complex lattice structures conventionally divided into cell signaling, metabolic and gene regulatory networks in a cell [1]. In recent years, large amounts of information and knowledge on cell signaling networks have been accumulated in the literature and databases [2,3].
Conventionally, this information is highly compartmental: various individual signaling pathways are mostly stored in separated and non-linked diagrams. Traditional pathway diagrams, where molecules are represented as nodes and their interactions are depicted as lines and arrows have significant limitations as they lack spatial and temporal context [4]. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Not surprisingly, the current state of pathway representation does not allow of complex investigation of qualitative or quantitative changes in cell signaling networks in response to external perturbations such as bacterial infections. Thus, an adequate computational environment for modeling cell signaling networks is needed for proper biological data integration as well as for simulation and prediction of cellular behaviors [5].
Recently, many models have been proposed for representing, storing and retrieving interactions among various biological entities. BIND [6] and IntAct [7] focus on protein-protein interactions and their resulting complexes. BioCyc [8] developed models for metabolic events and curated metabolic pathways from many organisms. The model developed by aMAZE [9] combines interactions in cell-signaling, metabolic and gene regulatory pathways. In addition, the System Biology Markup Language (SBML) has been developed for representing biochemical reaction networks and for communicating models used for various simulation programs [10]. Programs such as E-cell [11], Gepasi 3 [12] and Virtual Cell [13] use differential equations to represent molecular interactions, and their simulation results are obtained by solving these questions numerically [14]. It should be noted, however, that many cellular processes are sensitive to the stochastic behavior of a small number of molecules, and therefore, the assumptions in differential-equation methods can often be compromised [15]. Several studies have attempted to address the stochastic property of a cell. Vasudeva and Bhalla [16] proposed a hybrid simulation method that combined both deterministic and stochastic calculations. In addition, a stochastic simulator, StochSim [15] represented molecules as individual software objects that interact according to probabilities. Thus, it is feasible to suggest that useful cell signaling simulators should be capable of representing each molecule individually and reflecting the stochastic behavior of molecular interactions in a cell.
Semantic networks
Recently an artificial intelligence approach known as semantic networks (SN) have gained the attention of the biological community as a potentially powerful tool for organizing and integrating large amounts of biological information [17]. For instance, the semantic network in the Unified Medical Language System (UMLS) was designed to retrieve and integrate biomedical information from various resources [18]. The UMLS semantic network has also been applied and expanded to include information and knowledge from other domains such as genomics [19]. In addition, other studies have suggested a semantic approach where proteins are viewed as "adaptive and logical agents", whose properties and behaviors are affected by other agents in their spatial organization including intracellular compartments and protein complexes [20,21]. Defining the semantics among agents could characterize both local and global behaviors of a system, and therefore, it is potentially useful to apply such approach to study cell signalling in biological systems [21].
A semantic network is a method to represent information or knowledge by nodes and edges in a graphic form, where a node represents a concept and an edge represents a relationship [22]. A semantic network, which can exist abstractly in a human mind or be implemented by applying computer technology, can model many real-world problems [22]. Figure 1 illustrates a semantic network, where a concept such as a protein, a chemical reaction or a subcellular location is modeled by a semantic agent, and its relationships with other agents are represented as arrows. A proper semantic network implementation allows the identity and properties of an agent to arise from its relationships with other agents, not from descriptions or labels [23]. Hence, within a semantic network "things are what they do".
Previously an application development environment known as Visual Knowledge (VK) has been created, and VK is capable of different formalizations and implementations of semantic networks for various knowledge domains [23]. Visual Knowledge has been applied successfully to model and manipulate complex "interactomes", including corporate enterprise systems, flight scheduling networks, hardware maintenance simulators, and integrated currency exchange boards [23]. It has been anticipated that Visual Knowledge can address many of the current limitations on modeling cell signaling pathways. Using the latest VK-based environment, BioCAD [24], specifically designed for biological applications, we have developed a semantic model for cell signaling pathways occurring in human macrophages.
Bacterial invasions in macrophages
It is the current knowledge that many pathogenic bacteria are capable of entering and surviving within mammalian macrophages by modulating the host signaling pathways [25]. One well-studied example is the activation of the Fcγ macrophage receptor by the IgG antibody, which binds to the surface of bacteria such as Mycobacterium tuberculosis [26]. Activation of the Fcγ receptor induces phagocytosis of M. tuberculosis and the formation of a phagosome within the macrophage. These processes are mediated by the class I phosphoinositide 3-kinase (PI3K) – one of the most well-characterized enzymes to date [27]. The class I PI3K is a heterodimer composed a p110 catalytic subunit and a p85 regulatory subunit, which maintains a low-level activity of p110 [28]. The p110 subunit is activated when p85 binds at a phosphotyrosine site on a receptor or an adaptor protein, or by direct binding to activated Ras [29]. Activated PI3K-p110 phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) into phospatidylinositol-3,4,5-trisphosphate (PIP3), which is an essential signaling molecule that stimulates many downstream proteins, including PDK1 and Akt [30]. The formation of a phagosome is normally followed by the phagosome maturation process, which is responsible for intracellular killing of bacteria and is regulated by the class III PI3K [31]. However, it has been hypothesized that phosphatidylinositol analogs, such as ManLAM, produced by M. tuberculosis can inhibit the activity of the class III PI3K, arresting phagosome maturation process, and ensuring the survival of M. tuberculosis inside the macrophage [27,32].
In addition to their role in phagocytosis, PI3Ks are essential proteins that regulate cell survival, cell growth, cell cycle and other cellular processes [33]. Although, it is clear that PI3Ks play an important role in bacterial invasions, the knowledge of PI3K-mediated interactions is scattered in a number of literature and pathway databases. A coherent picture of detailed molecular interactions that link receptors to PI3Ks and to various cellular responses has yet to be constructed before bacterial invasions can be fully understood. To address this goal, a cell signaling network of the human macrophage was reconstructed with the semantic model, and qualitative changes in the network were investigated with a SN-simulator.
Results
A semantic model for cell signaling pathways
In the paper, the word "model" refers to a set of rules in two different but related contexts. In the context of the semantic network, the model refers to a set of rules that specify how a biological concept is mapped to one or multiple semantic agents/relationships. In the context of cell signaling pathways, the model is a set of rules that specify what, how, and when molecules interact with each other. The model has been formalized and implemented, using BioCAD software system, and it is presented in the following sections.
Overall classification of biological structures and their relationships
Within the semantic network, all biological structures are modeled by semantic agents that are members in one of the 6 different prototypes. Table 1 shows the 6 types of structures in the order of their hierarchy. From the highest to the lowest level, they are "Cell", "Subcellular Compartment", "Macromolecule", "Domain/Site", "Small Molecule/Molecular Fragment", and "Atom". A structure agent can be composed of multiple structures of the same prototype or a lower-level prototype, and the agent is connected to its components by the composition relationship in the SN. Thus, a human macrophage has been modeled as a semantic agent of the "Cell" prototype, and it was composed of various "Subcellular Compartment" agents, including plasma membrane, cytosol, nucleus and others. In addition, each compartment such as nucleus contained various agents of the "Macromolecule" prototype including proteins, DNA and RNA. A macromolecule such as a protein was further composed of "Domain/Site" agents like catalytic domains and phosphorylation sites, and a DNA was composed of sites such as promoters and gene regulatory sites.
Modeling interactions among biological structures
To create an adequate semantic model, we have postulated that structures of different levels in the cellular hierarchy can interact with one another. One example of such interactions is the movement of a molecule from one subcellular compartment (e.g. cytosol) to another (e.g. plasma membrane). This is referred to as a translocation event, and it is demonstrated on the left panel of Figure 2.
Table 2 shows that translocations have been modeled as one the five major "event" prototypes in the SN. Every translocation event has been connected to three structure agents: a molecule to be moved (macromolecule or small molecule), an original location (subcellular compartment), and a destination (subcellular compartment). Hence, the construction of translocation events has enabled us to confine all possible movements of molecules in a cell.
Interactions that occur by non-covalent or covalent forces have also been modeled as two distinct "event" prototypes as shown in Table 2. The right panel of Figure 2 illustrates a general case of a molecular interaction between a protein A and a protein B occurring via non-covalent forces. Such interaction can cause changes of the forms and functions of the interacting molecules, and these changes have been accommodated within the developed SN model by specifying two distinct types of states: "conformational states" and "binding states", also represented by semantic agents.
All hypothetical spatial changes occurring in the three-dimensional structure of a given macromolecule have been modeled within the SN as switches in the corresponding conformational states, and the changes do not lead to the creation of new semantic agents. Domains or sites for every protein encoded into the SN model have been assigned to either "Functional" or "Non-functional" conformational states. The "Functional" state represents that a domain/site is currently in a conformation that enables a certain interaction. On the other hand, a "Non-functional" state implies a domain/site is in a conformation that prevents an interaction. To illustrate this construct we have graphed the semantic agents and their relationships created within the developed SN. It should be noted that within the SN, all semantic agents are visualized as icons, and their relationships are depicted as connecting arrows. In addition, all agents are related by pairs of reciprocal relationships, and for simplicity, only one direction of each pair of the relationships was visualized. The left panel of Figure 3 features a p110 subunit of the class I PI3K that has been modeled as a "macromolecule" agent and contains a binging site for a Ras protein and a catalytic domain. The Ras binding site has been assigned a state of "Functional", depicted as a check symbol (square) on Figure 3. The "Functional" state enables the PI3K-p110 to bind to a Ras protein. On the other hand, the catalytic domain is "Non-functional', depicted as a cross symbol. Figure 8 shows the description of icons used in this paper.
In addition to the conformational states, a protein domain or site has been assigned one of the two binding states: "Bound" or "Not-bound". A "Bound" state implies that this domain/site currently associates with a domain/site of another molecule through a non-covalent interaction. On the other hand, a "Not Bound" state indicates such an association does not exist. Since ligand bindings can affect the conformation of a macromolecule through allosteric regulations, two types of such regulations have been implemented within the SN. A positive allosteric regulation event has been assigned to the scenario when a "Bound" binding state of a domain/site causes the conformational state of another domain/site to switch to "Functional". The right panel of Figure 3 shows that when the PI3K-p110 has bound to a Ras by a non-covalent interaction, the binding state of the Ras-binding site on p110 has switched to "Bound". As a result, the conformational state of the catalytic domain has switched to "Functional" due to a positive allosteric regulation. The "Functional" catalytic domain now enables the PI3K-p110 to phosphorylate its substrate. On the other hand, a negative allosteric regulation event has been attributed to those cases when a "Bound" state of a domain/site causes the conformational state of another domain/site to switch to "Non-functional". It should be noted that the semantic model stores the information that specifies the non-covalent event between the prototypic Ras and the prototypic PI3K-p110, and the condition for the event to occur. Figure 3 illustrates an instance of the Ras-binding event occurred during a simulation. The PI3K-p110 is an instance of the PI3K-p110 prototype, and it is the same agent before and after it binds to the Ras.
A more complex allosteric regulation event can be specified for mapping the binding states of multiple domains/sites (the condition or the input) to the conformational states of multiple domains/sites (the response or the output). Hence, a domain is switched to "functional" only if a certain combination of ligand bindings has occurred. The utilization of the states on domains/sites and allosteric regulation events in the SN has enabled us to express the cause-effect relationships among various signaling events explicitly.
In the developed semantic model, any molecular complex formed due to non-covalent interaction has been treated as a transient state of these two molecules, and a complex was not represented by a new semantic agent. Instead, the existence of a protein complex is inferred from the non-covalent interaction event. Thus, Figure 3 illustrates a protein complex of the PI3K-p110 and the Ras existed because of the occurrence of the non-covalent interaction event, which connected the two molecules.
Conventionally, there is often some inconsistency between representing chemical modifications of small molecules in metabolic pathways and modifications of proteins in signaling pathways. In the developed model, two molecules that interacted by covalent forces have resulted the creation of distinct semantic agents within the SN. This rule has been implemented consistently for both macromolecules such as proteins and small molecules such as ATP. As one example, Figure 4 features the phosphorylation of an Akt protein by an enzyme PDK1, yielding a distinct Akt-phosphate (Akt-P) agent and a free ADP. Within the SN, the Akt and the ATP are related to a covalent interaction event by "Substrate" relationships, depicted as arrows. In addition, the Akt-P and the ADP are related to the event by "Product" relationships, while the PDK1 is related by the "Enzyme" relationship. The PDK1 enzyme in this example contains a catalytic domain (not shown on the figure), which must be "functional" for the reaction to occur. The state of this domain is under the regulation of the binding of a ligand and an allosteric event as previously defined. In addition, new properties can be assigned to the modified protein. In this case, the phosphorylation by PDK1 switched the catalytic domain in Akt-P to "functional", while this domain was "non-functional" in Akt, the dephosphorylated form. Figure 4 illustrates that a covalent interaction event also applies to metabolites, and a metabolite such as glucose is phosphorylated into a glucose-6-phosphate by an enzyme Hexokinase. Other types of modifications including methylation, acetylation and glycosylation can also be modeled in a similar manner but involve different substrate types.
In the semantic model, a molecule can participate in different sets of interactions in different locations. The translocation events define all possible localizations of molecules, and therefore, an interaction can only occur if the participating molecules can be present in the same location at the same time. Alternatively, an interaction (non-covalent or covalent) can directly associate with a subcellular compartment, and this interaction is only available to molecules in that location.
In addition, all qualitative cellular responses such as cell survival and phagosome formation have been implemented within the SN under a distinct "event" prototype. They have been implemented in a way that the formation or the activation of certain signaling molecules such as PIP3 can trigger the occurrence of these cellular response events in a simulation.
As it has been mentioned previously, the behavior of any semantic agent can be clearly defined by its relationships or connections to other agents. Thus, the formalization of the five types of events, which are translocations, non-covalent interactions, covalent interactions, allosteric regulations and cellular responses, has enabled us to model the behaviors of molecules depicted in conventional pathways and to reconstruct a cell signaling network of the human macrophage.
Case study: a reconstruction of a cell signaling network in the macrophage
Data source
The molecular composition of human macrophages and information of known intracellular interactions have been extracted from various research articles [26,27,32,34-47], review articles [25,28-31,33,48-52] and pathway databases [2,3].
Translation and integration of pathway information into the semantic model
A pathway diagram in the literature or an electronic database, in principle, represents some scenario of what may happen in a call if every depicted molecule is expressed in the correct location, at the correct time and with the correct states. Hence, the aggregation of multiple pathway diagrams describes some, if not all, possible molecular events that can potentially occur in a cell under the right conditions. To utilize such information and build a cell signaling network, we have decomposed conventional pathways into individual pieces of information such as subcellular localization of a protein, a pairwise protein binding, a chemical reaction or a cellular response. Then, using the sets of semantic rules described in the model, we have represented and integrated each piece of those information in the form of semantic agents and their relationships. Table 3 illustrates the overall SN model for the cell signaling network contained a total of 93 prototypical macromolecules localized in several subcellular compartments. It included several cell receptors (such as Fcγ, CR3, CD 14, CD18, TLR2) relevant to the process of bacterial internalization of macrophages. Two distinct classes of PI3Ks have been modeled: the class I PI3K composed of p85 regulatory and p110 catalytic subunits, and the class III PI3K composed of p150 and Vps34p subunits [28]. The model also included various kinases such as Lyn, PDK1 and Akt, small GTPases including Ras, Rac1 and Rab5, and adaptor proteins like Gab2. Events of various prototypes have also been extrapolated from the literature and pathway diagrams.
Visualization and analysis of the cell signaling network
The defined semantic agents have been connected in the semantic network and can be visualized at different levels. Figure 5 shows one example of how various non-covalent and covalent interactions have been integrated into a unified cell signaling network. The longest path in the cell signaling network we have created contained 24 consecutive molecular interaction events, linking Fcγ receptor to the class I PI3K enzyme and further through class III PI3K to various cellular responses.
Such detailed semantic reconstruction of the cell signaling network has allowed thorough investigation of biochemical relationships between essential proteins. One such example is presented on Figure 5 featuring the connections among cell receptors Fcγ and CR3, and tyrosine kinase Lyn which they both activate. It has also been reconstructed by the SN model that both of these receptors can activate the class I PI3K via an adaptor protein, Gab2. The corresponding finding will now be subjected to testing in an experimental lab.
Another example of successful SN reconstruction is the relationship between CD14 macrophage receptor and the class I PI3K; such a relationship was previously suspected but not clear [39]. By incorporating the available literature data [35,45] into the semantic environment we were able to reconstruct the scenario where CD14 activates the class I PI3K by the association of Toll-like receptor 2 (TLR2), as it is illustrated in Figure 5. Such model will also be tested experimentally.
Simulation of changes in the cell signaling network during bacterial invasions
In the implemented semantic model, the "possible" behaviors of a molecule are defined through its relationships to other agents (for example a non-covalent event), and all instances of that prototypical molecule will inherit the same behaviors. However, the action of a molecule at any given time is affected by factors including its current states and its current location with respect to other molecules in the system. Hence, we have built an application that enabled us to produce instances of molecules in various locations and to observe the "current" action of a molecule qualitatively under different biological scenarios. We refer such scenario-play as simulation in this paper.
The application or the SN-simulator allows the molecules to move among various locations, to interact with each other and to create events when the conditions are met. In addition, every instance of a molecule has been represented as an individual agent while every instance of a molecular interaction has also been implemented as an individual event agent. Thus, the simulator provides a traceable "trajectory" of all the events that have happened on every molecule during a simulation.
As illustrated in Figure 6, the macrophage cell has been generally divided into four subcellular compartments or locations within the simulator. We have specified what molecules to be present initially in each subcellular location in the beginning of a simulation, and the simulator synthesized molecules in each location accordingly. At the very first simulation step, the simulator has created a translocation event moving a molecule (the current target) from one location to another. The initial translocation has been specified as the movement of an IgG molecule from the extracellular space to the plasma membrane as shown in the pathway-viewer on Figure 6. The occurrence of this initial event allowed the simulator to trigger a search and advanced to the next step. The search looked for other potential molecules (with the correct states) that can interact with the target molecule in the same location. If multiple instances of potentially interacting molecules were present in that location, a single molecule would be randomly selected to interact with the target.
Because an Fcγ receptor was the only interacting molecule (for the IgG) present at plasma membrane in the simulation, it has bound to the IgG by a non-covalent interaction event, as illustrated in Figure 7. This non-covalent interaction has switched the state of the Fcγ receptor's binding site for a Lyn kinase to "Functional", and thus it enabled the Fcγ receptor to bind to a Lyn. However, the Lyn was not initially present in plasma membrane, but it was localized in cytosol in the beginning of the simulation, as shown in Figure 6. Thus, when the Lyn has been translocated from the cytosol to the plasma membrane, a non-covalent interaction between the Lyn and the "Functional" Fcγ receptor occurred in the following step as shown in Figure 7. The search was iterated and the simulation continued until all interacting molecules have been depleted in the macrophage.
Figure 7 demonstrates the consecutive events in this simulation scenario where the Lyn protein phosphorylated a Gab2, which then bound to a class I PI3K. When activated, the PI3K phosphorylated a PIP2 into a PIP3, which in turn caused a phagosome formation response. Different setups of the initial localization of molecules have affected the outcome of the simulation. For instance, an initial presence of a Rab5 (a downstream protein of the PIP3) and a class III PI3K in the cytosol extended the previous pathway from the PIP3. This localization setup stimulated a PIP3-mediated activation of the class III PI3K, which led to phagosome maturation response in the simulation. However, if a phosphatidylinositol analog, ManLAM, of M. tuberculosis was initially present in the plasma membrane, it would inhibit the class III PI3K and thus arrest the phagosome maturation response in the macrophage. Table 4 shows that the activation of PI3Ks-mediated pathways by M. tuberculosis has caused several known cellular responses as well as additional responses such as cell survival of the macrophage, cell cycle entry, increase of protein synthesis and increase of intracellular glucose level in the simulation. We suspect that some of these responses have not yet be appreciated in previous studies of bacterial invasions. Further experiments can be formalized to test the simulation results.
In this study, the SN-simulator has enabled us to "play" different scenarios and observed their effects in the macrophage. It allowed us to investigate how changes on one molecule caused changes of another molecule in the cell signaling network during bacterial invasions.
Discussion
Features of the semantic model
In the present work we have developed a semantic model to represent the properties and the behaviors of molecules and their interactions in the context of cell signaling pathways. The proposed model offers some additional features, compared to other existing pathway models. Those features are essential for characterizing the complex behaviors of biological entities, and they include:
Specify the spatial organization of molecules
The semantic model has specified the hierarchical relationships among the different biological structures, from cells to compartments, molecules and domains/sites. The hierarchy between subcellular compartments and molecules has allowed us to specify the spatial organization of molecules, model the translocation events and represent the effects of locations on the different interactions among molecules.
Model proteins as integrating and logical devices
The hierarchy between molecules and their domains/sites has enabled us to explicitly model the relationship between forms and functions for proteins. Through the allosteric regulation events, proteins have been modeled and implemented as integrating and logical devices in the semantic network, and their conformational states (outputs) are switched by the combination of non-covalent ligand bindings or covalent modifications (inputs).
Provide a direct communication from models to simulations
Through the prototyping system in the semantic network, any rule or interaction specified on a prototypical molecule automatically define the properties and behaviors of all its instances. As demonstrated by the simulator, the semantic network provided a direct communication from the interaction model to an application where the actions of molecules can be observed under different scenarios. Therefore, the semantic network is dynamic as a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.
Reduce the need for labels
In addition, the current semantic model is different from the previous models in BioCAD. An essential difference is the representation of functional labels or roles on proteins. The meanings of functional descriptions or association words such as "enzyme", "activator/activates" or "inhibitor/inhibits", which are often used to characterize the behaviors of proteins in most pathway models, have been represented explicitly through events and relationships in the developed semantic network. For example, a protein acts as an "enzyme" if 1) the protein participates in a "covalent interaction event", 2) the presence of a "functional" catalytic domain on the protein is required for the occurrence of the event, and 3) the protein itself is not modified after the event. Similarly, a protein A "activates" a protein B if a non-covalent binding event from protein A turns on the "functional" state of a domain/site on protein B. Hence, the model has reduced the need for labels, which are often confusing or misleading on conventional pathway representation.
Future directions
The use of non-covalent and covalent events has enabled us to model protein-protein interactions and chemical modifications on molecules including proteins and metabolites. The next challenge is to model the complex interactions that govern gene regulations. The current construction of non-covalent interaction events can model the binding of an individual transcription factor to a particular site of a gene, and the covalent interaction event can represent the transcription process that leads to the production of an mRNA, and the translation process that produces a protein. However, a successful transcription in a eukaryotic cell requires the formation of a protein complex that involves more than one hundred subunits, and the complex may be assembled in various orders [53]. We anticipate the improvement of the current allosteric regulation model to characterize the more complex logic in gene regulation.
The semantic network representation can be exploited for performing analysis of cell signaling pathways. The examples of Fcγ receptor, CR and the class I PI3K demonstrated that connections can be queried and analyzed among different biological entities. The semantic model is also compatible with other pathway models. Therefore, the number of biological entities and interactions in the semantic network can be greatly increased as pathway data from existing databases is integrated. Previous study has shown the value of combining gene expression profiles with protein-protein interaction networks for identifying active subnetworks [54]. Similarly, data from gene and protein expression experiments could be integrated with the semantic network for "pathway filtering". For instance, within a particular cell, there could be multiple paths that connect two proteins, while each path consists of different number of nodes. When the cell receives a signal, the shortest path, the one with the least number of nodes that require activation, is more likely to be "walked" than a longer path. Hence, the gene/protein expression data will provide some estimation of an overall protein expression and activation states to identify "active" pathways in a cell under a given condition
In this study, the proposed semantic model has been applied to cell signaling pathways in the macrophage as a case study. The model is not limited to those pathways. The hierarchical classification of the biological structures and the events can model other cell signaling pathways for different cells and organisms. An interactive website is currently under development. We anticipate that through the web, researchers can utilize the semantic network approach for creating pathways in cells of their interest and for analyzing any existing pathways including the PI3K pathways of the human macrophage presented in the paper.
The current capability and applicability of the SN simulator
In this study, we have developed a simple simulator to demonstrate the dynamics of the semantic network and to observe the actions of molecules qualitatively. In order to perform a realistic cellular simulation in the future, three components need to be improved. First, quantitative factors should be integrated into the model. For example binding affinity, directly associated with non-covalent events, will affect the probability and the duration of the binding of molecules. Reaction kinetics, associated with covalent events, will determine the rate of production.
Second, the two parameters, the population of molecules and their localization, which influence the simulation outcome, could be initialized and supported by experimental data. For instance, gene expression data from microarrays supports the relative abundance of transcripts, and protein expression data supports the relative abundance of proteins. Computer algorithms such as PSORT [55] can assist in predicting the localization of proteins.
Third, the proximity of molecules has been represented by subcellular compartments in the simulation. This approximation can be improved in two different ways. First, a compartment can be further divided into smaller sub-locations. Increasing the number of locations and reducing the size of each location will improve the accuracy of the simulation. Second, the occurrence of non-covalent events in the simulation has allowed us to identify molecular complexes and their members effectively. Hence, the proximity can be approximated through molecular complexes, such that molecules in a complex have higher probability to interact with members of the same complex.
The simulator has demonstrated that a biological pathway can emerge from the creation of semantic agents and their relationships in the SN, and such a pathway represents a series of consecutive events resulting from the activation of a single molecule. It is anticipated that further development will improve our ability to track and visualize different instances of molecules participated in multiple pathways. Hence, the occurrence of a cellular response event can be triggered by the accumulation of certain molecular species with particular states.
Conclusions
We concluded from our results that the semantic network is an effective method to model cell signaling pathways. Utilizing the semantic agents and the relationships in the model, information on biological structures and their interactions at different levels has been properly represented and integrated in the hierarchical and spatial context. The reconstruction of the cell signaling network in the macrophage has allowed qualitative investigation of connections among various essential molecules and reflected the cause-effect relationships among the events. The simulation demonstrated the dynamics of the semantic network, where actions of molecules are affected by their current states and locations, and the history of events can be traced and analyzed. In addition, changes caused by the invading M. tuberculosis in the macrophage were investigated by the simulator. As a result, the simulation identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions.
Methods
The Visual Knowledge environment
Visual Knowledge (VK) is an application development environment, and its implementation has been influenced by the theory of semantic networks as well as other approaches including set theory, frame system, object-oriented modeling theory and systems based on networks of active software agents [23]. Different from other passive knowledge representation technology, VK is dynamic and scalable, and it is capable of active representation and integration of different domain knowledge. By manipulating a number of fundamental classes of semantic agents like "physical thing", "event" and "trigger", models of various complexity can be constructed with VK. In addition, VK allows the creation of "prototypes" within each basic class of agents, and therefore it enables any classification of agents based on their common characteristics and behaviors.
The BioCAD software
BioCAD, a Visual Knowledge-based development environment, is developed by Upstream Biosciences, Inc. and customized to model biological systems [24]. The BioCAD software provides tools for managing large-scale biological data and for visualizing and editing biological pathways and networks. BioCAD currently contains millions of biological concepts and hundreds of pathways that have been integrated and curated from publicly available data sources. A locally installed client program allows semantic agents to be created, stored and queried from a remote central server. The BioCAD software is available commercially, and a collaborative modeling server will be publicly accessible soon.
Authors' contributions
The semantic model was developed jointly by all authors and implemented by MH, JLB and CS. MH implemented the simulation, collected and analyzed data, constructed pathways in the macrophage and drafted the manuscript. JLB, CS, AC developed general concepts, provided scientific support, participated in the manuscript writing and coordinated the study. All authors read and approved the final version of the manuscript.
Acknowledgments
Authors acknowledge Zakaria Hmama, Neil E. Reiner and Jimmy Lee (Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia) for their knowledge and advice on the bacterial invasion process. We thank Kyle Recsky and Shawn Anderson (Upstream Biosciences, Inc) for their advice and help on the model development and implementation, and Ian Upright and Jason Ng (Visual Knowledge, Inc.) for the graphical interface and technical support.
This research was funded by the CIHR/MSFHR Strategic Training Program in Bioinformatics, sponsored by Canadian Institutes of Health Research and Michael Smith Foundation for Health Research.
Figures and Tables
Figure 1 An example of a semantic network. Characteristics and behaviors of a semantic agent (SA) are defined by its relationships (RE) with other agents. Semantic agents are represented as circles, and relationships are depicted as arrows. This SN-model represents that a protein A can be located at a nucleus, can interact with a protein B or catalyze a chemical reaction. For explanatory purpose, this figure illustrates an example of a semantic network. The implemented semantic network (as presented in the paper) is more complex and involves different types of relationships and agents.
Figure 2 Interactions among biological structures of different levels in the SN. The left panel shows an example of a translocation event when a protein B is moved from the cytosol to the plasma membrane. The right panel shows an example of a non-covalent interaction between a protein A and a protein B via non-covalent forces.
Figure 3 A model of a non-covalent interaction between a PI3K-p110 and a Ras. The figure was graphed from the developed SN to illustrate the relationships among different agents. The figure visualizes the agents as icons and their relationship as arrows. The left panel illustrates that a PI3K-p110 contains a "Not Bound" Ras-binding site and a "Non-Functional" catalytic domain. The right panel shows that when the PI3K-p110 has bound to a Ras, its Ras-binding site has switched to "Bound", and the catalytic domain has become "Functional" due to a positive allosteric regulation event. State changes as a result of the interaction are shown in bold. Note that the model stores the information, which specifies the non-covalent event between the prototypic Ras and the prototypic PI3K-p110, and the condition for the event to occur. This figure illustrates an instance of the Ras-binding event occurred during a simulation. The PI3K-p110 is an instance of the PI3K-p110 prototype, and it is the same agent before and after it binds to the Ras. Figure 8 shows the description of each icon.
Figure 4 A model for covalent interactions. Figure 4a shows that an Akt protein can be phosphorylated to an Akt-phosphate by an enzyme, PDK1, and an ATP is converted to an ADP in the process. Figure 4b shows a similar covalent interaction event where substrate Glucose can be converted to Glucose-6-phosphate by an enzyme Hexokinase.
Figure 5 Figure 5a- Phagocytosis of bacteria in macrophages. The picture shows macrophages ingesting green fluorescent mycobacteria (indicated by arrows). The host cell membrane was stained by red fluorochorme PKH to define the limit of the cell. (The picture was provided by Zakaria Hmama)
Figure 5b- A SN-representation of the cell signaling network that regulates phagocytosis in the human macrophage. Both molecules and their interactions (non-covalent and covalent interactions) are represented as semantic agents and visualized as nodes (with distinct icons) on the diagram. Arrows represent the semantic relationships between different agents.
Figure 6 A SN- simulator: at the beginning of the simulation. The simulation showed the actions of molecules under a biological scenario. 1. The initializing buttons synthesize molecules in each subcellular compartment. 2. The localization window shows molecules present in each subcellular compartment. In this simulation, an IgG molecule was present at the extracellular space (E.S.). There were 2 ATP molecules, an Fcγ receptor (FcγR), a Gab2 and a PIP2 (PI[4,5]P2) present at the plasma membrane (P.M.). The cytosol contained a Lyn kinase, a PI3K-p85 and a PI3K-p110 subunit. There was no molecule present at the nucleus in this simulation. 3. The "Start Simulation" button creates a previously specified translocation event. In this simulation, the translocation has already occurred and moved the IgG from the extracellular space to the plasma membrane. 4. The "Next" button triggers a search that determines a proper event to occur and advances to the next step. 5. The pathway-viewer shows a series of events occurred during the simulation.
Figure 7 A SN- simulator: at the end of the simulation. The pathway-viewer shows that the initial translocation of the IgG molecule has led to the occurrence of a series of events, which include several non-covalent interactions, covalent interactions, and translocations of various molecules: Event #1: the IgG was translocated from the extracellular space to the plasma membrane. Event #2: the IgG bound to the Fcγ receptor at the plasma membrane. Event #3: the Lyn was translocated from the cytosol to the plasma membrane. Event #4: the Lyn bound to the Fcγ receptor at the plasma membrane. Event #5: the Lyn phosphorylated the Gab2 to a Gab2-phosphate (Gab2-P) at the plasma membrane. Event #6: the PI3K-p85 and p110 (already bound to each other) were translocated together from the cytosol to the plasma membrane. Event #7: the PI3K-p85 bound to the Gab2-P at the plasma membrane. Event #8: the PI3K-p110 phosphorylated the PIP2 to a PIP3 (PI[3,4,5]P3) at the plasma membrane. Event #9: The formation of the PIP3 caused phagosome formation.
Figure 8 Description of icons used in other figures.
Table 1 Classification of biological structures in 6 prototypes in the semantic network.
Semantic Agent – Structure Biological Example
Cell Human macrophage, Mycobacterium tuberculosis
Subcellular Compartment Plasma membrane, cytosol, phagosome, nucleus
Macromolecule Protein, nucleic acid, polysaccharide, fat/lipid
Domain and Site Catalytic domain, SH2 domain, PH domain, binding site, phosphorylation site, promoter, gene regulatory site.
Small Molecule and Molecular Fragment Amino acid, nucleotide, sugar, fatty acid
Atom Hydrogen, carbon, oxygen, nitrogen, phosphorus, sulfur
Table 2 Classification of biological events in 5 prototypes in the semantic network.
Semantic Agent – Event Biological Example
Translocation A protein moves from cytosol to plasma membrane.
Non-covalent Interaction A ligand binds to a receptor.
Covalent Interaction An enzyme catalyzes a chemical reaction where substrates are converted to products.
Allosteric Regulation A ligand binding on site A of a protein causes a conformational change on site B of the protein.
Cellular Response Cell survival, cell death, phagosome formation, increase of intracellular glucose level.
Table 3 The number of structure and event prototypes modeled in the cell signaling network of the macrophage.
Structure Sum
Subcellular Compartment 9
Macromolecule 93
Domains and Site 20
Small Molecule 9
Event Sum
Translocation 24
Non-covalent Interaction 57
Covalent Interaction 28
Cellular response 22
Table 4 Simulation results from M. tuberculosis invasion in the human macrophage.
Known response in the macrophage Other potential response in the macrophage
Actin Polymerization Cell survival
Pseudopod Extension Cell cycle entry – S phase
Phagosome Formation Increase of protein synthesis
Phagosome Maturation Arrest Increase of intracellular glucose level
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| 15494071 | PMC528732 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Oct 19; 5:156 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-156 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1561549407110.1186/1471-2105-5-156Methodology ArticleModeling of cell signaling pathways in macrophages by semantic networks Hsing Michael [email protected] Joel L [email protected] Conor [email protected] Artem [email protected] CIHR/MSFHR Strategic Training Program in Bioinformatics, Genetics Graduate Program, Faculty of Graduate Studies, University of British Columbia, Vancouver, British Columbia, V5Z 3J5, Canada2 Upstream Biosciences, Inc., Vancouver, British Columbia, V6H 1H2, Canada3 Visual Knowledge, Inc., Vancouver, British Columbia, V6H 1H2, Canada4 Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, V5Z 3J5, Canada2004 19 10 2004 5 156 156 13 4 2004 19 10 2004 Copyright © 2004 Hsing et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways.
Results
We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages.
Conclusions
We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed investigation of connections among various essential molecules and reflected the cause-effect relationships among signaling events. The simulation demonstrated the dynamics of the semantic network, where a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.
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Background
Interactions among genes, gene products and small molecules regulate all cellular processes involving cell survival, cell proliferation, and cell differentiation among others. Such interactions are organized into complex lattice structures conventionally divided into cell signaling, metabolic and gene regulatory networks in a cell [1]. In recent years, large amounts of information and knowledge on cell signaling networks have been accumulated in the literature and databases [2,3].
Conventionally, this information is highly compartmental: various individual signaling pathways are mostly stored in separated and non-linked diagrams. Traditional pathway diagrams, where molecules are represented as nodes and their interactions are depicted as lines and arrows have significant limitations as they lack spatial and temporal context [4]. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Not surprisingly, the current state of pathway representation does not allow of complex investigation of qualitative or quantitative changes in cell signaling networks in response to external perturbations such as bacterial infections. Thus, an adequate computational environment for modeling cell signaling networks is needed for proper biological data integration as well as for simulation and prediction of cellular behaviors [5].
Recently, many models have been proposed for representing, storing and retrieving interactions among various biological entities. BIND [6] and IntAct [7] focus on protein-protein interactions and their resulting complexes. BioCyc [8] developed models for metabolic events and curated metabolic pathways from many organisms. The model developed by aMAZE [9] combines interactions in cell-signaling, metabolic and gene regulatory pathways. In addition, the System Biology Markup Language (SBML) has been developed for representing biochemical reaction networks and for communicating models used for various simulation programs [10]. Programs such as E-cell [11], Gepasi 3 [12] and Virtual Cell [13] use differential equations to represent molecular interactions, and their simulation results are obtained by solving these questions numerically [14]. It should be noted, however, that many cellular processes are sensitive to the stochastic behavior of a small number of molecules, and therefore, the assumptions in differential-equation methods can often be compromised [15]. Several studies have attempted to address the stochastic property of a cell. Vasudeva and Bhalla [16] proposed a hybrid simulation method that combined both deterministic and stochastic calculations. In addition, a stochastic simulator, StochSim [15] represented molecules as individual software objects that interact according to probabilities. Thus, it is feasible to suggest that useful cell signaling simulators should be capable of representing each molecule individually and reflecting the stochastic behavior of molecular interactions in a cell.
Semantic networks
Recently an artificial intelligence approach known as semantic networks (SN) have gained the attention of the biological community as a potentially powerful tool for organizing and integrating large amounts of biological information [17]. For instance, the semantic network in the Unified Medical Language System (UMLS) was designed to retrieve and integrate biomedical information from various resources [18]. The UMLS semantic network has also been applied and expanded to include information and knowledge from other domains such as genomics [19]. In addition, other studies have suggested a semantic approach where proteins are viewed as "adaptive and logical agents", whose properties and behaviors are affected by other agents in their spatial organization including intracellular compartments and protein complexes [20,21]. Defining the semantics among agents could characterize both local and global behaviors of a system, and therefore, it is potentially useful to apply such approach to study cell signalling in biological systems [21].
A semantic network is a method to represent information or knowledge by nodes and edges in a graphic form, where a node represents a concept and an edge represents a relationship [22]. A semantic network, which can exist abstractly in a human mind or be implemented by applying computer technology, can model many real-world problems [22]. Figure 1 illustrates a semantic network, where a concept such as a protein, a chemical reaction or a subcellular location is modeled by a semantic agent, and its relationships with other agents are represented as arrows. A proper semantic network implementation allows the identity and properties of an agent to arise from its relationships with other agents, not from descriptions or labels [23]. Hence, within a semantic network "things are what they do".
Previously an application development environment known as Visual Knowledge (VK) has been created, and VK is capable of different formalizations and implementations of semantic networks for various knowledge domains [23]. Visual Knowledge has been applied successfully to model and manipulate complex "interactomes", including corporate enterprise systems, flight scheduling networks, hardware maintenance simulators, and integrated currency exchange boards [23]. It has been anticipated that Visual Knowledge can address many of the current limitations on modeling cell signaling pathways. Using the latest VK-based environment, BioCAD [24], specifically designed for biological applications, we have developed a semantic model for cell signaling pathways occurring in human macrophages.
Bacterial invasions in macrophages
It is the current knowledge that many pathogenic bacteria are capable of entering and surviving within mammalian macrophages by modulating the host signaling pathways [25]. One well-studied example is the activation of the Fcγ macrophage receptor by the IgG antibody, which binds to the surface of bacteria such as Mycobacterium tuberculosis [26]. Activation of the Fcγ receptor induces phagocytosis of M. tuberculosis and the formation of a phagosome within the macrophage. These processes are mediated by the class I phosphoinositide 3-kinase (PI3K) – one of the most well-characterized enzymes to date [27]. The class I PI3K is a heterodimer composed a p110 catalytic subunit and a p85 regulatory subunit, which maintains a low-level activity of p110 [28]. The p110 subunit is activated when p85 binds at a phosphotyrosine site on a receptor or an adaptor protein, or by direct binding to activated Ras [29]. Activated PI3K-p110 phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) into phospatidylinositol-3,4,5-trisphosphate (PIP3), which is an essential signaling molecule that stimulates many downstream proteins, including PDK1 and Akt [30]. The formation of a phagosome is normally followed by the phagosome maturation process, which is responsible for intracellular killing of bacteria and is regulated by the class III PI3K [31]. However, it has been hypothesized that phosphatidylinositol analogs, such as ManLAM, produced by M. tuberculosis can inhibit the activity of the class III PI3K, arresting phagosome maturation process, and ensuring the survival of M. tuberculosis inside the macrophage [27,32].
In addition to their role in phagocytosis, PI3Ks are essential proteins that regulate cell survival, cell growth, cell cycle and other cellular processes [33]. Although, it is clear that PI3Ks play an important role in bacterial invasions, the knowledge of PI3K-mediated interactions is scattered in a number of literature and pathway databases. A coherent picture of detailed molecular interactions that link receptors to PI3Ks and to various cellular responses has yet to be constructed before bacterial invasions can be fully understood. To address this goal, a cell signaling network of the human macrophage was reconstructed with the semantic model, and qualitative changes in the network were investigated with a SN-simulator.
Results
A semantic model for cell signaling pathways
In the paper, the word "model" refers to a set of rules in two different but related contexts. In the context of the semantic network, the model refers to a set of rules that specify how a biological concept is mapped to one or multiple semantic agents/relationships. In the context of cell signaling pathways, the model is a set of rules that specify what, how, and when molecules interact with each other. The model has been formalized and implemented, using BioCAD software system, and it is presented in the following sections.
Overall classification of biological structures and their relationships
Within the semantic network, all biological structures are modeled by semantic agents that are members in one of the 6 different prototypes. Table 1 shows the 6 types of structures in the order of their hierarchy. From the highest to the lowest level, they are "Cell", "Subcellular Compartment", "Macromolecule", "Domain/Site", "Small Molecule/Molecular Fragment", and "Atom". A structure agent can be composed of multiple structures of the same prototype or a lower-level prototype, and the agent is connected to its components by the composition relationship in the SN. Thus, a human macrophage has been modeled as a semantic agent of the "Cell" prototype, and it was composed of various "Subcellular Compartment" agents, including plasma membrane, cytosol, nucleus and others. In addition, each compartment such as nucleus contained various agents of the "Macromolecule" prototype including proteins, DNA and RNA. A macromolecule such as a protein was further composed of "Domain/Site" agents like catalytic domains and phosphorylation sites, and a DNA was composed of sites such as promoters and gene regulatory sites.
Modeling interactions among biological structures
To create an adequate semantic model, we have postulated that structures of different levels in the cellular hierarchy can interact with one another. One example of such interactions is the movement of a molecule from one subcellular compartment (e.g. cytosol) to another (e.g. plasma membrane). This is referred to as a translocation event, and it is demonstrated on the left panel of Figure 2.
Table 2 shows that translocations have been modeled as one the five major "event" prototypes in the SN. Every translocation event has been connected to three structure agents: a molecule to be moved (macromolecule or small molecule), an original location (subcellular compartment), and a destination (subcellular compartment). Hence, the construction of translocation events has enabled us to confine all possible movements of molecules in a cell.
Interactions that occur by non-covalent or covalent forces have also been modeled as two distinct "event" prototypes as shown in Table 2. The right panel of Figure 2 illustrates a general case of a molecular interaction between a protein A and a protein B occurring via non-covalent forces. Such interaction can cause changes of the forms and functions of the interacting molecules, and these changes have been accommodated within the developed SN model by specifying two distinct types of states: "conformational states" and "binding states", also represented by semantic agents.
All hypothetical spatial changes occurring in the three-dimensional structure of a given macromolecule have been modeled within the SN as switches in the corresponding conformational states, and the changes do not lead to the creation of new semantic agents. Domains or sites for every protein encoded into the SN model have been assigned to either "Functional" or "Non-functional" conformational states. The "Functional" state represents that a domain/site is currently in a conformation that enables a certain interaction. On the other hand, a "Non-functional" state implies a domain/site is in a conformation that prevents an interaction. To illustrate this construct we have graphed the semantic agents and their relationships created within the developed SN. It should be noted that within the SN, all semantic agents are visualized as icons, and their relationships are depicted as connecting arrows. In addition, all agents are related by pairs of reciprocal relationships, and for simplicity, only one direction of each pair of the relationships was visualized. The left panel of Figure 3 features a p110 subunit of the class I PI3K that has been modeled as a "macromolecule" agent and contains a binging site for a Ras protein and a catalytic domain. The Ras binding site has been assigned a state of "Functional", depicted as a check symbol (square) on Figure 3. The "Functional" state enables the PI3K-p110 to bind to a Ras protein. On the other hand, the catalytic domain is "Non-functional', depicted as a cross symbol. Figure 8 shows the description of icons used in this paper.
In addition to the conformational states, a protein domain or site has been assigned one of the two binding states: "Bound" or "Not-bound". A "Bound" state implies that this domain/site currently associates with a domain/site of another molecule through a non-covalent interaction. On the other hand, a "Not Bound" state indicates such an association does not exist. Since ligand bindings can affect the conformation of a macromolecule through allosteric regulations, two types of such regulations have been implemented within the SN. A positive allosteric regulation event has been assigned to the scenario when a "Bound" binding state of a domain/site causes the conformational state of another domain/site to switch to "Functional". The right panel of Figure 3 shows that when the PI3K-p110 has bound to a Ras by a non-covalent interaction, the binding state of the Ras-binding site on p110 has switched to "Bound". As a result, the conformational state of the catalytic domain has switched to "Functional" due to a positive allosteric regulation. The "Functional" catalytic domain now enables the PI3K-p110 to phosphorylate its substrate. On the other hand, a negative allosteric regulation event has been attributed to those cases when a "Bound" state of a domain/site causes the conformational state of another domain/site to switch to "Non-functional". It should be noted that the semantic model stores the information that specifies the non-covalent event between the prototypic Ras and the prototypic PI3K-p110, and the condition for the event to occur. Figure 3 illustrates an instance of the Ras-binding event occurred during a simulation. The PI3K-p110 is an instance of the PI3K-p110 prototype, and it is the same agent before and after it binds to the Ras.
A more complex allosteric regulation event can be specified for mapping the binding states of multiple domains/sites (the condition or the input) to the conformational states of multiple domains/sites (the response or the output). Hence, a domain is switched to "functional" only if a certain combination of ligand bindings has occurred. The utilization of the states on domains/sites and allosteric regulation events in the SN has enabled us to express the cause-effect relationships among various signaling events explicitly.
In the developed semantic model, any molecular complex formed due to non-covalent interaction has been treated as a transient state of these two molecules, and a complex was not represented by a new semantic agent. Instead, the existence of a protein complex is inferred from the non-covalent interaction event. Thus, Figure 3 illustrates a protein complex of the PI3K-p110 and the Ras existed because of the occurrence of the non-covalent interaction event, which connected the two molecules.
Conventionally, there is often some inconsistency between representing chemical modifications of small molecules in metabolic pathways and modifications of proteins in signaling pathways. In the developed model, two molecules that interacted by covalent forces have resulted the creation of distinct semantic agents within the SN. This rule has been implemented consistently for both macromolecules such as proteins and small molecules such as ATP. As one example, Figure 4 features the phosphorylation of an Akt protein by an enzyme PDK1, yielding a distinct Akt-phosphate (Akt-P) agent and a free ADP. Within the SN, the Akt and the ATP are related to a covalent interaction event by "Substrate" relationships, depicted as arrows. In addition, the Akt-P and the ADP are related to the event by "Product" relationships, while the PDK1 is related by the "Enzyme" relationship. The PDK1 enzyme in this example contains a catalytic domain (not shown on the figure), which must be "functional" for the reaction to occur. The state of this domain is under the regulation of the binding of a ligand and an allosteric event as previously defined. In addition, new properties can be assigned to the modified protein. In this case, the phosphorylation by PDK1 switched the catalytic domain in Akt-P to "functional", while this domain was "non-functional" in Akt, the dephosphorylated form. Figure 4 illustrates that a covalent interaction event also applies to metabolites, and a metabolite such as glucose is phosphorylated into a glucose-6-phosphate by an enzyme Hexokinase. Other types of modifications including methylation, acetylation and glycosylation can also be modeled in a similar manner but involve different substrate types.
In the semantic model, a molecule can participate in different sets of interactions in different locations. The translocation events define all possible localizations of molecules, and therefore, an interaction can only occur if the participating molecules can be present in the same location at the same time. Alternatively, an interaction (non-covalent or covalent) can directly associate with a subcellular compartment, and this interaction is only available to molecules in that location.
In addition, all qualitative cellular responses such as cell survival and phagosome formation have been implemented within the SN under a distinct "event" prototype. They have been implemented in a way that the formation or the activation of certain signaling molecules such as PIP3 can trigger the occurrence of these cellular response events in a simulation.
As it has been mentioned previously, the behavior of any semantic agent can be clearly defined by its relationships or connections to other agents. Thus, the formalization of the five types of events, which are translocations, non-covalent interactions, covalent interactions, allosteric regulations and cellular responses, has enabled us to model the behaviors of molecules depicted in conventional pathways and to reconstruct a cell signaling network of the human macrophage.
Case study: a reconstruction of a cell signaling network in the macrophage
Data source
The molecular composition of human macrophages and information of known intracellular interactions have been extracted from various research articles [26,27,32,34-47], review articles [25,28-31,33,48-52] and pathway databases [2,3].
Translation and integration of pathway information into the semantic model
A pathway diagram in the literature or an electronic database, in principle, represents some scenario of what may happen in a call if every depicted molecule is expressed in the correct location, at the correct time and with the correct states. Hence, the aggregation of multiple pathway diagrams describes some, if not all, possible molecular events that can potentially occur in a cell under the right conditions. To utilize such information and build a cell signaling network, we have decomposed conventional pathways into individual pieces of information such as subcellular localization of a protein, a pairwise protein binding, a chemical reaction or a cellular response. Then, using the sets of semantic rules described in the model, we have represented and integrated each piece of those information in the form of semantic agents and their relationships. Table 3 illustrates the overall SN model for the cell signaling network contained a total of 93 prototypical macromolecules localized in several subcellular compartments. It included several cell receptors (such as Fcγ, CR3, CD 14, CD18, TLR2) relevant to the process of bacterial internalization of macrophages. Two distinct classes of PI3Ks have been modeled: the class I PI3K composed of p85 regulatory and p110 catalytic subunits, and the class III PI3K composed of p150 and Vps34p subunits [28]. The model also included various kinases such as Lyn, PDK1 and Akt, small GTPases including Ras, Rac1 and Rab5, and adaptor proteins like Gab2. Events of various prototypes have also been extrapolated from the literature and pathway diagrams.
Visualization and analysis of the cell signaling network
The defined semantic agents have been connected in the semantic network and can be visualized at different levels. Figure 5 shows one example of how various non-covalent and covalent interactions have been integrated into a unified cell signaling network. The longest path in the cell signaling network we have created contained 24 consecutive molecular interaction events, linking Fcγ receptor to the class I PI3K enzyme and further through class III PI3K to various cellular responses.
Such detailed semantic reconstruction of the cell signaling network has allowed thorough investigation of biochemical relationships between essential proteins. One such example is presented on Figure 5 featuring the connections among cell receptors Fcγ and CR3, and tyrosine kinase Lyn which they both activate. It has also been reconstructed by the SN model that both of these receptors can activate the class I PI3K via an adaptor protein, Gab2. The corresponding finding will now be subjected to testing in an experimental lab.
Another example of successful SN reconstruction is the relationship between CD14 macrophage receptor and the class I PI3K; such a relationship was previously suspected but not clear [39]. By incorporating the available literature data [35,45] into the semantic environment we were able to reconstruct the scenario where CD14 activates the class I PI3K by the association of Toll-like receptor 2 (TLR2), as it is illustrated in Figure 5. Such model will also be tested experimentally.
Simulation of changes in the cell signaling network during bacterial invasions
In the implemented semantic model, the "possible" behaviors of a molecule are defined through its relationships to other agents (for example a non-covalent event), and all instances of that prototypical molecule will inherit the same behaviors. However, the action of a molecule at any given time is affected by factors including its current states and its current location with respect to other molecules in the system. Hence, we have built an application that enabled us to produce instances of molecules in various locations and to observe the "current" action of a molecule qualitatively under different biological scenarios. We refer such scenario-play as simulation in this paper.
The application or the SN-simulator allows the molecules to move among various locations, to interact with each other and to create events when the conditions are met. In addition, every instance of a molecule has been represented as an individual agent while every instance of a molecular interaction has also been implemented as an individual event agent. Thus, the simulator provides a traceable "trajectory" of all the events that have happened on every molecule during a simulation.
As illustrated in Figure 6, the macrophage cell has been generally divided into four subcellular compartments or locations within the simulator. We have specified what molecules to be present initially in each subcellular location in the beginning of a simulation, and the simulator synthesized molecules in each location accordingly. At the very first simulation step, the simulator has created a translocation event moving a molecule (the current target) from one location to another. The initial translocation has been specified as the movement of an IgG molecule from the extracellular space to the plasma membrane as shown in the pathway-viewer on Figure 6. The occurrence of this initial event allowed the simulator to trigger a search and advanced to the next step. The search looked for other potential molecules (with the correct states) that can interact with the target molecule in the same location. If multiple instances of potentially interacting molecules were present in that location, a single molecule would be randomly selected to interact with the target.
Because an Fcγ receptor was the only interacting molecule (for the IgG) present at plasma membrane in the simulation, it has bound to the IgG by a non-covalent interaction event, as illustrated in Figure 7. This non-covalent interaction has switched the state of the Fcγ receptor's binding site for a Lyn kinase to "Functional", and thus it enabled the Fcγ receptor to bind to a Lyn. However, the Lyn was not initially present in plasma membrane, but it was localized in cytosol in the beginning of the simulation, as shown in Figure 6. Thus, when the Lyn has been translocated from the cytosol to the plasma membrane, a non-covalent interaction between the Lyn and the "Functional" Fcγ receptor occurred in the following step as shown in Figure 7. The search was iterated and the simulation continued until all interacting molecules have been depleted in the macrophage.
Figure 7 demonstrates the consecutive events in this simulation scenario where the Lyn protein phosphorylated a Gab2, which then bound to a class I PI3K. When activated, the PI3K phosphorylated a PIP2 into a PIP3, which in turn caused a phagosome formation response. Different setups of the initial localization of molecules have affected the outcome of the simulation. For instance, an initial presence of a Rab5 (a downstream protein of the PIP3) and a class III PI3K in the cytosol extended the previous pathway from the PIP3. This localization setup stimulated a PIP3-mediated activation of the class III PI3K, which led to phagosome maturation response in the simulation. However, if a phosphatidylinositol analog, ManLAM, of M. tuberculosis was initially present in the plasma membrane, it would inhibit the class III PI3K and thus arrest the phagosome maturation response in the macrophage. Table 4 shows that the activation of PI3Ks-mediated pathways by M. tuberculosis has caused several known cellular responses as well as additional responses such as cell survival of the macrophage, cell cycle entry, increase of protein synthesis and increase of intracellular glucose level in the simulation. We suspect that some of these responses have not yet be appreciated in previous studies of bacterial invasions. Further experiments can be formalized to test the simulation results.
In this study, the SN-simulator has enabled us to "play" different scenarios and observed their effects in the macrophage. It allowed us to investigate how changes on one molecule caused changes of another molecule in the cell signaling network during bacterial invasions.
Discussion
Features of the semantic model
In the present work we have developed a semantic model to represent the properties and the behaviors of molecules and their interactions in the context of cell signaling pathways. The proposed model offers some additional features, compared to other existing pathway models. Those features are essential for characterizing the complex behaviors of biological entities, and they include:
Specify the spatial organization of molecules
The semantic model has specified the hierarchical relationships among the different biological structures, from cells to compartments, molecules and domains/sites. The hierarchy between subcellular compartments and molecules has allowed us to specify the spatial organization of molecules, model the translocation events and represent the effects of locations on the different interactions among molecules.
Model proteins as integrating and logical devices
The hierarchy between molecules and their domains/sites has enabled us to explicitly model the relationship between forms and functions for proteins. Through the allosteric regulation events, proteins have been modeled and implemented as integrating and logical devices in the semantic network, and their conformational states (outputs) are switched by the combination of non-covalent ligand bindings or covalent modifications (inputs).
Provide a direct communication from models to simulations
Through the prototyping system in the semantic network, any rule or interaction specified on a prototypical molecule automatically define the properties and behaviors of all its instances. As demonstrated by the simulator, the semantic network provided a direct communication from the interaction model to an application where the actions of molecules can be observed under different scenarios. Therefore, the semantic network is dynamic as a change of states on a molecule can alter its function and potentially cause a chain-reaction effect in the system.
Reduce the need for labels
In addition, the current semantic model is different from the previous models in BioCAD. An essential difference is the representation of functional labels or roles on proteins. The meanings of functional descriptions or association words such as "enzyme", "activator/activates" or "inhibitor/inhibits", which are often used to characterize the behaviors of proteins in most pathway models, have been represented explicitly through events and relationships in the developed semantic network. For example, a protein acts as an "enzyme" if 1) the protein participates in a "covalent interaction event", 2) the presence of a "functional" catalytic domain on the protein is required for the occurrence of the event, and 3) the protein itself is not modified after the event. Similarly, a protein A "activates" a protein B if a non-covalent binding event from protein A turns on the "functional" state of a domain/site on protein B. Hence, the model has reduced the need for labels, which are often confusing or misleading on conventional pathway representation.
Future directions
The use of non-covalent and covalent events has enabled us to model protein-protein interactions and chemical modifications on molecules including proteins and metabolites. The next challenge is to model the complex interactions that govern gene regulations. The current construction of non-covalent interaction events can model the binding of an individual transcription factor to a particular site of a gene, and the covalent interaction event can represent the transcription process that leads to the production of an mRNA, and the translation process that produces a protein. However, a successful transcription in a eukaryotic cell requires the formation of a protein complex that involves more than one hundred subunits, and the complex may be assembled in various orders [53]. We anticipate the improvement of the current allosteric regulation model to characterize the more complex logic in gene regulation.
The semantic network representation can be exploited for performing analysis of cell signaling pathways. The examples of Fcγ receptor, CR and the class I PI3K demonstrated that connections can be queried and analyzed among different biological entities. The semantic model is also compatible with other pathway models. Therefore, the number of biological entities and interactions in the semantic network can be greatly increased as pathway data from existing databases is integrated. Previous study has shown the value of combining gene expression profiles with protein-protein interaction networks for identifying active subnetworks [54]. Similarly, data from gene and protein expression experiments could be integrated with the semantic network for "pathway filtering". For instance, within a particular cell, there could be multiple paths that connect two proteins, while each path consists of different number of nodes. When the cell receives a signal, the shortest path, the one with the least number of nodes that require activation, is more likely to be "walked" than a longer path. Hence, the gene/protein expression data will provide some estimation of an overall protein expression and activation states to identify "active" pathways in a cell under a given condition
In this study, the proposed semantic model has been applied to cell signaling pathways in the macrophage as a case study. The model is not limited to those pathways. The hierarchical classification of the biological structures and the events can model other cell signaling pathways for different cells and organisms. An interactive website is currently under development. We anticipate that through the web, researchers can utilize the semantic network approach for creating pathways in cells of their interest and for analyzing any existing pathways including the PI3K pathways of the human macrophage presented in the paper.
The current capability and applicability of the SN simulator
In this study, we have developed a simple simulator to demonstrate the dynamics of the semantic network and to observe the actions of molecules qualitatively. In order to perform a realistic cellular simulation in the future, three components need to be improved. First, quantitative factors should be integrated into the model. For example binding affinity, directly associated with non-covalent events, will affect the probability and the duration of the binding of molecules. Reaction kinetics, associated with covalent events, will determine the rate of production.
Second, the two parameters, the population of molecules and their localization, which influence the simulation outcome, could be initialized and supported by experimental data. For instance, gene expression data from microarrays supports the relative abundance of transcripts, and protein expression data supports the relative abundance of proteins. Computer algorithms such as PSORT [55] can assist in predicting the localization of proteins.
Third, the proximity of molecules has been represented by subcellular compartments in the simulation. This approximation can be improved in two different ways. First, a compartment can be further divided into smaller sub-locations. Increasing the number of locations and reducing the size of each location will improve the accuracy of the simulation. Second, the occurrence of non-covalent events in the simulation has allowed us to identify molecular complexes and their members effectively. Hence, the proximity can be approximated through molecular complexes, such that molecules in a complex have higher probability to interact with members of the same complex.
The simulator has demonstrated that a biological pathway can emerge from the creation of semantic agents and their relationships in the SN, and such a pathway represents a series of consecutive events resulting from the activation of a single molecule. It is anticipated that further development will improve our ability to track and visualize different instances of molecules participated in multiple pathways. Hence, the occurrence of a cellular response event can be triggered by the accumulation of certain molecular species with particular states.
Conclusions
We concluded from our results that the semantic network is an effective method to model cell signaling pathways. Utilizing the semantic agents and the relationships in the model, information on biological structures and their interactions at different levels has been properly represented and integrated in the hierarchical and spatial context. The reconstruction of the cell signaling network in the macrophage has allowed qualitative investigation of connections among various essential molecules and reflected the cause-effect relationships among the events. The simulation demonstrated the dynamics of the semantic network, where actions of molecules are affected by their current states and locations, and the history of events can be traced and analyzed. In addition, changes caused by the invading M. tuberculosis in the macrophage were investigated by the simulator. As a result, the simulation identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions.
Methods
The Visual Knowledge environment
Visual Knowledge (VK) is an application development environment, and its implementation has been influenced by the theory of semantic networks as well as other approaches including set theory, frame system, object-oriented modeling theory and systems based on networks of active software agents [23]. Different from other passive knowledge representation technology, VK is dynamic and scalable, and it is capable of active representation and integration of different domain knowledge. By manipulating a number of fundamental classes of semantic agents like "physical thing", "event" and "trigger", models of various complexity can be constructed with VK. In addition, VK allows the creation of "prototypes" within each basic class of agents, and therefore it enables any classification of agents based on their common characteristics and behaviors.
The BioCAD software
BioCAD, a Visual Knowledge-based development environment, is developed by Upstream Biosciences, Inc. and customized to model biological systems [24]. The BioCAD software provides tools for managing large-scale biological data and for visualizing and editing biological pathways and networks. BioCAD currently contains millions of biological concepts and hundreds of pathways that have been integrated and curated from publicly available data sources. A locally installed client program allows semantic agents to be created, stored and queried from a remote central server. The BioCAD software is available commercially, and a collaborative modeling server will be publicly accessible soon.
Authors' contributions
The semantic model was developed jointly by all authors and implemented by MH, JLB and CS. MH implemented the simulation, collected and analyzed data, constructed pathways in the macrophage and drafted the manuscript. JLB, CS, AC developed general concepts, provided scientific support, participated in the manuscript writing and coordinated the study. All authors read and approved the final version of the manuscript.
Acknowledgments
Authors acknowledge Zakaria Hmama, Neil E. Reiner and Jimmy Lee (Division of Infectious Diseases, Department of Medicine, Faculty of Medicine, University of British Columbia) for their knowledge and advice on the bacterial invasion process. We thank Kyle Recsky and Shawn Anderson (Upstream Biosciences, Inc) for their advice and help on the model development and implementation, and Ian Upright and Jason Ng (Visual Knowledge, Inc.) for the graphical interface and technical support.
This research was funded by the CIHR/MSFHR Strategic Training Program in Bioinformatics, sponsored by Canadian Institutes of Health Research and Michael Smith Foundation for Health Research.
Figures and Tables
Figure 1 An example of a semantic network. Characteristics and behaviors of a semantic agent (SA) are defined by its relationships (RE) with other agents. Semantic agents are represented as circles, and relationships are depicted as arrows. This SN-model represents that a protein A can be located at a nucleus, can interact with a protein B or catalyze a chemical reaction. For explanatory purpose, this figure illustrates an example of a semantic network. The implemented semantic network (as presented in the paper) is more complex and involves different types of relationships and agents.
Figure 2 Interactions among biological structures of different levels in the SN. The left panel shows an example of a translocation event when a protein B is moved from the cytosol to the plasma membrane. The right panel shows an example of a non-covalent interaction between a protein A and a protein B via non-covalent forces.
Figure 3 A model of a non-covalent interaction between a PI3K-p110 and a Ras. The figure was graphed from the developed SN to illustrate the relationships among different agents. The figure visualizes the agents as icons and their relationship as arrows. The left panel illustrates that a PI3K-p110 contains a "Not Bound" Ras-binding site and a "Non-Functional" catalytic domain. The right panel shows that when the PI3K-p110 has bound to a Ras, its Ras-binding site has switched to "Bound", and the catalytic domain has become "Functional" due to a positive allosteric regulation event. State changes as a result of the interaction are shown in bold. Note that the model stores the information, which specifies the non-covalent event between the prototypic Ras and the prototypic PI3K-p110, and the condition for the event to occur. This figure illustrates an instance of the Ras-binding event occurred during a simulation. The PI3K-p110 is an instance of the PI3K-p110 prototype, and it is the same agent before and after it binds to the Ras. Figure 8 shows the description of each icon.
Figure 4 A model for covalent interactions. Figure 4a shows that an Akt protein can be phosphorylated to an Akt-phosphate by an enzyme, PDK1, and an ATP is converted to an ADP in the process. Figure 4b shows a similar covalent interaction event where substrate Glucose can be converted to Glucose-6-phosphate by an enzyme Hexokinase.
Figure 5 Figure 5a- Phagocytosis of bacteria in macrophages. The picture shows macrophages ingesting green fluorescent mycobacteria (indicated by arrows). The host cell membrane was stained by red fluorochorme PKH to define the limit of the cell. (The picture was provided by Zakaria Hmama)
Figure 5b- A SN-representation of the cell signaling network that regulates phagocytosis in the human macrophage. Both molecules and their interactions (non-covalent and covalent interactions) are represented as semantic agents and visualized as nodes (with distinct icons) on the diagram. Arrows represent the semantic relationships between different agents.
Figure 6 A SN- simulator: at the beginning of the simulation. The simulation showed the actions of molecules under a biological scenario. 1. The initializing buttons synthesize molecules in each subcellular compartment. 2. The localization window shows molecules present in each subcellular compartment. In this simulation, an IgG molecule was present at the extracellular space (E.S.). There were 2 ATP molecules, an Fcγ receptor (FcγR), a Gab2 and a PIP2 (PI[4,5]P2) present at the plasma membrane (P.M.). The cytosol contained a Lyn kinase, a PI3K-p85 and a PI3K-p110 subunit. There was no molecule present at the nucleus in this simulation. 3. The "Start Simulation" button creates a previously specified translocation event. In this simulation, the translocation has already occurred and moved the IgG from the extracellular space to the plasma membrane. 4. The "Next" button triggers a search that determines a proper event to occur and advances to the next step. 5. The pathway-viewer shows a series of events occurred during the simulation.
Figure 7 A SN- simulator: at the end of the simulation. The pathway-viewer shows that the initial translocation of the IgG molecule has led to the occurrence of a series of events, which include several non-covalent interactions, covalent interactions, and translocations of various molecules: Event #1: the IgG was translocated from the extracellular space to the plasma membrane. Event #2: the IgG bound to the Fcγ receptor at the plasma membrane. Event #3: the Lyn was translocated from the cytosol to the plasma membrane. Event #4: the Lyn bound to the Fcγ receptor at the plasma membrane. Event #5: the Lyn phosphorylated the Gab2 to a Gab2-phosphate (Gab2-P) at the plasma membrane. Event #6: the PI3K-p85 and p110 (already bound to each other) were translocated together from the cytosol to the plasma membrane. Event #7: the PI3K-p85 bound to the Gab2-P at the plasma membrane. Event #8: the PI3K-p110 phosphorylated the PIP2 to a PIP3 (PI[3,4,5]P3) at the plasma membrane. Event #9: The formation of the PIP3 caused phagosome formation.
Figure 8 Description of icons used in other figures.
Table 1 Classification of biological structures in 6 prototypes in the semantic network.
Semantic Agent – Structure Biological Example
Cell Human macrophage, Mycobacterium tuberculosis
Subcellular Compartment Plasma membrane, cytosol, phagosome, nucleus
Macromolecule Protein, nucleic acid, polysaccharide, fat/lipid
Domain and Site Catalytic domain, SH2 domain, PH domain, binding site, phosphorylation site, promoter, gene regulatory site.
Small Molecule and Molecular Fragment Amino acid, nucleotide, sugar, fatty acid
Atom Hydrogen, carbon, oxygen, nitrogen, phosphorus, sulfur
Table 2 Classification of biological events in 5 prototypes in the semantic network.
Semantic Agent – Event Biological Example
Translocation A protein moves from cytosol to plasma membrane.
Non-covalent Interaction A ligand binds to a receptor.
Covalent Interaction An enzyme catalyzes a chemical reaction where substrates are converted to products.
Allosteric Regulation A ligand binding on site A of a protein causes a conformational change on site B of the protein.
Cellular Response Cell survival, cell death, phagosome formation, increase of intracellular glucose level.
Table 3 The number of structure and event prototypes modeled in the cell signaling network of the macrophage.
Structure Sum
Subcellular Compartment 9
Macromolecule 93
Domains and Site 20
Small Molecule 9
Event Sum
Translocation 24
Non-covalent Interaction 57
Covalent Interaction 28
Cellular response 22
Table 4 Simulation results from M. tuberculosis invasion in the human macrophage.
Known response in the macrophage Other potential response in the macrophage
Actin Polymerization Cell survival
Pseudopod Extension Cell cycle entry – S phase
Phagosome Formation Increase of protein synthesis
Phagosome Maturation Arrest Increase of intracellular glucose level
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| 15504240 | PMC528735 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Oct 26; 5:160 | latin-1 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-160 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1731551626010.1186/1471-2105-5-173SoftwareThe InDeVal insertion/deletion evaluation tool: a program for finding target regions in DNA sequences and for aiding in sequence comparison Stoneberg Holt Sierra D [email protected] Jason A [email protected] Department of Botany, Masaryk University, Brno, Czech Republic2 Rybkova 3, Brno, Czech Republic2004 29 10 2004 5 173 173 7 5 2004 29 10 2004 Copyright © 2004 Stoneberg Holt and Holt; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The program InDeVal was originally developed to help researchers find known regions of insertion/deletion activity (with the exception of isolated single-base indels) in newly determined Poaceae trnL-F sequences and compare them with 533 previously determined sequences. It is supplied with input files designed for this purpose. More broadly, the program is applicable for finding specific target regions (referred to as "variable regions") in DNA sequence. A variable region is any specific sequence fragment of interest, such as an indel region, a codon or codons, or sequence coding for a particular RNA secondary structure.
Results
InDeVal input is DNA sequence and a template file (sequence flanking each variable region). Additional files contain the variable regions and user-defined messages about the sequence found within them (e.g., taxa sharing each of the different indel patterns).
Variable regions are found by determining the position of flanking sequence (referred to as "conserved regions") using the LPAM (Length-Preserving Alignment Method) algorithm. This algorithm was designed for InDeVal and is described here for the first time.
InDeVal output is an interactive display of the analyzed sequence, broken into user-defined units. Once the user is satisfied with the organization of the display, the information can be exported to an annotated text file.
Conclusions
InDeVal can find multiple variable regions simultaneously (28 indel regions in the Poaceae trnL-F files) and display user-selected messages specific to the sequence variants found. InDeVal output is designed to facilitate comparison between the analyzed sequence and previously evaluated sequence. The program's sensitivity to different levels of nucleotide and/or length variation in conserved regions can be adjusted. InDeVal is currently available for Windows in Additional file 1 or from .
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Background
Gaps caused by insertion and deletion events (indels) are often important features of DNA sequence data, which is widely used in phylogenetic studies [1-13]. Although some authors consider indels to be potentially misleading [1,14], others consider indels to be important characters [8,9,13,15,16] and have argued that treating them as missing data can weaken phylogenetic analyses [3,7,10,17]. Even though it is generally accepted that indels that cannot be unambiguously positioned make confident homology assessment impossible and, therefore, regions that contain them should be excluded from phylogenetic analyses [3,4,6,8], it has been proposed that even these regions are valuable if properly coded [15].
Phylogenetic estimation depends on accurate character homology assessment (sequence alignment) [4,8,18], which is made more difficult by the presence of indels [2-4,6,8,15]. Indel occurrence is context-dependent, and it has been repeatedly reported that indels tend to be found clustered into specific length-variable regions [1,3,5,6,8,19-21]. Accurate assessment of these regions (proper alignment and recognition of relative indel rate, reversals, parallel events, and multiple, overlapping events) is aided by comparison among many sequences from various taxonomic levels [4,8,19].
Sequence comparisons are complicated by the ambiguities gaps introduce into alignments. Finding a target region recognized from one alignment within another can be time consuming and difficult to perform accurately. Because of the limits of computer screen size and human analytical ability, alignments of hundreds of sequences are difficult to evaluate, even when they can be prepared.
Poaceae is one of the largest families of flowering plants and is economically important [22]. Lower-level phylogenies within the family often make use of the largely non-coding plastid sequence between the trnL (UAA) 5' exon and trnF (GAA), hereafter called trnL-F [17,23-28]. As of Apr 2, 2004, the NCBI Entrez Nucleotides database [29] contained 505 Poaceae trnL-F sequences. Comparing indel regions across these sequences can reveal patterns in indel behavior and aid researchers in creating accurate alignments. A discussion of the indel regions in these sequences is being prepared separately for publication.
The program InDeVal was originally developed to help researchers find known indel regions (with the exception of isolated single-base indels) in newly determined Poaceae trnL-F sequences and simultaneously compare them with 533 previously determined sequences (those mentioned above, plus 28 determined by SDSH). It is supplied with input files designed for this purpose. More broadly, the program is applicable for finding specific target regions (referred to as "variable regions") in DNA sequence. A variable region is any specific sequence fragment of interest, such as an indel region, a codon or codons, or sequence coding for a particular RNA secondary structure. The LPAM algorithm, which was specifically designed for InDeVal, is used to find sequence (referred to as "conserved regions") flanking the variable region in the analyzed sequence. InDeVal can find multiple variable regions simultaneously (28 indel regions in the Poaceae trnL-F files). The program's sensitivity to different levels of nucleotide and/or length variation in conserved regions can be adjusted.
Implementation
Input files
InDeVal uses three types of input files: one conserved region file, separate variable region files for each variable region, and a DNA sequence file (Table 1). The conserved region file contains a template of sequence immediately flanking the variable regions (regions of interest). A variable region file contains messages that indicate which permutation of the variable region is in the analyzed sequence. The sequence file contains a set of sequences to be analyzed with InDeVal. All files are plain text (ASCII). Conserved region and variable region files are in InDeVal-specific formats. Detailed instructions for creating them are in the help files. The sequence file is in FASTA-format.
A conserved region file contains at least one template, created by taking a representative sequence, removing the variable regions, and replacing them with variable region file names (Additional file 2). Multiple (15) templates were used in the Poaceae trnL-F sequences to accommodate single, large deletions that spanned otherwise conserved regions. Treating them the same as the other indels would have resulted in a few large, difficult-to-interpret variable regions. InDeVal performance is improved by designing templates with conserved regions at least 20 bases long on either side of each variable region. However, the program still functions using templates with only one conserved region, conserved regions only 4 bases long, and variable regions flanked by other variable regions (such as clearly distinguishable, adjacent indel regions varying at different rates, which are found in Poaceae trnL-F). Although InDeVal parameters can be adjusted to reflect different degrees of nucleotide and length variation in conserved regions, it is a good idea to use a representative sequence for template design, especially if some conserved regions are short. Additional templates can be designed to accommodate distantly related taxa. (In the Poaceae trnL-F files, separate templates were designed for Pooideae, Ehrhartoideae, and the PACCAD clade).
A variable region file contains the sequence variations the researcher expects in the region and some output information about each variation (Additional file 2). In the Poaceae trnL-F files, the output information is the list of taxa with each variation. For coding sequence, the output information could be the amino acids for which the variations code. If the user is interested only in knowing if a particular variation is present, the output could be simply "Yes". Variants not found in the variable region file are also reported by InDeVal. The program works if the variable region file is completely blank (variable regions are all bases found between template conserved regions), but it obviously cannot provide output messages in this case. Symbols can be used in variable region sequence to draw attention to specific features of interest. They are ignored during alignment, but are displayed in the Variable Region Sequence List Box (Additional file 2). The Poaceae trnL-F variable region files include spacing that emphasizes repeat motifs, hyphens to indicate that an entire variable region has been deleted, and stars to indicate possible inversion sites. InDeVal can help the user create these input files, comparing new sequences to those already included and indicating what adjustments should be made.
Sequence files are in a less stringent FASTA-format (Additional file 2) and can be in either orientation. They can include spaces, numbers, capital or small letters, IUPAC ambiguity symbols, and carriage returns without disrupting InDeVal function.
A conserved region file (TemplatePtrnLF.txt) and 28 variable region data files, designed from 533 Poaceae trnL-F sequences, are provided with InDeVal. These files are based on the first author's critical examination (to be published elsewhere) of various alignments of these sequences using the web-based programs BLAST 2 SEQUENCES [30] and/or CLUSTAL W multiple alignments [31].
Aligning a sequence with a given template
InDeVal begins by sorting a template's conserved regions by length. It then searches for each conserved region in the analyzed sequence (using LPAM – see below), proceeding from longest to shortest. Found conserved regions are used to limit the search space for future searches (Figure 1).
Sometimes conserved regions are not found or are found at multiple locations within their subsequence. The program records this information and proceeds to the next longest sequence. A conserved region that cannot be aligned is recorded as "not found." A conserved region with multiple possible alignments is recorded as "found", but none of its possible alignments is used to limit the searches for the remaining regions.
This template alignment algorithm preserves the ordering found in the template, giving priority to the alignment suggested by the longer conserved regions, which are assumed to be more reliable. Using the alignment of longer regions to reduce the search space for smaller regions minimizes the probability of finding ambiguous or incorrect alignments.
Length-Preserving Alignment Method (LPAM)
InDeVal uses the LPAM algorithm, designed specifically for InDeVal and described here for the first time, to align the conserved regions within their subsequences. LPAM divides a conserved region into overlapping "words", strings of sequence of a user-defined length. That is, given a 4-base word length, the sequence "caatgt" would be represented as "caat", "aatg", "atgt". (Conserved regions shorter than the word length are found only if they match exactly.) LPAM searches the analyzed sequence for each of these words and notes multiple finds, single finds, and missing words. Each word is allowed one "vote" for a possible alignment, i.e., for the base in the analyzed sequence that begins the conserved region. If the word occurs once, it casts its vote for that alignment. If it occurs multiple times, its vote is divided equally among the possibilities. Words that are not found cast no vote. The alignment that receives the most votes is assumed to be the most probable.
LPAM permits a (user-definable) degree of length variation. A word's possible alignments are evaluated according to whether or not they agree with the most probable alignment for the region as a whole. Suppose, for example, that the most probable ungapped conserved region alignment would begin at base 53. If the tolerance is set to 3, LPAM will allow the first word to begin at any base from 50 to 56, preferring the possibility closest to 53. If the first word in the conserved region sequence has no acceptable alignment, LPAM searches for the first word that does have an acceptable alignment. All bases preceding this first found word are aligned with no gaps. In the above example, if the first and second words are not found and the third word aligns beginning at base 57, the first two bases of the conserved region will be aligned with bases 55 and 56.
Each subsequent base is aligned similarly. If the word it begins has no acceptable alignment, the base is aligned according to the previous acceptable word. If the word it begins has multiple acceptable alignments, the one closest to the previous alignment is chosen. Note that the range of acceptable alignments remains constant; it is a function of the initial voting and does not depend on any of the choices made in aligning individual bases.
The aligned bases from the analyzed sequence are compared with the template conserved region sequence, and a percentage similarity is established. If this is greater than the user-defined cut-off value, the conserved region is listed as found.
InDeVal then checks the most probable alignment that clearly differs from the first. (In the example above, this excludes any alignment beginning at bases 50–56.) If this alignment also yields a percentage similarity greater than the cut-off value, neither is selected, but the region is still considered to be "found" for purposes of measuring similarity to the template (see below).
The LPAM algorithm is able to align conserved regions despite both point mutations and indels. However, deletions in the first or last word of a conserved region are indistinguishable from point mutations (and are interpreted as such). LPAM proved effective in the Poaceae trnL-F sequences, where conserved regions are by definition length-conserved. In 533 sequences, there were only three instances where an indistinguishable mismatch was caused by an indel instead of a point mutation and resulted in misalignment. It is important to design templates so that the ends of conserved regions are not in sequence prone to indel activity.
The alignment suggested by LPAM is not necessarily locally maximal, i.e., there are cases where a slight adjustment of the alignment would produce a higher percentage of matched bases. Furthermore, a number of factors can prevent LPAM from finding a correct alignment. Repetitive sequence, resulting in multiple matches, can be very problematic. InDeVal may not be appropriate for analyzing repetitive sequence, and if it is used for this purpose, templates must be designed with great care. If every word contains a point mutation, the conserved region cannot be found. Indels can cause problems if they are dispersed so that several words are disrupted. In general, LPAM will correctly align regions if the mutations are clustered and the first and last base can be assigned correctly (which will be the case if they have no indels between them and the undisrupted word that will position them). Though hypothetical situations where LPAM would create incorrect alignments are easy to envision, in practice, the algorithm proved able to reliably determine where the conserved regions of a sequence begin and end. This is sufficient for the purposes of InDeVal. It is important to note that even when LPAM does not find a conserved region alignment, a possible alignment is displayed, and the user can rearrange it (to a limited extent) and interpret it.
Choosing the best template
Once conserved regions have been found, InDeVal calculates a "found conserved region score" for each template. Each matched conserved region is given a value equal to the number of bases in that region in the template (regardless of how many individual bases actually matched), and these values are summed to give a score for the template. This prevents a template with a major deletion from being selected over a template with more matching conserved regions (as could happen if the deletion template were a better match at the base-per-base level). Templates with higher scores are ranked above those with lower scores (Figure 2).
If two templates have roughly equal found conserved region scores (to within a user-selected tolerance), the one with the higher "score percentage" (calculated by dividing the score by the total number of conserved region bases in the template) is ranked higher. This ensures that sequences that actually do have deletions will be matched with deletion templates, even though the templates (theoretically) have an equal number of bases in found conserved regions (Figure 2).
All the templates are ranked in this way and listed in the Template List Box in order. The highest ranked template is automatically selected, and its variable regions are analyzed. The user has the option of comparing the sequence to a template other than the one selected by InDeVal.
Finding the correct variable region sequence
Each variable region is defined as the sequence between two specific conserved regions. If both flanking conserved regions are found, the bases between them are recorded, and the appropriate variable region file is searched for a matching string of bases.
Sequence between two found conserved regions is classified as a confused region if the template indicates that it should contain one or more conserved regions that were not found. Because the variable regions in a confused region are not clearly delimited, InDeVal searches the entire region for each variable region sequence in each applicable variable region file, and all potential matches are recorded. The user is able to select from among these possibilities, rearrange the display to reflect them, and study different sequence interpretations. In this manner, InDeVal is able to deal satisfactorily with most short conserved regions that cannot be found because of point mutations. In the Poaceae trnL-F file, one- and two-base conserved regions were incorporated into adjacent variable region files because they were impossible to find.
A short conserved region will be misaligned if a point mutation prevents it from being found in the proper place and a perfect match is found nearby. If this occurs, it is possible to set the program to disregard conserved regions of this size. (This situation has only been observed for 4- and 5-base conserved regions). The region can then be satisfactorily parsed using the confused region algorithm.
Results and discussion
InDeVal has two output windows (Table 1, Additional file 2). The Sequence Analysis Window displays the analyzed sequence broken into conserved and variable regions and lists template information. From this window, the user can load template and sequence files, export the analysis to a text file, set LPAM parameters and warning message options, convert the sequence to its complement prior to analysis, and choose a template other than the one selected by InDeVal. For conserved regions, the template sequence is listed so that it can be compared directly with the analyzed sequence. For variable regions, if the analyzed sequence matches one of the variants in the variable region file, the message for that variant is displayed.
The Variable Region Analysis Window displays the variable region file name, variants proposed by InDeVal, and the sequence and length of the line currently selected in the Sequence Analysis Window (which can be any line from any sequence). The user can request displays of some or all of the sequences in the variable region file.
A text file can be created once the user is satisfied with the display in the Sequence Analysis Window. This file gives information about the sequence and template, lists the positions of the various conserved and variable regions, and shows the entire analyzed sequence. The user can choose whether or not variable region file information is displayed.
InDeVal for the Windows platform is available in Additional file 1. The source code for InDeVal in Microsoft Visual Basic 6.0 is available in Additional file 3. InDeVal analyzes only DNA sequences, but a version for protein sequences could be created using the source code. It would have to be altered to recognize amino acid sequence (all non-nucleotide letters are presently ignored), and an algorithm to recognize frame-shifts would be helpful.
Conclusions
InDeVal is a program designed to search DNA sequence for target regions and to display information about them. It can find multiple target regions simultaneously and is relatively robust when challenged by conserved region variation and differences among analyzed sequences in length, spanned region, and format. InDeVal differs from other alignment software in that it breaks the analyzed sequence into user-defined units and emphasizes regions that are of most interest to the user. This makes it possible to quickly compare specific features among many hundreds of sequences. An advantage of InDeVal is that, while it can be used to quickly skim and classify hundreds of sequences, it displays all surrounding sequence. Therefore, if at any time questions arise about the initial classification, the researcher can recreate the InDeVal alignment, instantly find the area in question, and study it for alternative explanations.
Availability and requirements
Project name: InDeVal
Project home page:
Operating system(s): Windows Platform
Programming language: Microsoft Visual Basic 6.0
Other requirements: None
License: None
Any restrictions to use by non-academics: No restrictions
Authors' contributions
SDSH planned and oversaw the project, collected the data, prepared the data files, tested the program, and wrote the documentation. JAH suggested the project and designed, wrote, and debugged the program. Both authors read and approved the final manuscript.
Supplementary Material
Additional File 1
InDeVal 1.0 installation version InDeVal is currently available for the Windows platform. Instructions for use can be found in the help files, which are included with the program and are accessible from the Sequence Analysis Window. An installation version of InDeVal 1.0 with accessory files can be obtained by clicking on the link below or by visiting . Running the installation program installs (in addition to system files) an InDeVal program directory that contains the Indeval.exe file, the 7 InDeValHelp.txt files, the InDeValOptions.txt file, the InDeValParams.txt (parameters) file, and a Templates subdirectory containing TemplatePtrnLF.txt and a Vr subdirectory with 28 Poaceae trnL-F variable region files. The program is archived using WinZip® 9.0. WinZip is available from . The zipped package has 3.2 MB.
Click here for file
Additional File 2
Annotated illustrations of InDeVal files and displays This file (386 kB) is a 9-page pdf that contains annotated illustrations and explanations of InDeVal files and displays. The structure and format of the conserved region, variable region, sequence, and output files is illustrated. Annotated screenshots of the Sequence Analysis Window and Variable Region Analysis Window are also included. These illustrations, combined with the InDeVal help files, serve as the InDeVal Manual.
Click here for file
Additional File 3
InDeVal source code The source code for InDeVal in Microsoft Visual Basic 6.0 can be obtained by clicking on the link below or by visiting . The files are archived using WinZip® 9.0. The package (61 kB) includes the 32 code files, the InDeVal icon and the bitmap from which it was constructed, and InDeValSourceCodeHelp.txt, a file with advice on orienting within the source code files.
Click here for file
Acknowledgements
The authors wish to thank P Bureš for helpful discussions and advice and three anonymous reviewers for constructive suggestions. This research was undertaken during the graduate studies of SDSH, supported by a US National Science Foundation Graduate Research Fellowship, a US Student Fulbright Grant, and an Honor Society of Phi Kappa Phi Fellowship, and was supported in part by the Ministry of Education of the Czech Republic FRVŠ Project 556-G4 (Phylogenetic analysis of Poa L. on the basis of non-coding cpDNA sequences) and Research Project MSM 143100010 (Spatial and Temporal Biodiversity Dynamics in Ecosystems of Central Europe).
Figures and Tables
Figure 1 Limiting search space with found conserved regions This figure shows a schematic diagram of a template with four conserved regions of varying lengths (indicated by colored boxes and labeled CR1–CR4) and an analyzed sequence (indicated by two lines). Conserved regions are searched for in order of length, and each found conserved region limits the search space of future searches.
Figure 2 Ranking templates This figure shows a schematic diagram of two templates (conserved regions indicated by colored boxes) and two analyzed sequences (indicated by two lines). The first sequence is a better match with Template 1, because it has more conserved region bases in common. The second sequence is a better match with Template 2 (a major deletion template), because it matches a higher percentage of all the conserved regions in the template.
Table 1 This table summarizes the inputs and outputs for InDeVal. It provides the names of the different InDeVal file types and displays, the number involved in analyzing a single portion of DNA, whether or not they are required for InDeVal function, their basic format, whether or not they are provided with the InDeVal package, when they are produced, and a brief description.
Input Number Required Format Provided Description
Conserved region file One Yes InDeVal-specific Yes Representative sequence with variable regions replaced by variable region file names
Variable region file Multiple No InDeVal-specific Yes (28) List of sequences for which a variable region is to be searched and a message to be displayed for each
DNA sequence file One Yes FASTA No Set of sequences to be analyzed
Output Number Produced Format Description
Sequence Analysis Window One per analyzed sequence Always Display Displays analyzed sequence broken into user-defined units and allows InDeVal/user interaction
Variable Region Analysis Window One per variable region Upon user-request Display Displays one variable region so that the sequence found in the analyzed sequence can be contrasted with those found in all other studied sequences
Output file One per analyzed sequence Upon user-request Similar to NCBI default display Text file of the analyzed sequence, annotated with conserved and variable region positions and variable region sequence-specific messages
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| 15516260 | PMC528782 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Oct 29; 5:173 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-173 | oa_comm |
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BMC Cell BiolBMC Cell Biology1471-2121BioMed Central London 1471-2121-5-401549810110.1186/1471-2121-5-40Research ArticleGerminating fission yeast spores delay in G1 in response to UV irradiation Nilssen Esben A [email protected] Marianne [email protected]ård Tonje [email protected]ø Heidi [email protected] Erik [email protected] Beáta [email protected] Department of Cell Biology, Institute for Cancer Research, Montebello, 0310 Oslo, Norway2004 21 10 2004 5 40 40 25 3 2004 21 10 2004 Copyright © 2004 Nilssen et al; licensee BioMed Central Ltd.2004Nilssen et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Checkpoint mechanisms prevent cell cycle transitions until previous events have been completed or damaged DNA has been repaired. In fission yeast, checkpoint mechanisms are known to regulate entry into mitosis, but so far no checkpoint inhibiting S phase entry has been identified.
Results
We have studied the response of germinating Schizosaccharomyces pombe spores to UV irradiation in G1. When germinating spores are irradiated in early G1 phase, entry into S phase is delayed. We argue that the observed delay is caused by two separate mechanisms. The first takes place before entry into S phase, does not depend on the checkpoint proteins Rad3, Cds1 and Chk1 and is independent of Cdc2 phosphorylation. Furthermore, it is not dependent upon inhibiting the Cdc10-dependent transcription required for S phase entry, unlike a G1/S checkpoint described in budding yeast. We show that expression of Cdt1, a protein essential for initiation of DNA replication, is delayed upon UV irradiation. The second part of the delay occurs after entry into S phase and depends on Rad3 and Cds1 and is probably due to the intra-S checkpoint. If the germinating spores are irradiated in late G1, they enter S phase without delay and arrest in S phase, suggesting that the delay we observe upon UV irradiation in early G1 is not caused by nonspecific effects of UV irradiation.
Conclusions
We have studied the response of germinating S. pombe spores to UV irradiation in G1 and shown that S phase entry is delayed by a mechanism that is different from classical checkpoint responses. Our results point to a mechanism delaying expression of proteins required for S phase entry.
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Background
Checkpoint mechanisms are important for cell survival and genetic stability. They prevent cell cycle transitions until previous events have been completed or damaged DNA has been repaired [1]. Checkpoint pathways and proteins are evolutionarily conserved from yeast to man, underlining their importance in maintaining genomic integrity. In fission yeast several checkpoint pathways monitor the status of the DNA and arrest the cell cycle in response to DNA damage or inhibition of DNA replication [2,3] They include mechanisms to inhibit mitosis when the DNA is damaged (the G2/M checkpoint) or when S phase has not been completed (the S/M checkpoint) as well as a mechanism to inhibit ongoing DNA replication when the DNA is damaged (the intra-S checkpoint). Screens designed to reveal elements of the checkpoint pathways have led to the identification of the so-called checkpoint rad genes as well as crb2/rhp9, mrc1, chk1 and cds1 [4-13] The checkpoint rad genes consist of rad1, rad3, rad9, rad17, rad26 and hus1 (reviewed in ([14]). Rad3 is a member of the phosphatidylinositol 3-kinase family of proteins and the closest mammalian homologue is the ATR (ATM and Rad3 related) protein [15,16]. Rad3 forms a complex with Rad26 and this association is required for activation of Rad3 kinase activity in response to DNA damage or replication arrest [17,18] The Rad1, Rad9 and Hus1 proteins have similarities to PCNA, the sliding clamp of the replicative DNA polymerase, and the three proteins may form a similar ring-shaped structure [19-21]. Rad17 has similarities to all five subunits of replication factor C [22], a complex which loads PCNA onto chromatin.
There are two known effector kinases downstream of the checkpoint Rad proteins, Chk1 and Cds1. Chk1 is phosphorylated in response to DNA damage induced in late S or G2 in a Rad3 dependent manner [12,23,24]. Phosphorylation of Chk1 leads to an increase of Chk1 kinase activity [25] and is often used as a convenient molecular marker for Chk1 dependent checkpoint activation. Cds1 is activated only in S phase as part of the intra-S and the S/M checkpoints [8,26] Activation of either kinase leads to inhibition of Cdc2 activity by maintaining the inhibitory phosphorylation on Tyr15 [27-29].
Crb2 and Mrc1 act upstream of Chk1 and Cds1, respectively. Crb2 shares homology with the budding yeast RAD9 protein [10], which is involved in delaying entry into S phase upon DNA damage in G1 [30-32] In fission yeast, Crb2 is required both for activation of Chk1 and for subsequent inactivation of Chk1 for reentry into the cell cycle [10,33] Mrc1 plays a parallel role by binding to and activating Cds1. Expression of Mrc1 is regulated in the cell cycle, thus linking Cds1 activation to S phase [6,11].
In addition to the G2/M, S/M and intra-S checkpoints, three papers have reported the existence of G1 checkpoints that inhibit mitosis when the cells are arrested in G1 using cell cycle mutants. Arrest at the cdc10 arrest point was shown to depend on Chk1 [34] and Rum1 [35]. Arrest of orp1 mutant cells depends on the checkpoint Rad proteins and on Chk1 [36]. It should be noted that neither of these cell cycle mutants is able to replicate their DNA at the restrictive temperature, and failure of the checkpoints responsible for cell cycle arrest results in aberrant entry into mitosis and not into S phase.
A G1/S checkpoint has so far not been detected in S. pombe. The drop of CDK activity at the M/G1 transition allows the assembly of the pre-Replication Complex, preRC, which is the first step leading to initiation of S phase. The preRC consists of the ORC (Origin Recognition Complex), Cdc18, Cdt1 and the MCM proteins. Expression of Cdc18 and Cdt1 is cell cycle regulated, thus providing one of the means to regulate initiation of S phase [37,38] Once the preRC is assembled, the chromatin is competent to replicate, but replication is not initiated until other replication proteins are loaded, and two kinases, Cdc2 and Hsk1, are activated. It has been shown both in fission yeast and in Xenopus that the intra-S phase checkpoint cannot be engaged until polα-primase is loaded and replication begins [39,40] This observation poses the question whether the cells have any means to respond to DNA damage sustained in G1.
G1 in fission yeast is very short under standard laboratory growth conditions, rendering the investigation of a G1/S checkpoint(s) difficult. However, G1 might be much extended in the natural habitat of S. pombe due to poor nutrient availability. We decided to use several approaches to synchronise the cells and/or to extend G1. Recently we reported the existence of a mechanism that delays entry into S phase when cycling cells are UV-irradiated in G1, using cdc10 and cdc25 mutants to synchronise the cells or growing the cells in medium where G1 is extended [41].
Here we show that germinating S. pombe spores delay entry into S phase upon UV irradiation in early but not late G1. We demonstrate that there is a G1/S delay that is not dependent on any of the known checkpoint proteins and does not target Cdc2 phosphorylation. We argue that the delay is due to a novel mechanism that leads to delayed expression of Cdt1 and possibly other replication proteins.
Results
Entry into S phase is delayed by UV irradiation
Spores made from diploid cells were allowed to germinate for 3.5 h at 30°C before UV irradiation. At this time point, 1 – 2 hours before S phase entry, the spores showed visible signs of germination by phase contrast microscopy. The dose of UV light was 1200 J/m2, which gave a cell survival of about 30% in wild type cells (data not shown). At the time of irradiation, the majority of germinating spores had a 1C DNA content. The timing of S phase was measured, by flow cytometry, as an increase in cellular DNA content from 1C to 2C. The decrease of the 1C population was plotted against time, and the graphs for unirradiated control and UV irradiated cells were compared at the point where 50% of the cells had 1C DNA content.
An inherent problem in the present experiments is that the time of germination varies both within each population of spores (low degree of synchrony) and between the different preparations (experiment-to-experiment variation). Thus, the time from resuspension in medium until the cells enter S phase is variable, and it is difficult to ensure that irradiation occurs at exactly the same time point relative to S phase entry. Therefore the experiments were repeated at least twice and the averages were determined (Table 1). The experiments revealed that UV irradiation made wild type cells delay their S phase entry by 76 minutes relative to unirradiated control cells (Fig. 1A, Table 1). When the irradiated cells started to increase their DNA content, they did not delay appreciably within S phase compared to unirradiated control cells, suggesting that when they started to synthesise DNA, the DNA damage had been removed. However, when the germinating spores were irradiated shortly before S phase entry, they entered S phase without a delay and were arrested with a DNA content between 1C and 2C (Fig. 1B, Table 1), presumably due to the intra-S checkpoint. Indeed, the later the irradiation was performed the more pronounced the intra-S phase delay was (data not shown). We conclude that cells irradiated in early G1 arrest temporarily with 1C DNA content, then replicate their DNA with normal timing. Cells irradiated in late G1 exit from 1C with the same kinetics as unirradiated control cells do, but they are unable to complete S phase in normal time.
Table 1 Length of the delay in the investigated mutants
Mutant Length of the 1C delay (min)(*) Average length of the 1C delay (min)
wt early irradiation 70, 80, 90, 60, 80 76
wt late irradiation <10, <10 <10
caffeine <10, <10 <10
rad3 40, 20, 45, 15 30
rad26 45, 40 43
rad1 50, 40 45
rad9 40, 30 35
hus1 55, 40 50
rad17 40, 30 35
cds1 50, 40 45
chk1 90, 55, 70 72
chk1 cds1 35, 40 38
rum1 55, 45, 25 42
res2 <10, <10, <10 <10
(*) The length of the delay was measured at the point where 50% of the cells had a 1C DNA content on the quantitations. The second column shows the results of individual experiments, the third column shows the average lengths of the delay. Irradiations were carried out 3.5 hours after inoculation in medium, except for the entry "wt late irradiation", which was performed 4.5 hours after inoculation.
Figure 1 Irradiation of germinating wild type spores delays entry into S phase. Germinating spores were irradiated with UV light 3.5 h (A) and 4.5 h (B) (time 0) after inoculation into EMM medium, as described in Materials and Methods. Samples were taken for flow cytometry at the indicated times after treatment. The uppper panels show DNA histograms for the unirradiated control (shaded) and the irradiated cells (bold outline without shading). The lower panels show the quantification of cells with a 1C DNA content. Filled symbols represent the control cells, open symbols represent the irradiated cells.
The cells delay with low levels of Cdt1
Flow cytometry cannot distinguish between G1 and early S phase cells, therefore we sought to confirm that the cells arrest prior to S phase. PreRC formation is a prerequisite for initiation of S phase. The first step towards preRC formation is de novo synthesis of Cdc18 and Cdt1, which in turn are required for MCM loading. We investigated Cdt1 levels in germinated spores treated as above to establish the timing of the 1C delay relative to Cdt1 expression. Wild type spores carrying myc-tagged Cdt1 were UV irradiated as described above and samples of irradiated and control cells were removed for analysis by flow cytometry and by immunoblotting. Cdt1 expression was induced already at 30 minutes in the control cells, but not until 55 minutes later in the UV irradiated cells (Fig. 2, Table 2). These observations suggest that cells irradiated in early G1 may delay entry into S phase at least in part by delaying preRC formation.
Figure 2 The cells delay with low levels of Cdt1. Wild type spores carrying myc tagged Cdt1 were germinated and irradiated as described in the legend to Figure 1A. Samples were taken for protein extracts and flow cytometry at the times indicated. Total protein extracts were prepared and the amount of Cdt1-myc was investigated by SDS-PAGE and immunoblot analyses against total Cdc2, which served as loading control, and Cdt1-myc (top panel). Quantification of the fraction of cells with a 1C DNA content is also shown in the bottom panel (filled symbols: control; open symbols: UV).
Table 2 Cdt1 expression and Cdc2 phosphorylation are delayed upon UV irradiation
Event Length of the delay (min)(*) Average length of the delay (min)
Cdt1 expression 60, 50 55
Cdc2 phosphorylation 40, 50 45
(*) The levels of Cdt1 expression and Cdc2 phosphorylation were quantified and the length of the delay was measured at the point where Cdt1 expression and Cdc2 phosphorylation, respectively, reached 50% of its maximal value. Irradiations were carried out 3.5 hours after inoculation in medium.
Rum1 is required for part of the delay
Rum1 inhibits the mitotic CDK, Cdc2-Cdc13, and is required for efficient proteolysis of Cdc13 [42-44]. Furthermore, Rum1 is required for all G1 arrests and delays investigated so far. We irradiated germinating rum1Δ spores as above and progression into S phase was followed by flow cytometry. Irradiated spores delayed with a 1C DNA content for 40 minutes (Fig. 3A, Table 1). At the 60–90 minute timepoints the irradiated cells display a distinct delay in S phase, consistent with activation of the intra-S-phase checkpoint. The absence of Rum1 shortens G1, therefore some of the germinating spores were in fact in late G1 or early S at the time of irradiation, giving rise to significant activation of the intra-S-phase checkpoint.
Figure 3 Rum1 is required for part of the delay. A. rum1Δ spores were irradiated and analysed as described in the legend to Figure 1A. B. Wild type spores were germinated and irradiated as described in the legend to Figure 1A. Samples were taken for protein extracts and flow cytometry at the times indicated. Total protein extracts were prepared and the amount of Rum1 was investigated by SDS-PAGE and immunoblot analyses against total Cdc2, which served as loading control, and Rum1. Quantification of the fraction of cells with a 1C DNA content is also shown in the bottom panel (filled symbols: control; open symbols: UV).
Rum1 expression is cell cycle regulated such that it is only expressed in G1 [45]. We investigated Rum1 levels in germinated spores UV irradiated as described above and samples of irradiated and control cells were removed for analysis by flow cytometry and by immunoblotting. Rum1 expression was induced at 30 minutes in both cultures, but was maintained to a higher extent and longer in the irradiated cells (Fig. 3B). Both increased expression of Rum1 and the requirement for Rum1 for part of the delay demonstrate that part of the delay takes place in G1.
Is the 1C delay checkpoint dependent?
The definition of a checkpoint calls for the existence of mutations or chemicals that eliminate the delay. We addressed this issue by treating the germinating spores with caffeine. Caffeine is known to abolish checkpoint function in both higher eukaryotes and fission yeast, possibly through the inhibition of Rad3 [46]. Caffeine was added to the culture 15 minutes before UV irradiation. Flow cytometric analyses showed that the caffeine-treated spores entered S phase with the same kinetics as unirradiated cells (Fig. 4A, Table 1). This observation indicates, (but does not prove, see Discussion), that the delay might be caused by a checkpoint mechanism.
Figure 4 Checkpoint Rad proteins in the G1 checkpoint. A. Wild type spores germinating in the presence of caffeine were irradiated and analysed as described in the legend to Figure 1A. B. Germinating rad3 spores were irradiated and analysed as described in the legend to Figure 1A. C. The indicated mutants were sporulated and the germinating spores were irradiated and analysed as described in the legend to Figure 1A.
Given that caffeine can inhibit Rad3 related kinases, we investigated whether the G1/S delay is also abolished in rad3 mutant cells. Irradiated rad3 germinating spores delayed with a 1C DNA content for 30 minutes, in contrast to the 76 minute delay of wild type cells (Fig. 4B, Table 1).
We have investigated whether the other checkpoint Rad proteins are involved in the G1/S delay. rad26, rad1, rad9, hus1, and rad17 spores were germinated and UV irradiated as described above. Figure 4C (and Table 1) shows that rad26, rad1, rad9, hus1 and rad17 cells delay much less than wild type cells do (35–50 versus 76 min). We conclude that Rad26, Rad1, Rad9, Hus1 and Rad17 are required for at least a part of the 1C delay.
Mrc1 and Cds1, but not Crb2 and Chk1, are required for part of the delay
The products of the checkpoint genes cds1 and chk1 are both known downstream targets of the Rad3 protein kinase and they are required for Cdc2 phosphorylation in the DNA damage and replication checkpoints. We irradiated germinating spores carrying mutations of cds1, chk1 or both. In cds1 spores the delay was reduced to 45 minutes (Fig. 5A, Table 1). In chk1 spores (Fig. 5B, Table 1) the length of the delay was not reduced compared to that found in wild type cells. In cds1 chk1 double mutant spores the delay was somewhat shorter than in either single mutant, 35 minutes versus 45 and 73 minutes (Fig. 5C, Table 1). The shorter delay in the cds1 chk1 double mutant compared to that in cds1 indicates that chk1 might have a synthetic effect with the cds1 mutation.
Figure 5 Cds1, but not Chk1, is required for part of the delay. cds1Δ (A), chk1Δ (B) and chk1Δ cds1Δ (C) spores were irradiated and analysed as described in the legend to Figure 1A.
Crb2 and Mrc1 are required for activation of Chk1 and Cds1, respectively. We irradiated germinating crb2 and mrc1 spores [11]. Consistent with the above findings, in mrc1 the delay was reduced to 50 minutes, while in crb2 the delay was not reduced compared to that in wild type cells (data not shown). We conclude that Mrc1 and Cds1 are required for part of the delay, while Crb2 and Chk1 are not required.
The arrested cells maintain Cdc2 in the unphosphorylated form
Cdc2 kinase activity is required for the initiation of S phase and is inhibited by phosphorylation on Tyr15 as DNA replication commences [35,47]. We investigated whether the Cdc2 protein is phosphorylated when the cells are delayed with a 1C DNA content. Germinating wild type spores were treated as above and samples of irradiated and control cells were removed for analysis by flow cytometry and by immunoblotting. The results show an increase in the phosphorylation signal as the unirradiated cells enter S phase (Fig. 6), in agreement with previous findings [35,47] The irradiated cells increased phosphorylation of Cdc2 45 minutes later (Table 2). We conclude that the irradiated cells arrest with a 1C DNA content for a significant length of time with unphosphorylated Cdc2.
Figure 6 Cdc2 is not phosphorylated in the arrested cells. Wild type spores were germinated and irradiated as described in the legend to Figure 1A. Samples were taken for protein extracts and flow cytometry at the times indicated. Total protein extracts were prepared and the amount of phosphorylated Cdc2 was investigated by SDS-PAGE and immunoblot analyses against total Cdc2, which served as loading control, and phosphorylated Cdc2 (top panel). Quantification of the fraction of cells with a 1C DNA content is also shown in the bottom panel (filled symbols: control; open symbols: UV).
res2 mutant cells do not delay S phase entry after irradiation
A number of genes required for DNA replication are transcribed as the cells prepare for S phase. This activation depends on the cell cycle regulated transcription factor Cdc10/Res1/Res2 [48,49]. In the absence of Res2, transcription is constitutively active [50]. If the G1/S delay in fission yeast cells is brought about by inhibiting this transcription factor, constitutive activation of transcription in a res2 mutant should override the UV-induced G1 delay. We have irradiated germinating res2Δ spores as described above and found that S phase entry was not delayed compared to unirradiated control cells (Fig. 7, Table 1).
Figure 7 The res2Δ mutant cells do not display the delay. res2Δ mutant spores were germinated and irradiated as described in the legend to Figure 1A.
Cdc10 dependent transcription is not inhibited during the delay
The above result indicates that either constitutive expression of Cdc10 dependent genes required for S phase entry can override the delay or inhibiting Cdc10 dependent transcription might be the mechanism of the delay. A prediction of the latter alternative is that cells arrested with 1C DNA content upon UV irradiation should not have performed the Cdc10-dependent transcriptional events, including induction of the cdc18, cdt1, and cig2 genes. We isolated total RNA from irradiated and unirradiated germinating wild type spores and followed the transcription of cig2, cdt1 and cdc18. There was no delay in the appearance of the Cdc10 dependent transcripts upon UV irradiation (Fig. 8, only data for cdt1 are shown). We conclude that Cdc10 dependent transcription is not the mechanism of the delay.
Figure 8 Cdc10 dependent transcription is not inhibited during the delay. Wild type spores were germinated and irradiated as described in the legend to Figure 1A. Samples were taken for RNA extracts and flow cytometry at the indicated times. Total RNA extracts were prepared and the amount of cdt1 mRNA was investigated by Northern analysis.
Discussion
We have provided evidence for the existence of a mechanism in germinating fission yeast spores that delays entry into S phase upon UV irradiaton in early G1. Germinating wild type spores displayed a pronounced delay in entering S phase after UV irradiation. The delay was observed only when irradiation was carried out in early but not in late G1. We have investigated the dependence of the delay on classical checkpoint proteins and showed that they are required for some but not all of the delay with 1C DNA content. We argue that the observed delay is caused by two separate mechanisms, the first taking place before entry into S phase, and the second in early S phase (see below).
The delay in exit from the 1C population was demonstrated by means of flow cytometry, which does not allow us to distinguish between a G1/S and an early S delay. The following data represent strong evidence that part of the delay takes place before entry into S phase. First, the irradiated cells delay expression of Cdt1. In the absence of Cdt1 the cells cannot form preRCs and thus cannot initiate S phase. Second, the irradiated cells express Rum1 longer than unirradiated control cells. Since Rum1 expression is cell cycle regulated such that it is only expressed in G1, [45], this observation implies that the irradiated cells do delay in G1. Furthermore, the delay is shorter in a rum1 mutant, which presumably loses the G1 part of the delay. Third, mutants lacking Mrc1 or Cds1, which are essential for S-phase checkpoints reported so far in fission yeast [6], still delay for a significant length of time, pointing to the existence of a non-S mechanism [11]. Fourth, cells delay with the Cdc2 kinase in an unphosphorylated state. Cdc2 is normally inhibited by phosphorylation on Tyr15 as DNA replication commences [35,47], arguing that the cells arrest before S phase. Fifth, the delay is not observed in a res2 mutant, which can not turn off the Cdc10 dependent transcription signal. The finding that a mutation affecting expression of proteins crucial for preparation for S phase abolishes the delay argues that the wild type cells first stop in G1 and only later stop in S phase.
On the basis of these results we conclude that there is a UV-induced G1 delay, which is not checkpoint Rad dependent and is brought about by an as yet undescribed mechanism. This part of the total delay with 1C DNA content is ca 40 minutes, since rad, cds1 and mrc1 mutants delay 30–50 minutes and rum1Δ cells lose ca 40 minutes of the delay, compared to wild type cells. The remaining ca 40 minutes of the total delay requires the checkpoint Rads, Mrc1, Cds1 and Cdc2 is phosphorylated. We argue that this part of the delay is brought about by the intra-S checkpoint. However, the resolution of our experiments is not high enough to exclude the possibility that some of the checkpoint Rad- and Cds1-dependent part of the delay occurs in late G1. We consider this possibility unlikely for two reasons; first, previous work has shown that the role of Cds1 is specific for S phase [26] and second, we have shown in the current paper that if irradiation occurs later, the cells enter S phase without delay and delay in S phase.
Since the level of synchrony is low in germinating spores, we have not emphasised minor differences in the timing of S phase entry. In spite of poor synchrony, we deem germinating spores a good model system, since spore germination is a natural phenomenon, it involves an extended G1 period and we observed clear-cut effects. Furthermore, this model system allowed us to investigate the effects of a number of mutations that would have not been possible using synchronisation by other methods.
We have explored whether the G1/S delay is caused by a checkpoint mechanism. We have shown that caffeine abolishes the delay, but this is not entirely due to inhibition of Rad3 activity, since a rad3 mutation does not abolish all of the G1/S delay. Since we have not identified a checkpoint mutation which abolishes the delay, we attribute the effect of caffeine to another effect than the inhibition of checkpoint proteins. Interestingly, recent data suggest that caffeine inhibits checkpoint responses without inhibiting the ATR and ATM kinases in human cells [51,52].
Previously, Rhind and Russell [53] showed that UV-irradiation during G1 delays passage through S-phase. However, this checkpoint arrests cells in S phase, requires Cds1 function and probably represents the intra-S checkpoint.
We have recently discovered a mechanism that delays entry into S phase in cells irradiated in early G1 in synchronised or in cycling cells [41] This inhibitory mechanism has several features in common with that described here. Both pathways are activated in early but not in late G1; both inhibit entry into S phase; both pathways are independent of classical checkpoint genes and of Cdc2 phosphorylation. These similarities argue that the G1/S mechanism demonstrated in germinating spores and in cycling cells is one and the same.
In budding yeast there is a G1 DNA damage checkpoint response that depends upon Mec1 [31,32,54], a homologue of the mammalian ATM/ATR and the fission yeast Rad3 protein. However, the budding yeast G1 checkpoint response depends on Rad53 [55,56], whereas its homologue in S. pombe, Cds1, is not involved in the present pathway. The budding yeast G1/S checkpoint delays entry into S phase by phosphorylating and thereby downregulating Swi6, the homologue of Cdc10 [57]. In contrast, in fission yeast Cdc10 dependent transcription is not delayed during the G1/S delay (Fig. 8).
Other possible mechanisms for the G1/S delay include inhibition of Cdc2 by Rum1 or an as yet unidentified mechanism such as preventing the formation of Cdc2-cyclin complexes or by restricting the availability of cyclins. We have shown that Rum1 is expressed during the delay and is required for the G1 delay. This observation does not imply that Rum1 is a direct target of the G1/S delay, but this remains an attractive possibility. Another possible mechanism for the delay is delaying expression of proteins required for the initiation of DNA replication. In particular, the findings that (1) irradiation in late G1 does not cause a delayed entry into S phase, (2) increased transcription of Cdc10 dependent genes in res2Δ overrides the delay, (3) transcription of Cdc10 dependent genes is not downregulated during the delay and (4) expression of Cdt1 is delayed, suggest that the G1/S delay is caused by delayed expression of Cdt1 and probably also of Cdc18 and Cig2. Since we have shown that transcription of Cdc10 regulated genes is not downregulated, the most likely mechanism of the delay is reduced translation rate of Cdt1 and possibly other proteins required for initiation of DNA replication.
Conclusions
We studied the response of Schizosaccharomyces pombe cells to UV irradiation in G1. We used germinating spores to exploit a natural phenomenon where the cells have a long G1. In this paper we provide evidence for the existence of a mechanism in fission yeast that delays entry into S phase upon UV irradiaton in early G1. The G1 delay is independent of classical checkpoint proteins and Cdc2 phosphorylation. Our results point to a mechanism that delays translation of proteins required for S phase entry.
Methods
Fission yeast strains and methods
All our strains are derivatives of the Schizosaccharomyces pombe L972h- strain. All basic growth and media conditions were as described [58].
Sporulation and spore germination
Diploids were made by interrupted mating of h+ and h- strains carrying the met3 or ade1 complementing markers. The rum1:::ura4+/rum1+ diploid was made by protoplast fusion since rum1Δ is sterile [44]. All diploids, with the exceptions of res2Δ (which is deficient in meiosis [59]) and rum1Δ, were homozygous for the respective mutations. In case of these two mutants Δ/wild type ura4-D18/ura4-D18 diploids were sporulated and the spores were germinated in the absence of uracil. The diploids were sporulated in liquid malt extract medium at 30°C, then incubated with 3 μl/ml β-glucuronidase (Helix pomatia juice, Biosepra) at 30°C overnight. The spores were washed twice in water and resuspended in EMM2 supplemented with adenine and methionine for germination and UV irradiation.
UV irradiation
Cells were irradiated with 254 nm UV light while rapidly stirred in a thin layer (3 mm) of liquid medium. The dose administered was measured with a radiometer (UVP instruments) and an exposure time of 4 minutes gave an incident dose of about 1100 J/m2. Cell survival was monitored by conventional plating on YE plates. The incident dose does not reflect the dose absorbed by the cells because UV light of this wavelength penetrates poorly into water. However, since irradiation conditions were constant, the incident dose was proportional to the absorbed dose.
Protein extracts and western blots
Protein extracts for western blotting were made by TCA extraction, as described previously [19]. For western blot analysis the following antibodies were used: anti-phosphotyrosine Cdc2 (Sigma C0228) at a dilution of 1:400, anti-PSTAIRE against Cdc2 (Santa Cruz sc-53) at a dilution of 1:2000, anti-myc (PharMingen) at a dilution of 1:1000. The secondary antibodies were either HRP or AP conjugates, used at a dilution of 1/5000. Detection was performed using the enhanced chemiluminescence procedure (NEN ECL kit). Cdc2 and phosphorylated Cdc2 was measured using ECF detection (Amersham) and quantified with the Image Quant software.
RNA preparation and blotting
Total RNA was isolated as described [58], resolved on formaldehyde agarose gels and blotted onto a nitrocellulose membrane (NitroPure, Osmonics). All blots were hybridized with 32P-labelled RNA probes, generated with T7 RNA polymerase (Riboprobe System T7 Kit, Promega). For cig2 and cdc18, the ORFs were inserted into pGEM-3 MCS to serve as template for producing the RNA probes. For cdt1, a PCR fragment of the ORF with T7 promoter sequence attached to the lower primer was used as template. Hybridisation was carried out using standard procedures and visualised by a STORM 860 Phosphoimager (Molecular Dynamics).
Flow cytometry
About 107 cells were spun down for each sample and fixed in 70% ethanol before storing at 4°C. Samples were processed for flow cytometry as described [60] and stained with Sytox Green (Molecular Probes S-7020) [61], and analysed with a Becton-Dickinson FACSCalibur. The fraction of 1C cells was quantified using the CellQuest software (BD Biosciences).
Authors' contributions
EAN showed the existence of the delay and investigated the roles of Rad3, Chk1 and Cds1, Cdc2 phosphorylation and Cdc10-dependent transcription as a potential mechanism. MS, TT and HV investigated the roles of further checkpoint proteins and that of Res2. EB participated in the design and coordination of the study and in writing the manuscript. BG devised the study, analysed Rum1 and Cdt1 expression and drafted the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We would like to thank T. Carr, S. Moreno, H. Nishitani, P. Nurse and P. Russell for strains and antibodies and M. O. Haugli and J. A. Sandvik for technical assistance. Work in our laboratory is funded by The Norwegian Cancer Society, the Norwegian Research Council and the Astri and Birger Torsteds Legacy.
==== Refs
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| 15498101 | PMC528784 | CC BY | 2021-01-04 16:31:37 | no | BMC Cell Biol. 2004 Oct 21; 5:40 | utf-8 | BMC Cell Biol | 2,004 | 10.1186/1471-2121-5-40 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-401549622910.1186/1471-2148-4-40Research ArticlePhylogenetic inference in Rafflesiales: the influence of rate heterogeneity and horizontal gene transfer Nickrent Daniel L [email protected] Albert [email protected] Yin-Long [email protected] Romina [email protected] Frank E [email protected] Department of Plant Biology, Southern Illinois University, Carbondale, IL 62901-6509, USA2 Institute of Systematic Botany, University of Zurich, 8008 Zurich, Switzerland3 Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109-1048, USA4 Department of Zoology, Southern Illinois University, Carbondale IL, 62901-6501, USA2004 20 10 2004 4 40 40 25 8 2004 20 10 2004 Copyright © 2004 Nickrent et al; licensee BioMed Central Ltd.2004Nickrent et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The phylogenetic relationships among the holoparasites of Rafflesiales have remained enigmatic for over a century. Recent molecular phylogenetic studies using the mitochondrial matR gene placed Rafflesia, Rhizanthes and Sapria (Rafflesiaceae s. str.) in the angiosperm order Malpighiales and Mitrastema (Mitrastemonaceae) in Ericales. These phylogenetic studies did not, however, sample two additional groups traditionally classified within Rafflesiales (Apodantheaceae and Cytinaceae). Here we provide molecular phylogenetic evidence using DNA sequence data from mitochondrial and nuclear genes for representatives of all genera in Rafflesiales.
Results
Our analyses indicate that the phylogenetic affinities of the large-flowered clade and Mitrastema, ascertained using mitochondrial matR, are congruent with results from nuclear SSU rDNA when these data are analyzed using maximum likelihood and Bayesian methods. The relationship of Cytinaceae to Malvales was recovered in all analyses. Relationships between Apodanthaceae and photosynthetic angiosperms varied depending upon the data partition: Malvales (3-gene), Cucurbitales (matR) or Fabales (atp1). The latter incongruencies suggest that horizontal gene transfer (HGT) may be affecting the mitochondrial gene topologies. The lack of association between Mitrastema and Ericales using atp1 is suggestive of HGT, but greater sampling within eudicots is needed to test this hypothesis further.
Conclusions
Rafflesiales are not monophyletic but composed of three or four independent lineages (families): Rafflesiaceae, Mitrastemonaceae, Apodanthaceae and Cytinaceae. Long-branch attraction appears to be misleading parsimony analyses of nuclear small-subunit rDNA data, but model-based methods (maximum likelihood and Bayesian analyses) recover a topology that is congruent with the mitochondrial matR gene tree, thus providing compelling evidence for organismal relationships. Horizontal gene transfer appears to be influencing only some taxa and some mitochondrial genes, thus indicating that the process is acting at the single gene (not whole genome) level.
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Background
Combining gene sequences from multiple subcellular compartments continues to provide increasingly well-resolved flowering plant phylogenies [1] and these have precipitated a new classification for angiosperms [2]. Whereas most groups have been placed at the ordinal level, seven of the 18 "taxa of uncertain position" are holoparasitic, nonphotosynthetic flowering plants. These parasites have been difficult to ally with green plants owing to extreme reduction and/or loss of morphological features [3]. Chloroplast genes commonly used to infer land plant phylogenetic relationships either show elevated substitution rates or are absent in these holoparasites [3-5]. Moreover, nuclear ribosomal genes also show greatly increased rates [6], thus analytical methods that accommodate such among-lineage rate heterogeneity must be used.
Rafflesiales are a fascinating and enigmatic group of holoparasitic plants that includes Rafflesia, whose meter-wide flowers are the largest among all angiosperms, and Pilostyles, whose flowers are less than a centimeter in diameter. Such wide morphological variation has resulted in classifications that comprise four families: 1) the "small-flowered clade" (Apodanthaceae) with Apodanthes, Berlinianche, and Pilostyles, 2) the "large-flowered clade" (Rafflesiaceae s. str.) with Rafflesia, Rhizanthes, and Sapria, 3) the "inflorescence clade" (Cytinaceae) with Bdallophyton and Cytinus, and 4) the "hypogynous clade" (Mitrastemonaceae) with Mitrastema [7,8].
Recently, Barkman et al. [9] used DNA sequences of the mitochondrial gene matR to identify the closest photosynthetic relatives of two clades within Rafflesiales. Three genera, representing two of the four families in the order, were used in that study: Rafflesia and Rhizanthes (Rafflesiaceae s. str.) and Mitrastema (Mitrastemonaceae). Analyses of the matR data placed Rafflesiaceae s. str. within Malpighiales, an order that includes passionflowers (Passiflora), willow (Salix), and violet (Viola). Mitrastemonaceae was placed within Ericales, an order containing blueberries (Vaccinium), primroses (Primula), and tea (Camellia). The authors argued that these results were robust because they were congruent using different analytical methods (parsimony, neighbor-joining, Bayesian) and were not affected by long-branch attraction artifacts [10]. Moreover, because sequences from host plant lineages were included, and the parasites did not emerge as sister to these lineages, contamination and horizontal gene transfer (HGT) were discounted.
In this study we expand upon the previous analysis [9] by including representatives of all Rafflesiales genera and families, thus allowing us to address the question of monophyly of the order. Moreover, parsimony, likelihood and Bayesian analyses were conducted on genes derived from all three subcellular compartments. These results were compared to assess the impact of artifacts such as long-branch attraction and HGT on various relationships. The data sets used were 1) mitochondrial matR, 2) mitochondrial atp1 and 3) a "3-gene" data set consisting of nuclear SSU rDNA plus two chloroplast genes: rbcL and atpB (the latter two only from nonparasites).
Results
Maximum likelihood (ML), maximum parsimony (MP) and Bayesian inference (BI) analyses of mitochondrial matR resulted in trees congruent with each other and with those previously generated [9] (Figure 1 and additional data file 1). As shown on the ML tree (Figure 1), Rafflesia, Rhizanthes, and Sapria were placed with strong support in Malpighiales. Mitrastema was placed in Ericales sister to Vaccinium. The Cytinus and Bdallophyton clade (Cytinaceae) was strongly supported and this clade was sister to one composed of four genera of Malvales, an order that contains cotton (Gossypium), rockrose (Cistus) and chocolate (Theobroma). For Apodanthaceae, Apodanthes and Pilostyles were sister taxa and derived from within Cucurbitales, an order that contains squash/pumpkin (Cucurbita) and Begonia. For Berlinianche, sequences homologous to matR could not be obtained using several primer combinations.
Figure 1 ML strict consensus tree from mitochondrial matR. Strict consensus of two trees obtained from ML analysis of the 77-taxon mitochondrial matR matrix. Clades with Bayesian posterior probabilities between 0.9 and 1.0 are indicated by thick lines. Bootstrap percentages from MP analysis shown above lines. Rafflesiales taxa are shown in bold italics. Arrow represents a putative cases of horizontal gene transfer. The small phylogram is included to demonstrate branch length heterogeneity.
All three analytical methods of the atp1 data produced trees that were generally congruent, thus the ML tree is illustrative (Figure 2, additional data file 2). Clades among the monosulcates generally follow previously reported relationships, whereas the topology of the eudicot portion of the tree does not clearly reflect accepted clades, possibly owing to poor sampling within rosids and asterids (sequences for these taxa were not available from GenBank). Despite these shortcomings, this gene provides additional evidence useful in assessing the phylogeny and molecular evolution of Rafflesiales. With all three analytical methods, Mitrastema forms a clade with Beta (Caryophyllales), although this relationship does not receive strong support. This is remarkable given that 15 taxa from Ericales were included, yet a relationship with this order (as seen with matR) was not obtained with atp1. The large-flowered clade was strongly supported as monophyletic in all analyses, however, its position within the eudicots did not receive strong support. Parsimony analysis placed Pilostyles as sister to Pisum (Fabales) and this clade was sister to Berlinianche, but both with low bootstrap support. Apodanthes was strongly suported (90% bootstrap) as sister to Polemonium (Ericales) with MP but with ML this long-branch clade received lower support (Figure 2). The two genera of Cytinaceae, Cytinus and Bdallophyton, were sister to Malvales, with moderate (MP) to strong (BI) support.
Figure 2 ML tree from mitochondrial atp1. Phylogram obtained from ML analysis of the 71-taxon mitochondrial atp1 matrix. Clades with Bayesian posterior probabilities between 0.9 and 1.0 are indicated by thick lines. Rafflesiales taxa are shown in bold italics. Note that the clade with Apodanthes and Polemonium (asterisk) is poorly supported with a posterior probability of 0.54.
Maximum parsimony analyses of the full-length (103 taxon) and reduced (77 taxon) 3-gene matrices were generally congruent and both resulted in all taxa of Rafflesiales being associated with Malvales (Figure 3), although with low bootstrap support for the monophyly of this clade. The two accessions of Pilostyles were sister to a clade composed of Pavonia and Gossypium, also with low bootstrap support. In constrast, BI analysis of the 3-gene matrix placed Mitrastema with Ericales and the large-flowered clade was a component of Malpighiales, the latter with strong support. The inflorescence clade (Cytinus and Bdallophyton) and the small-flowered clade (Pilostyles) were allied with Malvales (see additional data file 3), although posterior probablilities of this association were lower.
Figure 3 Unconstrained MP tree from the 3-gene data matrix. Strict consensus of 12 trees obtained from an unconstrained maximum parsimony analysis of the 77-taxon "3-gene" matrix (nuclear SSU rDNA, rbcL, atpB). Bootstrap support is shown above the lines. Rafflesiales taxa are shown in bold italics.
Parsimony analysis of the nuclear SSU rDNA matrix, constrained to an accepted topology for nonparasites, showed the same pattern of relationships as the unconstrained 3-gene MP analysis, i.e., all Rafflesiales taxa were associated with Malvales (see additional data file 4). In contrast, the tree (Figure 4) resulting from ML analysis using the same constraint tree showed the same relationships as the BI tree for the 3-gene data set.
Figure 4 Constrained ML tree from nuclear SSU rDNA. Tree resulting from the constrained ML analysis of the 77-taxon nuclear SSU rDNA matrix. Rafflesiales taxa are shown in bold italics.
None of the consensus trees generated from MP analysis of the 100 nuclear SSU rDNA data sets simulated on 20-taxon trees matched the topology of the model tree. 58 of the 100 MP consensus trees showed a Mitrastema + Rafflesia/Rhizanthes/Sapria clade and 17 showed a Bdallophyton/Cytinus + Rafflesia/Rhizanthes/Sapria clade (Figure 5). Two other combinations, Bdallophyton/Cytinus + Pilostyles and Bdallophyton/Cytinus + Mitrastema + Rafflesia/Rhizanthes/Sapria accounted for 6% and 2% of the MP consensus trees, respectively. Thus, 83% of the MP trees contained incorrect clades, and most of these can be attributed to the long-branch Rafflesia clade. However, only two of the 100 MP trees showed all six long-branch taxa as monophyletic, a result seen on the original MP tree for the full 77-taxon data set. Results of parsimony analyses of data sets simulated on the full 77-taxon tree showed a similar pattern – 58 of the MP consensus trees showed a Mitrastema + Rafflesia/Rhizanthes/Sapria clade, 7 showed a Bdallophyton/Cytinus + Rafflesia/Rhizanthes/Sapria clade, and 14 showed a Bdallophyton/Cytinus + Pilostyles clade (Figure 5). In other words, MP returned an incorrect "long-branch" clade for 79% of the data sets simulated on the full 77-taxon model tree. In contrast, far fewer incorrect long-branch clades were recovered by ML for the 20-taxon simulations, and most (56%) ML trees matched the model tree in that the Rafflesia clade was sister to Passiflora, Mitrastema was sister to Helianthus/Nicotiana, and Pilostyles, Bdallophyton and Cytinus were associated with Gossypium.
Figure 5 Rafflesiales long branches mislead MP. Proportion of simulated data sets (replicates) for which incorrect "long-branch" clades are recovered in maximum parsimony (black bars, 77 taxa), maximum parsimony (grey bars, 20 taxa), and maximum likelihood (open bars, 20 taxa) analyses. Inset is the model tree used to generate the simulated data sets. M = Mitrastema, B = Bdallophyton + Cytinus, R = Rafflesia + Rhizanthes + Sapria, P = Pilostyles.
MP analyses of SSU data sets from which all but one parasite group had been removed resulted in phylogenetic placements that matched those found in the ML tree. MP analysis of a data set from which all Rafflesiales except Mitrastema had been removed resulted in trees that placed Mitrastema in Ericales. Removal of all parasites except Pilostyles or Bdallophyton + Cytinus individually placed both of these groups in Malvales. Finally, removal of all parasites except the large-flowered clade (Rafflesia, Rhizanthes and Sapria) placed this clade in Malpighiales. Thus, the positions of the parasite clades inferred in four separate MP analyses matched the positions found for these clades in the single ML tree.
Discussion
Rate heterogeneity and long-branch attraction artifacts
Determining the photosynthetic relatives of Rafflesiales has long presented a challenge owing to the extreme reduction and/or modification of morphological structures that have accompanied the evolution of this lineage [3,11]. Molecular phylogenetic approaches, although providing great promise in resolving such questions, also come with their own set of challenges that includes losses of some genes, substitution rate increases in other genes, and horizontal gene transfer. Examples of the first process can be seen in chloroplast genes such as rbcL that are typically used to infer phylogenetic relationships among angiosperms but have not yet been amplified from any Rafflesiales and are presumed lost [5]. Increased substitution rates in the normally conservative plastid rDNA has been demonstrated in these holoparasites [4,12]. Similarly, accelerated rates in mitochondrial SSU rDNA, typically very conservative in many photosynthetic angiosperms, occur in Rafflesia and Cytinus [13]. Despite these complications, molecular phylogenetic analyses of some holoparasite lineages with comparatively lower rates have been tractable. For example, the mitochondrial genes atp1 and matR were used, in combination with nuclear rDNA and chloroplast genes, to reliably place Hydnoraceae with Aristolochiaceae [11].
Long-branch attraction, a bias in certain phylogenetic inference methods in which similarity due to convergent or parallel changes produces an erroneous phylogenetic grouping of taxa [10], is often implicated as the reason for anomalous phylogenetic groupings [14]. It has been suggested that some data sets with marked among-lineage rate heterogeneity cannot be applied to particular phylogenetic problems owing to hypothesized long-branch attraction artifacts [15]. In their unconstrained parsimony analysis of several angiosperm SSU rDNA sequences, Barkman et al. [9] found that the branch leading to Rafflesia was several times longer than any other branch, and that this branch was attracted to the second-longest branch in the tree – the one between gymnosperms and angiosperms. For these reasons, they argued that nuclear SSU rDNA sequences are of limited utility for assessing the phylogenetic position of Rafflesia.
Barkman et al. [9] analyzed their SSU rDNA data using only parsimony, not model-based methods (e.g., ML or BI methods) that are less likely to be misled by long-branch attraction [16]. Our ML analysis of the SSU rDNA data recovers a topology that closely matches the matR topology presented by Barkman et al. [9] in which Rafflesia is closely related to Malpighiales and Mitrastema is a member of Ericales (Figure 4). These results highlight the requirement to analyze SSU rDNA data with methods less biased by long-branch attraction than parsimony, as well as the advantage gained by independent confirmation of results obtained from a single gene.
Several authors have suggested that adding taxa can "break up" long branches and allow parsimony to recover the correct topology [17-19]. Our parsimony analysis of the 103- and 77-taxon SSU rDNA data sets, in which we included representatives of all genera of Rafflesiales (i.e., sequences that could potentially break the Rafflesia long branch), recovers a nearly monophyletic Rafflesiales containing all of the longest terminal branches in the tree (see additional data file 3). Based on our simulation study and MP analyses of data sets from which all but one parasite group was removed, we believe that this topology represents a case of long-branch attraction. These simulation results support the contention that the branches leading to the parasitic taxa are long enough to attract one another (Figure 5), a result in agreement with previous work [3,6].
Taxon sampling is not a cure-all for long-branch attraction problems [20]. Even for the data sets simulated on the full 77-taxon tree, MP returned incorrect long-branch clades nearly 80% of the time. MP did nearly as poorly with data sets simulated on a 77-taxon tree as it did on data sets simulated on a 20-taxon tree. Evaluation of the ML tree for the SSU data (Figure 4) shows that increasing the number of taxa from 20 to 77 did not improve the result because the long parasite branches were not broken. Instead, shorter (nonparasite) branches were broken which did not help MP recover the true topology for the simulated data sets. MP analyses of the full 77-taxon SSU data set that included all parasite clades resulted in a worse estimate of the phylogeny than MP analyses of smaller data sets in which only single parasite clades were included. Thus, the frequently stated view that increased taxon sampling can help MP avoid long-branch attraction problems may only be true if the added taxa are not distantly related long-branch clades themselves.
Phylogenetic relationships of the four Rafflesiales clades
Rafflesiaceae (the large-flowered clade)
The results from analyses of Rafflesiales using independent data sets are summarized in Table 1. For Rafflesiaceae s. str., placement in Malpighiales is supported by ML and BI analyses of the 3-gene and nuclear SSU rDNA data sets as well as mitochondrial matR. This placement in Malpighiales is also supported by a molecular phylogenetic study that used a single copy nuclear gene phytochrome C [21]. These authors proposed that Rafflesiaceae are most closely related to Ochnaceae or Clusiaceae which contrasts with presumed synapomorphies with Passiflora given by Barkman et al. [9]. Within Malpighiales, tremendous morphological diversity exists among the 27 families and 16,000 species. Moreover, relationships among the major clades are still poorly resolved [22]. Although the evidence for a malpighialean affinity of Rafflesiaceae appears strong, it is possible that the molecular data have only identified the stem group that represents the sister to the parasitic lineage.
Table 1 Summary of phylogenetic analyses of Rafflesiales using different data partitions and methods of analysis.
3-Gene* 3-Gene nuSSU rDNA nuSSU rDNA matR matR atp1 atp1
Parsimony Bayesian Parsimony constrained Likelihood constrained Parsimony Likelihood & Bayesian Parsimony Likelihood & Bayesian
Mitrastema Malvales Ericales Malvales Ericales Ericales Ericales Caryophyllales Caryophyllales
Cytinus Malvales Malvales Malvales Malvales Malvales Malvales Malvales Malvales
Bdallophyton Malvales Malvales Malvales Malvales Malvales Malvales Malvales Malvales
Apodanthes N/A N/A N/A N/A Cucurbitales Cucurbitales Polemonium Polemonium
Pilostyles Malvales Malvales Malvales Malvales Cucurbitales Cucurbitales Fabales Fabales
Berlinianche N/A N/A N/A N/A N/A N/A Ericales/Fabales Ericales/Fabales
Rafflesia Malvales Malpighiales Malvales Malpighiales Malpighiales Malpighiales Eudicots Eudicots
Rhizanthes Malvales Malpighiales Malvales Malpighiales Malpighiales Malpighiales Eudicots Eudicots
Sapria Malvales Malpighiales Malvales Malpighiales Malpighiales Malpighiales Eudicots Eudicots
*Nuclear SSU rDNA plus chloroplast rbcL &atpB.
Possible HGT events
Long-branch artifact
Barkman et al. [9] suggested that the floral similarities between Rafflesia and Passiflora, first noted by Robert Brown [23] represent morphological synapomorphies that support the results obtained from the matR gene tree. Arguments in favor of a number of other, equally credible relationships within eudicots could be made based on hypothetical evolutionary transformation series of morphological characters. Indeed Brown concluded that Rafflesia may have affinity with Passifloraceae (Malpighiales) but he also considered other groups such as Aristolochiaceae ("Asarinae", Piperales), Sterculiaceae (Malvales) and Cucurbitaceae (Cucurbitales). In general, different characters supported relationships with one or another group and therefore he left the subject as unresolved. Three proposed synapomorphies between Passifloraceae and Rafflesia were cited by Barkman et al. [9]: a hypanthium (perigone tube in Rafflesia), an androgynophore (gynostemium or column in Rafflesia), and an annular corona (diaphragm in Rafflesia). Whether these structures are homologous is not clear and will likely require further morphological studies, possibly examining the floral development genes themselves. These hypotheses require scrutiny because the apparent similarities in structure are not clear when examined in detail. For example, the androgynophore of Passiflora is composed of a stalk that bears the androecium and gynoecium. In Rafflesia, the ovary is inferior (with no stalk), hence the central column must involve other gynoecial parts. The corona of Passiflora is very different in structure and function from the diaphragm of Rafflesia [24]. The observation of a physical union between Passiflora caerulea and Euonymus [25] was discussed by Barkman et al. [9] as a possible clue to the origin of parasitism in Rafflesia. Whether this association represents parasitism or not is a matter of semantics [26], for other similar associations exist such as Cissus and Opuntia growing on Yucca and Opuntia on Cercidium and Idria. In all of these cases, a true haustorium does not form and more likely these represent forms of grafting. It is difficult to state whether such rare occurrences have any bearing on the origin of parasitism in Rafflesiales or other parasitic flowering plants.
Mitrastemonaceae (the hypogynous clade)
Maximum likelihood and Bayesian analyses of the 3-gene and nuclear SSU rDNA data partitions placed Mitrastema in Ericales, a result congruent with that obtained using mitochondrial matR. As noted by Barkman et al. [9], this relationship within the asterids had not previously been proposed. Mitrastema has bisexual, protandrous flowers with a collar-shaped, four-merous perianth tube. The stamens are connate into a tube (androphore) crowned by a fertile zone of pollen-bearing locules. The staminal tube, open at the top by a small hole, circumscissally separates from the flower as it is pushed up by the growing gynoecium. The apical portion of the staminal tube is sterile, but below this is a series of vertical rings of ca. ten minute, pollen sacs each. The gynoecium is hypogynous, one-locular, with a thick, conical stigma. Placentation is parietal with 8–15 (-20) unequal placental lobes filling the locule. The numerous ovules are small (190 by 120 μm), anatropous, unitegmic (but with two cell layers), and tenuinucellar. Although some floral morphological features of Mitrastema are not in conflict with those seen in Ericales, such as extrorse anthers and cellular endosperm, features such as decussate leaves, circumscissile fruit dehiscence, and parietal placentation are too general to draw specific associations. Given that Mitrastema is an achlorophyllous holoparasite and that one clade of Ericaceae (Monotropoideae) contains achlorophyllous mycotrophs, it is intriguing to ask whether these groups share a common ancestor or evolved independently. The most specialized morphological feature found in Mitrastemonaceae, the athecal androecium, is not found in Ericales but in Malvaceae, the only angiosperm family that shows the entire gamut from taxa with normal stamens, to taxa with stamens deviating only slightly from the common pattern [27,28], to athecal androecia [29].
Cytinaceae (the inflorescence clade)
The most consistent phylogenetic signal that is seen across all data sets and types of analyses is a relationship between Cytinaceae and Malvales (Table 1). Because the relationship between Cytinaceae and Malvales is the strongest among all four Rafflesiales clades, it is possible that this clade acts as an "attracter" for the other three Rafflesiales clades in some analyses. This is seen when using nuclear SSU rDNA sequences, either alone or with the topology of the tree stabilized through the addition of two chloroplast genes. In both cases, parsimony produces a monophyletic Rafflesiales within Malvales which contrasts with the result seen with the constrained ML SSU rDNA and the matR results. These results and those obtained from the simulation study indicate that the large-flowered clade and Mitrastema are artifactually attracted to Cytinaceae when parsimony is utilized.
Unlike other Rafflesiales, members of Cytinaceae have multiple flowers arranged in an inflorescence. The floral structure called the diaphragm, seen in Rafflesia and Sapria (but not Rhizanthes), is lacking in Cytinaceae. Bdallophyton is dioecious and Cytinus is either dioecious (C. capensis, C. sanguineus) or monoecious (C. hypocistis). The perianth is tubular, composed of four to nine imbricate organs. The androecium is connate, forming a compact synandrium with extrorse anthers and the pollen is 2-, 3-, or 4-porate. The female flower is epigynous with a columnar style terminated by a globose or capitate, viscous stigma with commissural lobes [30]. The ovary is unilocular with 8–14 deeply intrusive, discrete parietal placentae that bear numerous, orthotropous, tenuinucellate ovules.
Apodanthaceae (the small-flowered clade)
Maximum parsimony and likelihood analyses of the 3-gene data set and nuclear SSU rDNA sequences alone also place Pilostyles (the only Apodanthaceae for which SSU rDNA sequences are available) within Malvales, however, a sister relationship with Cytinaceae is not consistently obtained. A 3-gene alignment that included additional representatives of Malvales (16 taxa) gave similar results as shown in Figure 3 (i.e., Pilostyles on a clade separate from other Rafflesiales). These data, in conjunction with the results from the mitochondrial genes, support an evolution of Apodanthaceae independent from Rafflesiaceae s. str. The well-supported relationship between Pilostyles and Apodanthes using matR is expected given their very similar floral morphology [31], yet this clade is sister to two representatives of Cucurbitales (Begonia and Cucurbita). Contamination with host tissue is excluded because neither parasite is known to currently occur on a member of Cucurbitales. Apodanthaceae are grouped with Pisum (Fabales) and Polemonium (Ericales) on the atp1 tree, but no atp1 sequences from representatives of Cucurbitales were available from GenBank to test the matR result. The sister relationship between Apodanthes and Polemonium is strongly supported on the MP tree (bootstrap support value = 90%; additional data file 3), but this pairing must be viewed with caution given the low Bayesian posterior probability of the clade (0.54) and that both taxa are very long branches (Figure 2). Although ML is less susceptible to long-branch attraction artifacts than MP, it is not immune to it; thus, it remains unclear whether or not this relationship is artifactual. Moreover, the Polemonium sequence is separate from the clade containing 12 other members of this order, thus raising the possibility that the sequence results from contamination or HGT (see below). Additional sampling within the eudicots will be required to better understand the atp1 gene tree topology.
Morphological features shared between Apodanthaceae and Cytinaceae are: unisexual flowers, a connate androecium, an inferior ovary, and a unilocular ovary with four parietal placentae bearing numerous, anatropous, tenuinucellate ovules [30,31]. Floral morphological features that might link Apodanthaceae and Cytinaceae with Malvales [31] include an androecial tube (e.g., Malvaceae), a trend toward synandria without anthers and thecae (e.g., Malvaceae) [29], tri- to hexamerous flowers (e.g., Thymelaeaceae), and parietal placentae (e.g., Cistaceae). The floral conditions of unisexuality and epigyny do occur in Malvales, albeit rarely. Unisexual flowers pose some difficulties for interpreting the morphological homologies of various floral organs. For Pilostyles and Apodanthes male flowers, a tubular synandrium surrounds and fuses with a central structure that could be interpreted as a sterile gynoecium. Support for the concept that such a central structure is a pistillode comes from Berlinianche where the upper portion of the synandrium is free from the central part. In female flowers of Apodanthaceae, there is no rudiment of an androecium, hence the central tissue is apparently entirely gynoecial.
In contrast to the above discussion, the matR data indicate Apodanthaceae are related to Cucurbitales, an order with seven families, 129 genera and 2300 species. Hosts for Apodanthaceae are generally legumes, although Apodanthes occurs most frequently on Casearia (Salicaceae, Malpighiales). Thus, neither recent HGT nor contamination explains this result. Apodanthaceae shares some morphological features with members of Cucurbitaceae, subfamily Cucurbitoideae: unisexual, five-merous flowers (Berlinianche); carpellate flower with a unilocular, inferior ovary with parietal placentation; anatropous, bitegmic ovules; staminate flower with connate filaments (monadelphous) and a rudimentary gynoecium (pistillode) [32]. Conflicting characters also occur, such as a three-carpellate gynoecium in Cucurbitoideae (vs. four-carpellate in Apodanthaceae) and a valvate perianth (vs. imbricate). All of these characters, however, are less specialized than those shared between Apodanthaceae and Malvales.
Background on horizontal gene transfer
A requirement of the molecular phylogenetic approach to inferring evolutionary histories of organisms is vertical transmission of genetic material from parent to offspring. In contrast, horizontal gene transfer (HGT) describes the movement of genetic material between organisms of no direct ancestor-descendant relationship. Although the frequency of HGT is currently not well understood among prokaryotic and eukaryotic organisms, it is clear that HGT can compromise accurate inference of genealogical history. In plants, lateral movement of genetic material has been documented for mobile genetic elements such as introns [33-37] but only recently has convincing evidence emerged documenting HGT of mitochondrial genes [38,39]. Genes of the mitochondrion are extensively used to infer evolutionary relationships in plants [40-42], thus highlighting the importance of characterizing the frequency of HGT across genes and taxa.
Incongruence among gene trees derived from different data sets can derive from a number of factors such as technical causes (insufficient data, gene choice, sequencing error, taxon sampling and identification), gene/genome-level processes, and organism-level processes (e.g., hybridization/introgression, lineage sorting, and HGT) [43]. HGT has only recently been recognized as a potentially important force in the evolution of plant mitochondrial genomes and detecting HGT is highly dependent upon the presence of multiple gene data sets with robust taxon sampling [38,39].
Evidence for horizontal gene transfer in parasitic plants
We believe that incongruence between the the mitochondrial and the nuclear gene trees (Table 1) stem not just from long-branch attraction artifacts but also from cases of HGT. The placement of Apodanthes and Pilostyles on the atp1 tree as sister to Pisum (a legume, the family of hosts for Pilostyles) represents a likely case of HGT. The atp1 data conflict with those from matR that associates Apodanthaceae with Cucurbitales. Moreover, we infer that the SSU rDNA tree better represents the organismal phylogeny because it seems less likely that nuclear genes would be influenced by HGT [44,45]. The main rationale for this is that nuclear rDNA cistrons are repeated hundreds to thousands of times in tandem arrays at nucleolar organizing regions of the chromosomes. Although it can be envisioned that concerted evolution could homogenize all rDNAs in the parasite with a form obtained via HGT, the probability of this happening is small given the vastly different number of starting copies.
In their study of Rafflesiaceae s. str. and Mitrastemonaceae, Barkman et al. [9] discounted HGT as a possible explanation for their results because they state the phenomenon is rare and the overall topology of the matR tree closely matched results from other molecular phylogenetic investigations of angiosperms. The present study confirms that HGT is not implicated for the two lineages studied by Barkman as well as Cytinaceae, but this process could be invoked for Apodanthaceae. More recent work by these authors [46] identified several cases of HGT from host to parasite for atp1. These included Dalea to Pilostyles, Tetrastigma to Rafflesia, and Lithocarpus to Mitrastema. In addition, HGT of another mitochondrial gene, nad1, has been reported for Rafflesia and Sapria, both of which occur on the same clade as their hosts (Tetrastigma) on a gene tree [20]. These examples demonstrating the presence of host genes in parasitic plants provide the most compelling evidence for HGT. This form of transfer is intuitively logical given the intimate contact between cells of the two organisms via the endophytic haustorium. However, parasitic plants exist in complex ecosystems where they are in physical contact with many other organisms (bacteria, fungi, phytophagous and pollinating animals, etc.) that could potentially affect HGT. That such nonhost HGT may also be occurring is evidenced by the presence of an apparent cucurbitalean matR gene in Pilostyles and Apodanthes. Moreover, present-day hosts of parasitic angiosperms do not represent the only conduit for HGT if host choice has shifted through time as the parasite lineage evolves. For example, Barkman et al. [9] state that Mitrastema only parasitizes Fagales (e.g., Lithocarpus and Castanopsis, both Fagaceae) but this parasite has also been recorded from Aquifoliaceae, Asteraceae, Elaeocarpaceae, Juglandaceae, and Myrtaceae [47]. Host latitude for this species would be broader if rare hosts and hosts of parasite ancestors were fully known, thus expanding the taxonomic spectrum of potential HGT sources.
Formidable contamination issues
Contamination of parasite DNA with DNA from the host plant is an issue that must be given serious attention. Indeed, two sequences shown on the matR tree (Figure 1), Tetrastigma2 and Julbernardia are hosts for Rafflesia tuan-mudae and Berlinianche, respectively. These sequences were obtained by PCR amplification and sequencing from what was originally thought to be pure parasite genomic DNA. Sequences of the host (obtained from separate samples) were found to be identical to these "parasite" sequences, strongly suggesting contamination. In the case of Rafflesia, the DNA was obtained from a bud still attached to the host vine, both of which had been sectioned longitudinally. Disruption of these tissues likely resulted in transfer of host sap to the bud region where the tissue was sampled. Other samples of R. tuan-mudae from the same population, obtained as floral bracts with no host tissue, resulted in matR sequences that were similar to the other two Rafflesia species.
For Berlinianche, whose flowers are much smaller than those of Rafflesia (5 mm in diameter), extreme care (using a stereo microscope) was exercised to remove floral parts devoid of any host tissue. Despite this, the matR sequence obtained from the first sample was that of the host, Julbernardia. Later, silica gel dried samples of other populations of the parasite were extracted, again using extreme care in avoiding host contamination. PCR products were obtained using several mitochondrial matR primers, but none were found to be homologous to this gene following BLAST searches. This result shows that host DNA was not present in this sample in sufficient amounts to amplify and that the parasite matR gene, if present, is highly divergent at the priming sites used.
For all three Apodanthaceae genera, the conical style in female flowers is papillate and heavily secretory [31]. This sticky surface tends to capture a variety of environmental debris, likely including extraneous pollen, fungal spores, and host tissues that have been disrupted upon collecting. Obtaining a proper nuclear SSU rDNA sequence for Pilostyles was extremely difficult. Despite PCR products of the correct sizes using a variety of primer combinations, the sequences obtained from genomic DNA derived from flowers were deemed contaminants following BLAST searches that showed them to be most similar to monocots, fungi, etc. Only when sequences from two accessions of Pilostyles (Texas and California) both were most similar to Malvales was this considered good evidence for their true phylogenetic affiliation. Retrospectively, it is likely that the sticky flowers had accumulated wind-dispersed pollen (e.g., grasses) and that this DNA, despite being in low concentration, had less divergent priming sites than the parasite target DNA, allowing PCR to preferentially amplify the contaminant DNA.
The mechanism of horizontal gene transfer: some considerations
Given the accumulating molecular evidence for HGT from host to parasitic plant, it is worthwhile to consider potential mechanisms, along with their constraints, that may suggest further research. Relatively little information exists on the structure of the endophyte of Rafflesiales. Ultrastructural studies have been conducted on two species of Pilostyles: P. hamiltonii [48] and P. thurberi [49]. These authors conflict, however, as to whether there exists symplastic continuity between host and parasite via plasmodesmata; the former indicated that such connections are the major path of nutrient uptake by the parasite whereas the latter rejected this idea. Despite this controversy, heteroplastic plasmodesmatal connections have been documented in another parasitic plant, Cuscuta [50] and indeed such connections can even form in heterografts between distantly related plant taxa [51]. Given this, we assume that host-parasite plasmodesmatal connections exist in the endophytes of Rafflesiales. Transmission electron micrographs of Pilostyles suggest that intact, mature mitochondria are too large to pass through heteroplastic plasmodesmata, however, mitochondrial genomes or portions of the genome are certainly small enough for transmission. Once inside the parasite cell, there are various fates for the host gene. It could become incorporated into the parasite mitochondrial genome, and then either replace the parasite copy or exist as a duplicate, or the host gene could reside in the parasite nuclear genome. For the latter case, the gene would likely become a pseudogene given the requirement of mitochondrial-specific patterns of RNA editing. Two forms of atp1 are present in the primitive angiosperm Amborella trichopoda [38], one of which is derived from a HGT event from a eudicot. It is not known whether both forms of the gene exist in a single mitochondrial genome, in different mitochondrial genomes within the cell (i.e., heteroplasmy), or if one is nuclear and the other mitochondrial. Future work to address these questions would involve sequencing flanking regions of purported horizontally transferred genes to determine their subcellular location. Additionally, cDNA sequences obtained from matR mRNA would be useful to determine whether the gene is expressed and whether mitochondrial-specific RNA editing patterns are present.
Conclusions
In this study we have used data derived from nuclear, mitochondrial and chloroplast DNA and a variety of analytical approaches to address long-standing questions about the holoparasitic flowering plant order Rafflesiales. We show that Rafflesiales are not monophyletic but composed of at least three and possibly four independent lineages. Rafflesiaceae (Rafflesia, Rhizanthes, and Sapria) representing the large-flowered clade are monophyletic and are related to Malpighiales. The monogeneric family Mitrastemonaceae, the only member of the order with a superior ovary, is related to Ericales. The first of the remaining two families that have previously not been sampled is Cytinaceae (Bdallophyton and Cytinus) which is strongly supported as a member of Malvales. The last remaining unsampled family, Apodanthaceae (Apodanthes, Berlinianche, and Pilostyles) is either related to Malvales or Cucurbitales. Our simulation studies indicate that Mitrastema, Bdallophyton/Cytinus, and Rafflesia/Rhizanthes/Sapria have branches that are long enough to mislead parsimony. All of these relatively long branches appear to be attracted toward the Cytinaceae clade within Malvales. When nuclear SSU rDNA sequences are analyzed with ML, results fully congruent with those previously reported for two Rafflesiales clades using mitochondrial matR are obtained. If the phylogenetic affinityof Apodanthaceae are with Malvales, the results from the mitochondrial matR gene must represent a case of horizontal gene transfer (HGT) from Cucurbitales. If this proves to be the case, this provides an example of HGT from a nonhost plant to a parasitic angiosperm.
To properly discern phylogenetic relationships in enigmatic parasitic taxa, our results demonstrate the need for robust taxon sampling, gene sequences from multiple subcellular compartments, and the use of analytical methods that accommodate rate heterogeneity and avoid the pitfalls of long-branch attraction. When the phylogenetic relationships among such holoparasitic taxa are poorly known, the strongest phylogenetic signal that can be obtained is congruence among results derived from independent sources (i.e., genes from different subcellular compartments). Comparisons among gene trees allows for the identification of HGT, a phenomenon that requires further investigation to determine its modes of action and frequency among taxa and through evolutionary time.
Methods
DNA extraction, PCR, sequencing
DNA was extracted, amplified, cloned, and sequenced by using methods formerly reported [52-54]. The nuclear and mitochondrial sequences were PCR-amplified using primers reported elsewhere [6,55,56] and are also given on the first author's web site [57]. Sequencing was conducted using manual and automated methods (ABI Prism® 377 automated DNA sequencer, Applied Biosystems) according to manufacturer's protocols.
DNA alignments
The initial matR alignment incorporated all of the Rafflesiales parasites and the nonparasite sequences previously published [9] as well as our newly generated sequences. The 106-taxon matrix represented over 40 orders and contained three gymnosperm outgroup taxa (Ginkgo, Pinus, and Zamia), 28 monosulcates, 63 nonparasitic eudicots, and 15 Rafflesiales. For two taxa (Mitrastema and Rhizanthes), our sequences, as well as those previously published, were from the same species but different accessions to test for consistency. Taking into account codon information, an alignment of 2177 sites was constructed manually using SeAl version 2.0 [58]. The full matrix was used for parsimony analyses whereas another, truncated to 77 taxa by removing all but three monosulcate taxa (Laurales used as outgroup), was constructed to facilitate likelihood analyses. This operation was justified because monosulcates were never implicated as relatives of Rafflesiales in any analyses. A 71-taxon, 1265-site atp1 alignment was similarly constructed and included the same gymnosperm outgroup genera as above, 24 monosulcates, 32 nonparasitic eudicots and 12 Rafflesiales. All of the monosulcate genera in the atp1 alignment were also represented in the matR data set, whereas eudicot sampling for atp1 was constrained by sequences available on GenBank (12 of the same genera as with matR or placeholders from same family).
To test the position obtained for Rafflesiales taxa using mitochondrial genes with an independent data set derived from different compartments, a 4646-site "3-gene" matrix combining sequences from nuclear SSU rDNA and chloroplast rbcL and atpB was constructed that included 103 taxa (3 gymnosperms, 28 monosulcates, 58 nonparasitic eudicots, and 14 Rafflesiales). Sampling across angiosperm orders was very similar to the matR matrix, differing only by the presence of 11 placeholders and a second accession of Pilostyles. For the holoparasites, only nuclear SSU rDNA sequences were included; the chloroplast gene data for these taxa were coded as missing. The two chloroplast genes were included to add stability to the tree topology given that nuclear SSU has been shown to contain lower phylogenetic signal when used alone [15]. As with matR, the 103-taxon matrix was truncated to 77 taxa by removing all but five monosulcate taxa to facilitate likelihood analyses. All alignments reported in this paper have been deposited with TreeBASE [59]: study accession number S1177, matrix accession numbers = M2034–M2037.
Data analysis
All three data sets were analyzed using maximum parsimony (MP) and maximum likelihood (ML) methods in PAUP* 4.0b10 [60] and Bayesian inference (BI) methods in MrBayes 3.0b4 [61].
Maximum parsimony
All MP searches were performed using 100 random addition sequence replicates with tree-bisection-reconnection (TBR) branch-swapping, holding ten trees at each addition step, with all sites equally weighted. For the 77-taxon SSU data set, a series of four MP analyses were performed in which all but one parasite group (Bdallophyton + Cytinus, Mitrastema, Pilostyles or the large-flowered clade comprising Rafflesia, Rhizanthes and Sapria) was removed to determine the position of each parasite group in the absence of other long-branch parasite taxa in the analysis. This is a form of the test proposed by Siddall and Whiting [62].
Maximum likelihood
For ML analyses, a MP tree was used in PAUP* to evaluate 56 nucleotide substitution models. ModelTest 3.06 [63] was used to select an appropriate model from the PAUP* output using hierarchical likelihood-ratio tests (hLRT's) and the Akaike Information Criterion (AIC). The general time-reversible (GTR) substitution model with among-site rate heterogeneity modeled with a "gamma + invariant sites distribution" (Γ + I) was chosen via the AIC as the best-fitting model for the atp1 data set. Investigation of the likelihood score output from PAUP* suggested that a simpler model not evaluated by ModelTest was not significantly worse than the GTR+Γ + I model (LRT; p = 0.520824). This submodel employed four (rather than six) relative rate parameters: one for A-C transversions and A-G transitions, one for A-T and C-G transversions, one for C-T transitions, and one for G-T transversions; the PAUP* LSET option used for analysis was "RCLASS = (a a b b c d)". Likewise, the models chosen by ModelTest for the matR data set were TVM+Γ (hLRT) and TIM+Γ (AIC), but a simpler statistically equal model (LRT; p = 0.583393) was used for analysis. This model employed three relative rate parameters: one for A-C, A-G, and G-T substitutions; one for A-T and C-G substitutions; and one for C-T substitutions; "RCLASS = (a a b b c a)", with among-site rate heterogeneity modeled with a gamma distribution. These simplified models were chosen to reduce computational time and to avoid estimation of unnecessary parameters, which can lead to greater variance in parameter estimates and higher topological uncertainty.
A successive approximations approach was used for all ML analyses [19,64]. Substitution model parameters were estimated from the data on a MP tree. With parameter estimates fixed, starting trees for ML analyses were produced via random stepwise addition using five starting seeds, with each tree subjected to a round of tree bisection-reconnection (TBR) branch swapping. Substitution model parameters were then re-estimated on all resulting trees, followed by another round of random stepwise addition and TBR swapping. The tree with the highest likelihood was accepted as the ML tree.
Nodal support
Nodal support for all data sets was estimated using one or more of the following methods: equal-weights MP bootstrap analysis (100 pseudoreplicates, each consisting of a heuristic search using 100 random sequence addition replicates), ML bootstrap analysis (100 pseudoreplicates generated with SEQBOOT in PHYLIP and analyzed using successive approximations in PAUP*) [65,66], and Bayesian analysis (10 million generations, with the first one or two million discarded as burn-in and trees sampled every 500 generations for the matR and atp1 data sets; 10 million generations, with the first 5 million discarded as burn-in and trees sampled every 500 generations for the 3-gene data set) [61]. The GTR+Γ + I submodels used in PAUP* are not available in MrBayes; a standard GTR+Γ + I model was used for the matR and atp1 data sets instead. A partitioned model was used for the 3-gene data set (see below). Two Bayesian runs were performed for all analyses in an attempt to determine if stationarity was reached, and plots of log likelihood and parameter convergence were also evaluated; log-likelihood plots alone are insufficient for monitoring chain mixing and convergence [67,68].
Partitioned analyses
The 3-gene data set was also analyzed in MrBayes 3.0. A "fully partitioned" analysis was used in which the 3-gene data set was divided into seven partitions: nuclear SSU; atpB first, second and third codon positions; rbcL first, second and third codon positions. Appropriate substitution models for each data partition were chosen by computing likelihood scores for each partition on a MP tree for the 3-gene data set under 56 substitution models in PAUP* and comparing the scores in ModelTest. The GTR+Γ + I model was the best-fitting model for all partitions. The Bayesian analysis was performed with all model parameters (except branch lengths) unlinked across partitions.
Constraints
For the nuclear SSU rDNA data, constrained analyses were also performed. A constraint tree for 63 nonparasitic taxa was constructed using the MP topology of the "B series" tree from Soltis et al. [1] with relationships for poorly supported clades left unresolved. This tree was used as a backbone constraint for MP and ML analyses of 77 taxa including Rafflesiales. MP analyses were performed as described above. ML analyses followed a successive approximations approach similar to that described above.
Simulations
To investigate possible long-branch attraction in parsimony analyses of the SSU rDNA data set, two sets of simulations were performed. For the first set of simulations, a reduced data set of SSU rDNA sequences for 20 taxa (13 nonparasites and 7 Rafflesiales) was constructed and analyzed under ML (GTR+Γ + I model) in PAUP*. The tree resulting from this analysis, with its associated ML branch lengths and model parameters, was used as the model tree on which 100 data sets of length 1766 (the length of the original SSU rDNA data set) were simulated in Seq-Gen 1.2.7 [69]. For the second set of simulations, the ML tree for the full 77-taxon data set, with associated branch lengths and model parameters, was used as a model tree to simulate 100 data sets of length 1766 in Seq-Gen 1.2.7. Either MP and ML trees (20-taxon simulation) or just MP trees (77-taxon simulation) were estimated for all 100 simulated data sets. The trees (or strict consensus trees, if more than one MP or ML tree was recovered for a given simulated data set) were then inspected to determine the presence of "incorrect" clades (containing two or more "long-branch" Rafflesiales taxa) that were not present on the model tree. We do not expect to recover such clades at high frequencies unless long-branch attraction is biasing the analyses.
List of abbreviations
Γ + I – gamma + invariant sites distribution
atp1 – ATP synthase alpha subunit
atpB– ATP synthase beta subunit
BI – Bayesian inference
GTR – general time reversible model
HGT – horizontal gene transfer
matK – maturase K
matR – maturase R
ML – maximum likelihood
MP – maximum parsimony
rbcL – ribulose bisphosphate carboxylase/oxygenase, large subunit
SSU – small subunit
TBR – tree bisection-reconnection branch swapping
Authors' contributions
DLN coordinated all aspects of the study, obtained many of the genomic DNAs, generated all the nuclear SSU rDNA, conducted the sequence alignments, and drafted the manuscript. AB conducted the majority of the mitochondrial atp1 and matR sequencing and revised the text regarding morphological character comparisons. YQ provided primers, introduced AB to the field of molecular systematics, and supervised his Ph.D thesis. RVR conducted the PCR experiments showing host contamination of Rafflesia DNA and generated the matR sequences for several taxa. FEA performed the phylogenetic analyses. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
MP strict consensus tree from mitochondrial matR Strict consensus of 200,000+ trees obtained from maximum parsimony (unconstrained MP) analysis of the 77-taxon mitochondrial matR matrix. Bootstrap percentages are shown above the lines. Rafflesiales taxa are shown in bold italics.
Click here for file
Additional File 2
Strict consensus MP tree from mitochondrial atp1 Strict consensus of 328 trees resulting from a MP analysis of the 71-taxon mitochondrial atp1 matrix. Rafflesiales taxa are shown in bold italics. Bootstrap percentages are given above the branches.
Click here for file
Additional File 3
Majority rule consensus BI tree from 3-gene data set Majority rule consensus of 20,000 trees (10 million generations, 5 million burn-in) resulting from Bayesian analysis of the 77-taxon nuclear 3-gene matrix. Clades with Bayesian posterior probabilities are indicated above the clades. Rafflesiales taxa are shown in bold italics.
Click here for file
Additional File 4
Strict consensus constrained MP tree from nuclear SSU rDNA Strict consensus of 6 trees resulting from the constrained MP analysis of the 77-taxon nuclear SSU rDNA matrix. Rafflesiales taxa are shown in bold italics. Bootstrap percentages are given above selected nodes (Rafflesiales).
Click here for file
Additional File 5
Taxa used in this study MS Excel file giving taxon names and GenBank numbers for all genes used.
Click here for file
Acknowledgements
The authors wish to thank all who have helped with the difficult task of attaining complete generic-level sampling in Rafflesiales by sending plant material or assisting with field work: L. Diego-Gomez (Apodanthes), T. Muller, D. Plowes, and M. Palgrave (Berlinianche), J. García-Franco (Bdallophyton), N. López Giménez, P. Bourgoyne, W. Barthlott (Cytinus), S.-C. Hsiao (Mitrastema), R. Mitchel Beauchamp (Pilostyles), W. Meijer (Rafflesia), J. Trice (Rhizanthes), and H. Bänziger (Sapria). Laboratory assistance was provided by R. J. Duff, M. A. García, M. P. Martín, E. Nicholson, M. O'Dell, and S. Whitcomb. Data analysis was greatly facilitated by Andrew Rambaut, who provided critical assistance with Seq-Gen, and by David Swofford and Peter Foster, who generously allowed access to their computer clusters at Florida State University and The Natural History Museum (London), respectively. We also thank Jim Wilgenbusch for his assistance with computational issues. P. K. Endress, J. Fuertes, and J. D. Palmer provided useful discussions that improved the manuscript. Financial support was provided by the Marie-Louise-Splinter-Legat. to AB, and the National Science Foundation (MCB-9808752) to DLN.
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| 15496229 | PMC528834 | CC BY | 2021-01-04 16:29:00 | no | BMC Evol Biol. 2004 Oct 20; 4:40 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-40 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-401551859110.1186/1475-925X-3-40ResearchA two-layered mechanical model of the rat esophagus. Experiment and theory Fan Yanhua [email protected] Hans [email protected] Ghassan S [email protected] Institute of Experimental Clinical Research, Skejby Hospital, Aarhus, Denmark2 Center for Biomechanics and Visceral Pain, Aalborg Hospital, Aalborg, Denmark3 Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark4 Haukeland University Hospital, Bergen, Norway5 Department of Biomedical Engineering, UC Irvine, Irvine, California2004 1 11 2004 3 40 40 15 7 2004 1 11 2004 Copyright © 2004 Fan et al; licensee BioMed Central Ltd.2004Fan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The function of esophagus is to move food by peristaltic motion which is the result of the interaction of the tissue forces in the esophageal wall and the hydrodynamic forces in the food bolus. The structure of the esophagus is layered. In this paper, the esophagus is treated as a two-layered structure consisting of an inner collagen-rich submucosa layer and an outer muscle layer. We developed a model and experimental setup for determination of elastic moduli in the two layers in circumferential direction and related the measured elastic modulus of the intact esophagus to the elastic modulus computed from the elastic moduli of the two layers.
Methods
Inflation experiments were done at in vivo length and pressure-diameters relations were recorded for the rat esophagus. Furthermore, the zero-stress state was taken into consideration.
Results
The radius and the strain increased as function of pressure in the intact as well as in the individual layers of the esophagus. At pressures higher than 1.5 cmH2O the muscle layer had a larger radius and strain than the mucosa-submucosa layer. The strain for the intact esophagus and for the muscle layer was negative at low pressures indicating the presence of residual strains in the tissue. The stress-strain curve for the submucosa-mucosa layer was shifted to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3. The tangent modulus was highest in the submucosa-mucosa layer, indicating that the submucosa-mucosa has the highest stiffness. A good agreement was found between the measured elastic modulus of the intact esophagus and the elastic modulus computed from the elastic moduli of the two separated layers.
BiomechanicsMucosa-submucosa layerMuscle layerOpening angleZero-stress state
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Introduction
The majority of previous mechanical studies on visceral organs, including the blood vessels, have considered them as homogenous tubes; i.e., a single layer structure. Most visceral organs are, however, multilayered, e.g. the arteries consist of intima, media and adventitia and the gastrointestinal tract has circumferential and longitudinal muscle layers, submucosa and mucosa layers.
The esophagus represents a very interesting biomechanical model since it is the only organ that can be separated into two layers without damage to either layer. Hence, the muscle layers can be separated from the mucosa-submucosa layer by dissection, leaving two intact tubes. Separation experiments of the esophagus in guinea pigs and rabbits showed that the submucosa-mucosa layer had larger residual strains and opening angles than the muscle layer [1-3]. Considering the multi-layered composite structure and the difference in zero-stress state between the layers, the stress distribution in the wall is expected to be non-homogeneous. Hence, the material constants likely differ between the layers. Such a finding impacts our understanding of biological tissue remodelling and the function of mechanosensitive receptors located in various layers of the wall [4-6]. Therefore, data on the strain and stress distribution in the layers will facilitate the understanding of the relationship between the stress, remodelling of the tissue and sensory responses. To pursue this line of study, however, it is necessary to know how the stress and strain in the esophagus can be computed for each layer, and how the composite can be put together to give the overall observed mechanical properties.
In this study we recognize that the esophagus consists of mucosa-submucosa and muscle layers. We analyze these layers as elastic shells. Each layer has its own zero-stress state, and its own elastic constants. We will determine the material properties of each layer separately. Specifically, the material properties in the individual layers will be computed from the pressure-diameter relation and zero-stress state with the method of analysis presented below. We will then propose a simple model to combine the two layers to predict the overall behavior of the esophagus under certain hypotheses. The limitations and implications of the model will be discussed.
Materials and methods
Eight male Wistar rats, weighing 380–420 grams, were used in the study. Approval of the protocol was obtained from the Danish Animal Experiment Committee. The animals were anesthetized with sodium pentobarbital (50 mg kg-1 ip). Papaverine (15 mg) was injected into the tail vein to relax the visceral muscles and to euthanize the rat. The cervical segment of the esophagus was dissected free from its adjacent tissue. Next, the thoracic and abdominal cavities were opened. After pouring cold Krebs solution into the thoracic cavity, the esophagus was quickly dissected free from adjacent tissues and its in situ length was measured. A 2-cm-long segment from the middle part, intended for the distension test, was marked. The length of this segment and that of the entire esophagus was measured. The entire esophagus was then cut at the proximal and distal ends including the very first part of the stomach, and immediately placed in calcium-free Krebs solution containing 6% dextran and 0.25% ethylene glycol-bis (β-aminoethyl ether)-N,N,N,N-tetraacetic acid (EGTA). The solution was aerated with a gas mixture of 95% O2-5% CO2 at pH of 7.4. After careful removal of all extra-esophageal tissue, the length was measured in vitro.
Pressure-diameter experiments
The middle part of the intact esophagus was mounted in an organ bath containing the Ca+2-free Krebs solution. The segment was stretched to the in vivo length and fixed. The distal end was closed whereas the proximal end was cannulated and connected to a fluid container. After preconditioning the tissue with pressures up to 8 cmH2O, a ramp test was performed where the pressure was changed continuously at a rate of 2 cmH2O per minute up to a pressure of 8 cmH2O. A video camera (Sony CCD camera) monitored the changes in diameter and length during the distension and images were grabbed by a PC.
After the test of the intact esophagus, the muscle and submucosa-mucosa layers were gently separated into two tubes. The tubes were studied separately using the same procedure outlined above. The only difference was that the maximum pressure was set to 6 cmH2O.
The zero-stress state of the esophagus
The zero-stress state of the esophagus was obtained in accordance with the method used for blood vessels [7]. Briefly, six rings of 1 mm length were cut from the intact esophagus and from the separated layers and were then cut in the radial direction to obtain the zero-stress state. The choice of the ring length was based on pilot studies. The radial cut caused the rings to open up into sectors. The shape of each ring segment at the zero-stress state was photographed 60 minutes after the radial cut to allow the creep to subside.
Data analysis
The morphometric measurements were made using SigmaScan Pro image analysis software (Jandel Scientific, Germany). The data were obtained from the images of the tubes in the distended state, rings in the no-load state and sectors in the zero-stress state. In the distended state for both the intact esophagus and the separated tubes, images were analyzed for each 0.5 cmH2O increment. The outer diameter was measured at each pressure level and averaged over three locations. At the no-load and zero-stress states, the inner and outer circumferential lengths were measured along with the thickness and area of the wall and layers (for calculation of inner, outer or mid-layer circumference). The opening angle was defined as the angle subtended between two radii drawn from the midpoint of the inner wall to the tips of the inner wall of the open sector.
The stresses and strains of the esophagus and its sublayers in the pressurized state were determined under the assumption that the geometric configuration of the lumen is cylindrical, the wall of the esophagus is incompressible, and the material in each layer is homogenous. Based on the above measurements and assumptions, parameters of the esophagus such as the luminal radius (ri-p), the wall thickness (Hp), the mid-wall circumference (Cm-p) at a given pressure were computed as ri-p = [(r2o-p - An/πλ1)]1/2, Hp = ro-p - ri-p, and Cm-p = 2π(ri-p + Hp/2). The outer radius (ro-p) of the intestine was computed according to the outer diameter (Do). The circumferential Green's strains and Kirchhoff's stress were computed according to the equations:
where Cm-z is the mid-wall circumference at the zero-stress state.
where
The tangent modulus can be estimated from the slope of the stress-strain relation as
The tangent modulus given by Eq. (3) corresponds to Young's modulus in the linear stress-strain regime.
Integration of Two-Layers: An Analytical Model
We assume that the circumferential stress-strain relationships for the inflation experiment obey Hooke's law for each layer
σθθ(sm) = Eθθ(sm)eθθ(sm) σθθ(m) = Eθθ(m)eθθ(m) (4)
where σ, E and e indicate the Cauchy stress, Tangent modulus and Green strain, respectively. θθ, sm and m indicate the circumferential direction and the submucosal and muscle layers, respectively.
In general, the circumferential stress is a function of circumferential and longitudinal strain. In the present analysis, we assume that the cross-modulus is small such that the longitudinal term is negligible in comparison with the circumferential term and hence eq. (4). We further assume that the esophagus is a circular cylinder. The basic equations of equilibrium and deformation are given in Flugge [8]. Let x denote the longitudinal axis, θ the circumferential axis and z the radial coordinate. Nθ denotes the tensile membrane stress resultant in θ direction. The displacement in the x, θ, and z directions of a point on the neutral axis surface are denoted by u, v, and w, respectively. The displacements of any point, A, is denoted by uA, vA, wA as follows: 1) uA = displacement along the generator, positive in the direction of increasing x; 2) vA = displacement along a circle of radius a + z, positive in the direction of increasing θ and 3) wA = radial displacement, positive outward.
According to the Bernoulli-Kirchhoff hypothesis (all points lying on one normal to the neutral surface before deformation remain on the normal after deformation), we have
where a indicate the neutral axis.
The circumferential strain which is assumed to be small is given by
The circumferential membrane stress resultant Nθ is given by
By substitution of Eqs. (4), (5), and (6) into Eq. (7) and noting that in the inflation experiments u, v, and w do not change with θ and w does not change with x, we obtain
Eq. (8) can be integrated to yield
where I stands for intact esophagus; aI, asm and am are the neutral axes for the submucosa (1.35) and muscle (1.15). Equation (9) can be solved in terms of EI as
Hence, we can compare measured EI from Eq. (3) with EI computed from Esm and Em as given by Eq. (9b).
Statistical Analysis
The data were assumed to be representative of a normal distribution. The results are expressed as means ± SE. Student's t test and analysis of variance were used to detect possible differences between curves obtained from the intact esophagus and the two sublayers. The results were regarded as significant if P < 0.05.
Results
The esophagi shortened by approximately 30% after excision. However, the segments were stretched to the in vivo length before the mechanical tests. The length was fixed during the distension protocol.
The outer radius, wall thickness, and circumferential Green's strain as function of pressure is shown in figure 1. The radius and the Green's strain increased as function of pressure in the intact as well as the separated esophagus. At pressures higher than 1.5 cmH2O the muscle layer had a higher radius and strain than the mucosa-submucosa layer. The strain for the intact esophagus and for the muscle layer was negative at low pressures indicating the presence of residual strains in the tissue. The thickness of the intact wall and the separated layers decreased as function of pressure. The mucosa-submucosa was the thinnest layer.
Figure 1 Outer radius (top graph), wall thickness (middle graph) and circumferential Green's strain in the intact esophagus and the muscle and mucosa-submucosa sublayers as function of pressure. Values are means ± SE.
Cross-sectional views of the esophagus and its two layers were obtained at the no-load state and zero-stress state. Upon reducing the no-load state to the zero-stress state by cutting the ring radially, the opened ring expanded itself into a sector with an opening angle of about 140° for the intact esophagus (figure 2, top). Separation of the mucosa-submucosa layer from the muscle layer resulted in the release of compressive forces in the mucosa-submucosal layer and tensile forces in the muscle layer. After separation the opening angle of the muscle and the mucosa-submucosa approached 45 and 90°, respectively. Statistically significant differences in opening angles were found between the intact segment and the mucosa-submucosa (p < 0.05), between the intact segment and the muscle layer (p < 0.05), and between the two separated layers (p < 0.05). In comparison to the specimens in the state of zero bending moment, the buckling of the mucosa became less apparent but still present after separation.
Figure 2 Opening angle after anterior cut in the intact esophagus and after separation into the muscle and mucosa-submucosa layers (top) and the stress-strain relationship in sense of Kirchhoff and Green (middle). Values are means ± SE from 8 rats. The bottom graph shows the tangent modulus as function of stress for the intact esophagus and after separation into the muscle and mucosa-submucosa layers
The stress-strain data are depicted in figure 2 (middle) in the sense of Kirchhoff stress and Green strain. A non-linear (exponential) curve was used to fit the data. The stress-strain curve for the submucosa-mucosa layer was located to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3, indicating that the submucosa-mucosa has the highest stiffness. Figure 2c shows the relationship between the tangent modulus and stress for the various layers. It can be seen that there is a linear relationship between the tangent modulus and the stress as a result of the exponential nature of the stress-strain relationship. Furthermore, the modulus is significantly higher in the submucosa layer than in the muscle layer and the intact esophagus as predicted from figure 2 (middle).
Figure 3 shows a composition of the predicted (Eq. 11) and measured Young's modulus for the intact esophagus. It can be seen that the agreement is good in the low stress-strain regime (pressure < 4 cmH2O) where the assumption of linear stress-strain relationship is most justified.
Figure 3 The elastic modulus as function of pressure for the intact esophagus. The curve with the error bar is the experimental date whereas the other curve is the theoretical curve. The theoretical fit is within one SD for the low pressure regime.
Discussion
Scope of Study and Major Findings
The peristaltic transport of swallowed material by the esophagus to the stomach is a neuromuscular function affected by a number of neuromuscular factors [9-14]. The nervous system and the contractile muscle behavior of the esophagus have been studied extensively [10,15] but the mechanics of the esophagus tissues is lagging far behind. The stress-strain-velocity history of the tissues of the esophagus is unknown. Since the esophagus is a tube, traditionally in mechanical analysis the wall material is treated as homogeneous without further analysis into layers. The effect of the two layers on the overall mechanics of the esophagus is examined in this article.
The unique structure of the esophagus as a composite of submucosa and muscle layers allows the separation of the two layers and an experimental determination of the constitutive properties of each layer. This is difficult to carry out in the blood vessels or in other organs. The coronary arteries can be separated at the external elastic laminae but only by tearing the adventitial layer [16]. The results show that the material properties differ between the esophageal intact wall, the muscle layer, and the submucosa layer. The submucosa layer is the stiffest.
A Two-Layer Model
A major difference between the structures of the esophagus and blood vessels is the ease with which the wall can be separated into layers. This fact allowed us to obtain the stress-strain relation and the zero-stress state of the esophageal tissue layers, as reported above. In contrast, in spite of the extensive effort on theoretical and experimental bilayer models of arteries by Berry et al [17], Demiray and Vito [18], Maltzahn et al [19,20], and Rachev [21], the mechanical properties and the zero-stress states of the bi-layers of arteries are still largely unknown. For the esophagus, a practical question is: Can we regard the wall of the esophagus as a homogeneous tissue. Or must we treat it as composed of a mucosa-submucosa layer and a muscle layer? Or must we model it with even more detailed structures? Or can we model it as simply two concentric, non-interacting layers. The answer depends on the purpose of our investigation: What features of the organ does the investigator wish to know. In this article, we examined both the bi-layer model and the monolayer model, present a comparison and propose a simple model to explain the interaction of the two layers.
The opening angle of the inner submucosa layer is larger than that of the outer muscle layer which agrees with our previous report [1,3] and layered artery [22,23]. However the opening angles were largest in the intact layer and smallest in the muscle layer which differs somewhat from those obtained in guinea-pig [1] and in rabbit [3]. The difference may be species related but also due to experimental technique. In the previous studies esophageal rings were first cut radially and then separated circumferentially [1,3]. In the present study we first separated the inner submucosal tube from the outer muscle tube and then cut the ring radially in each layer. We believe that this procedure minimizes any damage inferred by the cutting. We plan to investigate the differences in experimental protocol to rule what causes the differences in opening angle between the previous studies and this study.
The stress-strain curve for the submucosa-mucosa layer was shifted to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3, indicating that the submucosa-mucosa has the highest stiffness (figure 2). This corresponds to the finding of a lower Green strain at pressure above 1.5 cmH2O (figure 1). The difference found below strain 0.3 and pressure 1.5 is due to that the submucosa-mucosa is compressed in the intact esophagus. Hence, when we study this layer after separation, it has a higher strain at low loads when compared to the other specimens. Furthermore, the submucosa-mucosa layer is rich in collagen. Thus, a contributing factor to the observed difference between layers may be that collagen during stretch first uncrimps with little resistance, then at higher loads has a high stiffness [7]. We observed that the stress-strain relationship of the intact esophagus and its two layers is exponential. The tangent modulus, which is the slope of the stress-strain relationship, varies exponentially with the strain (according to the stress-strain figure) and linearly with the stress. Hence, it is simpler to examine the modulus as function of stress. For a nonlinear stress-strain relationship it is meaningless to specify the modulus unless a stress or strain level is prescribed. Fung proposed that the slope of the tangent modulus-stress relationship, α, can be used as a measure of stiffness [24]. The data in this study clearly shows that the mucosa-submucosa layer is the stiffest which is in accordance with previous experience and the fact that submucosa contains large amounts of collagen. Therefore, the esophageal wall should be modelled as at least a two-layered composite system, as has also been proposed for arteries [18-20,22,23,25,26].
Limitations of Study
A limitation of the study is that only uni-axial data were obtained in this study. Intuitively the circumferential direction seems to be the most important for cylindrical organs. Since longitudinal changes may also be important for esophageal function, future studies should implement bi- or tri-axial data. Another limitation is that the analytical model is restricted to the linear stress-strain regime. Furthermore, the esophagus and its layers are assumed to be cylindrical tubes, and that the esophageal tissue is incompressible. The last assumption is possibly true in the pressure range studied and it is also known from yet unpublished studies that the esophagus attains circular geometry both at the inner and outer surfaces even at low pressures. The linearity assumption needs to be generalized. We also assumed that each layer was homogeneous, though it is well known that the muscle layer is composed of longitudinal and circumferential muscle bundles. Hence, the muscle layer can be modeled into further sublayers.
Conclusions and significance of research
We have developed an analytical tool that can be used to analyze the mechanics of bilayered organs. The model was used to study the esophagus. The model may be useful for studying the mechanical properties of other organs that can be separated into layers. There are two immediate implications of the results in this study for the understanding of esophageal function and for clinical practice. It is well known that pain may arise from the esophagus and that the receptors involved in the mechanotransduction are located at different positions in the wall. Detailed information about the stress and strain distributions in the layers is therefore important for the interpretation of receptor-mediated responses. Furthermore, the stress reduction during loading (caused by the residual stresses in the layers) may serve as a mechanism to reduce damage to the esophagus during excessive loadings caused by swallowing of large objects or by acute esophageal obstruction.
For the esophagus the model may be applied to the study of remodeling of the individual layers in response to disease. For example, in systemic sclerosis the muscle layers in esophagus are slowly replaced by fibrotic tissue, creating a passive conduit (fall pipe). It is already known that the esophageal stiffness increase in patients with systemic sclerosis [27,28] but we know very little about the mechanical remodeling in each layer.
Authors contributions
Yanhua Fan carried out the experimental work, the measurements, and read and approved the final manuscript. Hans Gregersen designed the study, partly analyzed the data, and drafted the manuscript. Ghassan S. Kassab suggested the analysis, analyzed the data, drafted and approved the final manuscript.
Acknowledgements
This research was supported by the Obelske Family Foundation, Spar Nord Foundation and the National Institute of Health-National Heart, Lung, and Blood Institute Grant 2 R01 HL055554-06. Dr. Kassab is the recipient of the American Heart Association Established Investigator Award.
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Maltzahn WWV Warringar RG Keitzer WF Experimental measurement of soft elastic properties of media and adventitia of bovine carotid arteries J Biomech 1984 17 839 848 6520132 10.1016/0021-9290(84)90142-8
Rachev A Theoretical study of the effect of stress-dependent remodeling on arterial geometry under hypertensive conditions J Biomech 1997 30 819 827 9239567 10.1016/S0021-9290(97)00032-8
Greenwald SE Moore JEJ Rachev A Meister JJ Experimental investigation of the distribution of residual strains in the artery wall J Biomech Eng 1997 119 438 444 9407283
Taber LA Humphrey JD Stress-modulated growth, residual stress and vascular heterogeneity J Biomech Eng 2001 123 528 535 11783722 10.1115/1.1412451
Fung YC Elasticity of soft tissues in simple elongation Am J Physiol 1967 213 1532 1544 6075755
Xie JP Zhou JB Fung YC Bending of blood vessel wall: stress-strain laws of the intima-media and adventitial layers J Biomech Eng 1995 117 136 145 7609477
Yu QL Zhou JB Fung YC Neutral axis location in bending and Young's modulus of different layers of arterial wall Am J Physiol 1993 269 H52 H60 8342664
Villadsen GE Storkholm J Zachariae H Hendel L Bendtsen F Gregersen H Oesophageal pressure-cross-sectional area distributions and secondary peristalsis in relation to subclassification of systemic sclerosis Neurogastroenterol Motil 2001 13 199 210 11437982 10.1046/j.1365-2982.2001.00259.x
Villadsen GE Storkholm JH Hendel L Vilstrup H Gregersen H Impedance planimetric characterization of esophagus in systemic sclerosis patients with severe involvement of esophagus Dig Dis Sci 1997 42 2317 26 9398812 10.1023/A:1018831104549
| 15518591 | PMC528839 | CC BY | 2021-01-04 16:37:31 | no | Biomed Eng Online. 2004 Nov 1; 3:40 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-40 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-401551859110.1186/1475-925X-3-40ResearchA two-layered mechanical model of the rat esophagus. Experiment and theory Fan Yanhua [email protected] Hans [email protected] Ghassan S [email protected] Institute of Experimental Clinical Research, Skejby Hospital, Aarhus, Denmark2 Center for Biomechanics and Visceral Pain, Aalborg Hospital, Aalborg, Denmark3 Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark4 Haukeland University Hospital, Bergen, Norway5 Department of Biomedical Engineering, UC Irvine, Irvine, California2004 1 11 2004 3 40 40 15 7 2004 1 11 2004 Copyright © 2004 Fan et al; licensee BioMed Central Ltd.2004Fan et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The function of esophagus is to move food by peristaltic motion which is the result of the interaction of the tissue forces in the esophageal wall and the hydrodynamic forces in the food bolus. The structure of the esophagus is layered. In this paper, the esophagus is treated as a two-layered structure consisting of an inner collagen-rich submucosa layer and an outer muscle layer. We developed a model and experimental setup for determination of elastic moduli in the two layers in circumferential direction and related the measured elastic modulus of the intact esophagus to the elastic modulus computed from the elastic moduli of the two layers.
Methods
Inflation experiments were done at in vivo length and pressure-diameters relations were recorded for the rat esophagus. Furthermore, the zero-stress state was taken into consideration.
Results
The radius and the strain increased as function of pressure in the intact as well as in the individual layers of the esophagus. At pressures higher than 1.5 cmH2O the muscle layer had a larger radius and strain than the mucosa-submucosa layer. The strain for the intact esophagus and for the muscle layer was negative at low pressures indicating the presence of residual strains in the tissue. The stress-strain curve for the submucosa-mucosa layer was shifted to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3. The tangent modulus was highest in the submucosa-mucosa layer, indicating that the submucosa-mucosa has the highest stiffness. A good agreement was found between the measured elastic modulus of the intact esophagus and the elastic modulus computed from the elastic moduli of the two separated layers.
BiomechanicsMucosa-submucosa layerMuscle layerOpening angleZero-stress state
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Introduction
The majority of previous mechanical studies on visceral organs, including the blood vessels, have considered them as homogenous tubes; i.e., a single layer structure. Most visceral organs are, however, multilayered, e.g. the arteries consist of intima, media and adventitia and the gastrointestinal tract has circumferential and longitudinal muscle layers, submucosa and mucosa layers.
The esophagus represents a very interesting biomechanical model since it is the only organ that can be separated into two layers without damage to either layer. Hence, the muscle layers can be separated from the mucosa-submucosa layer by dissection, leaving two intact tubes. Separation experiments of the esophagus in guinea pigs and rabbits showed that the submucosa-mucosa layer had larger residual strains and opening angles than the muscle layer [1-3]. Considering the multi-layered composite structure and the difference in zero-stress state between the layers, the stress distribution in the wall is expected to be non-homogeneous. Hence, the material constants likely differ between the layers. Such a finding impacts our understanding of biological tissue remodelling and the function of mechanosensitive receptors located in various layers of the wall [4-6]. Therefore, data on the strain and stress distribution in the layers will facilitate the understanding of the relationship between the stress, remodelling of the tissue and sensory responses. To pursue this line of study, however, it is necessary to know how the stress and strain in the esophagus can be computed for each layer, and how the composite can be put together to give the overall observed mechanical properties.
In this study we recognize that the esophagus consists of mucosa-submucosa and muscle layers. We analyze these layers as elastic shells. Each layer has its own zero-stress state, and its own elastic constants. We will determine the material properties of each layer separately. Specifically, the material properties in the individual layers will be computed from the pressure-diameter relation and zero-stress state with the method of analysis presented below. We will then propose a simple model to combine the two layers to predict the overall behavior of the esophagus under certain hypotheses. The limitations and implications of the model will be discussed.
Materials and methods
Eight male Wistar rats, weighing 380–420 grams, were used in the study. Approval of the protocol was obtained from the Danish Animal Experiment Committee. The animals were anesthetized with sodium pentobarbital (50 mg kg-1 ip). Papaverine (15 mg) was injected into the tail vein to relax the visceral muscles and to euthanize the rat. The cervical segment of the esophagus was dissected free from its adjacent tissue. Next, the thoracic and abdominal cavities were opened. After pouring cold Krebs solution into the thoracic cavity, the esophagus was quickly dissected free from adjacent tissues and its in situ length was measured. A 2-cm-long segment from the middle part, intended for the distension test, was marked. The length of this segment and that of the entire esophagus was measured. The entire esophagus was then cut at the proximal and distal ends including the very first part of the stomach, and immediately placed in calcium-free Krebs solution containing 6% dextran and 0.25% ethylene glycol-bis (β-aminoethyl ether)-N,N,N,N-tetraacetic acid (EGTA). The solution was aerated with a gas mixture of 95% O2-5% CO2 at pH of 7.4. After careful removal of all extra-esophageal tissue, the length was measured in vitro.
Pressure-diameter experiments
The middle part of the intact esophagus was mounted in an organ bath containing the Ca+2-free Krebs solution. The segment was stretched to the in vivo length and fixed. The distal end was closed whereas the proximal end was cannulated and connected to a fluid container. After preconditioning the tissue with pressures up to 8 cmH2O, a ramp test was performed where the pressure was changed continuously at a rate of 2 cmH2O per minute up to a pressure of 8 cmH2O. A video camera (Sony CCD camera) monitored the changes in diameter and length during the distension and images were grabbed by a PC.
After the test of the intact esophagus, the muscle and submucosa-mucosa layers were gently separated into two tubes. The tubes were studied separately using the same procedure outlined above. The only difference was that the maximum pressure was set to 6 cmH2O.
The zero-stress state of the esophagus
The zero-stress state of the esophagus was obtained in accordance with the method used for blood vessels [7]. Briefly, six rings of 1 mm length were cut from the intact esophagus and from the separated layers and were then cut in the radial direction to obtain the zero-stress state. The choice of the ring length was based on pilot studies. The radial cut caused the rings to open up into sectors. The shape of each ring segment at the zero-stress state was photographed 60 minutes after the radial cut to allow the creep to subside.
Data analysis
The morphometric measurements were made using SigmaScan Pro image analysis software (Jandel Scientific, Germany). The data were obtained from the images of the tubes in the distended state, rings in the no-load state and sectors in the zero-stress state. In the distended state for both the intact esophagus and the separated tubes, images were analyzed for each 0.5 cmH2O increment. The outer diameter was measured at each pressure level and averaged over three locations. At the no-load and zero-stress states, the inner and outer circumferential lengths were measured along with the thickness and area of the wall and layers (for calculation of inner, outer or mid-layer circumference). The opening angle was defined as the angle subtended between two radii drawn from the midpoint of the inner wall to the tips of the inner wall of the open sector.
The stresses and strains of the esophagus and its sublayers in the pressurized state were determined under the assumption that the geometric configuration of the lumen is cylindrical, the wall of the esophagus is incompressible, and the material in each layer is homogenous. Based on the above measurements and assumptions, parameters of the esophagus such as the luminal radius (ri-p), the wall thickness (Hp), the mid-wall circumference (Cm-p) at a given pressure were computed as ri-p = [(r2o-p - An/πλ1)]1/2, Hp = ro-p - ri-p, and Cm-p = 2π(ri-p + Hp/2). The outer radius (ro-p) of the intestine was computed according to the outer diameter (Do). The circumferential Green's strains and Kirchhoff's stress were computed according to the equations:
where Cm-z is the mid-wall circumference at the zero-stress state.
where
The tangent modulus can be estimated from the slope of the stress-strain relation as
The tangent modulus given by Eq. (3) corresponds to Young's modulus in the linear stress-strain regime.
Integration of Two-Layers: An Analytical Model
We assume that the circumferential stress-strain relationships for the inflation experiment obey Hooke's law for each layer
σθθ(sm) = Eθθ(sm)eθθ(sm) σθθ(m) = Eθθ(m)eθθ(m) (4)
where σ, E and e indicate the Cauchy stress, Tangent modulus and Green strain, respectively. θθ, sm and m indicate the circumferential direction and the submucosal and muscle layers, respectively.
In general, the circumferential stress is a function of circumferential and longitudinal strain. In the present analysis, we assume that the cross-modulus is small such that the longitudinal term is negligible in comparison with the circumferential term and hence eq. (4). We further assume that the esophagus is a circular cylinder. The basic equations of equilibrium and deformation are given in Flugge [8]. Let x denote the longitudinal axis, θ the circumferential axis and z the radial coordinate. Nθ denotes the tensile membrane stress resultant in θ direction. The displacement in the x, θ, and z directions of a point on the neutral axis surface are denoted by u, v, and w, respectively. The displacements of any point, A, is denoted by uA, vA, wA as follows: 1) uA = displacement along the generator, positive in the direction of increasing x; 2) vA = displacement along a circle of radius a + z, positive in the direction of increasing θ and 3) wA = radial displacement, positive outward.
According to the Bernoulli-Kirchhoff hypothesis (all points lying on one normal to the neutral surface before deformation remain on the normal after deformation), we have
where a indicate the neutral axis.
The circumferential strain which is assumed to be small is given by
The circumferential membrane stress resultant Nθ is given by
By substitution of Eqs. (4), (5), and (6) into Eq. (7) and noting that in the inflation experiments u, v, and w do not change with θ and w does not change with x, we obtain
Eq. (8) can be integrated to yield
where I stands for intact esophagus; aI, asm and am are the neutral axes for the submucosa (1.35) and muscle (1.15). Equation (9) can be solved in terms of EI as
Hence, we can compare measured EI from Eq. (3) with EI computed from Esm and Em as given by Eq. (9b).
Statistical Analysis
The data were assumed to be representative of a normal distribution. The results are expressed as means ± SE. Student's t test and analysis of variance were used to detect possible differences between curves obtained from the intact esophagus and the two sublayers. The results were regarded as significant if P < 0.05.
Results
The esophagi shortened by approximately 30% after excision. However, the segments were stretched to the in vivo length before the mechanical tests. The length was fixed during the distension protocol.
The outer radius, wall thickness, and circumferential Green's strain as function of pressure is shown in figure 1. The radius and the Green's strain increased as function of pressure in the intact as well as the separated esophagus. At pressures higher than 1.5 cmH2O the muscle layer had a higher radius and strain than the mucosa-submucosa layer. The strain for the intact esophagus and for the muscle layer was negative at low pressures indicating the presence of residual strains in the tissue. The thickness of the intact wall and the separated layers decreased as function of pressure. The mucosa-submucosa was the thinnest layer.
Figure 1 Outer radius (top graph), wall thickness (middle graph) and circumferential Green's strain in the intact esophagus and the muscle and mucosa-submucosa sublayers as function of pressure. Values are means ± SE.
Cross-sectional views of the esophagus and its two layers were obtained at the no-load state and zero-stress state. Upon reducing the no-load state to the zero-stress state by cutting the ring radially, the opened ring expanded itself into a sector with an opening angle of about 140° for the intact esophagus (figure 2, top). Separation of the mucosa-submucosa layer from the muscle layer resulted in the release of compressive forces in the mucosa-submucosal layer and tensile forces in the muscle layer. After separation the opening angle of the muscle and the mucosa-submucosa approached 45 and 90°, respectively. Statistically significant differences in opening angles were found between the intact segment and the mucosa-submucosa (p < 0.05), between the intact segment and the muscle layer (p < 0.05), and between the two separated layers (p < 0.05). In comparison to the specimens in the state of zero bending moment, the buckling of the mucosa became less apparent but still present after separation.
Figure 2 Opening angle after anterior cut in the intact esophagus and after separation into the muscle and mucosa-submucosa layers (top) and the stress-strain relationship in sense of Kirchhoff and Green (middle). Values are means ± SE from 8 rats. The bottom graph shows the tangent modulus as function of stress for the intact esophagus and after separation into the muscle and mucosa-submucosa layers
The stress-strain data are depicted in figure 2 (middle) in the sense of Kirchhoff stress and Green strain. A non-linear (exponential) curve was used to fit the data. The stress-strain curve for the submucosa-mucosa layer was located to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3, indicating that the submucosa-mucosa has the highest stiffness. Figure 2c shows the relationship between the tangent modulus and stress for the various layers. It can be seen that there is a linear relationship between the tangent modulus and the stress as a result of the exponential nature of the stress-strain relationship. Furthermore, the modulus is significantly higher in the submucosa layer than in the muscle layer and the intact esophagus as predicted from figure 2 (middle).
Figure 3 shows a composition of the predicted (Eq. 11) and measured Young's modulus for the intact esophagus. It can be seen that the agreement is good in the low stress-strain regime (pressure < 4 cmH2O) where the assumption of linear stress-strain relationship is most justified.
Figure 3 The elastic modulus as function of pressure for the intact esophagus. The curve with the error bar is the experimental date whereas the other curve is the theoretical curve. The theoretical fit is within one SD for the low pressure regime.
Discussion
Scope of Study and Major Findings
The peristaltic transport of swallowed material by the esophagus to the stomach is a neuromuscular function affected by a number of neuromuscular factors [9-14]. The nervous system and the contractile muscle behavior of the esophagus have been studied extensively [10,15] but the mechanics of the esophagus tissues is lagging far behind. The stress-strain-velocity history of the tissues of the esophagus is unknown. Since the esophagus is a tube, traditionally in mechanical analysis the wall material is treated as homogeneous without further analysis into layers. The effect of the two layers on the overall mechanics of the esophagus is examined in this article.
The unique structure of the esophagus as a composite of submucosa and muscle layers allows the separation of the two layers and an experimental determination of the constitutive properties of each layer. This is difficult to carry out in the blood vessels or in other organs. The coronary arteries can be separated at the external elastic laminae but only by tearing the adventitial layer [16]. The results show that the material properties differ between the esophageal intact wall, the muscle layer, and the submucosa layer. The submucosa layer is the stiffest.
A Two-Layer Model
A major difference between the structures of the esophagus and blood vessels is the ease with which the wall can be separated into layers. This fact allowed us to obtain the stress-strain relation and the zero-stress state of the esophageal tissue layers, as reported above. In contrast, in spite of the extensive effort on theoretical and experimental bilayer models of arteries by Berry et al [17], Demiray and Vito [18], Maltzahn et al [19,20], and Rachev [21], the mechanical properties and the zero-stress states of the bi-layers of arteries are still largely unknown. For the esophagus, a practical question is: Can we regard the wall of the esophagus as a homogeneous tissue. Or must we treat it as composed of a mucosa-submucosa layer and a muscle layer? Or must we model it with even more detailed structures? Or can we model it as simply two concentric, non-interacting layers. The answer depends on the purpose of our investigation: What features of the organ does the investigator wish to know. In this article, we examined both the bi-layer model and the monolayer model, present a comparison and propose a simple model to explain the interaction of the two layers.
The opening angle of the inner submucosa layer is larger than that of the outer muscle layer which agrees with our previous report [1,3] and layered artery [22,23]. However the opening angles were largest in the intact layer and smallest in the muscle layer which differs somewhat from those obtained in guinea-pig [1] and in rabbit [3]. The difference may be species related but also due to experimental technique. In the previous studies esophageal rings were first cut radially and then separated circumferentially [1,3]. In the present study we first separated the inner submucosal tube from the outer muscle tube and then cut the ring radially in each layer. We believe that this procedure minimizes any damage inferred by the cutting. We plan to investigate the differences in experimental protocol to rule what causes the differences in opening angle between the previous studies and this study.
The stress-strain curve for the submucosa-mucosa layer was shifted to the left of the curves for the muscle layer and for the intact esophagus at strains higher than 0.3, indicating that the submucosa-mucosa has the highest stiffness (figure 2). This corresponds to the finding of a lower Green strain at pressure above 1.5 cmH2O (figure 1). The difference found below strain 0.3 and pressure 1.5 is due to that the submucosa-mucosa is compressed in the intact esophagus. Hence, when we study this layer after separation, it has a higher strain at low loads when compared to the other specimens. Furthermore, the submucosa-mucosa layer is rich in collagen. Thus, a contributing factor to the observed difference between layers may be that collagen during stretch first uncrimps with little resistance, then at higher loads has a high stiffness [7]. We observed that the stress-strain relationship of the intact esophagus and its two layers is exponential. The tangent modulus, which is the slope of the stress-strain relationship, varies exponentially with the strain (according to the stress-strain figure) and linearly with the stress. Hence, it is simpler to examine the modulus as function of stress. For a nonlinear stress-strain relationship it is meaningless to specify the modulus unless a stress or strain level is prescribed. Fung proposed that the slope of the tangent modulus-stress relationship, α, can be used as a measure of stiffness [24]. The data in this study clearly shows that the mucosa-submucosa layer is the stiffest which is in accordance with previous experience and the fact that submucosa contains large amounts of collagen. Therefore, the esophageal wall should be modelled as at least a two-layered composite system, as has also been proposed for arteries [18-20,22,23,25,26].
Limitations of Study
A limitation of the study is that only uni-axial data were obtained in this study. Intuitively the circumferential direction seems to be the most important for cylindrical organs. Since longitudinal changes may also be important for esophageal function, future studies should implement bi- or tri-axial data. Another limitation is that the analytical model is restricted to the linear stress-strain regime. Furthermore, the esophagus and its layers are assumed to be cylindrical tubes, and that the esophageal tissue is incompressible. The last assumption is possibly true in the pressure range studied and it is also known from yet unpublished studies that the esophagus attains circular geometry both at the inner and outer surfaces even at low pressures. The linearity assumption needs to be generalized. We also assumed that each layer was homogeneous, though it is well known that the muscle layer is composed of longitudinal and circumferential muscle bundles. Hence, the muscle layer can be modeled into further sublayers.
Conclusions and significance of research
We have developed an analytical tool that can be used to analyze the mechanics of bilayered organs. The model was used to study the esophagus. The model may be useful for studying the mechanical properties of other organs that can be separated into layers. There are two immediate implications of the results in this study for the understanding of esophageal function and for clinical practice. It is well known that pain may arise from the esophagus and that the receptors involved in the mechanotransduction are located at different positions in the wall. Detailed information about the stress and strain distributions in the layers is therefore important for the interpretation of receptor-mediated responses. Furthermore, the stress reduction during loading (caused by the residual stresses in the layers) may serve as a mechanism to reduce damage to the esophagus during excessive loadings caused by swallowing of large objects or by acute esophageal obstruction.
For the esophagus the model may be applied to the study of remodeling of the individual layers in response to disease. For example, in systemic sclerosis the muscle layers in esophagus are slowly replaced by fibrotic tissue, creating a passive conduit (fall pipe). It is already known that the esophageal stiffness increase in patients with systemic sclerosis [27,28] but we know very little about the mechanical remodeling in each layer.
Authors contributions
Yanhua Fan carried out the experimental work, the measurements, and read and approved the final manuscript. Hans Gregersen designed the study, partly analyzed the data, and drafted the manuscript. Ghassan S. Kassab suggested the analysis, analyzed the data, drafted and approved the final manuscript.
Acknowledgements
This research was supported by the Obelske Family Foundation, Spar Nord Foundation and the National Institute of Health-National Heart, Lung, and Blood Institute Grant 2 R01 HL055554-06. Dr. Kassab is the recipient of the American Heart Association Established Investigator Award.
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Gregersen H Lee TC Chien S Skalak R Fung YC Strain distribution in the layered wall of the esophagus J Biomech Eng 1999 121 442 448 10529910
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Lu X Gregersen H Regional distribution of axial strain and circumferential residual strain in the layered rabbit oesophagus J Biomech 2001 34 225 233 11165287 10.1016/S0021-9290(00)00176-7
Gregersen H Kassab GS Biomechanics of the gastrointestinal tract Neurogastroenterol Motil 1996 8 277 297 8959733
Matsumoto T Hayashi K Stress and strain distribution in hypertensive and normotensive rat aorta considering residual strain J Biomech Eng 1996 118 62 73 8833076
Peterson SJ Okamoto RJ Effect of residual stress and Heterogeneity on circumferential stress in the arterial wall J Biomech Eng 2000 122 454 456 11036572 10.1115/1.1288210
Fung YC Biomechanics: Motion, Flow, Stress and Growth 1990 New York: Springer Verlag
Flügge W Stresses in shells Berlin 1960 Springer Verlag
Arndorfer RC Stef JJ Dodds WJ Linehan JH Hogan WT Improved infusion system for intraluminal esophageal manometry Gastroenterology 1977 73 23 27 324861
Christensen J Johnson LR, Christensen J, Jackson MJ, Jacobson ED, Walsh JH The esophagus In Physiology of the Gastrointestinal Tract 1987 1 2 New York: Raven Press 595 612
Humphries TJ Castell DO Pressure profile of esophageal peristalsis in normal humans as measured by direct intraesophageal transducers Am J Dig Dis 1977 22 641 646 879131
Li M Brasseur JG Kern MK Dodds WJ Viscosity measurements of barium sulfate mixtures for use in motility studies of the pharynx and esophagus Dysphagia 1992 7 17 30 1424824
Ravinder KM Ren J McCallum RW Shaffer HA Sluss J Modulation of feline oesophageal contractions by bolus volume and outflow obstruction Am J Physiol 1990 258 G208 G215 2305886
Ren J Massey BT Dodds WJ Kern MK Brasseur JG Shaker R Harrington SS Hogan WJ Arndorfer RC Determinants of the bolus pressure during esophageal peristaltic bolus transport Am J Physiol 1993 264 G407 G413 8460696
Christensen J Freeman BW Miller JK Some physiological characteristics of the esophagogastric junction in the opossum Gastroenterology 1973 64 1119 1125 4706130
Lu X Yang J Zhao J Gregersen H Kassab GS Shear modulus of coronary arteries Contribution of media and adventitia Am J Physiol Heart Circ Physiol 2003 285 H1966 H1975 14561679
Berry J Rachev A Moore JE jrMeister JJ Analysis of the effect of a non-circular two-layer stress-free state on arterial wall stress Proceedings of IEEE, EMBS, Paris 1992 65 66
Demiray H Vito RP A layered cylindrical shell model for an aorta Int J Eng Sci 1991 29 47 54 10.1016/0020-7225(91)90075-E
Maltzahn WWV Besdo D Wiemer W Elastic properties of arteries. A nonlinear two-layer cylindrical model J Biomech 1981 14 389 397 7263731 10.1016/0021-9290(81)90056-7
Maltzahn WWV Warringar RG Keitzer WF Experimental measurement of soft elastic properties of media and adventitia of bovine carotid arteries J Biomech 1984 17 839 848 6520132 10.1016/0021-9290(84)90142-8
Rachev A Theoretical study of the effect of stress-dependent remodeling on arterial geometry under hypertensive conditions J Biomech 1997 30 819 827 9239567 10.1016/S0021-9290(97)00032-8
Greenwald SE Moore JEJ Rachev A Meister JJ Experimental investigation of the distribution of residual strains in the artery wall J Biomech Eng 1997 119 438 444 9407283
Taber LA Humphrey JD Stress-modulated growth, residual stress and vascular heterogeneity J Biomech Eng 2001 123 528 535 11783722 10.1115/1.1412451
Fung YC Elasticity of soft tissues in simple elongation Am J Physiol 1967 213 1532 1544 6075755
Xie JP Zhou JB Fung YC Bending of blood vessel wall: stress-strain laws of the intima-media and adventitial layers J Biomech Eng 1995 117 136 145 7609477
Yu QL Zhou JB Fung YC Neutral axis location in bending and Young's modulus of different layers of arterial wall Am J Physiol 1993 269 H52 H60 8342664
Villadsen GE Storkholm J Zachariae H Hendel L Bendtsen F Gregersen H Oesophageal pressure-cross-sectional area distributions and secondary peristalsis in relation to subclassification of systemic sclerosis Neurogastroenterol Motil 2001 13 199 210 11437982 10.1046/j.1365-2982.2001.00259.x
Villadsen GE Storkholm JH Hendel L Vilstrup H Gregersen H Impedance planimetric characterization of esophagus in systemic sclerosis patients with severe involvement of esophagus Dig Dis Sci 1997 42 2317 26 9398812 10.1023/A:1018831104549
| 15511297 | PMC528844 | CC BY | 2021-01-04 16:39:18 | no | Lipids Health Dis. 2004 Oct 28; 3:23 | latin-1 | Lipids Health Dis | 2,004 | 10.1186/1476-511X-3-23 | oa_comm |
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J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-361550069910.1186/1479-5876-2-36CommentaryGranzyme B; the chalk-mark of a cytotoxic lymphocyte Waterhouse Nigel J [email protected] Karin A [email protected] Chris JP [email protected] Cancer Immunology Program, Peter MacCallum Cancer Centre, Locked Bag 1, A'Beckett Street, Melbourne, 8006, Australia2004 25 10 2004 2 36 36 27 9 2004 25 10 2004 Copyright © 2004 Waterhouse et al; licensee BioMed Central Ltd.2004Waterhouse et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
During cytotoxic lymphocyte (CL) mediated killing of target cells, granzyme B is released from the CL into the immune synapse. Recent studies have found that ELISPOT-detection of granzyme B correlated well with conventional assays for CL mediated killing. In this way, the released granzyme B can be used to mark the spot where a target cell was murdered. We discuss the benefits and potential limitations of using this assay to measure CL mediated killing of target cells.
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Introduction
Cytotoxic Lymphocytes (CLs) eliminate virally infected cells or tumour cells either by activating death receptors or by delivering cytotoxic granule proteins (granule exocytosis) to the target cell [1,2]. The ability of a virus or a tumour cell to evade detection or survive an attack by CLs is likely to result in a more aggressive disease. The ability to measure specific killing of target cells by CLs is therefore of great interest to clinicians and researchers alike. Any assay for CL-induced death involves mixed cultures of target and effector cells and must include some means of distinguishing between the two. The current approach is to measure the release of a label, such as 51Cr or, more recently calcein-AM [3], that has been preloaded into the target cells. Radioactivity limits the utility of 51Cr and, although this type of assay is presumed to measure rupture of the plasma membrane (cell lysis), it is not formally known what is being measured.
Discussion
Various alternative assays have been developed to assay CL-induced killing of target cells [4-10], however 51Cr remains the gold standard. Recently, Shafer-Weaver et al and others have utilized an interesting strategy aimed at measuring the functions of effector cells rather than death of the target cell [9,11]. During granule-mediated killing, granule enzymes (granzymes) are transferred to the target cell [2,12]. In the target cell granzyme B, can initiate target cell death by apoptosis [13,14]. Shafer-Weaver et al., [11] demonstrated that detection of granzyme B by ELISPOT correlated well with 51Cr release during antigen specific target cell death induced by cytotoxic T-lymphocytes and now report utility of this assay for measuring MHC non-restricted killing by natural killer cells [15]. Following incubation of CL with their targets, Shafer-Weaver et al., measured granzyme B by ELISPOT and found that the number of SPOTS correlated well with results obtained by the 51Cr release assay. Unlike the 51Cr release assay, this ELISPOT assay measures a specific and well-characterized event that occurs following target recognition. Assessing granzyme B by ELISPOT appears superior to other markers, such as IFNγ, because it assays a molecule that directly participates in CL mediated killing. Furthermore, the assay is non-radioactive and under the experimental parameters reported, it appears possible to detect cytolytic activity using fewer cells than are required for 51Cr release.
This assay appears to provide an effective alternative method for assessing CL-mediated cell death, however, users should be aware of possible limitations. The assay measures granzyme B release, not cell death. Frequently, the two will be closely correlated, but under certain circumstances using granzyme B release as a marker could lead either to an under or over estimate of target cell death. For example, perforin-deficient CLs are unable to kill targets [16,17], yet they may release granzyme B in the same way as wild type cells -leading to a false positive result. Alternatively, cells lacking, or expressing small amounts of granzyme B may retain the ability to kill targets by means of other granule components or through death receptor mediated pathways leading to an underestimate of cytotoxic activity [18]. In addition, a CL may degranulate normally, but certain targets may be inherently resistant to their effects [19]. Thus, to be certain that degranulation is inducing target cell death, chromium release assays should be performed alongside the granzyme B ELISPOT.
The limits of detection of this assay are not clear. It is not known whether the granzyme B released at a single death-inducing synapse are sufficient to produce a spot or whether a CL must degranulate several times, possibly killing multiple targets, to facilitate detection. Even if one spot reflects degranulation by one CL and is directly equivalent to one target cell death, it remains possible that CLs expressing granzyme B below the level of detection by ELISPOT may express sufficient granzyme B to kill their targets. These are difficult issues to address, but the correlation between 51Cr and granzyme B ELISPOT shown under the conditions used by Shafer-Weaver et al [15] suggests that the levels of detection of the assay are likely to be broadly equivalent to those required for cell death. It is however too difficult to directly compare these two assays. For example, 316 spots were detected in an assay using 50,000 target cells and 10,000 effectors (Table 1). This is equivalent to 0.6 +/- 0.1 % (as the number of spots must be related to the number of targets for comparison with 51Cr). Increasing the effectors generated too many spots to count. Therefore an experiment optimised for 51Cr assay, (0–70% release as reported in Table 1), will only have a dynamic range of between 0 and 0.6% using the ELISPOT assay. In contrast, an assay optimized for ELISPOT is likely to be off scale in a 51Cr release assay. These data suggest that a small amount of killing (e.g in a sample with low level killing) may easily generate a positive result by ELISPOT. It is therefore likely that stringent titration of both effectors and targets over a narrow range will be essential.
Conclusion
The granzyme B-ELISPOT introduces a new assay for measuring CL mediated toxicity that will have a widespread utility in experimental systems where granzyme B is present in the effector cell and the target is susceptible to CL mediated killing. However, no assay used in isolation can be the answer to everyone's prayers and the granzyme B ELISPOT, like all others, has limitations. There is no doubt that this assay measures triggering of degranulation, but it does not directly address the question of cell death. Therefore it is likely that the greatest utility of this assay will be found by using it in combination with other existing measures of cytotoxic activity. It may also be extremely valuable as a quick reference to determine whether killing can occur in an assay with defined targets and effectors.
Abbreviations
CL, cytotoxic lymphocytes; ELISPOT, enzyme linked immunospot; Cr, Chromium;
Competing Interests
The authors declare that they have no competing interests.
Author's contributions
All authors contributed to the ideas, discussion and preparation of this manuscript.
Acknowledgements
NJW is a Peter Doherty fellow and CJPC is a PI of the Cancer Immunology Program funded by the NHMRC Australia
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| 15500699 | PMC528853 | CC BY | 2021-01-04 16:39:24 | no | J Transl Med. 2004 Oct 25; 2:36 | utf-8 | J Transl Med | 2,004 | 10.1186/1479-5876-2-36 | oa_comm |
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J NeuroinflammationJournal of Neuroinflammation1742-2094BioMed Central London 1742-2094-1-191548557910.1186/1742-2094-1-19EditorialFunding free and universal access to Journal of Neuroinflammation Mrak Robert E [email protected] W Sue T [email protected] Department of Pathology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA2 Department of Geriatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA2004 14 10 2004 1 19 19 12 10 2004 14 10 2004 Copyright © 2004 Mrak and Griffin; licensee BioMed Central Ltd.2004Mrak and Griffin; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Journal of Neuroinflammation is an Open Access, online journal published by BioMed Central. Open Access publishing provides instant and universal availability of published work to any potential reader, worldwide, completely free of subscriptions, passwords, and charges. Further, authors retain copyright for their work, facilitating its dissemination. Open Access publishing is made possible by article-processing charges assessed "on the front end" to authors, their institutions, or their funding agencies. Beginning November 1, 2004, the Journal of Neuroinflammation will introduce article-processing charges of around US$525 for accepted articles. This charge will be waived for authors from institutions that are BioMed Central members, and in additional cases for reasons of genuine financial hardship. These article-processing charges pay for an electronic submission process that facilitates efficient and thorough peer review, for publication costs involved in providing the article freely and universally accessible in various formats online, and for the processes required for the article's inclusion in PubMed and its archiving in PubMed Central, e-Depot, Potsdam and INIST. There is no remuneration of any kind provided to the Editors-in-Chief, to any members of the Editorial Board, or to peer reviewers; all of whose work is entirely voluntary. Our article-processing charge is less than charges frequently levied by traditional journals: the Journal of Neuroinflammation does not levy any additional page or color charges on top of this fee, and there are no reprint costs as publication-quality pdf files are provided, free, for distribution in lieu of reprints. Our article-processing charge will enable full, immediate, and continued Open Access for all work published in Journal of Neuroinflammation. The benefits from such Open Access will accrue to readers, through unrestricted access; to authors, through the widest possible dissemination of their work; and to science and society in general, through facilitation of information availability and scientific advancement.
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Introduction
Journal of Neuroinflammation is an Open Access, online journal that is published by BioMed Central, an independent publisher committed to Open Access for peer-reviewed biomedical research [1]. Among the many benefits of Open Access publishing are (1) instant and universal availability of published work to any potential reader, worldwide, completely free of subscriptions, passwords, and charges; and (2) copyright retention by the authors rather than the publisher. This and many other benefits led us to select Open Access publishing, and to select BioMed Central, for the Journal of Neuroinflammation. Open Access publishing is made possible by article-processing charges (APCs) assessed "on the front end" to authors, their institutions, or their funding agencies. The Journal of Neuroinflammation will introduce APCs of around US$525 per article for manuscripts submitted on or after November 1, 2004. This charge will be waived for all authors from institutions that are BioMed Central members, and in additional cases for reasons of genuine financial hardship.
Problems with the traditional publishing model
Traditional journals generally do not charge authors for publication (although assessed page or color charges may easily exceed our APCs). Instead, article access is traditionally paid for by readers, either through subscriptions or through fees assessed for online viewing and downloading. Over the past decade, escalating journal subscriptions have resulted in cash-strapped libraries cancelling journal subscriptions [2], thus limiting the range of articles available to many readers and limiting the potential audience available to authors.
The Open Access publishing model
The Journal of Neuroinflammation's Open Access policy changes the way in which articles are published. First, all articles become freely and universally accessible online, immediately upon acceptance, so an author's work can be read by anyone at no cost. Second, the article authors retain copyright for their work, and grant to anyone the right to reproduce and disseminate the article, provided that it is correctly cited and no errors are introduced [1]. Third, a copy of the full text of each article is permanently archived in several separate online repositories. Journal of Neuroinflammation's articles are permanently archived in PubMed Central [3], the US National Library of Medicine's full-text repository of life science literature, and also in repositories at the University of Potsdam [4] in Germany, at INIST [5] in France and in e-Depot [6], the National Library of the Netherlands' digital archive of all electronic publications.
Benefits of Open Access publishing
Open Access has four broad benefits for science and the general public. First, published work is disseminated freely and instantly to the widest possible audience, without barriers to access. Authors are free to reproduce and distribute their work at will, for example by placing it on their institution's website. Open Access publication has been shown to actually increase article citations and impact because of this easier availability [7]. Second, availability of Open Access articles enhances literature searching [8], as information available to researchers is not limited by what their libraries can afford. Third, results of publicly funded research are accessible to all taxpayers and not just those with access to libraries with journal subscriptions. Such public accessibility would actually become a legal requirement in the USA if the proposed Public Access to Science Act is enacted into law [9]. Fourth, article access is not limited by the economic resources of a scientist's country or institution; resource-poor countries and institutions are able to access the same material as wealthier ones, subject only to the availability of internet access [10].
Journal of Neuroinflammation's article-processing charges
Article-processing charges will allow continued Open Access to all article published in Journal of Neuroinflammation. Authors will be asked to pay around US$525 upon acceptance of their article for publication. Submitted articles that are not accepted will incur no charge. There will be no charges for authors from institutions that are institutional members of BioMed Central. Currently this includes NHS England and all universities in the UK, the US National Institutes of Health and 136 other institutions and universities in the USA, the World Health Organization, and almost 200 additional institutions in 37 other countries [11]. Potential authors who are not associated with these institutions can avoid article-processing charges by getting their institution to join this list of BioMed Central institutional members. The annual institutional membership fee covers APCs for all authors at that institution for that year. In addition, many funding agencies have recognized the importance of Open Access publishing and have specified that funds from their grants may be used directly to pay APCs [12]. Finally, APC waivers are available for cases of genuine financial hardship. These will be considered on a case-by-case basis by the Editors-in-Chief.
What do article-processing charges pay for?
The APC pays for an electronic submission process that facilitates efficient and thorough peer review, for publication costs involved in providing the article freely and universally accessible in various formats online, and for the processes required for the article's inclusion in PubMed and its archiving in PubMed Central, e-Depot, Potsdam and INIST. There is no remuneration of any kind provided to the Editors-in-Chief, to any members of the editorial board, or to peer reviewers; all of whose work is entirely voluntary. Although some authors may consider US$525 expensive, it must be remembered that Journal of Neuroinflammation does not levy any additional page or color charges on top of this fee. Because we are an online-only journal, any number of color figures, photographs, and 'extra' pages can be included at no extra cost. Such color and page charges, as assessed by more traditional journals, can easily exceed our flat US$525 per-article APC. Another common expense with traditional journals is the purchase of reprints for distribution, and the cost of these reprints is also frequently greater than our APCs. The Journal of Neuroinflammation provides free, publication-quality pdf files for distribution, in lieu of reprints.
Free access versus Open Access
Several traditional journals now offer free access to their articles online, but this is different from Open Access as defined by the Bethesda Statement [13]. First, this access may be delayed for 6–2 months after publication. Second, readers are not free to reproduce and/or disseminate the work because of restrictions imposed by publishers' copyright policies. Even these restrictive policies do not ensure continued free access; the British Medical Journal, for instance, recently announced that it cannot continue to provide free access to its website [14]. They are considering various sources of revenue, including APCs [15].
APC-funded Open Access is not unique to BioMed Central or to the Journal of Neuroinflammation. The USA-based Public Library of Science (PLoS) is a new, non-profit organization that, like BioMed Central, is dedicated to online, Open Access publishing. PLoS has started two new Open Access journals, with APCs of US$1500 for each accepted article [16]. PLoS has used television advertising to promote their new journals [9], providing a high profile that should raise awareness of Open Access publishing in general. This, in turn, should encourage researchers in all disciplines to understand and accept Open Access, and to accept APCs as an acceptable funding method.
Conclusion
Article-processing charges will enable full, immediate, and continued Open Access for all work published in Journal of Neuroinflammation. The benefits from such Open Access will accrue to readers, through unrestricted access; to authors, through the widest possible dissemination of their work; and to science and society in general, through facilitation of information availability and scientific advancement. We ask for your support in this important movement by submitting your next article to Journal of Neuroinflammation or to another Open Access journal.
Competing interests
At Journal of Neuroinflammation, the work of the Editors-in-Chief, the Editorial Board, and of all invited outside peer reviewers is entirely voluntary, without tangible remuneration of any kind. Our goal is publication of biomedical research of the highest quality, and our (intangible) rewards lie in the achievement of these goals. Decisions about manuscripts are based entirely on the quality of the work, and not on the ability of authors to pay article-processing charges.
Abbreviations
APC = article-processing charge.
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BioMed Central Open Access Charter
Tamber PS Is scholarly publishing becoming a monopoly? BMC News and Views 2000 1 1
PubMed Central
Potsdam
INIST
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Lawrence S Free online availability substantially increases a paper's impact Nature 2001 411 521 11385534 10.1038/35079151
Velterop J Should scholarly societies embrace Open Access (or is it the kiss of death)? Learned Publishing 2003 16 167 169 10.1087/095315103322110932
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Tan-Torres Edejer T Disseminating health information in developing countries: the role of the internet BMJ 2000 321 797 800 11009519 10.1136/bmj.321.7264.797
BioMed Central Institutional Members
Which funding agencies explicitly allow direct use of their grants to cover article-processing charges?
Bethesda Statement on Open Access Publishing
Delamothe T Smith R Paying for bmj.com BMJ 2003 327 241 242 10.1136/bmj.327.7409.241
Smith R The BMJ will experiment with the 'author pays' model (Rapid response to BMJ 2003;327:241-2)
Public Library of Science to launch new free-access biomedical journals with $9 million grant from the Gordon and Betty Moore Foundation
| 15485579 | PMC528856 | CC BY | 2021-01-04 16:38:19 | no | J Neuroinflammation. 2004 Oct 14; 1:19 | utf-8 | J Neuroinflammation | 2,004 | 10.1186/1742-2094-1-19 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-171550930010.1186/1465-9921-5-17ReviewAdd-on therapy options in asthma not adequately controlled by inhaled corticosteroids: a comprehensive review Kankaanranta Hannu [email protected] Aarne [email protected] Eeva [email protected] Peter J [email protected] The Immunopharmacological Research Group, Medical School, University of Tampere, Tampere, Finland2 Department of Pulmonary Diseases, Tampere University Hospital, Tampere, Finland3 Department of Clinical Chemistry, Tampere University Hospital, Tampere, Finland4 Department of Thoracic Medicine, National Heart and Lung Institute, Imperial College, London, UK2004 27 10 2004 5 1 17 17 2 6 2004 27 10 2004 Copyright © 2004 Kankaanranta et al; licensee BioMed Central Ltd.2004Kankaanranta et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Many patients with persistent asthma can be controlled with inhaled corticosteroids (ICS). However, a considerable proportion of patients remain symptomatic, despite the use of ICS. We present systematically evidence that supports the different treatment options. A literature search was made of Medline/PubMed to identify randomised and blinded trials. To demonstrate the benefit that can be obtained by increasing the dose of ICS, dose-response studies with at least three different ICS doses were identified. To demonstrate whether more benefit can be obtained by adding long-acting β2-agonist (LABA), leukotriene antagonist (LTRA) or theophylline than by increasing the dose of ICS, studies comparing these options were identified. Thirdly, studies comparing the different "add-on" options were identified. The addition of a LABA is more effective than increasing the dose of ICS in improving asthma control. By increasing the dose of ICS, clinical improvement is likely to be of small magnitude. Addition of a LTRA or theophylline to the treatment regimen appears to be equivalent to doubling the dose of ICS. Addition of a LABA seems to be superior to an LTRA in improving lung function. However, addition of LABA and LTRA may be equal with respect to asthma exacerbations. However, more and longer studies are needed to better clarify the role of LTRAs and theophylline as add-on therapies.
Asthmainhaled corticosteroidslong-acting β2-agoniststheophyllineleukotriene antagonists
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Introduction
Inhaled corticosteroids (ICS) are the mainstay of current asthma management and should be used in all patients with persistent asthma. Many patients with persistent asthma can be controlled with regular ICS. However, a considerable proportion of patients treated with ICS remain symptomatic, despite the use of low to moderate doses (doses defined according to the ATS classification for adults [1,2]: beclomethasone dipropionate (BDP) 200 – 1000 μg/d, budesonide 200 – 800 μg/d or fluticasone propionate (FP) 100 – 500 μg/d) of ICS. Based on the differences in potency and pharmacokinetics the doses could also be defined differently [3,4]. Recent treatment guidelines [1,2,5,6] classify these patients as having moderate to severe persistent asthma (steps 3 and 4). According to the recent guideline [2] the typical clinical features of step 3 asthma include symptoms daily, nocturnal symptoms at least once a week, exacerbations that may affect activity or sleep, forced expiratory volume in one second (FEV1) 60 – 80% of predicted or peak expiratory flow (PEF) between 60 and 80% of the personal best reading. Daily rescue therapy is usually needed. Typical findings include low values of PEF or FEV1, a marked variation in daily PEF recordings and/or a significant response to bronchodilators. Thus, asthma is not adequately controlled, and the treatment needs to be optimized.
According to current guidelines the therapeutic options in the treatment of asthma not adequately controlled by low to moderate doses of ICS are as follows: 1. Increase in the dose of the ICS, 2. Addition of long-acting β2-agonist (LABA; formoterol or salmeterol), 3. Addition of a leukotriene receptor antagonist (LTRA; montelukast, pranlukast or zafirlukast) and 4. Addition of theophylline. Currently, the National Heart, Lung and Blood Institute guideline [2] recommends addition of LABA as the first choice and gives the other choices as secondary options, but leave the clinician alone to make the decision without offering comprehensive data to support the different options. Recently, this "step-3" dilemma on the different treatment options has gained attention [7,8]. Several of these options have been separately assessed in several reviews, systematic reviews and metaanalyses [7,9-16]. However, no comprehensive reviews exist on the subject. The aim of our article is to review the evidence that supports the increase in the dose of ICS and use of the different "add-on" options. Firstly to demonstrate the benefit that can be obtained by increasing the dose of ICS, dose-response studies with at least three different ICS doses were identified. Secondly, to demonstrate whether more benefit can be obtained by adding LABA, LTRA or theophylline to the treatment than by increasing the dose of ICS, we aimed to identify studies where the addition of a LABA, LTRA or theophylline to the treatment regimen was compared with the addition of a corresponding plabeco to an increased dose (usually doubled dose) of ICS. Thirdly, we aimed to identify studies comparing the different "add-on" options. In this review, we hope to help the clinician facing the "step-3 dilemma" by presenting in a systematic way the evidence obtained from randomised clinical trials that supports the use of these different treatment options.
Methods
The paper reviews studies where participants were adults or adolescents (≥12 years) with clinical evidence of asthma not adequately controlled with ICS. The general inclusion criteria in this review were: randomized, blinded and controlled trials with either parallel group or cross-over design published as a full-length paper. Steroid-tapering studies were not included as they are difficult to interpret. Studies published in abstract form only were not included. Similarly, studies lasting less than 4 weeks, containing less than 10 patients per group or studies containing a significant proportion (>10%) of patients using systemic steroids were excluded. Similarly "add-on" studies where a significant proportion (>10%) of patients were not using inhaled steroids were excluded.
We made a search of Medline from January 1 1966 to October 2001. All searches were limited to studies published in the English language. To identify the latest studies published, another search was made by using the drug names (budesonide, beclomethasone, fluticasone, flunisolide, mometasone, triamcinolone, formoterol, salmeterol, montelukast, pranlukast, zafirlukast, theophylline) from Medline on October 2003. The searches were manually (HK) evaluated to identify studies fulfilling the inclusion criteria and full papers were retrieved. In the case of uncertainty based on the abstract full papers were retrieved. All studies fulfilling the inclusion criteria for the ICS dose-response studies or "add-on" studies (see below) were scored for quality using the method described by Jadad et al. [17]. Furthermore, relevant systematic reviews were identified from the Cochrane Library (Issue 2, 2003). In addition, some in vitro results or results from open, non-randomized or uncontrolled trials or meta-analysis of particular relevance to the present topic may be cited.
Inclusion criteria for dose-response studies with ICS
To find the dose-response studies with ICS the term "anti-inflammatory agents, steroidal" was combined with the term: "dose-response relationship, drug" (MeSH), which combination produced 249 papers. To demonstrate the dose-response effect of ICS only controlled studies with at least three different ICS doses and a parallel-group design were included. Studies using consecutive doses of steroids were not included because it makes it impossible to differentiate the dose-response relation from the time course relation of efficacy.
Inclusion criteria for "add-on" studies with long-acting β2-agonists, leukotriene antagonists and theophylline
When the basic search done with the term "anti-inflammatory agents, steroidal" was combined with another made with terms: "salmeterol OR formoterol" it produced 97 papers, when combined with a search made with a term "leukotriene antagonists" (MeSH), it produced 26 papers and when combined with a search with a term "theophylline" (MeSH) it produced 342 papers. Only studies where the addition of LABA, LTRA or theophylline to the treatment with inhaled steroid was compared with the addition of a corresponding placebo to an increased dose (usually double-dose) of inhaled steroid were included. In addition, studies comparing the different "add-on" options were identified.
Increasing the dose of inhaled corticosteroid
On the design of dose-response studies with ICS
We identified 14 studies [18-31] assessing the dose-response relationship of ICS in the treatment of chronic asthma. All included studies were of fair to excellent quality (Jadad score 3–5). The main characteristics of these studies are presented in Table 1 (see Additional file 1). The inclusion criteria in most of these studies were moderate to severe chronic asthma but previous use of small to moderate doses of ICS was not required in all studies. The studies included patients with a relatively wide range of FEV1 % predicted and based on that these patients belong to steps 2–4 according to the recent guideline [2]. In all except three studies a ≥12% reversibility in FEV1 or PEF in response to a bronchodilator was required. There was 1 study that assessed the dose-response of budesonide, 7 of FP, 1 of BDP, 3 of mometasone furoate, and 2 of triamcinolone acetonide. The studies utilized two main approaches to identify a dose-response relationship. Some studies considered dose-response relationship to be present if the results obtained with the lowest and highest dose of ICS were significantly different, whereas in others the presence or absence of dose-response relationship was characterized with more advanced statistical analysis (e.g. analysis for linear trend or Jonckheere's nonparametric trend test). In this review, both ways of analysis are accepted as evidence for the presence of dose-response. In the following discussion the important difference between the formal dose-response studies presented in this review and the results reported in some meta-analysis is that the data of the meta-analyses may result from studies assessing one or more doses of ICS and comparing their effects with placebo or baseline. Thus, the data derived from some the published meta-analyses [9,11,14,32], although showing a dose-response effect, is obtained by combining different doses from several studies, and is not resulting from a strict dose-response relationship study. In addition, the data obtained using meta-analysis may be derived only from one or two studies.
Overview on lung function and symptoms in the 14 included studies
Studies with ICS show a statistically significant dose-response effect for morning PEF and FEV1 in the treatment of chronic asthma in 9 (69%) and 5 (31%) studies of the 14 studies included, respectively (Table 2a, see Additional file 1). However, statistical analysis of dose-dependency fails to show any significant dose-related effect for FVC in 5 (71%) studies of 7 where it was analysed. Similarly, no statistical dose-dependency was found for evening PEF in 6 (50%) studies out of 12 where it was analysed (Table 2a, see Additional file 1). The total or daytime symptom scores show a statistically significant dose-response effect in 5 (38%) out of 13 studies, whereas nighttime symptom score showed a dose-dependency in only three (25%) studies out of 13 where it was analysed. A dose-response for the rescue β2-agonist use was found in 4 (33%) out of 12 studies where it was analyzed (Table 2b, see Additional file 1). The difference between the highest and the lowest dose of ICS was most often statistically significant for morning PEF (7/12 studies; 58%) and to a lesser extent for evening PEF (3/10 studies; 30%), FEV1 and total or daytime symptom scores (both 2/12 studies; 16.7%), night-time symptom score and rescue β2-agonist use (both 1/11 studies; 9%) and FVC (0/6 studies; 0%). Similarly, the difference between the two consecutive doses of ICS was very seldom statistically significant (Table 2ab, see Additional file 1). Thus, taken together, the results suggest that morning and evening PEF and FEV1 are more sensitive to show a statistically significant dose-response effect for ICS, whereas symptom scores and rescue β2-agonist use are in general less sensitive to the increase in steroid dose. However, this conclusion may also be influenced by the duration of treatment. Inclusion of relatively short studies in this review, may either under- or over-estimate the dose-response differences depending on the outcome measure being used.
Beclomethasone dipropionate – studies included in this systematic review
The dose-response relationship of the effects of BDP (100 – 800 μg/d in two different formulations) was evaluated in asthmatic subjects who had deterioration in asthma control after discontinuation of ICS [18]. There was a statistically dose-dependent effect on morning PEF, FEV1, FVC, days free from wheeze or chest tightness and β2-agonist use, but not on evening PEF or nights free from asthma related sleep disturbance (Table 2ab, see Additional file 1). The dose-response effects detected in this study may reflect the fact that the patient population was carefully identified to show a well-defined responsiveness to ICS. Thereafter ICS were withdrawn to induce a clinically meaningful deterioration of asthma control. Thus, the design may not directly reflect clinical practice, where a patient is symptomatic, despite the use of low to moderate doses of ICS.
Beclomethasone dipropionate – other literature
A recent meta-analysis [10] analysed the dose-response effect of BDP in the treatment of chronic asthma. Eleven studies with variable methodological quality involved 1614 subjects were included in the analysis. Most of the endpoints were based on only 1–2 studies. In asthmatic patients not treated with oral steroids a small advantage of BDP 800 μg/d over 400 μg/d was apparent for improvement in FEV1 and morning PEF and reduction in night-time symptom score compared to baseline. Studies that assessed BDP 1000 v 500 μg/d and BDP 1600 v 400 μg/d demonstrated a significant advantage of the higher dose compared to the lower dose for percentage improvement in airway responsiveness to histamine and FEV1 compared to baseline. No differences between higher and lower daily doses of BDP were apparent for daytime symptoms, withdrawals due to asthma exacerbations or oropharyngeal side effects.
Budesonide – studies included in this systematic review
A 6 weeks dose-response study in Japanese asthmatics previously not on ICS showed that increasing the dose of budesonide (200–800 μg/d) [19] results in a dose-related improvement in morning and evening PEF and daytime and nighttime symptom scores, but not for FEV1. In this study, there was no statistically significant difference between the doubling doses of budesonide (Table 2ab, see Additional file 1). Instead, even the lowest dose of budesonide (200 μg/d) was superior to placebo in the case of morning and evening PEF and daytime and night-time symptom scores, but not for FEV1.
Budesonide – other literature
In a randomised, double-blind, placebo-controlled study of parallel-group design lasting 12 weeks four different doses of budesonide (200, 400, 800 and 1600 μg/d were compared in patients suffering from moderate to severe asthma. This study was not included in the systematic analysis due to a high proportion of patients on oral glucocorticoids (15.6%). Increasing the dose of budesonide [33] results in a dose-related improvement in morning PEF and FEV1, but not in evening PEF, FVC, symptom scores or rescue β2-agonist use. Instead, even the lowest dose of budesonide (200 μg/d) was superior to placebo for all parameters studied. The improvement induced by these low doses is much greater than the difference between the lowest and highest doses of budesonide studied, despite the 8-fold difference in the dose (Figure 1) [33]. There was a statistically significant difference only between the lowest (200 μg/d) and the highest (1600 μg/d) doses of budesonide when morning PEF or FEV1 were analysed. Instead, the lowest (200 μg/d) or the highest dose (1600 μg/d) did not differ from the two medium doses (400–800 μg/d). When evening PEF, FVC, daytime or nighttime asthma symptom scores or the use of rescue medication were analysed, there was no significant differences between any of the studied budesonide doses [33].
Figure 1 Mean change from baseline in morning peak expiratory flow (PEF) in patients treated with placebo or various doses of budesonide. A significant dose-response effect is seen. However, it should be noted that the difference between placebo and low-dose budesonide is greater than the difference between low-dose budesonide and high-dose budesonide and that there is no statistically significant difference between the various doses of budesonide. Reproduced from reference 33 with permission.
The dose-relationship of budesonide in the treatment of chronic asthma is a subject of a recent Cochrane review [12]. In this meta-analysis including both children and adults (n = 3907) in non-oral steroid-treated mild to moderately severe asthmatics no clinically worthwhile differences in FEV1, morning PEF, symptom scores or rescue β2-agonist use were apparent across a dose range of 200–1600 μg/d. However, in moderate to severe asthma there was a significant reduction in the likelihood of trial withdrawal due to asthma exacerbation with budesonide 800 μg/d compared with budesonide 200 μg/d. The reviewers also conclude that budesonide exhibits a significant improvements favouring high dose (1600 μg/d) over low dose (200 μg/d) for improvement in FEV1 in severe asthma [12]. Another recent meta-analysis combining 3 placebo-controlled studies with at least two different budesonide doses demonstrated a statistically significant dose-response for morning PEF and FEV1 but not for evening PEF [14].
Fluticasone propionate – studies included in this systematic review
The dose-dependency of FP has been studied in seven studies in patients with mild to moderate asthma. In two of the studies, patients were previously not on ICS (Table 1, see Additional file 1). The difference between the highest and lowest dose was 4- to 20-fold. In all studies almost all parameters improved significantly better with all doses of FP as compared with placebo. Only three studies [20,21,26] show a dose-response effect on morning PEF, only two studies [20,26] show a dose-response relationship for evening PEF and rescue medication use and only one study [20] shows a dose-response relationship for FEV1, FVC and daytime symptom score (Table 2ab, see Additional file 1). When different doses of FP (50–200–1000 μg/d) were studied in a randomized, double-blind dose-response setting, there was no difference in FEV1, FVC, evening PEF, symptom scores, use of rescue medication or the number of night awakenings between the lowest and highest FP dose, despite a 20-fold difference in the dose [21]. Only for morning PEF was the high (1000 μg/d) dose of FP better than the two lower doses, whereas even the lowest dose of FP (50 μg/d) was significantly better than placebo in improving all these parameters.
In a dose-response study [20] with patients with symptomatic chronic asthma (n = 672) patients were randomized to four different doses of FP (100, 200, 400, 800 μg/d). FP improved lung function and symptoms in a dose-related manner. The linear trend for doubling the dose of FP was calculated to be as follows: morning PEF increased 4.3 L/min (95% CI 1.8–6.8) and FEV1 increased 0.03 L (95% CI 0–0.05 in two weeks). How does this translate into clinical practice? When assessing a response to a bronchodilator or when assessing a response to inhaled or oral steroid an improvement of 10–20% above the previous values is often considered significant. Thus, in the above study, this would mean >36 L/min increase in morning PEF values. Recently, the average minimal patient perceivable improvements have been estimated as 18.8 L/min for PEF and 0.23 L for FEV1 [34]. Based on that the increase in lung function obtained by doubling the dose of fluticasone in the above study seems to be only of very limited clinical benefit.
Fluticasone propionate – other literature
In a recent meta-analysis [9] the dose-response relation of inhaled FP in adolescents or adults with asthma in eight studies [n = 2324] employing 2–3 different doses of inhaled FP were analysed. The dose-response curve for the raw data began to reach a plateau at around 100–200 μg/d and peaked by 500 μg/d. A negative exponential model for the data indicated that 80% of the benefit at 1000 μg/d was achieved at doses of 70–170 μg/d and 90% by 100–250 μg/d. A quadratic meta-regression showed that the maximum achievable efficacy was obtained by doses of around 500 μg/d. Another recent meta-analysis [11] of 28 studies with 5788 patients (children and adults) with chronic asthma evaluated the dose-response effect of FP, compared to placebo. Evidence for a dose-response effect was apparent for likelihood of trial withdrawal due to lack of efficacy, change in FEV1, morning PEF, evening PEF, nighttime awakening score and physician-rated efficacy. It is important to appreciate that this was only evident when improvements over placebo were compared for the highest dose of FP (1000 μg/d) and lowest dose of FP (100 μg/d). There were no significant differences when any other doses were compared (e.g. FP 200 v 100 μg/d, FP 500 v 200 μg/d, FP 1000 v 500 μg/d). Sixty percent (0.31 L; 95% CI 0.27–0.36 L) of the effect on FEV1 with FP 1000 μg/d (0.53 L; 95% CI 0.43–0.63 L) was achieved with tenth of the dose. No dose-response effect was apparent for change in symptom score or for rescue β2-agonist use [11]. Another recent meta-analysis from the same authors [32] found a statistically significant advantage of FP 200 μg/d over 100 μg/d for morning PEF (6 L/min; 95% CI 1–10 L/min), evening PEF (6 L/min, 95% CI 2–11 L/min) and night-time awakening score (0.17, 95% CI 0.04 – 0.30), but not for FEV1, daily symptom score, night-time awakenings and daily use of rescue β2-agonist use. No significant advantage was obtained with the use of FP at doses of 400–500 μg/d over 200 μg/d for morning or evening PEF, FEV1, daily symptom score or rescue β2-agonist use. Patients treated with higher dose (800 – 1000 μg/d) of FP achieved significantly greater improvements in morning PEF (22 L/min, 95% CI 15–29 L/min) and evening PEF (13 L/min, 95% CI 6–19 L/min) compared to the lower dose (50–100 μg/d). Another recent meta-analysis [14] including eight trials with at least 2 different doses of FP demonstrated a statistically significant dose-response in morning PEF, evening PEF and asthma symptom score but not in FEV1 or β2-agonist use.
Mometasone furoate and triamcinolone acetonide – studies included in this systematic review
Mometasone furoate is a corticosteroid closely related to FP and is being investigated in a dry powder inhalation formulation for the treatment of asthma [35]. Studies with mometasone furoate [27-29] show a dose-related efficacy in the treatment of mild to moderate asthma when morning PEF is analysed (Table 2a, see Additional file 1). Interestingly, even doubling doses of mometasone furoate produced statistically significant improvements in morning and evening PEF (Table 2a, see Additional file 1) [27-29]. Occasionally, a statistically significant dose-dependency or difference between the highest and lowest dose was found for evening PEF, FEV1 or daytime or total symptom score. In contrast, no significant dose-dependency was found for FVC, nighttime symptom score or rescue β2-agonist use (Table 2ab, see Additional file 1).
Linear trend analyses showed a dose-response for triamcinolone acetonide (TAA) in the treatment of moderate to severe asthma across the dose-range of 150 to 600 μg/d or 200 to 1600 μg/d for most variables in the two studies included in this review (Table 2ab, see Additional file 1) [30,31]. Occasionally, a statistically significant difference was reported even between two consecutive doses of TAA. As compared with placebo, therapeutic activity was generally evident at doses of 150–200 μg daily for all variables with significant clinical efficacy demonstrated for all doses.
Mometasone furoate and triamcinolone acetonide – other literature
A four-week randomised, double-blind, double-dummy and parallel group study [36] comparing the efficacy and safety of mometasone furoate administered by metered dose inhaler (112, 400 and 1000 μg/d) with BDP (336 μg/d) and placebo recruited adult patients with moderate asthma (n = 395). The patients were required to have a stable ICS dose, FEV1 or 50–90% and a bronchodilator response of ≥15% in absolute FEV1 at baseline. This study reported significantly better improvement in FEV1, FVC and morning PEF with doses of 400 and 1000 μg/d than with 112 μg/d. Also, physician's evaluation of asthma symptoms, but not salbutamol use was significantly better with dose 1000 μg/d than with 112 μg/d. This study, although fulfilling the criteria for dose-response study as defined in materials and methods, was excluded from the systematic evaluation, as the published statistical analysis did not include any formal dose-response analysis, and the reported difference between different mometasone doses always required a statistically significant difference to the active comparator BDP.
In contrast to the results presented in this review (Table 2ab, see Additional file 1), a meta-analysis [14] including 2 studies with mometasone furoate (200 μg/d versus 400 μg/d) failed to show any significant dose-response in FEV1. In the meta-analysis, there was not enough data to analyse other parameters than FEV1. The 3 studies [27-29] included in this review were not included in the meta-analysis [14]. The data suggests that 200 μg/d of mometasone furoate may be a relatively small dose. As both the inhaler device and mometasone have not been available for the treatment of asthma, it is difficult to define their exact position in the treatment of asthma, although there are data to suggest that a total daily dose of 400 μg of mometasone furoate administered with dry powder inhaler may be equal to total daily dose of 500 μg of FP via a Diskhaler or a daily dose of 800 μg budesonide via a Turbuhaler [28,29].
A placebo-controlled, double-blind parallel-group study assessed the effects of three different doses of TAA (450, 900 and 1800 μg/d for 12 weeks; delivered using a non-chlorofluorocarbon propellant) in patients with chronic symptomatic asthma and using ICS [37]. The data for all variables (FEV1, FEF25–75, morning and evening PEF, symptom scores and rescue salbutamol use) shows that even the lowest dose significantly differs from placebo, and there appears to be no clear dose-response. However, no formal statistical analysis was reported for the presence of a dose-response and thus this study is not included in Tables 1–2. A recent meta-analysis [14] including 3 studies with TAA, demonstrated a statistically significant dose-response in morning PEF, evening PEF and asthma symptom score, but not in FEV1.
Conclusions on the effects of ICS on lung function and asthma symptoms
Taken together these results indicate that the change in the ICS dose from low dose to moderate dose is at the flat part of the ICS dose-response curve for most lung function and symptom parameters studied (Figure 2). Furthermore, it appears that the low and moderate doses of currently used ICS are in the flat part of the steroid dose-response curve. Thus, it is predicted that doubling the dose of ICS is not sufficient to significantly improve lung function or reduce symptoms. Rather, the data suggest that the increase in the dose of ICS should be at least 4-fold to produce a clinically significant improvement in variables such as symptoms, use of rescue β2-agonists, PEF or lung function. However, the steepness of the dose-response curve for different outcomes may vary. For example, an open dose-response evaluation of different sequential doses of budesonide in patients with mild-to-moderate asthma (38) shows that the dose-response curves for FEV1/PEF and FEF25–75 are not identical. Similarly, the dose-response curves of budesonide on adenosine monophosphate (AMP) and methacholine bronchial challenges were significantly different [38]. It should also be noted that patients often receive higher doses of ICS in their daily routine treatment than required [3].
Figure 2 The dose-response curve of inhaled glucocorticoids.
The studies discussed above present mean data for groups of patients, but do not address the issue of differences in responsiveness to the anti-inflammatory effects of corticosteroids between individual patients. It may be possible that increasing the dose of ICS may be beneficial for some patients.
Is there a dose-response in the anti-inflammatory effects of ICS?
Studies included in this systematic review
We were not able to identify any studies that would have studied the dose-dependency of the anti-inflammatory effects of ICS in asthma and would have satisfied the inclusion criteria for the present review.
Other literature
In a study [39] with patients with chronic asthma (n = 66) treated with moderate doses of ICS the dose-dependency of consecutive doses of budesonide (800, 1600 and 3200 μg/d) and FP (500, 1000 and 2000 μg/d) were studied. Budesonide increased methacholine PD20 from 259 to 467 μg and FP from 271 to 645 μg, both showing a dose-dependency. However, no statistical comparison was made between individual doses. The PD20 was increased 1.67-fold and 1.96-fold when the patients were switched from the lowest dose to the highest dose of budesonide and FP, respectively. An apparently dose-dependent decrease in the blood eosinophil count was obtained with budesonide but not with FP treatment [39]. In contrast, no significant differences were observed for either treatment, when morning or evening PEF, symptom scores, and consumption of β2-agonist were analysed. Allergen PC15 and methacholine PC20 values were determined before and after treatment with budesonide at 200, 400 and 800 μg/d for 7 days in a double-blind, randomized and cross-over study (6 day washout period) in eleven atopic subjects with inhalation allergy [40]. The allergen PC15 and methacholine PC20 were significantly larger for all doses of budesonide as compared with placebo, but there was no significant difference between the 3 doses of budesonide. In an open trial with patients with moderate to severe asthma the effects of progressively increasing doses of budesonide (400, 800, 1600 and 2400 μg/d) were studied [41]. Budesonide decreased the blood eosinophil count in a dose-dependent manner. In a double-blind, randomized placebo-controlled study combining two separate studies, the dose-dependency of the anti-inflammatory effects of budesonide (100, 400 and 1600 μg/d) was assessed in patients with mild asthma (n = 31). Based on trend analysis, there were dose-dependent changes in exhaled NO, sputum eosinophils and PC20 to inhaled budesonide but a plateau response of exhaled NO was found at a dose of 400 μg/d [42]. In a study with a novel ICS ciclesonide, its effects were studied in a parallel-group, double-blind, placebo-controlled, randomized cross-over study (washout period 3–8 weeks) in patients (n = 29) with mild to moderate asthma [43]. Compared with placebo, ciclesonide for 14 days (100, 400 and 1600 μg/d) reduced airway responsiveness to AMP by 1.6, 2.0 and 3.4 doubling doses, respectively, and this effect was dose-dependent. A significant reduction in the percentage of eosinophils in induced sputum was observed after 400 and 1600 μg daily ciclesonide, but this was not dose-dependent. Sputum eosinophil cationic protein (ECP) was significantly reduced after 400 μg daily ciclesonide only, and no dose-dependent effect was seen. In a recent single-cohort, prospective placebo-controlled study with four 1 week periods with nonsteroid-treated asthmatic patients (n = 15) the effects of different doses of BDP (100, 400 and 800 μg/d) were measured on FEV1, exhaled nitric oxide (FENO) and methacholine PC20 [44]. All doses of BDP resulted in a significant change in FEV1 and methacholine PC20 from baseline or placebo treatment, but with no significant separation of active BDP doses. All doses of BDP resulted in a significant change in FENO from placebo treatment, but with significant separation of only the 100 μg and 800 μg doses by FENO. Another study assessed the dose-response relationship of the anti-inflammatory effects of BDP (50, 100, 200 and 500 μg/d) in the treatment of mild to moderate asthma for 8 weeks in a randomised, placebo-controlled, double-blind trial of parallel-group design [45]. Maintenance ICS therapy was discontinued and patients were randomised to different treatment groups and inflammatory markers such as exhaled NO, sputum eosinophil counts and PD15 to saline were followed. There was a significant linear relationship between BDP dose and exhaled NO concentration, FEV1 and changes in sputum eosinophils at the end of treatment. In contrast no relationship was found between BDP dose and PD15 to saline. However, the results of this study may be confounded because the patients were treated with oral prednisolone for two days in the beginning of the study.
In a recent randomized and double-blinded study, 12 atopic mild stable asthmatic subjects were treated with placebo or mometasone furoate (100, 200 and 800 μg/d) for six days [46] in a cross-over fashion. All three doses of MF demonstrated similar attenuation of early responses and allergen-induced airway hyperresponsiveness relative to placebo with no dose-response relationship. In contrast, the late maximal % fall in FEV1 after placebo treatment was 24% and was significantly reduced in a dose-dependent manner to 12%, 11% and 6% for the 100, 200 and 800 μg daily treatments. The allergen-induced sputum eosinophilia (×104 cells/ml) 24 h after challenge during placebo treatment was 60.2 and was significantly reduced to 24.0, 15.3 and 6.2 for the 100, 200 and 800 μg daily treatments, respectively. Although a statistically significant dose-response relationship was present, the difference between the lowest and highest dose (8-fold difference) for late maximal fall in FEV1 or allergen-induced sputum eosinophilia was less than the difference between placebo and the lowest dose of MF.
Taken together, the results suggest that there is tendency towards slightly higher anti-inflammatory efficacy with higher doses of ICS. At the moment there are only a few studies that assess the dose-dependency of the anti-inflammatory effects of ICS. Most of these studies included only small numbers of patients. However, despite the 4–8–16-fold differences in the doses of ICS studied, it has not been easy to demonstrate the dose-dependency of the anti-inflammatory effects of inhaled glucocorticoids. Thus, based on the scarce published evidence we would predict that doubling of the commonly used low to moderate doses of ICS is likely to produce only a small increase in the anti-inflammatory effect, suggesting that inflammation may be suppressed in most patients by relatively low doses of ICS.
Is there a dose-response with the adverse effects of ICS?
Glucocorticoids suppress corticotrophin levels, which may eventually lead to atrophy of the adrenal cortex and diminished levels of endogenous cortisol. The diminished levels of endogenous cortisol or reduced cortisol excretion have been used as markers of systemic activity of ICS. These systemic effects may include osteoporosis, behavioural effects, growth suppression, posterior subcapsular cataracts, risk for ocular hypertension and glaucoma as well as skin thinning and bruising [47]. In the following sections the literature on the dose-related effects of different steroids on HPA axis as well as on local adverse effects is discussed.
Studies included in the systematic review
Of the 14 studies included in this review, in 8 the effects on HPA-axis suppression were analysed. No data on the effects of BDP, budesonide or TAA on HPA-axis were reported. Six of the 7 randomised, double-blind dose-response studies with FP also analysed its effect on HPA axis, measuring either basal morning cortisol levels, post-cosyntropin stimulation test levels or urinary excretion of cortisol metabolites (Table 2b, see Additional file 1). Only one study reported a statistically significant dose-response effect (3% decrease per doubling dose of FP) in morning plasma cortisol levels [20] and one study [21] reported slight transient reductions in urinary free cortisol and urinary 17-hydroxy steroids in the group receiving the highest dose of FP (1000 μg/d). However, in 5 studies made with FP, no dose-related effects on HPA-axis suppression were described (Table 2b, see Additional file 1). There was no indication for the dose-dependent HPA-axis suppression in 2 studies with mometasone furoate. One needs to note that these studies were not planned and powered to detect differences in systemic or adverse effects.
Beclomethasone dipropionate – other literature
The dose-related effects of HFA-BDP (200–800 μg/d) were studied in 43 steroid-naïve asthmatic patients in a randomized double-blind fashion for 14 days [48]. When the HFA-BDP dose increased a greater decrease in the percent change from baseline in steady state 24 h urinary free cortisol was found suggesting a dose-response. Despite the observed statistically significant differences between placebo and the two highest dose-groups in mean percent change in 24 h urinary free cortisol, only one patient among all the treatment groups fell below the reference range for this parameter. In another small, randomized study 26 steroid-naïve asthmatic patients were treated with increasing doses of BDP (400 – 1600 μg/d) [49]. Only the highest dose of BDP produced a significant suppression of 24 h urinary free cortisol. In a recent Cochrane review [10], the dose-response relationship of BDP on HPA axis function was analysed. Only two small studies with adult patients not treated with oral steroids were identified, and showed no effect on morning plasma cortisol by two to five-fold increase in the BDP dose.
Budesonide – other studies
A randomized double-blind study with consecutive dose design [39] comparing FP (500–2000 μg/d) and budesonide (800–3200 μg/d) reported that budesonide, but not FP (or at least to a lesser extent) reduced 24 h urine cortisol excretion, plasma-cortisol and serum osteocalcin in a dose-related manner. Similar results have been reported from an open, randomized, parallel group trial with budesonide at doses of 400, 800, 1600 and 2400 μg/d for 2 weeks at each dose level, in adult patients with moderate to severe asthma [41]. Budesonide decreased the 24 h urinary cortisol excretion, serum cortisol and osteocalcin in a dose-dependent manner. In a randomized, double-blind parallel-group study [33], budesonide (1600 μg/d for 12 weeks) induced a mean change from baseline in synthetic corticotrophin (cosyntrophin)-stimulated plasma cortisol levels that was significantly different from placebo and the lowest dose of budesonide. However, the difference from placebo was only 10%, and all other doses of budesonide were not statistically different from placebo. In contrast, the mean basal morning plasma cortisol levels among different budesonide treatment groups and placebo did not differ. In a randomized cross-over study [50], budesonide (1600 μg/d) reduced serum osteocalcin and blood eosinophil count as compared with placebo, but these effects were not dose-dependent. In contrast, budesonide (400–1600 μg/d) had no significant effects on adrenal function as assessed by 8 am serum cortisol or overnight urinary cortisol excretion. In a recent open study, budesonide (400–1600 μg/d) was given to patients with mild to moderate asthma (n = 26) sequentially for 3 weeks each dose, a total of 9 weeks [38]. There was a significant dose-related suppression of morning cortisol levels and overnight urinary cortisol values, but not of serum osteocalcin. For example, the percentages of patients with a stimulated plasma cortisol response less than 500 nM were 7% at baseline, 13% at 400 μg/d, 40% at 800 μg/d and 66% at 1600 μg/d. The authors reported that the proportions of patients with a beneficial airway response together with a minimal systemic response – that is, an optimal therapeutic index – were approximately 50% at all three doses of budesonide. However, the proportion of patients with a good airway response together with a marked systemic response – that is, a suboptimal therapeutic index – increased from 4% at low dose to 38% at high dose [38]. In a recent Cochrane meta-analysis, statistically significant, dose-dependent suppression by budesonide of 24 hour urinary free cortisol excretion and serum cortisol post synthetic ACTH infusion over the dose range 800 – 3200 μg/d were apparent, but the authors concluded that the clinical significance of these findings is unclear [12].
Fluticasone propionate – other literature
FP has also been shown to suppress 8 am serum cortisol and urinary cortisol/creatinine ratio in a dose-dependent manner in a single-blind placebo-controlled cross-over study for 9 days in patients (n = 12) with mild to moderate asthma [51]. Similar dose-dependent suppression of adrenocortical activity was reported in four other studies with patients with mild to moderate asthma from the same research group [52-55]. Interestingly, the suppressive effects of FP on adrenocortical activity were greater than those observed on osteocalcin or eosinophils.
A Cochrane review [11] collected data on the effects of FP on HPA-axis function. Significant differences were not apparent between any daily dose of FP in the range of 100–1000 μg/d and placebo on basal plasma cortisol values or urinary cortisol excretion. However, the authors were not able to make a meta-analysis of the cortisol values. In another Cochrane review [32] the same authors found no evidence for dose-dependent suppression of HPA function. However, no decent meta-analysis could be made due to limited availability of data. In contrast to these findings another meta-analysis [47] found that FP exhibits a significantly steeper dose-related systemic bioavailability than BDP, budesonide, or triamcinolone when 21 studies of urinary cortisol levels and 13 studies of suppression of 8 am plasma cortisol levels were analysed. Thus, there clearly exists a discrepancy in the published literature concerning the systemic effects of FP.
Based on the recent Cochrane review and meta-analysis [32] it seems obvious that there is a dose-response relationship in the appearance of local side-effect hoarseness and/or dysphonia so that FP at doses of 400–500 μg/d and 800–1000 μg/d has a significantly higher risk than at lower doses (50–100 μg/d). Similarly FP at doses of 50–100 μg/d induces significantly less oral candidiasis than at doses of 800–1000 μg/d. However, there seemed to be no significant difference in the incidence of sore throat/pharyngitis between any of the FP doses. Another systematic review [16] collected data from fluticasone studies and calculated NNT (number needed to treat) to prevent worsening of asthma and NNH (number needed to harm) to induce oral candidiasis. Three patients needed to be treated with fluticasone 100 μg/d to prevent worsening of asthma (NNT 3), and for fluticasone 1000 μg/d the NNT was 2.1 patients. In contrast, the dose-response curve for side effects was steep. For a dose of fluticasone 100 μg/d, oral candidiasis developed in one of every 90 subjects treated (NNH 90), whereas the NNH for fluticasone 1000 μg and 2000 μg daily were 23 and 6, respectively.
Triamcinolone acetonide – other literature
In two randomized studies, TAA in the dose range of 400–1600 μg/d [50,51] did not significantly affect 8 am serum cortisol or the 24 h or overnight urinary excretion of corticosteroid metabolites. In an open non-controlled 6 months study with 400–800–1600 μg/d TAA the plasma cortisol levels before and after cosyntrophin injection were analysed in patients with asthma [56]. Although all treatment regimens caused some reduction in the 24 h excretion of corticosteroid products, none of the mean values was below the normal ranges and no significant suppression in the cosyntrophin test was seen. The mean data indicated that TAA had overall no significant effect on adrenal function at any dose or at any time. However, three patients exhibited some reduction in adrenal function. In another small, randomized study 26 steroid-naïve asthmatic patients were treated with increasing doses of TAA (800 – 3200 μg/d) [49]. Only the highest dose of TAA produced a significant suppression of 24 h urinary free cortisol.
Conclusions on the effects of ICS on HPA axis and local side effects
Taken together, the data on the systemic adverse effects of ICS is conflicting and seems also to reflect the study design. Several studies have measured only the basal morning cortisol levels or levels after stimulation with high cosyntrophin doses. However, these may be insensitive markers for HPA-axis suppression [47]. Different, a possibly more sensitive endpoint could be plasma cortisol profile during 20–24 h period, which has been shown to be affected by a short course of fluticasone and/or budesonide or even after single inhaled doses [57-59]. There is disagreement between the relative potency of budesonide and FP on HPA-axis function. In addition to the different ways to measure HPA-axis function, this may be due to the use of different inhalers, duration of the treatment period, the selection of the patient group or different design and sponsoring of the studies by pharmaceutical companies. In addition there are differences in the delivery of ICS between normal subjects and patients with asthma and in patients with severe versus mild asthma [60-62]. Although generally safe, it appears that there is at least some degree of dose-dependency in the HPA-axis effects of inhaled steroids. Some smaller studies [39,41,54] suggest that there is a significant decrease in the therapeutic index with higher doses of ICS. Recently, a statistical meta-analysis using regression was performed for parameters of adrenal suppression in 27 studies [47]. Marked adrenal suppression, and thus a marked risk for systemic adverse effects, occurs at doses of ICS above 1500 μg/d (budesonide and BDP) or 750 μg/d (FP), although there is a considerable degree of inter-individual susceptibility. Meta-analysis showed significantly greater potency for dose-related adrenal suppression with FP compared with BDP, budesonide, or TAA. The author concludes that ICS in doses above 1500 μg/d (750 μg/d for FP) may be associated with a significant reduction in bone density [47]. Long-term, high-dose ICS exposure increases the risk for posterior subcapsular cataracts, and to a much lesser degree, the risk for ocular hypertension and glaucoma. Skin bruising, which correlates with the degree of adrenal suppression, is most likely to occur with high-dose exposure [47].
Adding a long acting-β2-agonist (LABA)
The rationale
LABA provide long-lasting relaxation of airway smooth muscle, while the ICS provide potent topical anti-inflammatory action. In addition to these complementary actions, β2-agonists may have several other actions that may contribute to their efficacy in relieving asthma symptoms. β2-Agonists inhibit plasma exudation in the airways by acting on β2-receptors on postcapillary venule cells. They inhibit the secretion of bronchoconstrictor mediators from airway mast cells and may inhibit release of mediators from eosinophils, macrophages, T-lymphocytes and neutrophils. In addition, β2-agonists may have an inhibitory effect on the release of neuropeptides from sensory nerves [63]. Corticosteroids may also increase the expression of β2-receptors in inflammatory cells to overcome the desensitisation in response to chronic β2-agonist exposure [64]. In addition, LABA may prime the glucocorticoid receptor facilitating activation by corticosteroids [65,66].
Design of 12 LABA add-on studies included in the review
The literature search identified 3 studies with formoterol [67-69] and 9 studies with salmeterol [70-78]. All these studies included adult or adolescent patients with symptomatic asthma. Generally, patients used low to moderate doses of inhaled glucocorticoids. In two studies [68,73] previous use of ICS was not required. In all studies PEF or FEV1 reversibility of at least 10–15% was required (Table 3, see Additional file 1). Diurnal or period PEF variation >15% was required in four studies. FEV1 of >(40)–50% of predicted and a clearly positive symptom score was required in most studies (Table 3, see Additional file 1). In general, the mean FEV1 (% predicted) varied between 61 and 87% in different studies, being 61–70% in 4 studies, 70–80% in 3 studies, 81–87% in two studies and was not reported in three studies. The mean absolute PEF values varied from 299 to 404 L/min and FEV1 from 2.12 to 2.54 L (Table 5, see Additional file 1). Thus, the patient population in these studies represents mainly those with moderate to severe persistent asthma. This as well as the fact that patients with recent exacerbations are excluded may produce a selection bias, compared with the real life. In one study [78] patients were required to have at least two exacerbations during the previous year to be eligible for the inclusion in the study. One study [68] was performed in patients mainly affected with mild persistent asthma. In salmeterol and formoterol studies, the comparison dose of ICS was increased 2–2.5 (-4)-fold, whereas in the formoterol study [67] the comparison dose of budesonide was 4-fold higher (Table 4, see Additional file 1). Another significant difference between formoterol and salmeterol studies is that in the formoterol [67] study the main outcome parameter was the incidence of exacerbations whereas the salmeterol studies mainly focused on lung function and asthma symptoms. Most studies allowed a constant dose of theophylline but not oral steroid use (Table 3, see Additional file 1). Six out of the 12 studies excluded patients having previous exacerbations (generally during previous month). Only 2 studies lasted one year [67,68], whereas most studies lasted at least 24 weeks. Most reports did not identify whether the study were performed by respiratory specialists or general practitioners. All studies were financially supported by pharmaceutical companies.
Lung function and asthma symptoms
Formoterol – studies included in this systematic review
The addition of formoterol was compared with the increase (4-fold) in the dose of inhaled budesonide (from 200 μg/d to 800 μg/d) in patients with moderate to severe symptomatic chronic asthma [67]. The patients (n = 852) in this study had a FEV1 of at least 50% of predicted (mean 75–76%) with an increase in FEV1 ≥15% after inhalation of terbutaline. Addition of formoterol was superior to the increase in steroid dose in increasing FEV1 and morning PEF (Figure 3A; Table 5, see Additional file 1). Similarly, addition of formoterol was equal or superior to the 4-fold increase in ICS dose in reducing day- or night-time symptom scores or rescue medication use (Table 6, see Additional file 1). Most importantly, the effect of formoterol was sustained over the one-year treatment period. In this study, no statistical comparison was made between the low-dose budesonide + formoterol and high dose budesonide groups.
Figure 3 Formoterol add-on study showing forced expiratory volume in one second (FEV1) (panel A, from ref 64 with permission) and the estimated yearly rates (no. patients/year) of severe asthma exacerbations in the different treatment groups of the study (panel B). For estimated yearly rate of exacerbations, the P-values given were formoterol vs placebo P = 0.01 and lower vs higher dose of budesonide P < 0.001.
Another study [69] compared the addition of formoterol (4.5 μg bid) to a small dose of budesonide (160 μg/d) in single inhaler (Symbicort®) with an increased dose of budesonide (400 μg/d) in adults with mild to moderate asthma (mean FEV1 81–82%) not fully controlled on low doses of ICS alone. The increase in mean morning and evening PEF was significantly higher for budesonide/formoterol compared with budesonide alone. In addition, the percentage of symptom-free days and asthma control days were significantly improved in the budesonide/formoterol group. Budesonide and formoterol decreased the relative risk of an asthma exacerbation by 26% as compared with higher dose budesonide alone.
The results of the formoterol study [67] on the benefits of addition of formoterol were confirmed in patients with mild asthma (mean FEV1 86–87% of predicted and using approximately 1 rescue inhalation per day) [68]. In this study, the addition of formoterol was superior to doubling the dose of budesonide in increasing FEV1 and morning PEF in the patients already treated with a low dose of ICS, but not in steroid-naïve patients (Table 5), or in reducing the percentage of days with symptoms, number of rescue inhalations or nights with awakenings in the patients with mild persistent asthma already treated with low doses of ICS (Table 6, see Additional file 1).
A subgroup of the patients participating in the formoterol study [67] was analysed for asthma quality of life parameters using the Asthma Quality of Life Questionnaire (AQLQ) [79]. Following randomisation there was a significant increase in the AQLQ score only in the group with higher budesonide + formoterol group. Although the patterns of mean responses for AQLQ scores and for the clinical variables were very similar, correlations between change in AQLQ scores and change in clinical measures over the randomized period were only weak to moderate (maximum r = 0.51). The data confirm that the benefit from the addition of formoterol is sustained. However, instead of improving pulmonary function parameters patients are usually more interested in how their normal everyday life and activities are limited by the disease. The analysis of AQLQ parameters and their comparison with the clinical data in that analysis also suggest that if only pulmonary function parameters are to be analysed, the benefits of addition of LABA to the treatment may be over-estimated. Also, it should be noted that no correlation has been found between measures of pulmonary function and daytime asthma symptoms [80].
Formoterol – other literature
As compared with the abovementioned three studies, similar superiority of addition of formoterol on morning PEF, rescue medication use and asthma symptoms were reported in an open randomised parallel-group study comparing the addition of formoterol to the low-dose BDP with 2-fold higher dose of BDP in patients suffering from symptomatic asthma, despite the use of inhaled BDP [81].
Salmeterol – studies included in this systematic review
Addition of salmeterol as compared with the increase in the dose of ICS BDP or FP has been studied in 9 randomised parallel group studies with 3651 patients with moderate to severe persistent asthma (Tables 3 and 4, see Additional file 1). Addition of salmeterol improved FEV1 better than increasing the dose of ICS 2–4-fold in 5 studies (analysed in 6 studies) and mean morning PEF in 7 studies (analysed in 9 studies), respectively (Table 5, see Additional file 1). Similarly, addition of salmeterol was significantly better than the increase in the dose of ICS in increasing the number of days or nights without symptoms or without rescue medication or reducing day- or night-time symptom score as well as daytime or night-time rescue medication use in most studies (Table 6, see Additional file 1). However, although addition of salmeterol seems to be superior to increased dose of ICS, a statistically significant difference was not always reached (Tables 5 and 6, see Additional file 1) in the single studies when FEV1, morning PEF, asthma symptom scores or rescue medication use were analysed. Another feature typical of these studies is that the results favour the addition of salmeterol more at early time points and this difference is reduced as the study proceeds.
Salmeterol – other literature
Most of the studies mentioned above, (except ref [72]), have recently been analysed in a meta-analysis [13]. In addition, the published meta-analysis included 1 study (n = 488) that remains unpublished at the present. At baseline these patients (n = 3685, aged ≥12) used BDP 200 – 400 – 1000 μg/d or FP 200 – 500 μg/d. The addition of salmeterol to those doses was compared with increasing the dose of BDP or FP up to 2–2.5-fold. The mean FEV1 was <75% in most studies included in the meta-analysis and a reversibility of ≥10–15% in PEF or FEV1 after inhalation of short-acting bronchodilator was required for inclusion in all but three studies. In patients receiving salmeterol the morning PEF was 22–27 L/min greater and FEV1 was 0.10 – 0.08 L greater after three to six months of treatment, compared to the response to increased steroids. Similarly, the mean percentage of days and nights without symptoms was increased 12–15% and 5%, respectively, as well as the mean percentage of days and nights without need for rescue treatment increased 17–20% and 8–9%, respectively.
Effect of LABA on asthmatic inflammation
The results of the above mentioned studies favour the addition of a LABA instead of increasing the dose of ICS in patients not adequately controlled with low to moderate doses of ICS. However, there have been concerns that regular use of inhaled β2-agonists may mask an increase in the underlying airway inflammation in asthma. Also, some proinflammatory effects have been described for β2-agonists such as delay of constitutive eosinophil apoptosis [82] or reversal of corticosteroid-induced apoptosis [83]. Furthermore, development of tolerance to their protective effects against various asthma-provoking stimuli has been reported. There is some disagreement whether the addition of formoterol or salmeterol changes the level of pulmonary inflammation in patients already treated with inhaled glucocorticoids or whether they may even mask the inflammation. Three studies [84-86] do not indicate any significant increase in the inflammatory indices following addition of formoterol or salmeterol, whereas treatment of asthma with salmeterol with concomitant steroid tapering has been shown to increase the numbers of eosinophils in sputum [87].
Formoterol – studies included in this systematic review
In a randomised, double-blind and parallel-group study (n = 61) with similar inclusion and exclusion criteria than in the formoterol add-on study [67], the effect of adding formoterol (12 μg bid) to a low dose of budesonide (200 μg/d) was compared with a higher dose of budesonide (800 μg/d) for 1 year after a run-in with budesonide (1600 μg/d) for 4-wk [84]. Budesonide (1600 μg/d) during run-in significantly reduced median sputum eosinophils. No significant changes in the proportion of eosinophils, other inflammatory cells, or ECP levels in sputum were observed over the ensuing one year treatment with formoterol + budesonide (200 μg/d) or higher dose budesonide (800 μg/d). Clinical asthma control was not significantly different between both groups.
Salmeterol – other literature
In a small study (n = 9) with asthma patients using regular inhaled glucocorticoids and inhaled salbutamol for symptom relief, the addition of salmeterol for 8 weeks was studied in a double-blind crossover placebo-controlled protocol [86]. Bronchoalveolar lavage (BAL) cell profile, albumin and tryptase levels, percentages of CD4+ and CD8+ lymphocytes and lymphocyte activation as assessed as proportions of lymphocytes expressing HLA-DR were measured in BAL samples before and after treatment. There were no significant changes after salmeterol treatment. In another double-blind, parallel-group, placebo-controlled study [85] the effect of addition of salmeterol (50 μg bd) or fluticasone (200 μg/d) for 12 weeks was studied in 45 symptomatic patients with asthma who were receiving ICS (range 100–500 μg/d). Bronchial biopsies and BAL were analysed before and after the treatment. After treatment with salmeterol there was no deterioration of airway inflammation, as assessed by mast cell, lymphocyte, or macrophage numbers in BAL or biopsies, but a significant fall in EG1-positive eosinophils in the lamina propria was found, which was not seen after treatment with FP. The only cellular effect of added FP was a decrease in BAL lymphocyte activation as assessed as proportions of lymphocytes expressing HLA-DR. There was a concurrent improvement in clinical status, more marked with salmeterol than with increased ICS. These two studies thus suggest that adding salmeterol to ICS is not associated with increased airway inflammation. In another study in 13 asthmatic individuals requiring ≥1500 μg ICS daily, the steroid sparing and "masking" effects of salmeterol versus placebo were studied in a randomised, placebo-controlled, double-blind and crossover trial [87]. Subjects were re-stabilised on their original dose of ICS for 4 wk before crossover to the alternative treatment. Corticosteroid doses were reduced weekly until criteria were met for an exacerbation or the corticosteroid was fully withdrawn. Mean ICS dose was reduced significantly more (87%) during salmeterol treatment, than with placebo (69%). Sputum eosinophils increased before exacerbation, despite stable symptoms, FEV1 and PEF. In the week before clinical exacerbation, sputum eosinophil counts were higher in the salmeterol-treatment arm as compared with placebo, whereas there were no differences in PC20 or serum ECP. Five subjects showed >10% sputum eosinophilia before exacerbation during salmeterol treatment, compared to two receiving placebo. This suggests that the use of salmeterol allowed subjects to tolerate a greater degree of inflammation without increased symptoms or reduced lung function. Thus, during progressive reduction of ICS the bronchodilator and symptom-relieving effects of salmeterol may mask increasing inflammation and delay awareness of worsening asthma. These findings strengthen guideline recommendations that LABA should not be described as sole anti-asthma medication and that they should be used as "add-on" therapy rather than for steroid tapering purposes.
The effect of addition of salmeterol (50 μg bd), FP (200 μg/d) or placebo for 3 months on airway wall vascular remodelling has been studied in 45 symptomatic patients with asthma who were receiving treatment with ICS (range 400–1000 μg/d) [88]. Bronchial biopsies were analysed before and after treatment. There was a decrease in the density of vessels of lamina propria after treatment only in the salmeterol group compared to baseline. There was no significant change within the FP or placebo groups and no treatment was associated with increased airway wall vascularity.
Asthma exacerbations
If there were a marked masking of pulmonary inflammation by LABA, one would expect to see an increase in the number and severity of asthma exacerbations during their long-term use. There is some difficulty in comparing the different studies done with formoterol and salmeterol as the definition of exacerbation varies. In formoterol studies [67,68] a severe exacerbation was defined as need for treatment with oral corticosteroids, as judged by the investigator, or hospital admission or emergency treatment for worsening of asthma or a decrease in morning PEF >25%–30% from baseline on two consecutive days. In contrast, in the salmeterol "add-on" studies the exacerbation was not defined at all or was more loosely defined for example as "a clinical exacerbation", "any worsening of asthma symptoms requiring a change in prescribed therapy, other than increased use of rescue medication" or "any asthma event that required treatment with oral or parenteral steroids".
Formoterol – studies included in this systematic review
In the formoterol study [67] the main outcome parameter was the rate of exacerbations during combination therapy. The results show that the 4-fold increase in the dose of budesonide reduced the rates of severe and mild exacerbations by 49% and 37%, respectively, whereas addition of formoterol to the lower dose of budesonide reduced the rates of severe and mild exacerbations by 26% and 40%, respectively. Patients treated with formoterol and the higher dose of budesonide had the greatest reductions, 63% and 62%, respectively (Figure 3B; Table 7, see Additional file 1). This suggests that if frequent asthma exacerbations are a major problem, increasing the dose of ICS may help to reduce the number of exacerbations. The results of the formoterol study [67] as well as the salmeterol meta-analysis [13] suggest that addition of LABA has divergent effects on asthma control: it is superior to the increased steroid dose in improving lung function, but is equal or less efficient in reducing exacerbations (Figure 3AB). The data also suggest that to achieve a better control of asthma exacerbations, the dose of ICS should be increased 4-fold. When 425 exacerbations of the formoterol study [67] were analysed [89], the use of higher dose of ICS or the use of formoterol was shown not to affect the pattern of change in PEF values or in symptoms during asthma exacerbation (Figure 4B).
Figure 4 A. Change in supplemental salbutamol use before and after exacerbation in patients treated with fluticasone and salmeterol combination or with high-dose fluticasone (with permission from ref 90), B. Change in morning PEF (percent fall from day -14) over the 14 d before and 14 d after an exacerbation in relation to treatment as analyzed from a subgroup of a FACET study (with permission from ref 89).
In contrast to that described in moderate to severe asthma, in the other formoterol study [68] addition of formoterol (6 μg bid) to either the lower (200 μg/d) or higher (400 μg/d) dose of budesonide in patients suffering from mainly mild asthma reduced the risk of the first asthma exacerbation by 43% (RR = 0.57, 95% CI 0.46–0.72). There was also a significant 52% reduction in the rate of severe exacerbations (RR = 0.48; 95% CI 0.39–0.59). In addition, significant improvement was observed for the rate of severe exacerbations (RR = 0.58, 95% CI 0.44–0.76). Thus, the data suggest that there may be a difference in the effect of ICS and formoterol on the exacerbations between mild and moderate to severe asthma so that in mild asthma addition of LABA may be more efficient in preventing exacerbations, whereas in moderate to severe asthma increasing the dose of ICS may be more efficient (Table 7, see Additional file 1). However, the formoterol studies [67,68] are not fully comparable in that way that in the other study [67] the increase in the dose of budesonide was 4-fold whereas in the other study [68] it was 2-fold.
Another study [69] compared the addition of formoterol (4.5 mg/d) to a small dose of budesonide (160 μg/d) in single inhaler (Symbicort®) with an increased dose of budesonide (400 μg/d) in adults with mild to moderate asthma (mean FEV1 81–82%) not fully controlled on low doses of ICS alone. Budesonide/formoterol combination significantly decreased the relative risk of an asthma exacerbation by 26% as compared with higher dose budesonide alone. In contrast, the estimated risk of having a severe exacerbation was 6% lower in patients treated with budesonide/formoterol compared with those receiving budesonide alone, but this was not statistically significant.
Salmeterol – studies included in this systematic review
Only two studies [70,78] of those included in this systematic review reported the actual monthly or annual rates for moderate or severe exacerbations. In those studies there were no significant differences in the yearly rate of exacerbations or percentages of patients experiencing at least exacerbation. The other studies generally reported the percentages of patients experiencing at least one exacerbation (Table 7). In salmeterol studies, the data were presented mostly in a form, which did not allow us to calculate the yearly rate of exacerbations.
Salmeterol – other literature
In the salmeterol studies lasting 3–6 months the numbers of patients with exacerbations were analysed. The meta-analysis [13] revealed that fewer patients experienced any exacerbation with salmeterol (difference 2.7%), and the proportion of patients with moderate or severe exacerbations was also lower (difference 2.4%). Thus, to prevent one exacerbation 37–41 patients should be treated with salmeterol instead of increasing the dose of ICS. Rather than indicating salmeterol being superior, the result suggests that there is no increased risk for exacerbations with the use of salmeterol. Unfortunately, in most salmeterol studies the severity and/or yearly incidence of exacerbations was not analysed. As one patient can experience more than one asthma exacerbation during the study, the parameter used in the salmeterol studies (proportion of patients experiencing an exacerbation) may not reflect the actual number of exacerbations. Another factor that may affect our interpretation of the effect of these therapies on asthma exacerbations is that in 6 of the 12 LABA studies, patients could be withdrawn from the study if they experienced >1–5 exacerbations (Table 7, see Additional file 1). This may underestimate the total incidence of exacerbations, as those patients experiencing several exacerbations were excluded from analysis. However, these are the patients the "add-on" therapies are most frequently prescribed.
Recently, the exacerbation rates and clinical measures of asthma worsening were assessed in an analysis combining results from two double-blind studies (n = 925) comparing addition of salmeterol to low-dose-FP with increasing the dose of FP 2.5-fold [90]. The addition of salmeterol resulted in a significantly lower rate (0.23 vs. 0.39 per patient per year) of exacerbations compared with higher dose FP. Salmeterol combined with low-dose FP was significantly more protective than 2.5-fold higher dose of FP in preventing asthma exacerbations, as assessed by the time to first exacerbation. In both groups clinical indicators of worsening of asthma showed parallel changes before asthma exacerbation, and greater improvements in morning PEF, supplemental salbutamol use and asthma symptom score were observed after exacerbation with salmeterol compared with higher dose FP (Figure 4A). Thus, the ability to detect deteriorating asthma and the severity of exacerbation is not negatively affected by salmeterol.
Adverse effects of LABA
The addition of LABA to the treatment regimen usually results in a slight increase in those pharmacologically predictable adverse events such as tremor and tachycardia. However, generally these do not lead to the discontinuation of the treatment. In the formoterol studies [67-69], no significant differences were reported on the adverse effects between the groups, but no detailed data was presented. Also, in the salmeterol studies [70-78], the incidence of adverse events was very low and generally was not different between the treatment groups. Although LABA appear to be generally very safe, one should not forget that they are generally not suitable for patients with symptomatic coronary heart disease or hyperthyroidism and may provoke more severe adverse events such as supraventricular tachycardias, atrial fibrillation and extrasystoles. Rarely hypersensitivity reactions and painful muscular cramps may occur. Also one should note that the "add-on" studies included in this review are not originally planned and powered to detect significant differences in the adverse effects.
Adding a leukotriene receptor antagonist (LTRA)
Rationale
Cysteinyl leukotriene receptor-antagonists (LTRA), such as montelukast, pranlukast and zafirlukast, are a new class of asthma medication, whose role in the stepwise management of asthma has not yet been fully established. Leukotriene antagonists blunt the obstructive response and have weak anti-inflammatory activity. In some studies corticosteroids are not very effective inhibitors of cysteinyl leukotriene pathways, at least when assessed by their inability to reduce cysteinyl leukotriene concentrations [91,92] and thus combination of these therapeutic classes may offer some benefit.
Montelukast – studies included in this systematic review
We identified one randomised, double-blind, parallel-group 16 week study (Jadad score 3) comparing the addition of montelukast (10 mg/d) to budesonide (800 μg/d) with doubling the dose of budesonide (1600 μg/d) in patients inadequately controlled on inhaled budesonide (800 μg/d, n = 448) [93]. The inclusion criteria were: patients (aged 15–75 years) who were not optimally controlled as judged by the investigators in spite of a regular ICS (600–1200 μg/d for BDP, budesonide, TAA, flunisolide or 300–800 μg/d for FP). Patients were required to have FEV1 ≥50% predicted at visits 1 and 3, with a ≥12% bronchodilator response and symptoms requiring β-agonist treatment of at least 1 puff/day during the last 2 weeks of the run in period (total 4 weeks). Both groups showed progressive improvement in several measures of asthma control compared with baseline. Mean morning PEF improved similarly in the last 10 weeks of treatment compared with baseline in both the montelukast + budesonide group and in the double dose budesonide group (33.5 vs 30.1 L/min). The improvement in montelukast + budesonide group was faster as the mean morning PEF was significantly higher during days 1–3 after start of treatment in this group as compared with the double dose budesonide group (20.1 vs 9.6 L/min) (Figure 5). Both groups showed similar improvements with respect to rescue β2-agonist use, mean daytime symptom score, nocturnal awakenings, exacerbations, asthma free days, peripheral blood eosinophil counts, and asthma specific quality of life. The authors conclude that addition of montelukast to ICS offers comparable asthma control to doubling the dose of ICS. However, it needs to be remembered that, in most cases, to obtain a statistically significant improvement in asthma control at least a 4-fold increase in the dose of ICS is needed (see above).
Figure 5 Effect of addition of montelukast (10 mg/d) or doubling the dose of ICS on morning peak expiratory flow (AM PEF) over 12 week treatment period in patients not adequately controlled by budesonide 800 μg/d (solid line = montelukast + budesonide 800 mg/d, dashed line = budesonide 1600 μg/d) (with permission from ref 93).
Montelukast – other literature
A large (n = 639) study [94] recruited patients with asthma not optimally controlled by ICS (stable dose equivalent to budesonide 400–1600 μg/d). The patients were required to have FEV1 ≥55%, a bronchodilator response greater than 12%, symptoms and rescue β2 agonist use of at least 1 puff/day. The mean FEV1 at baseline was 81% predicted. The patients were randomised to obtain either montelukast (10 mg/d) or placebo in a double-blind manner. The ICS dose remained constant throughout the study. The primary efficacy end point was the percentage of asthma exacerbation days. The major advantage of this study is that this study adopted several different definitions for asthma exacerbation days from previously published other studies, making comparison to other studies more easy. The median percentage of asthma exacerbation days was 35% lower (3.1% vs 4.8%, p = 0.03) and the median percentage of asthma free days was 56% higher (66.1% vs 42.3%, p = 0.001) in the montelukast group than in the placebo group. Thus, the NNT with montelukast to avoid one exacerbation day was 13, and the NNT to avoid one day not free of asthma – that is, to gain an asthma free day – was 10. Patients receiving concomitant treatment with montelukast had significantly less (25.6% vs 32.2%, p = 0.01) nocturnal awakenings, and significantly greater reductions in β2-agonist use (17.26% v 4.92%, p = 0.05, baseline use was 3.2–3.3 puffs/day), and morning PEF (16.86 L/min vs 11.30 L/min, p = 0.05, baseline 365–373 L/min). No significant difference was found in asthma specific quality of life or in morning FEV1. The results of this study suggest that although the effect of montelukast on endpoints such as morning PEF, FEV1 and rescue β2-agonist use are only small or modest, addition of montelukast may produce a significant improvement of asthma control by reducing the number of asthma exacerbation days.
In another study with patients (n = 642) with symptomatic persistent asthma despite the treatment with BDP (400 μg/d), addition of montelukast (10 mg/d), improved morning FEV1 and PEF, asthma symptom score and the percentage of asthma exacerbation free days better than placebo during 16 week treatment period [95]. The increase in morning FEV1 was approximately 140 mL and in morning PEF 10 L/min. There was a tendency towards reduced rescue medication use with the combination therapy, but the reduction was only 0.2 puffs/day. Addition of montelukast to ICS seemed to prevent the increase in the number of peripheral blood eosinophils seen in other treatment groups.
In an atypical "add-on" study (randomised double-blind, placebo-controlled and crossover trial), addition of montelukast (10 mg/d) was compared with placebo in patients with asthma (n = 72) and symptoms despite treatment with ICS and additional therapy [96]. Most of the patients used several different types of combination therapy, except leukotriene antagonists, at baseline. The inclusion criteria were defined as "any patient with physician diagnosis of asthma in whom the recruiting physician felt a trial of montelukast was indicated for continued asthma symptoms despite other anti-asthma therapy". A current worsening of asthma requiring oral corticosteroid treatment, or worsening in the preceding month were both exclusion criteria, but did not exclude any of those referred for inclusion in the trial. In this setting corresponding to a typical hospital outpatient clinic, addition of montelukast did not result in any significant change in symptom scores, rescue inhaled β2-agonist use, or morning or evening PEF. When treatment response was defined as a 15% or greater increase in mean PEF recordings, there were four responders to montelukast and seven responders to placebo. Although several points in this study may be criticised (loose inclusion criteria, small sample size, short 2 week treatment period, no wash-out period, encapsulation of the tablets, exacerbations not analysed as end-point), the results suggest that the effects of montelukast are not as evident in unselected population than in the more clearly defined patients included in other trials [93-95].
The additional anti-inflammatory activity obtained by adding montelukast to the treatment regimen has been assessed in three randomised, double-blind, cross-over studies lasting 10 days–8 weeks. In one study [97], addition of montelukast (10 mg/d) to salmeterol (50 μg bid) and fluticasone (250 μg bid) combination was compared with placebo in patients with mild-moderate asthma for 3 weeks. Compared with salmeterol/fluticasone run-in period, adding montelukast was better (p < 0.05) than placebo for inflammatory markers such as AMP-threshold, recovery, exhaled NO, and blood eosinophils but not for lung function. In another study [98], addition of montelukast for 8 weeks to FP (100 μg bid) was compared with placebo in patients with mild asthma. There were no differences in FEV1 or histamine PC20 between the two treatment regimens. There was no difference in the efficacy of either treatment in decreasing T cell, CD45RO+, mast cell or activated eosinophil numbers in bronchial biopsies. In a third study [99], the addition of montelukast (10 mg/d) to budesonide (400 μg/d) for 10 days to steroid-naïve patients with asthma was reported not to produce any additional anti-inflammatory benefit when compared with budesonide alone in reducing airway hyperresponsiveness or sputum eosinophilia.
Zafirlukast – other studies
Addition of high-dose zafirlukast (80 mg b.i.d.: 4-fold greater than the approved dose) improved asthma control better than placebo in patients (n = 368) on high-dose ICS (1000 – 4000 μg/d) [100]. Compared with placebo, addition of zafirlukast improved morning and evening PEF and reduced daytime symptom score and rescue medication use [100]. According to a recent meta-analysis [101,102], in symptomatic asthmatic adults, addition of zafirlukast (80 mg bid) to ICS did not reduce the risk of an exacerbation requiring systemic steroids after 12 weeks of treatment, compared to double dose ICS [RR = 1.08; 95% CI 0.47, 2.50]. There were no differences in any other measure of outcome. Higher doses of zafirlukast than currently licensed were associated with increased risk of liver enzyme elevation.
Conclusions on adding a LTRA
According to recent meta-analyses (12 adult studies and 1 in children) [101,102], leukotriene antagonists (zafirlukast or pranlukast at 2–4 times the licensed dose) combined with ICS (300–2000 μg/d BDP equivalent) reduce the number of patients with exacerbations that require systemic corticosteroids, compared to ICS alone [RR = 0.34; 95% CI 0.13, 0.88]. This equates to 20 patients (95% CI 1,100) treated to prevent one needing systemic corticosteroids. There was no difference in side effects [101,102]. The addition of licensed doses of LTRA to ICS resulted in a non-significant reduction in the risk of exacerbations requiring systemic steroids (two trials, RR 0.61, 95% CI 0.36, 1.05). This systematic review did not include the recent study comparing the addition of montelukast to double-dose ICS [93]. As that systematic review did not include any data of LTRA drugs at currently licensed doses compared with high dose ICS, the author came to a conclusion that the addition of LTRA to ICS may modestly improve asthma control compared with ICS alone but this strategy cannot be recommended as a substitute for increasing the dose of ICS [101]. However, based on one relatively large trial [93], the evidence suggests that addition of montelukast may be equal to doubling the dose of ICS. However, one might criticise this conclusion as this study [93] lacked placebo arm, ie. it is possible that increasing (doubling) the dose of ICS does not produce any real improvement in asthma control as compared with lower ICS dose and thus the result showing non-inferiority to double dose ICS might mean no effect at all. Thus, more data is needed to compare the efficacy of LTRA at currently licensed doses with increasing the dose of ICS.
Adding theophylline
Rationale
Although theophylline has traditionally been classified as a bronchodilator, its ability to control chronic asthma is greater than can be explained by its relatively small degree of bronchodilator activity. In fact, theophylline has immunomodulatory, anti-inflammatory and bronchoprotective effects that may contribute to its efficacy as an anti-asthma drug [103]. There is some evidence that addition of theophylline to ICS treatment improves pulmonary function and asthma symptoms [104], although all studies have not been able to confirm this result [105].
Theophylline – studies included in this systematic review
The addition of theophylline has been compared with doubling the dose of ICS (BDP and budesonide; 400 μg/d → 800 μg/d) in two separate studies with 195 patients with symptomatic asthma for 6 to 12 weeks [106,107]. Theophylline was used at relatively low doses, the mean serum theophylline concentrations were 8.7 and 10.1 mg/L in these studies.
In the study (Jadad score 4) of Evans and coworkers [106] addition of low-dose theophylline to budesonide (400 μg/d) was compared with doubling the dose of budesonide (800 μg/d) in a randomised double-blind trial for 3 months. Patients (n = 62) were required to have FEV1 predicted normal ≥50%, bronchodilator response of at least 15% and to have symptoms despite the use of ICS (equivalent to budesonide dose of 800–1000 μg/d). The overall treatment effect of addition of theophylline was superior to double-dose budesonide in improving FVC and FEV1 (Figure 6), although at single timepoints there were no significant differences between the treatments. There was no significant difference between the treatments in improving home PEF recordings or reducing β2-agonist use or symptom scores. There was no difference in the occurrence of possibly drug-related adverse effects between the groups. The statistical power of this study was calculated to detect significant changes over baseline, but not to detect differences (superiority) or non-inferiority between the treatments.
Figure 6 Mean (+- SE) change in FEV1 in 31 asthma patients treated with high-dose budesonide (1600 μg/d) and 31 patients given low-dose budesonide (800 μg/d) and theophylline (with permission from ref 106).
A randomised, double-blind parallel-group study (Jadad score 3) by Ukena and coworkers [107] compared the addition of theophylline to low dose BDP (400 μg/d) with double-dose BDP (800 μg/d) for 6 weeks. Patients (n = 133) were required to have FEV1 50–85% predicted normal and a documented reversibility of at least 15% of FEV1 over baseline and to be not controlled by BDP (400 μg/d) or equivalent. The sample size of this study was powered to detect equivalence. No significant differences were found between the high-dose BDP and low-dose BDP plus theophylline groups in outcomes such as morning or evening PEF, PEF variability, FEV1, daytime or nighttime symptom scores or rescue medication use. Both treatments were well tolerated.
Lim et al. [108] recruited asthmatic patients that were symptomatic while being treated with low dose inhaled steroids (400 μg BDP, 200 μg FP or 400 μg BDP daily). Patients (n = 155) were required to have PEF ≥50% of the predicted normal with at least 15% variability in PEF. The patients were randomised to treatment either with low dose BDP (400 μg/d) alone, theophylline plus BDP (400 μg/d) or high-dose BDP (1000 μg/d) for six months in a double-blind trial (Jadad score 5). No significant differences were found between any of the treatment groups in morning PEF, evening PEF, PEF variability, rescue β2-agonist use, symptom scores or in the number of exacerbations. Of note is that there were no difference between the low dose BDP alone and high dose BDP groups in any of the parameters. This study was powered to detect superiority of theophylline plus BDP as compared with high-dose BDP. There were no significant differences between the treatment groups for any of the commonly reported adverse effects. The results of this study suggest that when the benefit of an "add-on" therapy is evaluated as compared with double-dose inhaled steroid, additional group using low-dose steroid alone should be included to see whether even the doubling of the dose of steroid produces any benefit to the patient.
Conclusions on the addition of theophylline
Taken together, the results from two relatively small studies suggest that addition of low-dose theophylline may be equal to doubling the dose of ICS in the treatment of asthma not adequately controlled by low dose of ICS. However, one needs to remember that the effect of doubling the dose of ICS on asthma control is generally small or negligible (see above). Furthermore, a placebo group should be included in these studies to see whether an improvement in asthma control is obtained by doubling the dose of ICS. Thus, more data is needed to confirm the present results. Use of theophylline at concentrations at the lower limit or slightly below the recommended therapeutic range may help to limit the adverse effects.
Comparison between LTRA, theophylline and LABA as add-on options
Montelukast versus salmeterol – studies included in this systematic review
Combination of fluticasone (100 μg bid) and salmeterol (50 μg bid) in a single inhaler has recently been shown to provide more effective asthma control than montelukast (10 mg daily) combined with FP (100 μg bid) in a 12 weeks study (randomised, double-blind, double-dummy, Jadad score 3) in patients (n = 447) whose symptoms were suboptimally controlled by ICS only [109]. The inclusion criteria were FEV1 between 50% and 80% predicted normal, and at least 1 additional sign of inadequate asthma control during the 7 preceding days. Salmeterol/FP combination was superior to montelukast/FP in improving morning PEF (24.9 vs 13.0 L/min), evening PEF (18.9 vs 9.6 L/min), FEV1 (0.34 vs 0.20 L) and shortness of breath symptom score (-0.56 vs -0.40) as well as increasing the percentage of days without rescue medication (26.3 vs 19.1%). In contrast, there was no significant difference in outcomes such as chest tightness, wheeze and overall symptom scores. Asthma exacerbation rates were significantly (P = 0.031) lower in the FP + salmeterol group (2%) than in the FP+ montelukast group (6%). Adverse event profiles were reported to be similar.
A similar study [110] comparing the efficacy of combination of FP (100 μg bid) and salmeterol (50 μg bid) in a single inhaler with combination of montelukast (10 mg daily) and FP (100 μg bid) in a 12 weeks study (randomised, double-blind, double-dummy, Jadad score 4) in patients (n = 725) whose symptoms were suboptimally controlled by ICS (BDP, budesonide, flunisolide 400–1000 μg/d or FP 200–500 μg/day) only. The inclusion criteria were FEV1 above 50% and at least 15% bronchodilator response, and asthma symptoms at least at 4/7 days during run-in. Salmeterol/FP combination was superior to montelukast/FP in improving morning PEF (36 vs 19 L/min), evening PEF (29 vs 14 L/min), FEV1 (0.26 vs 0.17 L), percentage of symptom-free days (42.9 vs 31.5%), percentage of symptom-free nights (46.5 vs 41.1%) as well as increasing the percentage of days without rescue medication (47.9 vs 46%). In contrast, there was no significant difference in percentage of rescue free nights. The number of patients experiencing at least one asthma exacerbation (any severity) was significantly (P < 0.05) lower in the FP + salmeterol group (9.6%) than in the FP+ montelukast group (14.6%). The percentage of patients who had at least one asthma exacerbation of either moderate or severe intensity was 4.8% in the salmeterol + FP group and 8.4% in the montelukast + FP group, but this difference did not reach statistical significance. The time to the first exacerbation was significantly (P < 0.05) longer in the salmeterol + FP group than in the montelukast + FP group. Adverse event profiles were reported to be similar.
Another very similar study [111] was designed to demonstrate the non-inferiority of combination of montelukast (10 mg daily) and FP (100 μg bid in dry powder inhaler) as compared with combination of FP (100 μg bid in dry powder inhaler) and salmeterol (50 μg bid; metered dose inhaler) on asthma exacerbations. This 48 weeks study (randomised, double-blind, double-dummy, Jadad score 5) included patients (n = 1490) whose symptoms were suboptimally controlled by ICS (equivalent to BDP 200–1000 μg/d). The inclusion criteria were FEV1 50–90% predicted and at least 12% bronchodilator response, short-acting β2-agonist use of one puff/day or more and asthma symptoms. Salmeterol/FP combination was superior to montelukast/FP in improving morning PEF (34.6 vs 17.7 L/min), FEV1 (0.19 vs 0.11 L). In contrast, there was no significant difference in nocturnal awakenings and asthma specific quality of life score. The percentage of patients experiencing at least one asthma exacerbation (any severity) was shown to be similar in the FP + salmeterol group (19.1%) than in the FP+ montelukast group (20.1%). Also there was no difference in the time to the first exacerbation between the salmeterol + FP and the montelukast + FP groups. Peripheral blood eosinophils were reported to be reduced significantly more in the montelukast + FP group (-0.04 × 103/μl) than in the salmeterol + FP group (-0.01 × 103/μl). Interestingly more serious adverse events were reported in the salmeterol + FP group.
In another randomised, double-blind, double-dummy, parallel-group study (Jadad score 3) in patients (n = 948) with symptomatic asthma despite treatment with ICS, addition of montelukast (10 mg daily) was compared with addition of salmeterol (50 μg bid) for 12 weeks [112]. Patients were required to have symptoms despite the constant dose of ICS (any brand at any dose) and FEV1 between 50% and 80% predicted and at least 12% bronchodilator response. Treatment with salmeterol resulted in significantly greater improvements from baseline compared with montelukast for most efficacy measurements, including morning PEF (35.0 vs 21.7 L/min), percentage of symptom-free days (24 v 16%) and percentage of rescue-free days (27 vs 20%). Also total supplemental salbutamol use (-1.90 vs -1.66 puffs per day) and nighttime awakenings per week (-1.42 vs -1.32) decreased significantly more with salmeterol than with montelukast. Six percent of patients in the salmeterol group experienced a total of 27 asthma exacerbations compared with 5% of patients in the montelukast group who experienced 24 asthma exacerbations during the 12 weeks treatment period. However, the patients experiencing an asthma exacerbation were withdrawn from the study. Thus, annualised incidences of exacerbations cannot be compared [112]. The safety profiles of the two treatments were reported to be similar.
Taken together, addition of salmeterol seems to produce better improvement of asthma control when lung function is assessed than addition of montelukast in patients with asthma suboptimally controlled by small to moderate doses of ICS. However, in one long-term study [111] addition of montelukast to fluticasone was shown to be non-inferior to addition of salmeterol when the percentage of patients with at least one asthma exacerbation was used as the primary endpoint. Whereas addition of salmeterol may produce a better improvement in lung function, addition of montelukast may provide additional anti-inflammatory efficacy to ICS that is reflected in a long-term efficacy on asthma exacerbations. A factor that may produce a selection bias in these studies [109-111] is that a positive response to bronchodilator was required for inclusion. In fact, the reported mean improvements in FEV1 in response to β2-agonist were 23–24% [109], 27.0–27.4% [110] and 18.4–18.8% [111] in the single studies. This may produce a selection bias favouring long-acting β2-agonist. However, one needs to remember that many of those studies done with leukotriene receptor antagonist to prove their efficacy in the treatment of asthma have been performed with patients displaying a significant response to β2-agonist. Another factor that might be considered to produce bias is that all the above three studies that report salmeterol to be better have been sponsored by the producer of salmeterol and that study reporting the non-inferiority of montelukast as compared with salmeterol has been sponsored by producer of montelukast.
Montelukast versus salmeterol – other literature
In addition to the normal clinical endpoints, the effects of addition of salmeterol (50 μg bid) or montelukast (10 mg/d) to the treatment regimen were analysed on AMP bronchial challenge, blood eosinophil counts and exhaled NO in a placebo-controlled, double-dummy, crossover study in patients (n = 20) with persistent asthma not controlled with ICS [113]. For the provocative concentration of AMP causing a 20% fall in FEV1, compared to placebo, there were significant differences with the first and last doses of montelukast as well as the first but not the last dose of salmeterol, thus indicating the development of some tolerance with salmeterol. Only montelukast produced a significant, albeit trivial, suppression of blood eosinophil count. There were significant improvements with the first doses of salmeterol for all parameters of lung function. After 2 weeks of treatment, there were significant improvements with both drugs on rescue bronchodilator requirement and morning PEF. There were no significant differences between drugs for any endpoints except blood eosinophils. Thus, the results suggest some anti-inflammatory activity for montelukast when used as an "add-on" therapy.
Salmeterol versus zafirlukast – studies included in this systematic review
In a randomised, double-blind, double-dummy parallel-group trial (Jadad score 3) addition of zafirlukast (20 mg bid) was compared with the addition of salmeterol (50 μg bid via MDI) for 4 weeks in adult and adolescent patients (n = 429) with persistent asthma [114]. Patients were required to have FEV1 percentage predicted normal between 50 and 70% with or without asthma symptoms, or FEV1 of 70.1% to 80% of predicted normal values and symptoms or requirement for rescue β2-agonist use ≥4 puffs/day or diurnal PEF-variation of more than 20% at two days during 6 days run-in. Both inhaled salmeterol and oral zafirlukast resulted in within-group improvements from baseline in measures of pulmonary function (morning and evening PEF and FEV1), asthma symptoms, and supplemental salbutamol use. Salmeterol treatment resulted in significantly greater improvements from baseline compared with zafirlukast for most efficacy measurements, including morning PEF (28.8 vs 13.0 L/min), evening PEF (21.8 vs 11.2 L/min), combined patient-rated symptom scores for all symptoms (-35 vs 21%), daytime albuterol use (41 vs 25%) and night-time salbutamol use (42% vs 16%). Also, statistically significant differences favouring the addition of salmeterol were noted on patient-rated symptom scores for shortness of breath and chest tightness, percentage of symptom-free days, sleep symptoms, nighttime awakenings and percentage of days and nights with no albuterol use. There was no difference between the groups in symptom score for wheezing. Interestingly, the difference between salmeterol and zafirlukast was clear at week 1, but not at 4 weeks when the effect on FEV1 was analysed. One factor that may affect the results of this study is that there may be a randomisation bias as the proportions of patients using FP or TAA were not similar in the salmeterol and zafirlukast groups. This study was funded by the producer of salmeterol.
Salmeterol versus zafirlukast – other literature
As a part of the above study [114], a randomised, double-blind, double-dummy parallel-group trial comparing the addition of zafirlukast (20 mg b.i.d) with the addition of salmeterol (50 μg bid) for 4 weeks in patients (n = 289) with persistent asthma, 80% of whom were on a concurrent ICS regimen has been published [115]. Both inhaled salmeterol and oral zafirlukast resulted in within-group improvements from baseline in measures of pulmonary function (morning and evening PEF and FEV1), asthma symptoms, and supplemental salbutamol use. Salmeterol treatment resulted in significantly greater improvements from baseline compared with zafirlukast for most efficacy measurements, including morning PEF (29.6 vs 13.0 L/min), percentage of symptom-free days (22.2% vs 8.8%) and percentage of days and nights with no supplemental albuterol use (30.5% vs. 11.3%).
Formoterol versus zafirlukast versus theophylline – other literature
An open, randomised Turkish study [116] recruited patients with moderate persistent asthma having symptoms despite the use of moderate to high doses of ICS. The patients were required to have a FEV1 reversibility of at least 15%. Patients (n = 64) were randomised to three different treatments budesonide (800 μg/d) plus formoterol (9 μg bid), budesonide (800 μg/d) plus zafirlukast (20 mg bid) or budesonide (800 μg/d) plus sustained-release theophylline (400 mg/d) for three months. After three months there were no between group differences in endpoints such as morning and evening PEF, PEF variability, FEV1, daytime or nighttime symptom scores and rescue terbutaline use. However, the addition of formoterol produced earlier improvements compared with the two other groups in criteria such as PEF variability, day- and night-time asthma symptom scores and supplemental terbutaline use. Patients in budesonide plus zafirlukast group experienced most adverse effects, but no statistical analysis was presented. The authors conclude that in patients who still have symptoms despite the treatment with ICS, the addition of any of these medications to the treatment is a logical approach and may be chosen.
Conclusions on the comparisons between LABA, LTRA and theophylline as add-on options
LABA (salmeterol) seem to have superior efficacy as add-on therapy in persistent asthma not controlled by low to moderate doses of ICS as compared with LTRA (montelukast; four studies or zafirlukast; one study). More studies comparing the different add-on options are needed as well as studies with longer duration as the current evidence is mostly limited to follow-up period of 3 months.
Compliance and treatment strategies
When assessing a patient with persistent asthma who is not adequately controlled by low to moderate doses of ICS:
• It is important to find out whether the patient is using the prescribed medication correctly. Poor compliance in asthma patients treated with ICS is a very common reason for treatment failure. Compliance with ICS is often less than 50% [117,118]. Oral asthma therapies may result in better compliance [119].
• Secondly, it is important to check whether the inhalation technique is adequate. Problems with the inhalation techniques are very common, especially among children and the elderly [120]. Good patient education, especially if it is self-management oriented improves health outcomes in adults with asthma [121].
• Thirdly, it is important to search for possible environmental factors, such as changes in home and working environment, hobbies and pets.
If asthma exacerbations are the dominant problem, guided self-management of asthma has been proven to be an efficient treatment strategy. In a Cochrane review [121] self-management of asthma was compared with usual care in 22 studies. Self-management reduced hospital admissions (odds ratio; OR 0.58, 95% confidence interval; CI 0.38 to 0.88), emergency room visits (OR 0.71; 95% CI 0.57–0.90), unscheduled visits to the doctor (OR 0.57; 95% CI 0.40 to 0.82), days off from work or school (OR 0.55; 95% CI 0.38 to 0.79) and nocturnal asthma (OR 0.53; 95% CI 0.39 to 0.72).
Conclusions
Addition of formoterol or salmeterol seems to be superior as compared with the increase in the dose of the ICS in improving lung function, controlling asthma symptoms and reducing the use of rescue bronchodilator treatment. By increasing (doubling) the dose of the ICS the clinical improvement is likely to be of small magnitude. However, if frequent exacerbations are the major problem, increasing the dose of ICS may significantly help to reduce the number of exacerbations. By avoiding doses above 1000 – 1500 μg/d (budesonide and BDP) or 500 – 750 μg/d (FP) the risk of systemic adverse effects remains low. However, it should be noted that the evidence on the superiority of LABA is limited to symptomatic patients with mild to severe persistent asthma currently treated with low to moderate doses of ICS and presenting with a significant bronchodilator response. Also, addition of the LTRA montelukast or zafirlukast may improve asthma control in patients remaining symptomatic with ICS and addition of montelukast may be equal to double-dose ICS. Addition of LABA (salmeterol) seems to produce better asthma control as compared with a LTRA (montelukast or zafirlukast) whereas the long-term efficacy of LTRA (montelukast) on asthma exacerbations may be equal to LABA (salmeterol). There is evidence that addition of low-dose theophylline to the treatment regimen may be equal to doubling of the dose of ICS. However, more studies are needed to better clarify the role of leukotriene antagonists and theophylline as "add on"-therapies. For patients with inappropriate inhalation technique the value of LTRA or theophylline are especially worth considering. More studies are now needed to compare between different add-on therapies and to explore the effect of more than one add-on therapy in patients with more severe asthma as well as in those having symptoms but not significant bronchodilator response.
Another issue not addressed by these studies of large patient groups are the different responses of patients to the different add-on therapies. This needs to be studied by comparing add-on treatments in the same patients, but these studies are difficult and prolonged. In the future it may be possible to predict factors that predict the value of a particular add-on therapy in a particular patient, but the currently published studies unfortunately provide no guidance.
Abbreviations
ACTH: corticotrophin, AMP: adenosine monophosphate, AQLQ: asthma quality of life questionnaire, BAL: bronchoalveolar lavage, BDP: beclomethasone dipropionate, ECP: eosinophil cationic protein, FEF50: forced expiratory flow when 50% of vital capacity has been exhaled, FENO: exhaled nitric oxide, FEV1: forced expiratory volume in one second, FP: fluticasone propionate, FVC: forced vital capacity, HFA: hydrofluoroalkane-134a formulation, HPA: hypothalamic-pituitary-adrenal, ICS: inhaled corticosteroid, LABA: long-acting β2-agonist, LTRA: leukotriene receptor antagonist, MDI: metered dose inhaler, NNH: number needed to harm, NNT: number needed to treat, PC20: provocative concentration causing a 20% fall in FEV1, PD20: provocative dose causing a 20% fall in FEV1, PEF: peak expiratory flow, TAA: triamcinolone acetonide
Authors' contributions
HK carried out the literature searches, evaluated the studies, conceived the review and drafted the manuscript. AL, EM and PJB participated in the design and writing of the review. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Tables 1–7-Kankaanranta.doc contains tables 1–7 of this review.
Click here for file
Acknowledgements
Production of this review was supported by Tampere Tuberculosis Foundation (Finland), the Finnish Anti-Tuberculosis Association Foundation, Jalmari and Rauha Ahokas Foundation (Finland), the Academy of Finland and the Medical Research Fund of Tampere University Hospital (Finland). No support was obtained from the pharmaceutical industry.
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| 15509300 | PMC528858 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Oct 27; 5(1):17 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-17 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-101550713210.1186/1743-7075-1-10ReviewUric acid: A new look at an old risk marker for cardiovascular disease, metabolic syndrome, and type 2 diabetes mellitus: The urate redox shuttle Hayden Melvin R [email protected] Suresh C [email protected] Department of Family and Community Medicine, University of Missouri, Columbia, Missouri USA2 Department of Physiology and Biophysics, University of Louisville, School of Medicine, Louisville, Kentucky USA2004 19 10 2004 1 10 10 23 8 2004 19 10 2004 Copyright © 2004 Hayden and Tyagi; licensee BioMed Central Ltd.2004Hayden and Tyagi; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The topical role of uric acid and its relation to cardiovascular disease, renal disease, and hypertension is rapidly evolving. Its important role both historically and currently in the clinical clustering phenomenon of the metabolic syndrome (MS), type 2 diabetes mellitus (T2DM), atheroscleropathy, and non-diabetic atherosclerosis is of great importance.
Results
Uric acid is a marker of risk and it remains controversial as to its importance as a risk factor (causative role). In this review we will attempt to justify its important role as one of the many risk factors in the development of accelerated atherosclerosis and discuss its importance of being one of the multiple injurious stimuli to the endothelium, the arterial vessel wall, and capillaries. The role of uric acid, oxidative – redox stress, reactive oxygen species, and decreased endothelial nitric oxide and endothelial dysfunction cannot be over emphasized.
In the atherosclerotic prooxidative environmental milieu the original antioxidant properties of uric acid paradoxically becomes prooxidant, thus contributing to the oxidation of lipoproteins within atherosclerotic plaques, regardless of their origins in the MS, T2DM, accelerated atherosclerosis (atheroscleropathy), or non-diabetic vulnerable atherosclerotic plaques. In this milieu there exists an antioxidant – prooxidant urate redox shuttle.
Conclusion
Elevations of uric acid > 4 mg/dl should be considered a "red flag" in those patients at risk for cardiovascular disease and should alert the clinician to strive to utilize a global risk reduction program in a team effort to reduce the complications of the atherogenic process resulting in the morbid – mortal outcomes of cardiovascular disease.
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Background
While the topicality of serum uric acid (SUA) being a risk factor is currently controversial [1,2], there is little controversy regarding its association as a risk marker associated with cardiovascular (CVD) and renal disease (especially in patients with hypertension, diabetes, and heart failure). SUA seems to be a graded marker of risk for the development of coronary heart disease (CHD) or cerebrovascular disease and stroke compared with patients with normal uric acid levels and especially those in the lower 1/3 of its normal physiological range [1,3-13].
LK Niskanen's et al. recently published article has demonstrated new information regarding this subject. They were able to demonstrate that elevations of SUA levels were independent of variables commonly associated with gout or the metabolic syndrome in association with CVD mortality in middle aged men [3].
In 1951, Gertler MM and White PD et al. sat out to determine the clinical aspects of premature coronary heart disease in 100 male patients 40 years old and younger. Their findings were increased mesomorphic body build, shorter stature, increased anterior posterior chest wall diameter, and increased cholesterol and uric acid (5.13 +/- .11 vs. 4.64 +/-.06) as compared to the normal population [14].
A much larger trial (1967) confirmed the initial interest in SUA and CVD with the publication of the early, large (5,127 participants), epidemiologic, seminal Framingham study. This classical paper by Kannel et al. noted an elevated SUA was also associated with an increased risk of coronary heart disease for men aged 30–59 [15]. In addition to the important finding of elevations in lipoproteins (specifically cholesterol levels greater than 250 mg/100 ml) being associated with CHD, there also appeared a definite association of elevated SUA, which was associated with an increase in the incidence rate of CHD. The above authors also noted that subjects in this study with evidence of impaired carbohydrate metabolism or disordered purine metabolism could be assumed to have accelerated atherogenesis [15].
This controversy regarding SUA being a risk factor or a risk marker is not as important as understanding its overall role in the association with endothelial cell damage, dysfunction, decreased endothelial nitric oxide (eNO) bioavailability, and how SUA interacts with other substrate toxicities and increased reactive oxygen species (ROS) of the A-FLIGHT-U acronym, which result in accelerated atherosclerosis (table 1). Johnson RJ et al. have nicely demonstrated that hyperuricemia predicts cardiovascular events in the general population, the hypertensive population, and patients with pre-existing CVD. Furthermore hyperuricemia predicts the development of future hypertension [11].
Table 1 A-FLIGHT-U ACRONYM Identification of multiple metabolic toxicities and injurious stimuli responsible for reactive oxygen species production. (figure 2)
A Angiotensin II (also induces PKC-β isoform)
Amylin (hyperamylinemia) / amyloid toxicity
AGEs/AFEs (advanced glycosylation/fructosylation endproducts)
Apolipoprotein B
Antioxidant reserve compromised
Absence of antioxidant network
Aging
ADMA (Asymmetrical DiMethyl Arginine)
F Free fatty acid toxicity: Obesity toxicity: Triad
L Lipotoxicity – Hyperlipidemia – Obesity toxicity: Triad
I Insulin toxicity (endogenous hyperinsulinemia-hyperproinsulinemia)
Inflammation toxicity
G Glucotoxicity (compounds peripheral insulin resistance) reductive stress
Sorbitol/polyol pathway
Pseudohypoxia (increased NADH/NAD ratio)
H Hypertension toxicity
Homocysteine toxicity
hs-CRP
T Triglyceride toxicity: Obesity toxicity: Triad
U Uric Acid toxicity:Antioxidant early in physiological range and a conditional prooxidant late when elevated through the paradoxical (antioxidant → prooxidant)
URATE REDOX SHUTTLE
Endothelial cell dysfunction with eNOS uncoupling, decreased eNO and increased ROS.
Vulnerable atherosclerotic plaque milieu of being acidic, proinflammatory, excess metal ions (Fe) (Cu) from vasa vasorum rupture and red blood cell plasma membranes due to intraplaque hemorrhage and plaque thrombus formation.
There are certain clinical clustering groups with increased cardiovascular risk, which have associated hyperuricemia (table 2). Non-diabetic patient groups with accelerated atherosclerosis, T2DM patient groups with accelerated atherosclerosis (atheroscleropathy), congestive heart failure patient groups with ischemic cardiomyopathy, metabolic syndrome patient groups (with hyperinsulinemia, hypertension, dyslipidemia, impaired glucose tolerance, and obesity), renal disease patient groups, hypertensive patient groups, African American patient groups, patient groups taking diuretics, and patient groups with excessive alcohol usage. Each of these clustering groups has metabolic mechanisms that may help to explain why SUA may be elevated (table 2). In addition to the recurring finding of an elevated tension of oxidative- redox stress and ROS in many of the groups is the importance of the MS and insulin resistance.
Table 2 Hyperuricemia: clinical clusters at cardiovascular risk
GROUPS Abbreviated Mechanisms
Patients with CVD
Accelerated atherosclerosis
Congestive heart failure Increased apoptosis – necrosis of the arterial vessel wall and capillary resulting in increased purine metabolism and hyperuricemia.
Increased oxidative – redox stress
Antioxidant – Prooxidant Paradox:
Urate Redox Shuttle
Patients with (T2DM)
Accelerated atherosclerosis
(Atheroscleropathy) Acting through obesity and insulin resistance.
Accelerated atherosclerosis with increased vascular cell apoptosis and inflammatory necrosis with increased purine metabolism resulting in hyperuricemia and increased oxidative stress through ischemia-reperfusion and xanthine oxidase.
Additional reductive stress associated with glucotoxicity and pseudohypoxia.
Increased oxidative-redox stress
Antioxidant – Prooxidant Paradox:
Urate Redox Shuttle
Obesity – Insulin resistance
Hyperinsulinemia – Insulin toxicity
Metabolic Syndrome (figure 1):
Hyperinsulinemia
Hypertension
Hyperlipidemia dyslipidemia, obesity
Hyperglycemia Leptin may induce hyperuricemia.
Insulin increases sodium reabsorption and is tightly linked to urate reabsorption.
Increased oxidative – redox stress
Antioxidant – Prooxidant Paradox:
Urate Redox Shuttle
Men and Postmenopausal females Estrogen is uricosuric
Renal diseases Decreases in GFR increases uric acid levels
Hypertension Urate reabsorption increased in setting of increased renal vascular resistance, microvascular disease predisposes to tissue ischemia that leads to increased urate generation (excess purine metabolism) and reduced excretion (due to lactate competing with urate transporter in the proximal tubule).
Increased oxidative – redox stress
Antioxidant – Prooxidant Paradox:
Urate Redox Shuttle
African American Unknown (assumed genetic causes as yet unidentified)
Diuretic use Volume contraction promotes urate reabsorption
Alcohol use (in excess) Increases urate generation and decreased urate excretion
Uric acid, MS, T2DM, and atheroscleropathy
The importance of hyperuricemia and the clustering phenomenon of the metabolic syndrome were first described by Kylin in 1923 when he described the clustering of three clinical syndromes: hypertension, hyperglycemia, and hyperuricemia [16]. In 1988, Reaven GM described the important central role of insulin resistance in the seminal Banting lecture where he described Syndrome X, which has now become known as the metabolic syndrome (MS) and/or the insulin resistance syndrome (IRS) [17]. Seven decades after the clustering phenomenon was reported by Kylin (1993), Reaven GM and Zavaroni I et al. suggested that hyperuricemia be added to the cluster of metabolic and hemodynamic abnormalities associated with insulin resistance and/or hyperinsulinemia of Syndrome X [18].
The four major players in the MS are hyperinsulinemia, hypertension, hyperlipidemia, and hyperglycemia. Each member of this deadly quartet has been demonstrated to be an independent risk factor for CHD and capable of working together in a synergistic manner to accelerate both non-diabetic atherosclerosis and the atheroscleropathy associated with MS, PD, and T2DM.
In a like manner, hyperuricemia, hyperhomocysteinemia, ROS, and highly sensitive C- reactive protein (hsCRP) each play an important role in expanding the original Syndrome X described by Reaven in the atherosclerotic process. The above quartet does not stand alone but interacts in a synergistic manner resulting in the progression of accelerated atherosclerosis and arterial vessel wall remodeling along with the original players and the A-FLIGHT-U toxicities (table 1). The MS of clinical clustering has been renamed multiple times over the past 16 years indicating its central importance to cardiovascular disease and was included in the recent National Cholesterol Educational Program – Adult Treatment Panel III (NCEP ATP III) clinical guidelines in order to assist the clinician in using this important tool to evaluate additional cardiovascular risk [16-19].
Hyperinsulinemia and Hyperamylinemia
Insulin, proinsulin, and amylin individually and synergistically activate the renin – angiotensin system (RAS) with subsequent increase in Ang II. Ang II is the most potent endogenous inducer of NAD(P)H oxidase, increasing NAD(P)H, which increases vascular – intimal reactive oxygen species (ROS) and superoxide (O2-•) [19,20]. There are many deleterious effects of hyperinsulinemia in addition to its being responsible for sodium, potassium, water, and urate retention in proximal kidney (table 3) [21].
Table 3 Deleterious effects of hyperinsulinemia (HI)
1. HI, hyperproinsulinemia, and hyperamylinemia synergistically activate RAS with subsequent increase in Ang II, renin, and aldosterone.
2. HI promotes Na+ and H2O retention, which increases blood volume and pressure. In turn this activates the reabsorption of uric acid resulting in elevation of SUA. In turn increased SUA has been shown to increase tubular reabsorption of Na+.
3. HI increases membrane cation-transport increasing intracellular Ca++, which increases tone and pressure.
4. HI activates the sympathetic nervous system.
5. HI stimulates vSMC proliferation and migration and remodeling.
6. HI increases the number of AT-1 receptors.
7. HI creates cross talk between the insulin receptor and AT-1 receptor, resulting in a more profound Ang II effect.
8 HI promotes PI3 kinase Akt-MAP kinase Shunt. Impairing the metabolic (PI3 kinase-AKT pathway while promoting the MAPkinase remodeling pathway.
9. HI induces Ang II, which promotes the MAP kinase pathway and remodeling.
10. HI induces Ang II, which is the most potent stimulus for production of NAD(P)H oxidase with reactive oxygen species generation (superoxide production) and resultant vascular oxidative stress.
Hypertension
Hypertension is strongly associated with hyperuricemia. SUA levels are elevated in hypertension and are present in 25% of untreated hypertensive subjects, 50% of subjects taking diuretics, and greater than 75% of patients with malignant hypertension [22]. Potential mechanisms involved with the association of hyperuricemia and hypertension include the following: 1. Decreased renal blood flow (decreased GFR) stimulating urate reabsorption, 2. Microvascular (capillary) disease resulting in local tissue ischemia. 3. Ischemia with associated increased lactate production that blocks urate secretion in the proximal tubule and increased uric acid synthesis due to increased RNA-DNA breakdown and increased purine (adenine and guanine) metabolism, which increases uric acid and ROS through the effect of xanthine oxidase (XO). 4. Ischemia induces increased XO production and increased SUA and ROS. These associations with ischemia and XO induction may help to understand why hyperuricemia is associated with preeclampsia and congestive heart failure.
Because endothelial dysfunction, local oxidant generation, elevated circulating cytokines, and a proinflammatory state are common in patients with cardiovascular disease and hypertension there is an increased level of oxidative – redox stress within vascular tissues. Oxidative – redox stress results in impaired endothelium-dependent vasodilation with quenching of endothelial nitric oxide (eNO) and allows the endothelium to become a net producer of ROS specifically superoxide as the endothelial nitric oxide synthase (eNOS) enzyme uncouples to produce superoxide instead of eNO. This similar mechanism applies equally well to that associated with type 2 diabetes and congestive heart failure [11,19]. It is important to note that allopurinol and oxypurinol (XO inhibitors) are capable of reversing the impaired eNO production in both heart failure [23-25] and type 2 diabetes mellitus (T2DM) [26].
Lin KC et al. were able to demonstrate that blood pressure levels were predictive for cardiovascular disease incidence synergistically with serum uric acid level [27]. Two separate laboratories have demonstrated the development of systemic hypertension in a rat model of hyperuricemia developed with a uricase inhibitor (oxonic acid) after several weeks of treatment [28,29]. This hypertension was associated with increased renin and a decrease in neuronal nitric oxide synthase in the juxtaglomerular apparatus. Prevention of this hypertension was accomplished by an ACE inhibitor and to a lesser extent L-arginine. These findings indicate an interacting role of the renin- angiotensin system and the NOS enzyme. Hypertension, neural nitric oxide synthase (nNOS) and renin changes were also prevented by maintaining uric acid levels in the normal range with allopurinol or benziodarone (a uricosuric).
These above models have provided the first challenging evidence that uric acid may have a pathogenic role in the development of hypertension, vascular disease, and renal disease [11].
Obesity
Obesity has reached epidemic proportions in the past decade and represents one of the confounding factors associated with the MS and T2DM [19,30] (figure 1).
Hyperuricemia has been associated with increasing body mass index (BMI) in recent studies and are even apparent in the adolescent youth [30-33].
Figure 1 Metabolic syndrome: hyperuricemia. This image focuses on the "H" phenomenon consisting of the four major players in the MS: Hyperinsulinemia, Hypertension, Hyperlipidemia and the Lipotoxicity – Obesity toxicity triad, and Hyperglycemia. These players have frequently been referred to as the "deadly quartet" and the "H" phenomenon. It is important to note the central position of insulin resistance in this image and also hyperuricemia. Hyperuricemia is flanked by hyperhomocysteinemia to indicate its importance in the MS. Each of these players has its own important role and this image helps to portray the clustering effect and synergism to contribute to an overall increased oxidative – redox stress to the endothelium of the vasculature.
Leptin levels are elevated and associated with insulin resistance in MS and early T2DM. Bedir A et al. have recently discussed the role of leptin as possibly being a regulator of SUA concentrations in humans and even suggested that leptin might be one of the possible candidates for the missing link between obesity and hyperuricemia [34]. Furthermore, hypertriglyceridemia and free fatty acids are related to hyperuricemia independently of obesity and central body fat distribution [30,33] (table 1: (T): Triglyceride toxicity and (F): Free fatty acid toxicity).
Hyperglycemia: Impaired glucose tolerance: Type 2 Daibetes Mellitus (T2DM)
Glucotoxicity places an additional burden of redox stress on the arterial vessel wall and capillary endothelium. Hyperglycemia induces both an oxidative stress (glucose autoxidation and advanced glycosylation endproducts (AGE) – ROS oxidation products) and a reductive stress through pseudohypoxia with the accumulation of NADH and NAD(P)H in the vascular intima [19,35,36].
This redox stress consumes the natural occurring local antioxidants such as: SOD, GPX, and catalase (table 4). Once these local intimal antioxidants are depleted uric acid can undergo the paradoxical antioxidant – prooxidant switch or the urate redox shuttle [37,38]
Table 4 Antioxidants: enzymatic – nonenzymatic inactivation of free radicals.
ENZYMATIC ANTIOXIDANTS
SUPER OXIDE DISMUTASE (SOD)
Reactions catalyzed: [O2- + SOD → H2O2 + O2]
Various isoforms: ecSOD (extracellular); Mn-SOD (mitochondrial); Cu/Zn-SOD (intracellular)
CATALASE – Location: peroxisome.
Reaction catalyzed: [2 H2O2 + catalase → 2 H2O + O2]
GLUTATHIONE PEROXIDASE – Location: mitochondrion, cytosol, and systemic circulation.
Glutathione (GSH or glutamyl-cysteinyl-glycine tripeptide): the reduced -SH of GSH is oxidized to disulfide GSSG.
Glutathione peroxidase-catalyzed reation: [GSH + 2 H2O2 → GSSG + H2O + O2]
Glutathione reductase-catalyzed reaction: [GSSG → GSH] at the expense of [NADH → NAD+] and/or [NAD(P)H → NAD(P)+]
ENZYMATIC – NONENZYMATIC INACTIVATION OF FREE RADICALS. NITRIC OXIDE SYNTHASE Location: membrane.
Isoforms:
eNOS (endothelial): good
nNOS (neuronal): good
iNOS (inducible-inflammatory): bad
O2- and nitric oxide (NO) are consumed in this process with the creation of reactive nitrogen species (RNS).
O2- + NO → ONOO-(peroxynitrite) + tyrosine → nitrotyrosine.
Nitrotyrosine reflects redox stress and leaves a measurable footprint.
NO the good; O2• the bad; ONOO- the ugly *
NONENZYMATIC ANTIOXIDANTS
Vitamins (A, C, and E):
Thiols: Sulfhydryl (-SH)-containing molecules.
Albumin: Is an antioxidant because of it is a thiol-containing macromolecule.
Apoproteins: Ceruloplasmin and transferrin. Bind copper and iron in forms, which cannot participate in the Fenton reaction.
Uric acid:Early on in the atherosclerotic process in physiologic ranges: antioxidant.
PARADOX:Late in elevated range prooxidant with loss of supporting antioxidants above and in a milieu of oxidative – redox stress within the atherosclerotic intima. In MS, T2DM and advanced vulnerable atherosclerotic plaques SOD, Catalase, and GPX are depleted. The Urate Redox Shuttle.
PARADOX: antioxidants may become prooxidant in a certain milieu.
* Beckman JS and Koppenol WH [1996] Nitric oxide, superoxide, and peroxynitrite: the good, the bad, and ugly. Am J Physiol 271(5 Part 1): C1424–C1437
Homocysteine
A direct relation between homocysteine levels and SUA levels is known to occur in patients with atherosclerosis. Not only do these two track together (possibly reflecting an underlying elevated tension of redox stress) but also may be synergistic in creating an elevated tension of redox stress, especially in the rupture prone, vulnerable atherosclerotic plaque with depletion of local occurring antioxidants [39-41] (figure 1).
Atherosclerosis and Atheroscleropathy
Non-diabetic atherosclerosis and atheroscleropathy (accelerated atherosclerosis associated with MS, prediabetes, and T2DM) are each impacted with the elevation of uric acid [42,43].
Prothrombotic milieu
In MS and T2DM there is an observed increased thrombogenecity, hyperactive platelets, increased PAI-1 (resulting in impaired fibrinolysis), and increased fibrinogen in the atherosclerotic milieu associated with the dysfunctional endothelial cell. Additionally, the vulnerable atherosclerotic plaque includes increased tissue factor, which increases the potential for thrombus formation when the plaque ruptures and exposes its contents to the lumen [19,42,43].
Uric acid as one of the multiple injurious stimuli to the endothelium of the arterial vessel wall and capillary
The upper 1/3 of the normal physiologic – homeostatic range (> 4 mg/dl) and abnormal elevations (> 6.5 or 7 mg/dl in men and > 6.0 mg/dl in women) in SUA definitely should be considered as one of the multiple injurious stimuli to the arterial vessel wall and capillary, which may contribute to endothelial dysfunction and arterial – capillary vessel wall remodeling through oxidative – redox stress [2,3,19] (figure 2). There are multiple injurious stimuli to the endothelium and arterial vessel wall in the accelerated atherosclerosis associated with MS and T2DM (atheroscleropathy)(figure 2). It is important to note that redox stress occurs upstream from inflammation by activating the nuclear transcription factor: NFkappa B [39]. Over time, individually and synergistically injurious stimuli of the A-FLIGHT-U acronym (table 1) result in the morbid – mortal complications of MS, T2DM, atheroscleropathy, and non-diabetic atherosclerosis.
Figure 2 Multiple injurious stimuli to the endothelium in non-diabetic atherosclerosis and atheroscleropathy. This image portrays the anatomical relationship between the endothelium, intima, media and the adventitia. Each of these layers plays an important role in the development of accelerated atherosclerosis (atheroscleropathy) of the MS, PD, and overt T2DM. Of all the different layers the endothelium seems to play a critical and central role. It is placed at a critical location and acts as an interface with nutrients and toxic products not only at its luminal surface of musculo-elastic arteries but also at the endothelial extracellular matrix interface of the interstitium in capillary beds. The intima, sandwiched between the medial muscular layer and the endothelium, is the site of atherosclerosis, intimopathy, and the atheroscleropathy associated with MS, PD, and overt T2DM. There are multiple injurious stimuli to the endothelium including ROS and hyperuricemia. It is important to note that redox stress occurs upstream from inflammation by activating the nuclear transcription factor: NFkappa B [39]. Over time, individually and synergistically these injurious stimuli (table 1) result in the morbid – mortal vascular complications of MS, T2DM, atheroscleropathy, and non-diabetic atherosclerosis.
Each of these A-FLIGHT-U toxicities may be viewed as an independent risk marker – factor and is known to have a synergistic effect when acting in concert [19,21,39,42,43]. Additionally, low density lipoproteins such as LDL-cholesterol are capable of being modified and retained within the intima through a process of oxidative modification through free radicals, hypochlorous acid, peroxynitrite, and selected oxidative enzymes such as xanthine oxidase, myeloperoxidase and lipoxygenase (table 5) [19,44-50].
Table 5 Origin, enzymatic pathways of reactive oxygen species, and their oxidized products.
[Origin and Location]
Enzymatic Pathways: [ROS]
Potent Oxidants: [Products]
Oxidized lipids and proteins:
Mitochondrial Respiratory
Chain O2•
-OH• Oxidized lipids, proteins, nucleic acids, and autoxidation byproducts
Inflammatory Macrophage
Membranous NAD(P)H
Oxidase O2•
-OH•
H2O2 Advanced lipoxidation endproducts (ALE)
ortho o-tyrosine
meta m-tyrosine
Granular Myeloperoxidase
(MPO) Hypochlorous acid
HOCL
Tyr (Tyrosine)
NO2 3-Chlorotyrosine
di-Tyrosine
NO2-(Nitrotyrosine)
Macrophage
Nitric Oxide Synthase (iNOS)
Inducible (iNOS)
Large bursts – uncontrolled ONOO• NO2-(Nitrotyrosine)
Endothelial Cell
Nitric Oxide Synthase (NOS)
Constitutive (cNOS)
eNOS → NO
nNOS → NO
Small bursts – controlled NO + O2• → ONOO•
ONOO• NO2-(Nitrotyrosine)
NO2-(Nitrotyrosine)
eNOS-derived NO NO The GOOD * Natural-occurring, local-occurring, chain-breaking, antioxidant
Superoxide O2• The BAD * Toxic effects of ROS on proteins, lipid, nucleic acids
Peroxynitrite ONOO• The UGLY * Toxic effects of ROS on proteins, lipid, nucleic acids
Hypochlorous acid HCLO The UGLY * Toxic effects of ROS on proteins, lipid, nucleic acids
Restoration of eNO
Via the eNOS reaction Antioxidant
Antioxidant Prevention of the toxic effects of ROS
* Beckman JS and Koppenol WH [1996] Nitric oxide, superoxide, and peroxynitrite: the good, the bad, and ugly. Am J Physiol 271(5 Part 1): C1424–C1437
The simple concept that SUA in patients with CVD, MS, T2DM, hypertension, and renal disease may reflect a compensatory mechanism to counter oxidative stress is intriguing. However, this does not explain why higher SUA levels in patients with these diseases are generally associated with worse outcomes [11].
An antioxidant – prooxidant urate redox shuttle
Antioxidants may become prooxidants in certain situations [51-55]. Therefore we propose the existence of an antioxidant – prooxidant redox shuttle in the vascular milieu of the atherosclerotic macrovessel intima and the local sub endothelial capillary interstitium of the microvessel [38,51,52] (figure 3).
Figure 3 Antioxidant – prooxidant urate redox shuttle. The antioxidant – prooxidant urate redox shuttle is an important concept to understand regarding accelerated atherosclerosis. This shuttle is important in understanding the role of how the antioxidant uric acid becomes prooxidant in this environmental milieu, which results in its damaging role to the endothelium and arterial vessel wall remodeling with an elevated tension of oxidative – redox stress (ROS), accelerated atherosclerosis and arterial vessel wall remodeling.
SUA in the early stages of the atherosclerotic process is known to act as an antioxidant and may be one of the strongest determinates of plasma antioxidative capacity [53].
However, later in the atherosclerotic process when SUA levels are known to be elevated (in the upper 1/3 of the normal range >4 mg/dl and outside of the normal range >6 mg/dl in females and 6.5–7 mg/dl in males) this previously antioxidant (SUA) paradoxically becomes prooxidant. This antioxidant – prooxidant urate redox shuttle seems to rely heavily on its surrounding environment such as timing (early or late in the disease process), location of the tissue and substrate, acidity (acidic – basic – or neutral ph), the surrounding oxidant milieu, depletion of other local antioxidants, the supply and duration of oxidant substrate and its oxidant enzyme. In the accelerated atherosclerotic – vulnerable plaque the intima has been shown to be acidic [54], depleted of local antioxidants with an underlying increase in oxidant stress and ROS (table 1) (table 5) and associated with uncoupling of the eNOS enzyme and a decrease in the locally produced naturally occurring antioxidant: eNO and endothelial dysfunction. This process is also occurring within the microvascular bed at the level of the capillary within various affected hypertensive and diabetic end organs [19,51,52] (figure 4).
Figure 4 Uncoupling of the eNOS reaction. It is important to understand the role of endothelial dysfunction in accelerated atherosclerosis and even more important to understand the role of eNOS enzyme uncoupling and how it relates to MS, PD, T2DM, and non-diabetic atherosclerosis. Oxygen reacts with the eNOS enzyme in which the tetrahydrobiopertin (BH4) cofactor has coupled nicotinamide dinucleotide phosphate reduced (NAD(P)H) emzyme with L-arginine to be converted to nitric oxide (NO) and L-citrulline. When uncoupling occurs the NAD(P)H enzyme reacts with O2 and the endothelial cell becomes a net producer of superoxide (O2•) instead of the protective endothelial NO. This figure demonstrates the additional redox stress placed upon the arterial vessel wall and capillaries in patients with MS, PD, and overt T2DM.
Nitric oxide and vitamin C have each been shown to inhibit the prooxidant actions of uric acid during copper-mediated LDL-C oxidation [38,55].
The ANAi acronym
We have devised an acronym, to better understand the increase in SUA synthesis within the accelerated atherosclerotic plaque termed: ANAi. A – apoptosis, N – necrosis, A – acidic atherosclerotic plaque, angiogenesis (both induced by excessive redox stress), i – inflammation, intraplaque hemorrhage increasing red blood cells – iron and copper transition metal ions within the plaque.
This acronym describes the excess production of purines: (A) adenine and (G) guanine base pairs from RNA and DNA breakdown due to apoptosis and necrosis of vascular cells in the vulnerable – accelerated atherosclerotic plaques; allowing SUA to undergo the antioxidant – prooxidant urate redox shuttle (figure 3).
Reactions involving transitional metal ions such as copper and iron are important to the oxidative stress within atherosclerotic plaques. Reactions such as the Fenton and Haber- Weiss reactions and similar reactions with copper lead to an elevated tension of oxidative – redox stress.
FENTON REACTION:
Fe2+ + H2O2 → Fe3+ + OH• + OH-
Fe3+ + H2O2 → Fe2+ + OOH• + H+
HABER – WEISS REACTION:
H2O2 + O2- → O2 + OH- + OH
H2O2 + OH- → H2O + O2- + H+
The hydroxyl radicals can then proceed to undergo further reactions with the production of ROS through addition reactions, hydrogen abstraction, electron transfer, and radical interactions. Additionally, copper (Cu3+ - Cu2+ - Cu1+) metal ions can undergo similar reactions with formation of lipid peroxides and ROS. This makes the leakage of iron and copper from ruptured vasa vasorum very important in accelerating oxidative damage to the vulnerable accelerated atherosclerotic plaques, as well as, providing a milieu to induce the SUA antioxidant – prooxidant switch within these plaques [42].
These same accelerated – vulnerable plaques now have the increased substrate of SUA through apoptosis and necrosis of vascular cells (endothelial and vascular smooth muscle cells) and the inflammatory cells (primarily the macrophage and to a lesser extent the lymphocyte).
Endothelial function and endothelial nitric oxide (eNO)
The endothelium is an elegant symphony responsible for the synthesis and secretion of several biologically active molecules. It is responsible for regulation of vascular tone, inflammation, lipid metabolism, vessel growth (angiogenesis – arteriogenesis), arterial vessel wall – capillary sub endothelial matrix remodeling, and modulation of coagulation and fibrinolysis. One particular enzyme system seems to act as the maestro: The endothelial nitric oxide synthase (eNOS) enzyme and its omnipotent product: endothelial nitric oxide (eNO) (figure 2).
The endothelial nitric oxide synthase (eNOS) enzyme reaction is of utmost importance to the normal functioning of the endothelial cell and the intimal interstitium. When this enzyme system uncouples the endothelium becomes a net producer of superoxide and ROS instead of the net production of the protective antioxidant properties of eNO (table 6) (figure 4).
Table 6 The positive effects of eNOS and eNO
• Promotes vasodilatation of vascular smooth muscle.
• Counteracts smooth muscle cell proliferation.
• Decreases platelet adhesiveness.
• Decreases adhesiveness of the endothelial layer to monocytic WBCs (the "teflon effect").
• Anti-inflammatory effect.
• Anti-oxidant effect. It scavenges reactive oxygen species locally, and acts as a chain-breaking antioxidant to scavenge ROS.
• Anti-fibrotic effect. When NO is normal or elevated, MMPs are quiescent; conversely if NO is low, MMPs are elevated and active.
MMPs are redox sensitive.
• No inhibits prooxidant actions of uric acid during copper-mediated LDL oxidation.
• NO has diverse anti-atherosclerotic actions on the arterial vessel wall including antioxidant effects by direct scavenging of ROS – RNS acting as chain-breaking antioxidants and it also has anti-inflammatory effects.
There are multiple causes for endothelial uncoupling in addition to hyperuricemia and the antioxidant – prooxidant urate redox shuttle: A-FLIGHT -U toxicities, ROS, T2DM, prediabetes, T1DM, insulin resistance, MS, renin angiotensin aldosterone activation, angiotensin II, hypertension, endothelin, dyslipidemia – hyperlipidemia, homocysteine, and asymmetrical dimethyl arginine (ADMA) [19,39,43].
Xanthine oxidase – oxioreductase (XO) has been shown to localize immunohistochemically within atherosclerotic plaques allowing the endothelial cell to be equipped with the proper machinery to undergo active purine metabolism at the plasma membrane surface, as well as, within the cytoplasm and is therefore capable of overproducing uric acid while at the same time generating excessive and detrimental ROS [56] (figure 3,4). To summarize this section:
The healthy endothelium is a net producer of endothelial nitric oxide (eNO).
The activated, dysfunctional endothelium is a net producer of superoxide (O2-) associated with MS, T2DM, and atheroscleropathy [43].
Uric acid and inflammation
Uric acid and highly sensitive C reactive protein (hsCRP) each now share a respected inclusion as two of the novel risk markers – risk factors associated with the metabolic syndrome. It is not surprising that these two markers of risk track together within the MS. If there is increased apoptosis and necrosis of vascular cells and inflammatory cells in accelerated – vulnerable atherosclerotic plaques as noted in the above section then one would expect to see an increase in the metabolic breakdown products of RNA and DNA with arginine and guanine to its end product of uric acid. SUA elevation may indeed be a sensitive marker for underlying vascular inflammation and remodeling within the arterial vessel wall and capillary interstitium.
Is it possible that SUA levels could be as similarly predictive as hsCRP since it is a sensitive marker for underlying inflammation and remodeling within the arterial vessel wall and the myocardium [57].
Should the measurement of SUA be part of the national cholesterol educational program adult treatment panel III and future IV (NCEP ATPIII or the future NCEP ATPIV) clinical guidelines (especially in certain ethnic groups such as females and in the African Americans)?
Uric acid is known to induce the nuclear transcription factor (NF-kappaB) and monocyte chemoattractant protein-1 (MCP-1) [58]. Regarding TNF alpha it has been shown that SUA levels significantly correlate with TNF alpha concentrations in congestive heart failure and as a result Olexa P et al. conclude that SUA may reflect the severity of systolic dysfunction and the activation of an inflammatory reaction in patients with congestive heart failure [59]. Additionally, uric acid also stimulates human mononuclear cells to produce interleukin-1 beta, IL-6, and TNF alpha [11].
Tamakoshi K et al. have shown a statistically significant positive correlation between CRP and body mass index (BMI), total cholesterol, triglycerides, LDL-C, fasting glucose, fasting insulin, uric acid, systolic blood pressure, and diastolic blood pressure and a significant negative correlation of CRP with HDL-C in a study of 3692 Japanese men aged 34–69 years of age. They conclude that there are a variety of components of the MS, which are associated with elevated CRP levels in a systemic low-grade inflammatory state [60].
CRP and IL-6 are important confounders in the relationship between SUA and overall mortality in elderly persons, thus when evaluating this association the potential confounding effect of underlying inflammation and other risk factors should be considered [61].
Uric acid and chronic renal disease
Hyperuricemia can be the consequence of increased uric acid production or decreased excretion. Any cause for decreased glomerular filtration, tubular excretion or increased reabsorption would result in an elevated SUA. Increased SUA has been found to predict the development of renal insufficiency in individuals with normal renal function [11]. In T2DM hyperuricemia seems to be associated with MS and with early onset or increased progression to overt nephropathy, whereas hypouricemia was associated with hyperfiltration, and a later onset or decreased progression to overt nephropathy [62]. An elevated SUA could be advantageous information for the clinician when examining the global picture of T2DM in order to detect those patients who might gain from more aggressive global risk reduction to delay or prevent the transition to overt nephropathy. Elevated SUA contributes to endothelial dysfunction and increased oxidative stress within the glomerulus and the tubulo-interstitium with associated increased remodeling fibrosis of the kidney and as noted earlier in this discussion to be pro-atherosclerotic and proinflammatory. This would have a direct effect on the vascular supply affecting macrovessels, particularly the afferent arterioles. The glomeruli would be affected also through the effect of uric acid on the glomerular endothelium with endothelial dysfunction due to oxidative – redox stress and result in glomerular remodeling. SUA's effect on hypertension would have an additional affect on the glomeruli and the tubulo-interstitium with remodeling changes and progressive deterioration of renal function. Increased ischemia – ischemia reperfusion would activate the xanthine oxidase mechanism and contribute to an increased production of ROS through H2O2 generation and oxidative stress within the renal architecture with resultant increased remodeling. Hyperuricemia could increase the potential for urate crystal formation and in addition to elevated levels of soluble uric acid could induce inflammatory and remodeling changes within the medullary tubulo-interstitium.
A recent publication by Hsu SP et al. revealed a J-shaped curve association with SUA levels and all-cause mortality in hemodialysis patients [63]. They were able to demonstrate that decreased serum albumin, underlying diabetic nephropathy, and those in the lowest and highest quintiles of SUA had higher all-cause mortality. It is interesting to note that almost all of the large trials with SUA and cardiovascular events have demonstrated this same J shaped curve regarding all-cause mortality with the nadir of risk occurring in the second quartile [11].
Johnson RJ et al. have speculated that the increased risk for the lowest quartile reflects a decreased antioxidant activity, while the increased risk at higher levels reflects the role of uric acid in inducing vascular disease and hypertension through the mechanism of the previously discussed antioxidant prooxidant urate redox shuttle. This would suggest that treatment with xanthine oxidase inhibitors (allopurinol) should strive to bring levels to the 3–4 mg/dl range and not go lower [11].
Nutritional support for hyperuricemia
While it is not within the scope of this review to discuss this important topic with an in- depth examination, it is important to discuss some prevailing concepts and provide some clinical nutritional guidelines for hyperuricemia (table 8).
Table 8 Nutritional guidelines for hyperuricemia
Obesity Caloric restriction to induce weight loss in order to decrease insulin resistance of the MS.
Exercise to aid in weight reduction by increased energy expenditure, also to increase eNOS and eNO, as well as, increase HDL-C with its antioxidant – anti-inflammatory effects. Both will result in REDOX STRESS REDUCTION
Alcohol Avoidance and or moderation. Especially beer with the increased purines from hops and barley. Also improve the liver antioxidant potential.
REDOX STRESS REDUCTION
Low purine diet (moderation) Moderation in meats and seafood's, especially shrimp and barbeque ribs (all you can eat specials).
Vegetables and fruits higher in purine should not be completely avoided as they provide fiber and naturally occurring antioxidants.
Lists should be provided to demonstrate the vegetables and fruits that are higher in purines to allow patients healthier choices
REDOX STRESS REDUCTION
Fiber Emphasize the importance of fiber in the diet as fiber helps to bind excess purines in the gastrointestinal track.
REDOX STRESS REDUCTION
Moderation is the key element in any diet approaching hyperuricemia. The nutritional "gold standard" for the treatment of hyperuricemia has been "the low purine diet". This traditional diet has recently come into question as it may limit the intake of high purine vegetables and fruits. Vegetables and fruits are important for the fiber they supply in addition to naturally occurring antioxidants. Recently, of greater importance is controlling obesity through generalized caloric restriction and increased exercise to combat the overnutrition and underexercise of our modern-day society, as well as, controlling the consumption of alcohol [64].
Nutritional support by the nutritionist and the diabetic educator (an integral part of the health care team) is of utmost importance when dealing with the metabolic syndrome, T2DM, and the cardiovascular atherosclerotic afflicted patients in order to obtain global risk reduction, because we are what we eat.
Conclusion
From a clinical standpoint, hyperuricemia should alert the clinician to an overall increased risk of cardiovascular disease and especially those patients with an increased risk of cardiovascular events. Hyperuricemia should therefore be a "red flag" to the clinician to utilize a team effort in achieving an overall approach to obtain a global risk reduction program through the use of the RAAS acronym (table 7).
Table 7 The RAAS Acronym: GLOBAL RISK REDUCTION
R Reductase inhibitors (HMG-CoA). Decreasing modified LDL-cholesterol, i.e., oxidized, acetylated LDL-cholesterol. Decreasing triglycerides and increasing HDL-cholesterol.
Improving endothelial cell dysfunction. Restoring the abnormal Lipoprotein fractions.
Thus, decreasing the redox and oxidative stress to the arterial vessel wall and myocardium.
Redox stress reduction
A AngII inhibition or receptor blockade:
ACEi-prils. ARBs-sartans. Both inhibiting the effect of angiotensin-II locally as well as systemically. Affecting hemodynamic stress through their antihypertensive effect as well as the deleterious effects of angiotensin II on cells at the local level – injurious stimuli -decreasing the stimulus for O2• production. Decreasing the A-FLIGHT toxicities. The positive effects on microalbuminuia and delaying the progression to end stage renal disease. Plus the direct-indirect antioxidant effect within the arterial vessel wall and capillary. Antioxidant effects.
Aspirin antiplatelet, anti-inflammatory effect on the diabetic hyperactive platelet.
Adrenergic (non-selective blockade) in addition to its blockade of prorenin → renin conversion.
Amlodipine – Felodipine with calcium channel blocking antihypertensive effect, in addition to their direct antioxidant effects.
Redox stress reduction
A Aggressive control of diabetes to HbA1c of less than 7. This usually requires combination therapy with the use of insulin secretagogues, insulin sensitizers (PPAR-gamma agonists), biguanides, alpha-glucosidase inhibitors, and ultimately exogenous insulin.
Decreasing modified LDL cholesterol, i.e., glycated-glycoxidated LDL cholesterol. Improving endothelial cell dysfunction. Also decreasing glucotoxicity and the oxidative-redox stress to the intima and pancreatic islet.
Aggressive control of blood pressure, which usually requires combination therapy, including thiazide diuretics to attain JNC 7 guidelines.
Aggressive control of homocysteine with folic acid with its associated additional positive effect on re-coupling the eNOS enzyme reaction by restoring the activity of the BH4 cofactor to run the eNOS reaction via a folate shuttle mechanism and once again produce eNO.
Aggressive control of uric acid levels with xanthine oxidase inhibitors (allopurinol and oxypurinol) should be strongly considered in view of the prevailing literature in order to achieve more complete: Global Risk Reduction
Redox stress reduction
S Statins. Improving plaque stability (pleiotropic effects) independent of cholesterol lowering. Improving endothelial cell dysfunction. Moreover, the direct/indirect antioxidant anti-inflammatory effects within the islet and the arterial vessel wall promoting stabilization of the unstable, vulnerable islet and the arterial vessel wall.
Style. Lifestyle modification (weight loss, exercise, and change eating habits).
Stop Smoking.
Redox stress reduction
SUA may or may not be an independent risk factor especially since its linkage to other risk factors is so strong, however there is not much controversy regarding its role as a marker of risk, or that it is clinically significant and relevant.
Regarding the MS and epidemiologic evaluations: A multivariate model could well eliminate hyperuricemia as an independent risk factor even if it were contributing to the overall phenotypic risk of the syndrome. Additionally, we must remember that it was Reaven that called for the inclusion of hyperuricemia to Syndrome X we now call MS – insulin resistance syndrome -IRS in 1993 [18].
A quote by Johnson RJ and Tuttle KR is appropriate for the concluding remarks:
"The bottom line is that measuring uric acid is a useful test for the clinician, as it carries important prognostic information. An elevation of uric acid is associated with an increased risk for cardiovascular disease and mortality, especially in women" [64].
Abbreviations
Serum uric acid (SUA); cardiovascular disease (CVD); coronary heart disease (CHD); endothelial nitric oxide (eNO); endothelial nitric oxide synthase (eNOS); endothelial nitric oxide (eNO); reactive oxygen species (ROS); metabolic syndrome (MS); insulin resistance syndrome (IRS); nicotine adenine dinucleotide phosphate oxidase reduced NAD(P)H; superoxide (O2-•); xanthine oxidase (XO); type 2 diabetes mellitus (T2DM); angiotensin converting enzyme (ACE); renin-angiotensin-aldosterone system (RAAS); advanced glycosylation endproducts (AGE); superoxide dismutase (SOD); glutathione (GPX); plasminogen activator inhibitor (PAI-1); angiotensin II (AngII); low density lipoprotein cholesterol (LDL-C); asymmetrical dimethyl arginine (ADMA); highly sensitive C reactive protein (hsCRP); national cholesterol educational program adult treatment panel III (NCEP ATPIII); nuclear transcription factor (NF-kappaB); monocyte chemoattractant protein-1 (MCP-1); tumor necrosis factor alpha (TNF alpha); interleukin one beta (IL-1beta); interleukin 6 (IL-6); body mass index (BMI); high density lipoprotein (HDL); hydrogen peroxide (H2O2); free fatty acids (FFA).
Competing interests
The authors declare that they have no competing interests.
Author's contribtions
MRH and SCT envisioned, wrote and edited jointly.
Acknowledgements
A part of this study was supported by NIH grants HL-71010 and HL-74185.
The authors would like to acknowledge Dr. Charles Kilo and Dr. Joe Williamson of Washington University School of Medicine for their devotion to teaching medical students, residents, fellows and patients with diabetes in the pursuit of knowledge regarding diabetes. Their research and 31 years of providing CME exposure through a nationally recognized annual CME Diabetes Symposium have been an inspiration to all interested in delivering the best diabetic care possible to their patients.
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| 15507132 | PMC529248 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Oct 19; 1:10 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-10 | oa_comm |
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Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-111550713310.1186/1743-7075-1-11ResearchManagement of multifactorial idiopathic epilepsy in EL mice with caloric restriction and the ketogenic diet: role of glucose and ketone bodies Mantis John G [email protected] Nicole A [email protected] Mariana T [email protected] Richard [email protected] Thomas N [email protected] Biology Department, Boston College, Chestnut Hill, MA, USA2004 19 10 2004 1 11 11 29 7 2004 19 10 2004 Copyright © 2004 Mantis et al; licensee BioMed Central Ltd.2004Mantis et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The high fat, low carbohydrate ketogenic diet (KD) was developed as an alternative to fasting for seizure management. While the mechanisms by which fasting and the KD inhibit seizures remain speculative, alterations in brain energy metabolism are likely involved. We previously showed that caloric restriction (CR) inhibits seizure susceptibility by reducing blood glucose in the epileptic EL mouse, a natural model for human multifactorial idiopathic epilepsy. In this study, we compared the antiepileptic and anticonvulsant efficacy of the KD with that of CR in adult EL mice with active epilepsy. EL mice that experienced at least 15 recurrent complex partial seizures were fed either a standard diet unrestricted (SD-UR) or restricted (SD-R), and either a KD unrestricted (KD-UR) or restricted (KD-R). All mice were fasted for 14 hrs prior to diet initiation. A new experimental design was used where each mouse in the diet-restricted groups served as its own control to achieve a 20–23% body weight reduction. Seizure susceptibility, body weights, and the levels of plasma glucose and β-hydroxybutyrate were measured once/week over a nine-week treatment period.
Results
Body weights and blood glucose levels remained high over the testing period in the SD-UR and the KD-UR groups, but were significantly (p < 0.001) reduced in the SD-R and KD-R groups. Plasma β-hydroxybutyrate levels were significantly (p < 0.001) increased in the SD-R and KD-R groups compared to their respective UR groups. Seizure susceptibility remained high in both UR-fed groups throughout the study, but was significantly reduced after three weeks in both R-fed groups.
Conclusions
The results indicate that seizure susceptibility in EL mice is dependent on plasma glucose levels and that seizure control is more associated with the amount than with the origin of dietary calories. Also, CR underlies the antiepileptic and anticonvulsant action of the KD in EL mice. A transition from glucose to ketone bodies for energy is predicted to manage EL epileptic seizures through multiple integrated changes of inhibitory and excitatory neural systems.
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Background
Epilepsy is a neurological disorder involving recurrent abnormal discharges of neurons that produce epileptic seizures [1]. With the exception of stroke, epilepsy is one of the most prevalent human neurological afflictions affecting about 1% of the US population [2,3]. Many persons with epilepsy manifest partial or generalized seizures without symptoms of brain abnormality, i.e., idiopathic epilepsy [1,4,5]. In contrast to idiopathic epilepsy, symptomatic or acquired epilepsy often accompanies brain trauma, injury, or neurostructural defects. While some idiopathic epilepsies are inherited as simple Mendelian traits, most are multifactorial where more than one gene together with environmental factors contribute to the disease phenotype [6,7].
Epilepsy animal models are used widely to test the influence of environmental and genetic factors on seizure mechanisms. The epileptic EL mouse is a natural model for human multifactorial idiopathic epilepsy and was first discovered in 1954 in an outbred DDY mouse colony [6,8-10]. EL mice experience complex partial seizures with secondary generalization similar to those seen in humans [6,10]. Seizures in EL mice commence with the onset of puberty (50–60 days), originate in or near the parietal lobe, and then spread to the hippocampus and to other brain regions [6,11-13]. The seizures are accompanied by electroencephalographic abnormalities, vocalization, incontinence, loss of postural equilibrium, excessive salivation, and head, limb, and chewing automatisms [10,12,14-17]. A reactive gliosis accompanies seizure progression in adult EL mice involving both astrocytes and microglia [18,19]. Epileptic seizures in EL mice also model Gowers' dictum, where each seizure increases the likelihood of recurrent seizures [6]. Seizure susceptibility can be managed with phenytoin and phenobarbital as well as with diet therapies to include the ketogenic diet and caloric restriction [20-22]. Gene-environmental interactions play a significant role in the determination of seizure frequency and onset in EL mice as with multifactorial human idiopathic epilepsies [6,9,23].
Despite intensive antiepileptic drug (AED) research and development, seizures remain unmanageable or refractory in many persons with epilepsy [24-26]. As an alternative to AEDs diet therapies can be effective in the management or control of epilepsy. Fasting has long been recognized as an effective antiepileptic therapy for a broad range of seizure disorders [27-29]. Since fasting produces ketonemia, it was originally thought that ketone bodies (β-hydroxybutyrate and acetoacetate) might underlie the antiepileptic effects of fasting [27,30]. Consequently, high fat, low protein, low carbohydrate KDs were developed to mimic the physiological effects of fasting [25,27,31,32]. Although the KD significantly elevates circulating ketone body levels, later studies showed that ketone bodies alone were unable to account for the antiepileptic and anticonvulsant effects of the KD in humans or in animal epilepsy models [20,31,33-38]. Since the KD manages epilepsy best when administered in restricted amounts and since fasting lowers blood glucose levels, Seyfried and co-workers suggested that caloric restriction might contribute to the antiepileptic and anticonvulsant effects of the KD [21,29,38].
CR is a natural dietary therapy that improves health, extends longevity, and reduces the effects of neuroinflammatory diseases in rodents and humans [21,29,39,40]. CR is produced from a total dietary restriction and differs from acute fasting or starvation in that CR reduces total caloric energy intake without causing anorexia or deficiencies of any specific nutrients [38]. In other words, CR extends the health benefits of fasting while avoiding starvation. Besides improving health, CR has both antiepileptic and anticonvulsant effects in EL mice and in other animal epilepsy models [20,21,41]. A reduction in blood glucose with a corresponding elevation in blood ketone bodies is thought to underlie the antiepileptic and anticonvulsant effects of CR [21,29,38].
Glucose uptake and metabolism increases more during epileptic seizures than during most other brain activities [42-44]. Blood glucose also positively correlates with flurothyl-induced seizures in rats and high glucose may exacerbate human seizure disorders [45]. Neuronal excitability and epileptic seizures are directly related to rapid glucose utilization and glycolysis [42,43,45-51]. It is not yet clear, however, to what extent enhanced glycolysis is related to the cause or effects of seizure activity [29]. Nevertheless, a transition in brain energy metabolism from glucose utilization to ketone body utilization reduces neural excitation and increases neural inhibition through multiple integrated systems [29,38]. Based on these and other observations [50,52-54], we proposed that many epilepsies, regardless of etiology, might ultimately involve altered brain energy homeostasis [29].
In this study, we compared the antiepileptic and anticonvulsant effects of both the KD and CR in adult EL mice that experienced at least 15 recurrent complex partial seizures. The results show that seizure control in EL mice is more associated with the amount than with the origin of dietary calories, and that CR underlies the antiepileptic and anticonvulsant action of the KD in EL mice. A preliminary report of these findings was recently presented [55].
Results
Diet composition and tolerance
The composition of each diet is shown in Table 1 and in the Methods. No adverse effects of the diets were observed in either R-fed mouse group. Despite the 20–23% body weight reduction, mice in both R-fed groups appeared healthy and were more active than the mice in the UR-fed groups as assessed by ambulatory and grooming behavior. With the exception of oily fur, the KD-fed mice appeared active and healthy throughout the study as previously found [20]. No signs of vitamin or mineral deficiency were observed in the R-fed mice according to standard criteria for mice [56]. These findings are consistent with the well-recognized health benefits of mild to moderate caloric restriction in rodents [57], and support our previous findings that both the KD and a moderate CR are well tolerated by EL mice [20,21].
Table 1 Composition (%) of the Standard Diet and the Ketogenic Diet 1
Components Standard Diet (SD) Ketogenic Diet (KD)
Carbohydrate 62 0
Fat 6 75
Protein 27 14
Fiber 5 12
Energy (Kcal/gr) 4.4 7.8
1 According to manufacturer's specifications (see Methods).
Influence of caloric restriction on body weight
All mice were matched for age (approximately 210 days) and body weight (approximately 31.0 ± 1.5 g) before the start of the dietary treatment (Fig. 1). All mice lost approximately 7–9% of their body weight during the 14 hr fast. Body weight remained relatively stable over the nine-week treatment period in both UR-fed mouse groups (Fig. 1). The 20–23% body weight reduction was achieved in the R-fed groups after about two weeks of gradual food restriction. However, more difficulty was encountered initially in maintaining a stable body weight reduction for the KD-R group than for the SD-R group. This difficulty may result from the high caloric content of the KD that produces greater body weight changes per calorie adjustment than the SD. We also estimated that the degree of CR necessary to maintain the 20–23% body weight reduction was about 38–45% for the SD and about 45–52% for the KD.
Figure 1 Influence of diet on body weight in adult EL mice fed the SD (A) or the KD (B). Squares represent the pre-trial period when all mice were fed the SD-UR. Circles and triangles represent the UR-fed and R-fed groups, respectively. Values are expressed as the mean ± SEM (n = 6 mice per group). Arrow indicates initiation of CR.
Influence of diets on seizure susceptibility in adult EL mice
All mice had at least 15 recurrent seizures before the start of dietary treatment (arrow, Fig. 1). The seizures occurred occasionally during routine cage changing prior to the pre-trial period and regularly from handling during the pre-trial test period. Seizure susceptibility was analyzed in all mouse groups after the R-fed mice achieved a stable body weight reduction, i.e., week five of treatment (Figs. 1 and 2). Seizure susceptibility was high for both UR-fed groups throughout the study. In both R-fed groups, seizure susceptibility decreased from 1.0 to about 0.3 after two weeks and remained significantly lower than that of the UR-fed control groups from treatment weeks 5–12 (Fig. 2). Only a single mouse in the KD-R group had a break-through seizure on week 8. Taken together, our findings show that seizure management in EL mice is more associated with the amount than with the origin of dietary calories.
Figure 2 Influence of diet on seizure susceptibility in adult EL mice. Asterisks indicate that seizure susceptibility was significantly lower (p < 0.001) in the R-fed groups than in their respective UR-fed groups. Values were pooled from treatment weeks 5–12 (see Fig. 1) and are expressed as the mean ± SEM (n = 6 mice per group).
Influence of diets on plasma glucose and β-hydroxybutyrate levels
Plasma glucose levels were analyzed in all mouse groups after the R-fed mice achieved a stable body weight reduction (Figs. 1 and 3). Glucose levels remained high for both UR-fed groups throughout the study and were stable over treatment weeks 5–12. However, plasma glucose levels were somewhat lower (about 8 mM) in both UR-fed groups between treatment weeks 3–5 compared to the pre-trial glucose levels (about 10 mM). This reduction might result from a combination of repetitive handling, seizures, blood collection, and the initial fast. In both R-fed mouse groups, the plasma glucose levels decreased from about 10 mM to about 5.0 mM after three weeks and remained significantly lower than those of their respective UR-fed control groups (Fig. 3).
Figure 3 Influence of diet on plasma glucose levels in adult EL mice. Asterisks indicate that the plasma glucose levels were significantly lower (p < 0.001) in the R-fed groups than in their respective UR-fed groups. Other conditions are as in Figures 1 and 2.
Plasma β-hydroxybutyrate levels were also analyzed in all mouse groups after the R-fed mice achieved a stable body weight reduction (Figs. 1 and 4). These levels remained low in the SD-UR group throughout the study and were stable for treatment weeks 5–12. β-hydroxybutyrate levels were significantly higher in the R-fed groups than in their respective UR-fed control groups (Fig. 4). These levels were also significantly higher in the KD-UR group than in the SD-UR group. The levels increased from about 0.4 mM to about 1.7 mM in the SD-R group and to about 3.0 mM in the KD-R group. These findings demonstrate that circulating β-hydroxybutyrate levels were inversely related to circulating glucose levels and that elevated β-hydroxybutyrate levels alone are not associated with seizure susceptibility.
Figure 4 Influence of diet on plasma β-hydroxybutyrate levels in adult EL mice. Asterisks indicate that the plasma β-hydroxybutyrate levels were significantly higher (p < 0.001) in the R-fed groups than in their respective UR-fed groups. The cross indicates that the plasma β-hydroxybutyrate levels were significantly higher (p < 0.001) in the KD-UR group than in the SD-UR group. Other conditions are as in Figures 1 and 2.
Statistical relationships among variables
The relationship between body weight, food intake, plasma glucose levels, plasma β-hydroxybutyrate levels, and seizure susceptibility was determined using Pearson bivariate correlation analysis (Table 2). All variables were significantly (p < 0.01) correlated with each other. Positive correlations were found among body weight, food intake, glucose, and seizure susceptibility. On the other hand, β-hydroxybutyrate was negatively correlated with all variables. The correlations among glucose, β-hydroxybutyrate, and seizure susceptibility were also apparent from the data in Figures 2,3,4. Plasma glucose was significantly (p < 0.001) associated with seizure susceptibility in the EL mouse, as determined by chi-square analysis (Fig. 5). These results support our previous findings that glucose levels are predictive of seizure susceptibility in adult EL mice [21,29].
Table 2 Pearson bivariate correlation of body weight, food intake, plasma glucose levels, plasma b-hydroxybutyrate levels, and seizure susceptibility in adult EL mice 1
Parameter Body weight (g) Food Intake (Kcal) Glucose (mM) Ketones (mM) Seizure Susceptibility
Body weight (g) 1.000
Food Intake (Kcal) 0.488* 1.000
Glucose (mM) 0.509* 0.382* 1.000
Ketones (mM) -0.379* -0.379* -0.429* 1.000
Seizure Susceptibility 0.512* 0.464* 0.616* -0.510* 1.000
1 Data were obtained from all four dietary groups over the treatment weeks 3–12 for a total number of 210 seizure and glucose measurements (see figure 1).
* All correlations were significant at the 0.01 level (2-tailed).
Figure 5 Association of plasma glucose and seizure susceptibility in adult EL mice. Data were obtained from all four dietary groups over treatment weeks 3–12 for a total of 234 seizure and glucose measurements. Seizure frequency in the three plasma glucose groups (< 6.5 mmol, 6.5–8.5 mmol, and > 8.5 mmol/L) was 8/234, 44/234, and 70/234, respectively. The association between glucose and seizure susceptibility was highly significant as determined by Chi-square analysis (p < 0.001).
Binary logistic regression was also used to determine the relationship between seizure susceptibility, plasma glucose, and plasma β-hydroxybutyrate levels when mice were fed either the SD and/or the KD. The data indicate that regardless of diet, glucose could predict seizure susceptibility with an approximate 75 to 78 % accuracy (Table 3). Although β-hydroxybutyrate could also predict seizure susceptibility, we previously showed that β-hydroxybutyrate levels were dependent on and were inversely related to plasma glucose levels [21].
Table 3 Binary logistic regression analysis of the maximum likelihood estimates between plasma glucose, and seizure susceptibility in adult EL mice fed either the SD or KD1
Dietary groups Parameter Df2 B3 SEM4 Wald x2 5 p value6
SD Glucose 1 0.774 0.139 30.962 0.01
Constant 1 -0.584 1.013 29.292 0.01
KD Glucose 1 0.787 0.157 25.033 0.01
Constant 1 -5.801 1.180 24.177 0.01
Both Diets Glucose 1 0.752 0.102 54.682 0.01
Constant 1 -5.507 0.759 52.625 0.01
1 Data were obtained from all four dietary groups over the treatment weeks 3–12 for a total number of 210 individual measurements of plasma glucose and seizure susceptibility.
2 Df, degrees of freedom.
3 B, Estimate of the association between glucose and seizure susceptibility.
4 The estimated error of the mathematical weighting, indicating the precision of the estimated coefficient.
5 The Wald test statistic was computed from the data compared by using x2 distribution with 1 degree of freedom. The test statistic is used to determine the p value.
6 The probability of Type I error.
Discussion
We found that restriction of either a high carbohydrate low fat standard diet or a high fat low carbohydrate KD was equally effective in reducing seizure susceptibility in adult EL mice with active epilepsy. Moreover, seizure susceptibility remained similarly high in these mice when either diet was fed ad libitum or unrestricted. These findings indicate that the KD, when fed unrestricted, is unable to reduce seizure susceptibility in adult EL mice. Although the KD delays epileptogenesis in young seizure naïve EL mice when fed ad libitum, the effect is transient [20]. These findings are interesting since previous observations with children suggest that the antiepileptic and anticonvulsant effects of the KD are best when the diet is administered in restricted amounts [25,31]. Indeed, seizure protection is often less in children that gain weight than in those who maintain or reduce body weight on the KD (Freeman, personal communication). Previous studies also indicate that restriction of high carbohydrate diets elevate seizure threshold [58]. Our findings in EL mice support these observations and suggest that CR may be necessary for the antiepileptic and anticonvulsant effects of the KD.
We previously showed that mild to moderate CR delayed epileptogenesis and reduced seizure susceptibility in seizure naïve juvenile and adult EL mice by reducing blood glucose and elevating ketone bodies [21]. Although our data show that circulating β-hydroxybutyrate levels are inversely related to circulating glucose levels, elevated ketone body levels are not directly associated with reduced seizure susceptibility in EL mice. This conclusion derives from the finding that seizure susceptibility is high in the KD-UR mice despite elevated β-hydroxybutyrate levels and from finding that seizure protection was similar in the SD-R and KD-R groups despite significantly higher β-hydroxybutyrate levels in the KD-R than in the SD-R group. These results are consistent with previous studies in EL mice and in non-genetic seizure models that elevated ketone bodies alone are unable to account for the antiepileptic or anticonvulsant action of the KD [20,31,33-38].
Under normal physiological conditions brain cells derive most of their energy from glucose or glucose-derived metabolites, e.g., lactate [46,59,60]. Also, brain glucose uptake is greater during epileptic seizures than during most other brain activities [43]. During fasting or caloric restriction, however, circulating glucose levels fall causing brain cells to rely more heavily for energy on ketone bodies that gradually increase with food restriction [29,61]. It is the transition from glucose to ketone bodies for brain energy that is thought to underlie the antiepileptic and anticonvulsant effects of caloric restriction [29]. Although the KD we used contained no carbohydrates, the mice eating this diet maintained high glucose levels and seizure susceptibility. The persistence of high glucose levels in the KD-UR group would prevent the transition to ketones for energy despite high levels of circulating ketone bodies. Our results show that circulating glucose levels accurately predict seizure susceptibility in EL mice regardless of diet composition or circulating ketone body levels.
We used a new experimental design for caloric restriction in this study. Instead of restricting calories in the R-fed mice based on the average food consumption of the UR control mice as we did previously [21], each R-fed mouse served as its own control to achieve and maintain a 20–23% body weight reduction. We found in a pilot study that isocaloric restriction of the KD was unable to reduce body weight to the same degree as that observed for a similar restriction of the SD. The new experimental design reduces variability in body weights and in caloric intake among mice fed diets widely different in nutritional composition and caloric content. In using body weight, rather than caloric intake, as an independent variable we were able to more accurately measure the statistical associations among circulating energy metabolites and seizure susceptibility. Thus, this type of experimental design is recommended for those studies attempting to evaluate the relationships among nutrition, metabolism, and disease phenotype.
We previously discussed the potential mechanisms by which CR might reduce seizure susceptibility [21,29,38]. Some of the cellular systems potentially modulated through CR that could influence brain excitability are illustrated in Fig. 6. We suggest that the transition from glucose to ketone bodies as a major energy fuel for the brain produces multiple changes in gene-linked metabolic networks. It is these changes that gradually adjust neurotransmitter pools and membrane excitability to restore the physiological balance of excitation and inhibition [29]. CR could also influence seizure susceptibility through the neuroendocrine system involving leptin signaling and increased levels of neuropeptide-Y, a peptide with antiepileptic and anticonvulsant effects [62-65]. While the levels of γ-aminobutyric acid (GABA) are increased in synaptosomes via the increased action of glutamic acid decarboxylase during the metabolism of ketone bodies for energy, the levels of aspartate decrease due to the formation of glutamate [66]. In addition, ketone body metabolism could increase membrane ionic pump activity [67,68]. Increased pump activity could increase membrane potential in neurons while also increasing neurotransmitter uptake in glia [29]. We do not exclude the possibility that CR may reduce seizure susceptibility in EL mice through additional mechanisms [31,69]. It is our contention that CR reduces seizure susceptibility through multiple integrated systems providing a multifactorial therapy to a multifactorial disease. Further studies in the EL mouse and in other epilepsy models are needed to identify the exact mechanisms of CR action in managing epileptic events.
Figure 6 Perspectives on the metabolic management of epilepsy through a dietary reduction of glucose and elevation of ketone bodies. A dietary reduction in blood glucose levels will increase ketone utilization for energy. This is expected to shift the neural environment from excitation to inhibition through multiple integrated systems. Abbreviations: GLUT-1 (glucose transporter), MCT (monocarboxylate transporter), PFK (phosphofructokinase), PDH (pyruvate dehydrogenase), SCOT (succinyl-CoA-acetoacetate-CoA transferase), β-OHB (β-hydroxybutyrate), β-HBDH (β-hydroxybutyrate dehydrogenase), NPY (Neuropeptide Y), GABA (gamma-aminobutyric acid). Modified from Seyfried et al., 2004 [38].
Conclusions
We conclude that seizure susceptibility in EL mice is dependent on plasma glucose levels and that seizure control depends more on the amount than on the origin of dietary calories. Also, we found that CR underlies the antiepileptic action of the KD in EL mice. A transition from glucose to ketone bodies for energy is predicted to manage EL epileptic seizures through multiple integrated changes of inhibitory and excitatory neural systems.
Methods
Mice
The inbred EL/Suz (EL) mice were originally obtained from J. Suzuki (Tokyo Institute of Psychiatry). The mice were maintained in the Boston College Animal Care Facility as an inbred strain by brother × sister mating. The mice were group housed (prior to initiation of study) in plastic cages with Sani-chip bedding (P.J. Murphy Forest Products Corp., Montville, N.J.) and kept on a 12-hr light/dark cycle at approximately 22°C. Cotton nesting pads were provided for warmth when animals were individually housed. All cages and water bottles were changed once per week. Only females were used for these studies as adult males die sporadically with age from acute uremia poisoning due to urinary retention [70]. The procedures for animal use were in strict accordance with the NIH Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care Committee.
Seizure Susceptibility and Seizure Testing
Seizure onset in EL mice is generally between 60–70 days of age as previously described [6]. These seizures occur occasionally during routine cage changing. Our recently developed seizure handling protocol was used to regularly induce seizure susceptibility in EL mice [6,21]. Briefly, the testing procedure included repetitive handling and simulated the stress normally associated with weekly cage changing, i.e., picking the mouse up by the tail for short intervals and transferring it to a clean cage with fresh bedding. The test included two trials that were separated by 30 min. In each trial, a single mouse was held by the tail for 30 sec at approximately 10–15 cm above the bedding of its home cage. After 30 sec, the mouse was placed into a clean cage with fresh bedding for 2 min. The mouse was then held again for 15 sec before being returned to its home cage. Trial 2 was performed even if the mouse experienced a seizure in trial 1. The epileptic seizures commenced during holding or soon after the mice were placed on the clean bedding. Mice that developed an epileptic seizure while handling were placed immediately in either the clean cage or their home cage depending on the testing stage. Mice were tested each week for a total of 13 measurements over a 12-week period using this method. Mice were undisturbed between testing phases (no cage changing) and testing was performed between 12 to 3 pm.
Seizure Phenotype
Mice were designated seizure susceptible if they experienced a generalized seizure during seizure testing. Generalized seizures in EL mice involve loss of postural equilibrium and consciousness, together with excessive salivation, head, limb, and chewing/swallowing automatisms. An erect forward-arching Straub tail, indicative of spinal cord activation, was also seen in most mice having generalized seizures. Mice that displayed only vocalization and twitching without progression to generalized seizure were not considered seizure susceptible [6,21]. Seizure susceptibility scores were generated for each mouse according to the seizure severity scores previously described [6]. Mice having a score of 4 or 5 were assigned a susceptibility score of 1.0, whereas mice having a seizure severity score less than 4 were given a susceptibility score of 0. The seizure susceptibility for each mouse was then averaged over multiple tests and the mean seizure susceptibility for a mouse dietary group was determined.
Diets
All mice received PROLAB RMH3000 chow diet (LabDiet, Richmond, IN, USA) prior to the experiment. This is the standard food pellet diet (SD) and contained a balance of mouse nutritional ingredients. According to the manufacturer's specification, this diet delivers 4.4 Kcal/g gross energy, where fat, carbohydrate, protein, and fiber comprised 55 g, 520 g, 225 g, and 45 g/Kg of the diet, respectively. The ketogenic diet (KD) was obtained from the Zeigler Bros., Inc. (Gardners, PA, USA) in butter-like form and also contained a balance of mouse nutritional ingredients. According to the manufacturer's specification, the KD delivers 7.8 Kcal/g gross energy, where fat, carbohydrate, protein, and fiber comprised 700 g, 0 g, 128 g, and 109 g/Kg of the diet, respectively. The fat in this diet was derived from lard and the diet had a ketogenic ratio (fats: proteins + carbohydrates) of 5.48:1. The individual % composition of each dietary energy component for the SD and KD diet is shown on Table 1.
Pre-Trial Period
Seizure susceptibility, body weight, and food intake was measured four times over a three-week period in 24 singly caged female EL mice (approximately 210 days of age). All mice received the SD during the pre-trial period and food intake was determined by subtracting the weight of food pellets remaining in the food hopper after one week from the initial amount given (200 g). The difference was then divided by seven to estimate the average daily food intake. Thus, all mice were highly seizure susceptible at the initiation of the diet therapy.
Dietary Treatment
After the three-week pre-trial period, the mice were placed into four groups (n = 6 mice/group) where the average body weight of each group was similar (about 31.0 ± 1.5 g) (Fig. 1). All mice were then fasted for 14 hr to establish a similar metabolic set point at the start of the experiment (arrow, Fig. 1). The mice in each group were then given one of four diets to include: 1) the standard diet fed ad libitum or unrestricted (SD-UR), 2) the KD fed ad libitum or unrestricted (KD-UR), 3) the SD restricted to achieve a 20–23% body weight reduction from the pre-trial weight (SD-R), and 4) the KD restricted to achieve a 20–23% body weight reduction from the pre-trial weight (KD-R). Each mouse in the two R groups served as its own control for body weight reduction. Based on food intake and body weight during the pre-trial period, food in the R-fed mouse groups was reduced until each mouse achieved the target weight reduction of a 20–23%. In other words, the daily amount of food given to each R mouse was reduced gradually until it reached 77–80% of its initial (pre-trial) body weight.
The mice in the SD-UR group received 200 g of food in the hopper/week as in the pre-trial period. For mice in the SD-R group, weighed food pellets were dropped directly into each cage for easy access. The KD was administered to the mice in a modified plastic Falcon tissue culture dish (60 mm × 15 mm). The dish edges were shaved to reduce the height from 15 mm to about 6 mm. After placing about 5 g of KD in the dish for the KD-UR mice, the dish with the weighed KD was inverted and placed on the top of the food hopper. An empty water bottle was placed on top of the dish to prevent dish movement during animal feeding. The butter-like consistency adhered the KD to the inverted dish. This feeding apparatus allowed the mice easy access to the KD and prevented KD contact with bedding material. After about 24 hr, the amount of KD consumed was determined and another 5 grams of fresh KD were added to the dish. The KD was therefore given fresh every day without moving or disturbing the mice. The total amount of KD consumed per day was summed each week and divided by 7 to obtain the average weekly food intake of each mouse. For the KD-R mice, a calculated restricted amount of KD was placed directly on top of the food hopper bars for easy access. The R-fed mice licked the bars clean of the KD.
Measurement of plasma glucose and β-hydroxybutyrate
Blood was collected approximately 1 h after seizure testing except for the pre-trial period where blood was not collected. Blood was first collected from all mice about 24 hr prior to the initiation of the 14 hr fast (Fig. 1). Mice were anesthetized with isoflurane, USP (Halocarbon, River Edge, NJ, USA) and blood was collected in heparinized tubes by puncture of the retro-orbital sinus using a borosilated capillary tube (FHC, Bowdoinham, ME, USA). The blood was centrifuged at 6,000 × g for 10 min, the plasma was collected, and aliquots were stored at -80°C until analysis. Plasma glucose concentration was measured spectrophotometrically using the Trinder Assay (Sigma-Aldrich, St. Louis, MO, USA). Plasma β-hydroxybutyrate concentration was measured using either the Stanbio β-Hydroxybutyrate LiquiColor® procedure (Stanbio, Boerne, TX, USA), or a modification of the Williamson et al procedure [71].
Statistical Analysis
Both ANOVA and a two-tailed t test were used to evaluate the significance of differences of body weight, seizure susceptibility, plasma glucose levels, and plasma β-hydroxybutyrate levels between unrestricted and restricted groups. Chi-square analysis was performed on the association between glucose and seizures. Pearson bivariate correlation analysis (SPSS software) was used to determine the relationship between body weight, food intake, plasma glucose levels, plasma β-hydroxybutyrate levels, and seizure susceptibility. Binary logistic regression (SPSS) was used to determine the relationship between seizure susceptibility, plasma glucose, and β-hydroxybutyrate levels on mice fed either the SD or the KD. Differences were considered significant at p < 0.01. All values are expressed as mean ± SEM. All statistical data were presented according to the recommendations of Lang et al., [72].
Lists of abbreviations
AED, antiepileptic drug; CR, caloric restriction; KD, ketogenic diet; R, restricted; SD, standard diet; UR, unrestricted.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JGM carried out all described methods and drafted the manuscript. NAC helped with the blood assays, participated in the feeding of the mice and carried out the incorporation of the data in Excel spreadsheets. MTT helped with the design of the study. RM participated in the data discussion and the statistical analysis. TNS conceived the study, participated in its design and coordination and helped prepare the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This research was supported by the Boston College Research Fund, and NIH grant (HD 39722). Also, "this project was supported by the Epilepsy Foundation through the generous support of the Roger F. and Edna F. Evans Fund". The authors would like to thank Christina Y. Kim for technical help and her contribution in the early phases of the study.
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| 15507133 | PMC529249 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Oct 19; 1:11 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-11 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1641550713810.1186/1471-2105-5-164Methodology ArticleMultivariate search for differentially expressed gene combinations Xiao Yuanhui [email protected] Robert [email protected] Alexander [email protected] Lev [email protected] Andrei [email protected] Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA2 Departments of Otolaryngology, Neurobiology and Anatomy, and Biomedical Engineering, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA3 Department of Probability and Statistics, Charls University, Sokolovska 83, Praha-8, CZ-18675, Czech Republic2004 26 10 2004 5 164 164 7 8 2004 26 10 2004 Copyright © 2004 Xiao et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
To identify differentially expressed genes, it is standard practice to test a two-sample hypothesis for each gene with a proper adjustment for multiple testing. Such tests are essentially univariate and disregard the multidimensional structure of microarray data. A more general two-sample hypothesis is formulated in terms of the joint distribution of any sub-vector of expression signals.
Results
By building on an earlier proposed multivariate test statistic, we propose a new algorithm for identifying differentially expressed gene combinations. The algorithm includes an improved random search procedure designed to generate candidate gene combinations of a given size. Cross-validation is used to provide replication stability of the search procedure. A permutation two-sample test is used for significance testing. We design a multiple testing procedure to control the family-wise error rate (FWER) when selecting significant combinations of genes that result from a successive selection procedure. A target set of genes is composed of all significant combinations selected via random search.
Conclusions
A new algorithm has been developed to identify differentially expressed gene combinations. The performance of the proposed search-and-testing procedure has been evaluated by computer simulations and analysis of replicated Affymetrix gene array data on age-related changes in gene expression in the inner ear of CBA mice.
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Background
The set of microarray expression data on p distinct genes is represented by a random vector X = X1,..., Xp with stochastically dependent components. The dimension of X is typically very high relative to the number of observations (replicates of experiment). The standard practice is to test the hypothesis of no differential expression for each gene. Formulated in terms of the marginal distributions of all components of X, this hypothesis means that the expression levels of a particular gene are identically distributed under two (or more) experimental conditions. It is commonly believed that the only challenging problem here is that of multiple statistical tests, because the corresponding test statistics computed for different genes are stochastically dependent. This problem is discussed in [2] in the context of microarray data analysis. Resampling techniques [3,4] provide a universal approach to the problem of multiple dependent tests inherent in the most typical study designs. However, there is another aspect of the standard approach that warrants special attention. Any test constructed solely in terms of marginal distributions of gene expression levels disregards the multidimensional (dependence) information hidden in gene interactions, which is its most obvious deficiency.
In a recent paper, Szabo et al. [5] proposed to build a target set of interesting genes from non-overlapping subsets of genes of a given size (≥1) that have been declared differentially expressed in accordance with a pertinent statistical test. The size of each sought-for subset is naturally constrained by the available sample size. This approach strives to preserve the dependence structure at least within each of such building blocks, which is already a major step toward a more general methodology of microarray gene expression data analysis.
No matter what specific statistical techniques are chosen to approach the problem of identifying differentially expressed gene combinations rather than individual genes, the hypothesis that the expression levels of a given set of genes are identically distributed across the conditions under study is the most meaningful hypothesis to be tested. However, this hypothesis is now formulated in terms of the joint distribution of expression levels. The issue of multiple testing is dramatically magnified with multivariate methodology, because the total number of tests to be carried out at all steps of multivariate selection may be many orders of magnitude larger than with univariate methods. A constructive idea is to design a random search procedure for identifying differentially expressed sets of genes followed by testing significance of a final set. Szabo et al. [5,6] proposed a search procedure based on maximization of a new distance between multivariate distributions of gene expression signals. They used permutation techniques for hypotheses testing. To adjust for multiple testing, the null-distribution was estimated from the test statistics generated by each optimal (in terms of the adopted distance) set of genes found in each permutation sample. The authors provided an illustrative example of clear advantages of multivariate methodology over univariate approaches. In the present paper, we improve the cross-validation and multiple testing components of the earlier proposed algorithm. This new combination of the search-and-testing procedures furnishes a sound statistical methodology for multivariate analysis of microarray data.
Results
Mathematical framework: measure of differential expression
To compare gene expression signals in two different experimental conditions (states) one needs a pertinent distance between two random vectors. Such a distance is expected to satisfy the following requirements: (1) it should have a clear probabilistic meaning; (2) it should accommodate both continuous and categorical data; (3) its estimate should be stable to random fluctuations and numerical errors; (4) its computation should not be too time consuming. A distance that meets all the above requirements was proposed in [6].
Let X = X1,..., Xd and Y = Y1,..., Yd, d ≤ p, be two random sub-vectors with probability measures μ and ν, respectively, defined on the Euclidean space Rd. Let K(x, y) be a strictly negative definite kernel, that is for any x1,..., xs from Rd and any real numbers h1,..., hs, , with equality if and only if all hi = 0. Introduce the following expression
The quantity N(μ, ν) can be shown [7] to be a metric in the space of all probability measures Rd, so that the null hypothesis in two-sample comparisons can be formulated as H0 : N(μ, ν) = 0. A normalized version of N can be derived as , where
If K(x, y) = Ψ(x - y) and Ψ(·) is homogeneous of any order, then Nnorm is both location and scale invariant.
Consider two independent samples, consisting of n1and n2 observations respectively, represented by the d-dimensional vectors and , and introduce an empirical counterpart (nonparametric estimate) of N(μ, ν) as follows
A very important advantage of the empirical counterpart of the distance N is that it does not involve numerically unstable high-dimensional components (such as covariance matrix or its inverse), thus it is expected to be numerically stable even for small sample sizes. This was corroborated by a computer simulation study [5], in which this distance demonstrated a much higher stability than the Mahalanobis distance and the nearest neighbor classifier.
Another distinct advantage of the approach based on is a wide selection of negative definite kernels that are sensitive to various departures from the hypothesis: μ = ν. Let x and y denote observations in two samples on a particular set of variables. We consider either of these observations to be points in Euclidean space Rd. One natural choice is the Euclidean distance between points representing experimental measurements:
When this kernel is applied to logarithms of gene expression signals the corresponding distance is scale invariant. Another possible choice is a bounded kernel exemplified by
Yet another kernel based on the correlation coefficient tends to pick up sets of genes with separated means and differences in correlation in the two samples under comparison [6]. One can also use a convex combination of the above mentioned kernels with the weights chosen in such a way as to make the distance more sensitive to particular types of the alternative hypothesis.
The search-and-testing algorithm
Once a multivariate distance between expression signals has been selected, it can be employed in a search for differentially expressed genes with the target subset of genes being defined as a subset for which the distance between the two groups under comparison attains its maximum. Unlike univariate testing, an exhaustive multivariate search is computationally prohibitive because the number of possible subsets increases as the d-th power of the total number of genes. The issue of computational complexity can be resolved by applying random search methodology. Random search can be designed in a number of various ways. One simple algorithm was described in [6,8]. We used this algorithm, hereafter designated as Simple Random Search (SRS), with multiple random starts and long sequences of search steps in the application reported in the present paper. We also compared its performance with that of simulated annealing [1].
To reduce the selection bias associated with choosing a small number of variables from a large set [9], Szabo et al. [5,6] suggested to use cross-validation techniques with the search for a target subset of genes running in each cross-validation cycle. The basic structure of our cross-validation algorithm is as follows:
Algorithm A1: Cross-validated search for differentially expressed genes
1. Randomly draw (without replacement) u1 samples from one group of arrays and u2 samples from the other group.
2. Leave out the selected arrays and find the optimal (in accordance with the chosen criterion) subset of genes using only the data from the remaining arrays.
3. Repeat steps 1 and 2 in succession v times to obtain v "optimal" sets of genes.
The main problem here is that the algorithm results in many overlapping sub-optimal sets, and one needs to somehow combine them to report a single final set. Szabo et al. resorted to a somewhat unnatural way of forming a final set by selecting single genes with the highest frequencies of occurrence in sub-optimal sets. In our new algorithm, this is accomplished through designing a second-stage cross-validated search limited to the union of the previously selected sets. In the second-stage search procedure, cross-validation is carried out at each step of random search with the distance averaged over all cross-validation samples. With this approach, the correlation structure is better preserved when combining the results of cross-validation. The foregoing description of the second-stage search may be summarized in the following algorithm:
Algorithm A2: The second-stage cross-validated random search
1. Form the union of all sets resulted from Algorithm A1 to represent an initial target set. Drop the data on all other genes from the data set.
2. Initiate a random search algorithm.
3. At each step of the search algorithm, randomly draw (without replacement) l1 samples from one group of arrays and l2 samples from the other group. Leave out the selected arrays and compute the N-statistic using only the data from the remaining arrays. Perform this computation r times.
4. Compute the average (arithmetic mean) of the N-statistics resulted from step 3. Denote this average by .
5. Move to the next step of random search using the statistic as a pertinent objective function to be maximized.
In the application discussed in the present paper, we used Algorithm A2 with 200 cross-validated samples in the second stage of the search algorithm. The two-stage search algorithm runs with multiple random starts and returns the most differentially expressed (in terms of the distance ) gene combination of a given size.
Once an optimal set has been found, all genes pertaining to this set are discarded and a search for the next set of differentially expressed genes is initiated. Szabo et al. [5] proposed a stopping rule based on a permutation significance test. In the improved version of our algorithm, instead of testing significance at each step of the successive selection of subsets of genes, the selection procedure runs (without testing) for a preset number of steps, thereby forming a reasonably long sequence of non-overlapping "maximal" subsets. The same cross-validated random search procedure is applied to each permutation sample, generated to model the complete null hypothesis for disjoint subsets of genes, and finally the step-down multiple testing resampling algorithm by Westfall and Young [4] is applied to the subsets thus selected. If all the null hypotheses happen to be rejected, the selection procedure goes on eliminating subsets of genes resulting from the search algorithm, otherwise the procedure stops. The heuristic procedure thus designed mimics its univariate multiple testing (marginal hypotheses testing) counterpart with known properties [4], thereby ensuring an approximate control of the family-wise error rate (FWER).
Suppose that all tests are two-tailed and utilize the same test statistic , then the following resampling algorithm can be proposed:
Algorithm A3: Successive selection of differentially expressed gene combinations
1. Form m permutation samples of sizes n1and n2, respectively, from n1+ n2 replicated observations (arrays). For each of the m permutation samples, run (without testing) the successive selection algorithm to find a preset number I of disjoint sets. At each step of successive selection, an optimal k-element set is identified by the two-stage cross-validated search algorithm and the corresponding m sequences of -values are stored.
2. Returning to the original two-sample setting, find a sequence of I optimal sets of the same size k and compute the respective test statistics for the selected sets.
3. Apply the step-down multiple testing resampling algorithm by Westfall and Young [4] to the N-statistics resulting from Steps 1 and 2. If the number of rejected hypotheses is less than I then stop and declare all the rejected sets of genes differentially expressed, otherwise return to Step 1 and continue successively selecting sets of genes. A faster version of Step 3 uses the single-step resampling adjustment [4].
The above algorithm can be reformulated in terms of p-values. The algorithm is computationally more expensive than its prototype presented in [5]. We used a SunFire V480 station to implement the algorithm. This "brute force" approach is needed to extract more information from multivariate gene expression profiles.
With the above approach, no distributional assumptions are needed although the test statistic is not distribution free. For this statistic, however, it can be proven that permutations produce samples from a distribution that is, in some sense, the least favorable for rejecting an underlying composite null hypothesis. In other words, permutations provide an optimal choice of a null distribution. More precisely, this theoretical result is valid for the resampling (with replacement) analog of permutations, but regular (without replacement) permutations may be a good approximation to this resampling procedure if both samples under comparison are not too small. This concept and its mathematical framework is discussed at length in our previous report [10].
For efficient nonparametric estimation of adjusted p-values associated with sets of genes resulting from random search, it is also desirable that the test statistic be scale invariant for any sample size. A statistic that meets this requirement is an empirical counterpart of the normalized distance Nnorm with a properly chosen kernel function, see formula (2) and the succeeding explanation. Yet another possibility is to use the kernel K1 with log-intensities of gene expressions. We employed the latter pivoting structure of the N-statistic in the analysis of simulated and biological data presented in the subsequent sections.
Simulation studies
We first tested our methodology by computer simulations. To this end, we designed a simulation study as follows.
Two sets of data on 1,000 genes were simulated. For convenience we will label them as "control" and "treatment" samples, respectively. The size of each sample was equal to 10. In the treatment group, the first 12 genes were set to be differentially expressed. To simulate these genes, logarithms of gene expression signals were generated from a multivariate normal distribution with an exchangeable correlation structure. The algorithm designed to simulate such data is presented in the Appendix. The correlation coefficient for all pairs of gene log-intensities was set equal to 0.6, while the standard deviation was chosen to be either σ = 0.5 or σ = 1 for all individual genes. The mean log-expression values τ for the genes assigned to the target set of genes were specified as follows: τ = 5 for the first 4 genes (Subset 1), τ = 4 for the second group of 4 genes (Subset 2), τ = 3 for the third group of 4 genes (Subset 3). The remainder of the genes (not differentially expressed) were simulated as log-normally distributed random variables with τ = 1 and the same standard deviation (either σ = 0.5 or σ = 1) and correlation coefficient. The 1,000 genes in the control group were simulated just like those that were not differentially expressed in the treatment group.
Our search-and-testing procedure was applied to the data sets thus generated in order to see whether (and how frequently) it can find all subsets, as well as all individual genes, included in the target set of differentially expressed genes. In each experiment, the SRS algorithm was run with multiple random starts. At each step of the successive selection of genes, the algorithm sought for a subset of 4 genes. The parameter I in Algorithm A3 was set equal to 5. Since the sole purpose of our simulations was to check how well a given algorithm finds a maximum of the N-statistic over gene sets, no recourse to cross-validation was made in this study. The number of permutations was set at 200. Because such simulations are very time consuming the experiment was repeated only 100 times. Two samples (control and treatment) were generated in each of the 100 experiments.
First we tested the SRS algorithm with 8 random starts and 2,500 search steps. When σ = 0.5 for the treatment group the algorithm was able to correctly recover Subset 1 in 82%, Subset 2 in 72%, and Subset 3 in 76% of simulation runs. The proportion of cases where all 12 genes were correctly recovered (irrespective of the order they entered the selected subsets) was 61%. The false discovery rate, defined as the mean proportion of falsely discovered genes among the true differentially expressed genes, was equal to 0.02.
When σ = 1 the SRS algorithm recovers Subset 1 in 76%, Subset 2 in 56%, and Subset 3 in 39% of the simulation runs. The proportion of cases where all 12 genes were correctly recovered was 53%. The false discovery rate was equal to 0.04.
As one would expect, the SRS algorithm performed better with 16 random starts and 3,600 search steps. For σ = 0.5, the rate of correct discovery becomes 100% for all three sets. For σ = 1 the algorithm correctly recovers Subset 1 in 81%, Subset 2 in 65%, and Subset 3 in 48% of simulation runs. The proportion of cases where all 12 genes are correctly recovered is 62%. However, the false discovery rate remains essentially the same as when running the SRS algorithm with 8 starts and 2,500 search steps. The results on individual simulated genes are presented in Table 1.
By way of comparison, we ran the Westfall and Young algorithm with a univariate counterpart of the test statistic N at the same level of FWER. While the results for σ = 0.5 were identical (100% correct recovery), the univariate method recovered less genes (45%) in the target set when we set σ = 1. In the latter case, the univariate algorithm had a uniformly lower correct discovery rate for genes #9 through #12 (69%, 71%, 70%, 71%, respectively) in comparison to the multivariate method (Table 1). One should not expect much discrepancy between the univariate and multivariate methods in these simulations because the alternative hypotheses were modeled in a univariate way.
In another experiment we studied the simulated annealing optimization (SAO) with one random start and the same parameters of the simulation model. Although computationally expensive, the SAO algorithm is easier to handle when tuning its parameters in simulation experiments. Proceeding from the less favorable case of σ = 1, we determined parameters of the SAO algorithm that provide correct selection of all three sets of differentially expressed genes in all simulation runs.
Another way of testing the two algorithms is to apply them in a situation where the true global maximum of the N-distance is known. We randomly selected 2000 genes from the data set discussed in the next section. All possible pairs were formed from the 2000 genes and the corresponding N-statistic between the two samples (young versus old mice) was computed for each pair. The data were normalized before the analysis (see Section "Results and Discussion"). Having determined a maximum value of the N-statistic over all pairs, we ran the SRS and SAO algorithms (with parameters suggested by our simulation experiments) to see whether they could find the actual maximum. Both algorithms hit the target.
Results
The biological purpose of our experimental study was to better understand age-related changes in gene expression that occur in mouse inner ear (including the organ of Corti and stria vascularis). Since we do not expect numerous genes to be involved in the process of aging of the auditory system, this experimental system seems to be especially promising for the use of multivariate methods.
Hearing loss or deafness affects about 10% of the U.S. population, or about 30 million people, most of them over age 60. Presbycusis – age-related hearing loss – is a primary sensory problem in the elderly population, the number one communicative disorder, and one of the top three chronic medical conditions affecting the aged. It is often described as difficulty in understanding speech, especially in conditions of high ambient background noise. Most elderly persons have a reduction in hearing acuity. For example, cross-sectional and longitudinal studies have consistently demonstrated gradually decreasing pure tone thresholds by cohort groups of elderly [13,14]. The composite audiometric pattern is one of better hearing for low- and mid-speech frequencies than higher speech frequencies. The consequence of this pattern is difficulty in hearing and understanding, not only conversational speech, but in particular, speech that is softly spoken. In fact, a similar gradual reduction in speech recognition for words and phonemes in quiet has been shown to accompany the pure tone threshold decrease in cohort groups of the elderly [14-16].
Much progress had been made in the field of auditory aging research regarding sensitivity deficits and metabolic problems of the cochlea. As humans and animals age, they lose sensory hair cells, 8th cranial nerve (i.e., vestibulocochlear) fibers, and develop stria vascularis/potassium recycling metabolic problems that degrade audibility and spectral tuning [17-21].
In addition, the differing roles of the ear and brain in presbycusis, and aging deficits in speech understanding in background noise, and their respective neural bases are beginning to be understood. Age effects in these areas are distinguishable and age-related problems in the brain can be influenced by the peripheral etiologies of presbycusis [22-24]. Considering studies completed to date, presbycusis in humans, and corresponding age-related hearing loss in animal models such as the CBA mouse, have two major facets: 1) A peripheral hearing loss of cochlear origin, starting with sensitivity losses in the high pitches (high frequencies), involving loss of sensory hair cells, spiral ganglion neurons (8th nerve fibers) and metabolic malfunctions of the highly vascularized stria vascularis organ system that produces the potassium rich endolymph of the inner ear [25,26]; and 2) An inability to comprehend speech in background noise, that results from deficits in the inner ear and the central auditory nervous system [23,24]. For the animal model studies of presbycusis, the CBA mouse strain has been quite useful to date.
The goal of the present study is to explore the underlying cochlear gene expression changes that may predispose or cause presbycusis. Common neurodegenerative diseases such as presbycusis are likely to be caused by several fundamental problems that interact with each other and with environmental factors, including genetic pre-dispositions to environmental insults, noise and ototoxic medications [27]. Although over a hundred genes have been identified that cause congenital deafness (e.g. [28-30]), no candidate genes have yet been identified that are involved in human presbycusis. The present report attempts to gain some initial insights into gene expression changes related to inner ear problems that may predispose or cause age-related neurosensory disorders, such as age-related hearing loss – presbycusis, utilizing the CBA mouse strain.
The two groups of arrays under comparison included 9 and 12 arrays, respectively (see the next section). The data were normalized using the quantile normalization method [11,12] carried out at the probe feature level. Compared to our simulations, the number of permutations was increased to 400. Each search cycle in the SRS algorithm proceeded in 45,000 steps with 100 random starts. The algorithm was tuned to search for a set of 5 genes at each step of the successive selection procedure. We also changed parameters that control the efficiency of the SAO algorithm to account for an increased dimensionality of the problem. The latter algorithm also sought for sets consisting of 5 genes. We used the following parameter values in the combined two-stage cross-validated search algorithm: I = 5, u1 = 4 (out of 9 arrays), u2 = 6 (out of 12 arrays), v = 10, l1 = 4 (out of 9 arrays), l2 = 6 (out of 12 arrays), r = 200.
Although the lists of genes produced by both algorithms are quite similar, there are still some discrepancies between them which may be attributed to the choice of parameters for each method. Since the SAO algorithm is less sensitive to the choice of the initial gene combination, we present only the results obtained with this algorithm. In the "young" versus "old" comparison, the procedure selected two sets of 5 genes with an adjusted p-value of less than 0.05. For comparison, we applied the Wesfall and Young step-down multiple testing procedure with a univariate counterpart of as the test statistic. This method selects only 6 genes at the same FWER; all of them appear among those genes that have been selected by the multivariate search-and-testing procedure. The final list of 10 genes was evaluated further for consistency with the existing biological knowledge.
Discussion
Of the 10 identified genes (from 2 sets) exhibiting major expression changes with age, there are 6 differentially expressed genes having to do with immune system function. This is important from an aging point of view for two reasons. First, immunoprecipitations or immunoproducts can be damaging to nerve cells, and have been implicated as being responsible for age-related neurodegeneration in the brain in general, and in Alzheimer's disease specifically, but this is a new finding for the cochlea and age-related hearing loss – presbycusis. Second, autoimmune problems, where the immune system starts attacking its own nerve cells, is another leading candidate for a causative factor in neurodegenerative aging conditions. These immune products are likely to come from the vascular supply to the cochlea, yet may be a causative component for age-related hearing loss due to the resultant damage to the cochlea sensory cells.
There are 3 genes having to do with post-translational protein changes, including protein binding properties, with two of these genes involved in carbohydrate metabolism (sugar/glucose binding in mitochondria for cellular respiration). These genes are related to the production of reactive oxygen species (ROS), which damage nerve cells, and have been implicated in age-related neurodegenerative disorders, and in cases of cochlear sensorineural hearing loss. For example, problems in cellular respiration can lead to accumulation of toxic intracellular substances, causing damage to sensory cell structures and abnormal metabolic processing along with increased levels of ROS [31-33].
The last gene, involved in mammary gland functioning, showed a significant increase with age. A closer inspection of the expression levels for this gene have shown that the observed effect cannot be attributed to the presence of outliers in the data. Although not directly involved in sensory functioning, this gene may change its espression as part of general degenerative processes in inner ear. An error in this gene annotation cannot be ruled out as well. This observation is definitely worth another look.
The above-described initial observations are quite provocative, in that we have several groupings of genes that have important functional significance for aging and hearing, including important aspects of cochlear, inner ear functioning. These animal-model gene-array investigations are quite useful for guiding human genetics experiments aimed at identifying candidate genes involved in the susceptibility and progression of human age-related hearing loss and other age-dependent neurosensory disorders.
Regarding methodological aspects of this paper, we would like to note that a pertinent multivariate method for selection of differentially expressed genes should include two components: finding subsets of candidate genes that jointly separate the classes (states) under comparison and testing statistical significance of this separation; the latter does not necessarily refer to characteristics of a classification (allocation) rule such as classification error rates. We also would like to stress that the problem of significance testing in the multivariate formulation is not equivalent to the problem of statistical classification (supervised learning). While closely related, these problems are fundamentally different. For example, the use of the classification error rate as a criterion for selection of important variables is appropriate where the aim is to form a discriminant rule for the subsequent outright allocation of unclassified samples to one of the known classes. A very good separation between classes can sometimes be provided by looking at a single feature variable (gene) so that the classification error rate is difficult to reduce further by including other (probably quite significant) variables in the rule. However, one would like to keep the chance of missing other interesting variables to a minimum. The problem dealt with in this paper is not that of classification or prediction. Our method is designed to find gene combinations that change in concert (as a set) their expression due to some biological factors. The problem thus formulated reduces to that of significance testing.
It must be emphasized that our method is designed not only to identify sets of genes whose interrelationships differ but also those genes with marginal effects. More importantly, the method seeks to provide an alternative way of making a specific FWER-based multiple testing procedure less conservative and, to some extent, less dependent on the subset pivotality requirement (see [4] for definition), by extracting more information from the data. In addition, this approach can be used for ranking and clustering those genes that have been declared differentially expressed by univariate methods.
Conclusions
A new algorithm for identifying differentially expressed gene combinations has been developed. This algorithm is built on the earlier proposed multivariate test statistic [6] and successive selection of differentially expressed sets of genes [5]. The algorithm includes an improved random search procedure designed to generate candidate gene combinations of a given size. Cross-validation is used to provide replication stability of the search procedure. A permutation two-sample test is used for significance testing. We design a multiple testing procedure to control the family-wise error rate when selecting significant combinations of genes that result from a successive selection procedure. A target set of genes is composed of all significant combinations selected via random search. The performance of the proposed search-and-testing procedure has been evaluated by computer simulations and analysis of replicated Affymetrix gene array data on age-related changes in gene expression in the inner ear of CBA mice.
Methods
Subjects
CBA mice from the University of Rochester vivarium served as subjects for this study who had similar environmental, non-ototoxic life histories. Subjects were mice of the following age groups: Young adult (N = 9, 3–4 months) and old (N = 12, 24–33 months). All animal procedures were approved the University of Rochester Committee on Animal Resources.
Cochlear dissections
Subject groups of the present report had extensive behavioral and neurophysiological hearing testing prior to sacrifice, verifying that the old mice had age-related hearing loss. Mice were sacrificed by cervical dislocation. Then both cochleae for each mouse were immediately dissected using a Zeiss stereomicroscope. The cochleae were placed in cold saline for micro dissection of the cochlear partition (basilar membrane, organ of Corti and spiral ligament), and were then placed in cold Trizol. A detailed protocol for Trizol can be found at . All samples were stored at -80°C for microarray gene expression processing.
Gene expression microarrays
The RNA quality was assessed by electrophoresis using the Agilent Bioanalyzer 2100. Between 200 ng and 2 ug of total RNA from each sample was used to generate a high fidelity cDNA, which was modified at the 3' end to contain an initiation site for T7 RNA polymerase, while 1 ug of cDNA was used in an in vitro transcription (IVT). 20 ug of full-length cRNA, from each mouse (age groups as described above), was fragmented. After fragmentation, the cDNA, full-length cRNA, and fragmented cRNA were analyzed by electrophoresis using the Agilent Bioanalyzer 2100 to assess the appropriate size distribution prior to microarray hybridization. Detailed protocols for sample preparation using the Ambion MessageAmp protocol can be found at . Affymetrix M430A High density oligonucleotide array set (A) which queried 20,000 murine probe sets was used. Each gene on the subarray is represented by 11 pairs of 25 mer oligonucleotides that span the coding region for the 20,000 genes and ESTs represented (clear overlapping of genes is evident). Each probe pair consists of a perfect match (PM) sequence that is complementary to the cDNA target, and a miss-match (MM) sequence that has a single base pair mutation in a region critical for target hybridization; this sequence serves as a control for non-specific hybridization. Staining and washing of all arrays was performed in the Affymetrix fluidics module per manufacturer's protocol. Streptavidin phycroerythrin stain (SAPE, Molecular Probes) was the fluorescent conjugate used to detect hybridized target sequences. All arrays in this study were assessed for "array performance" prior to data analysis.
Methods for data analysis and computer simulations
The methodology of data analysis and design of computer simulations have been described at length in the preceding sections. The relevant software for data analysis and simulations is included in the Additional Material Files [see the folder "MultivariateSearch"]. Here we supplement this information with a description of the generator of multivariate exchangeable normal random vectors which we used in our simulations.
Suppose we want to generate a normal random vector X in Rd with mean vector M ∈ Rd and covariance matrix Σ whose entries are σ2 and ρσ2 on and off diagonal, respectively. It is well-known that X can be represented in the form
X = M + CZ,
where Z is the standard normal vector with mean 0 in Rd and C is a d × d matrix with CCT = Σ. (Here CT denotes the transpose of C.) The matrix C may be chosen symmetric and can be computed using well-known algebraic procedures. However, our matrix Σ has a special structure:
Σ = (1 - ρ)σ2Id + ρσ21d × d,
where Id is a unit matrix of size d and 1d × d is a square matrix with all the d2 entries being equal to 1. Using this we look for C of the same form:
C = αId + β1d × d.
From the relations C2 = Σ and we have α2 = σ2(1 - ρ), 2αβ = ρσ2, so that
Authors' contributions
YX is responsible for the computational component of this study. He also participated in the methodology development. LK, AG, and AY have equally contributed to various methodological aspects of the proposed multivariate analysis. RF provided experimental data and biological interpretation of the net results of data analysis.
Supplementary Material
Additional File 1
The additional folder "MultivariateSearch" includes the following three sub-folders: 1. SAO _Simulation 2. SRS_Simulation 3. TSSearch Each subfolder contains a Unix executable file. The executable file "SASearch" implements the algorithm based on simulated annealing optimization. The executable file "SRSearch" implement the version based on simple random search. The exectuable file "TSSearch" for the two-stage search is is located in the sub-folder "TSSearch". Each sub-folder also contains two input files. The file "simulation04_UI.txt" is an input file for data analysis. Suppose the data file is named xxxx.marr, then the input file should be named as xxxx_UI.txt. To analyze the data from the file xxxx.marr, type: [Executable file] xxxx or [Executable file] 0 xxxx. The input file "simulation04_ui.txt" is designed for simulation experiments. To conduct simulations, one has to prepare an input file with the name: XXX_simu_ui.txt, where XXX is a string that follows the naming convention of computer files. An input file for data analysis with the name XXX_ui.txt is also needed. To run simulations, type: [executable file] 1 xxxx.
Click here for file
Acknowledgements
We thank Dr. Andrew Brooks, Dr. Mary D'Souza, Dr. Xiaoxia Zhu, Martha Erhardt, John Housel and Cristine Brower for technical assistance. Methodological discussions with Dr. Anthony Almudevar are greatly appreciated. We are grateful to anonymous reviewers whose comments have helped us improve the manuscript. The research is supported by NIH Grants P01 AG09524 from the National Institute on Aging, P30 DC05409 from the National Institute on Deafness & Communication Disorders, and the International Center for Hearing & Speech Research, Rochester, NY.
Figures and Tables
Table 1 Proportions of correct discoveries for each gene in the target set.
Gene SRS: 8 starts, 2500 steps SRS: 16 starts, 3600 steps
Correct discovery μ = 1, σ = 0.5 Correct discovery μ = 1, σ = 1 Correct discovery μ = 1, σ = 0.5 Correct discovery μ = 1, σ = 1
1 100% 100% 100% 100%
2 100% 100% 100% 100%
3 100% 100% 100% 100%
4 100% 100% 100% 100%
5 100% 97% 100% 99%
6 100% 99% 100% 100%
7 100% 100% 100% 100%
8 100% 100% 100% 100%
9 99% 78% 100% 76%
10 99% 76% 100% 78%
11 96% 72% 100% 74%
12 97% 74% 100% 72%
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| 15507138 | PMC529250 | CC BY | 2021-01-04 16:35:47 | no | BMC Bioinformatics. 2004 Oct 26; 5:164 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-164 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-301548260310.1186/1471-244X-4-30Research ArticleSingle photon emission computed tomography (SPECT) of anxiety disorders before and after treatment with citalopram Carey Paul D [email protected] James [email protected] Dana JH [email protected] der Linden Geoffrey [email protected] Heerden Barend B [email protected] Brian H [email protected] Soraya [email protected] Dan J [email protected] MRC Unit on Anxiety Disorders, Department of Psychiatry, University of Stellenbosch, Tygerberg, 7505, Cape Town, South Africa2 Department of Nuclear Medicine, University of Stellenbosch, Tygerberg, 7505, Cape Town, South Africa3 School of Pharmacy (Pharmacology), North-West University, Potchefstroom, South Africa2004 14 10 2004 4 30 30 4 5 2004 14 10 2004 Copyright © 2004 Carey et al; licensee BioMed Central Ltd.2004Carey et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Several studies have now examined the effects of selective serotonin reuptake inhibitor (SSRI) treatment on brain function in a variety of anxiety disorders including obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and social anxiety disorder (social phobia) (SAD). Regional changes in cerebral perfusion following SSRI treatment have been shown for all three disorders. The orbitofrontal cortex (OFC) (OCD), caudate (OCD), medial pre-frontal/cingulate (OCD, SAD, PTSD), temporal (OCD, SAD, PTSD) and, thalamic regions (OCD, SAD) are some of those implicated. Some data also suggests that higher perfusion pre-treatment in the anterior cingulate (PTSD), OFC, caudate (OCD) and antero-lateral temporal region (SAD) predicts subsequent treatment response. This paper further examines the notion of overlap in the neurocircuitry of treatment and indeed treatment response across anxiety disorders with SSRI treatment.
Methods
Single photon emission computed tomography (SPECT) using Tc-99 m HMPAO to assess brain perfusion was performed on subjects with OCD, PTSD, and SAD before and after 8 weeks (SAD) and 12 weeks (OCD and PTSD) treatment with the SSRI citalopram. Statistical parametric mapping (SPM) was used to compare scans (pre- vs post-medication, and responders vs non-responders) in the combined group of subjects.
Results
Citalopram treatment resulted in significant deactivation (p = 0.001) for the entire group in the superior (t = 4.78) and anterior (t = 4.04) cingulate, right thalamus (t = 4.66) and left hippocampus (t = 3.96). Deactivation (p = 0.001) within the left precentral (t = 4.26), right mid-frontal (t = 4.03), right inferior frontal (t = 3.99), left prefrontal (3.81) and right precuneus (t= 3.85) was more marked in treatment responders. No pattern of baseline activation distinguished responders from non-responders to subsequent pharmacotherapy.
Conclusions
Although each of the anxiety disorders may be mediated by different neurocircuits, there is some overlap in the functional neuro-anatomy of their response to SSRI treatment. The current data are consistent with previous work demonstrating the importance of limbic circuits in this spectrum of disorders. These play a crucial role in cognitive-affective processing, are innervated by serotonergic neurons, and changes in their activity during serotonergic pharmacotherapy seem crucial.
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Background
Significant advances in our understanding of the mediating psychobiology and the development of effective treatments for anxiety disorders have been made in recent years. Modern brain imaging techniques have proved useful in exposing specific albeit overlapping neurocircuitry that underlies individual anxiety disorders [1,2]. However, relatively little work has focused on the extent to which the anxiety disorders overlap with respect to changes in brain perfusion that accompany response to first-line treatment that is after all pharmacologically similar for different disorders.
The selective serotonin reuptake inhibitors (SSRIs) are currently recommended as first line medications for most anxiety disorders, including obsessive-compulsive disorder (OCD) [3], posttraumatic stress disorder [4] and social anxiety disorder [5]. A number of imaging studies have now examined the effects of SSRI's on brain perfusion in individual anxiety disorders. In OCD, attenuation of pre-treatment regional activation has been shown to correlate with treatment response in the anterolateral orbitofrontal cortex (OFC), caudate nucleus, thalamus, and temporal regions [6-11]. Results for studies assessing pre-treatment cerebral perfusion as a predictor of response, have, however, yielded mixed results. In some, an inverse relationship appears to exist with pre-treatment regional activation of the OFC [12], anterior cingulate, caudate [6] and subsequent responses to treatment. Conversely findings of higher prefrontal, cingulate and basal ganglia activation correlating with subsequent treatment response have also been reported [13,14]. In OCD co-morbid with depression, substrates of response to the SSRI, paroxetine, appear to differ based on pretreatment [15] activation patterns as well as changes that accompany treatment response when an SSRI is given in identical doses for either of the two conditions separately [16].
In social anxiety disorder SSRI treatment response accompanies attenuation of frontal, anterior and lateral temporal cortex, cingulate, and thalamic activity [17,18]. Higher anterior and lateral temporal cortical perfusion at baseline correlated with subsequent treatment response in the former study. The latter study also demonstrated some overlap of regions demonstrating attenuation of activity for both cognitive and pharmacotherapy interventions.
In PTSD, a single study by our group demonstrated medial temporal lobe deactivation with treatment irrespective of clinical response and medial prefrontal cortex activation correlated with treatment response. In addition, no baseline differences distinguished responders and non-responders to subsequent SSRI treatment [19].
In this present study, we hypothesised firstly, that response to SSRI treatment in this combined group of subjects with anxiety disorders (OCD, PTSD, SAD) would effect shared changes in rCBF affecting primarily limbic and related prefrontal regions and thus suggest some overlap between disorders in the mechanism of their response to effective treatment with SSRI's. Secondly, pre-treatment differences in regional perfusion would likely differentiate responders to subsequent treatment with citalopram across the anxiety disorders.
Methods
Subjects
Adult subjects with a primary diagnosis of OCD (n = 11), PTSD (n = 11) or SAD (n = 15) were recruited from the Anxiety Disorders Clinic of our tertiary hospital. All subjects were interviewed with the Structured Clinical Interview for the Diagnosis of Axis-I Disorders [20] to ascertain diagnosis according to DSM-IV criteria. Results for the PTSD group have been reported previously [19].
Comorbid major depression was an exclusion criterion in the OCD and SAD, but not in the PTSD subjects. Nevertheless, in all cases comorbid disorders were considered secondary in terms of temporal course, symptom severity, and associated distress. Patients previously treated with SSRI's had been free of medication for a minimum of four weeks for fluoxetine and two weeks for other SSRI's. In total 30 (81%) of the group were SSRI naïve. Subjects with other central nervous system disorders including previous head injury or epilepsy were excluded. The Institutional Review Board of our University approved the protocol and all patients gave informed written consent after a full explanation of the possible risks and benefits.
Pharmacotherapy and measures
All patients underwent treatment with citalopram, the most selective of the currently available selective serotonin reuptake inhibitors (SSRIs). The duration of the trial of treatment was 12 weeks for OCD and PTSD, and 8 weeks for SAD.
Dosage was initiated at 20 mg daily for the first two weeks and then maintained at 40 mg daily for the remainder of the study. Measures of symptom improvement were made bi-weekly by clinicians using the Clinical Global Impressions (CGI) scale [21]. Subjects with a CGI change score of 2 or less post-treatment were defined as responders, while those with scores greater than 2 were defined as non-responders.
Anxiety symptoms were also rated using disorder specific scales including the Liebowitz Social Anxiety Scale(LSAS) [22], the Yale Brown Obsessive-Compulsive Scale (YBOCS) [23] and the Clinician Administered Scale for PTSD [24]. Depressive symptoms were rated using the Montgomery-Asberg Depression Rating Scale (MADRS) [25].
SPECT imaging
Single photon emission computed tomography (SPECT) was conducted before and after pharmacotherapy. Subjects lay supine in a quiet dimly lit room for 30 minutes prior to injection of the radiopharmaceutical. Apart from administration of the injection by a physician, they remained alone in the room during this period. Subjects were asked to remain at rest during the 30 minute period and for 10 minutes after injection of the radiopharmaceutical.
An injection of 555 MBq (15 mCi) of technetium-99 m hexamethylpropylene amine oxime (Tc-99 m HMPAO) was given into an arm vein through a previously placed intravenous cannula. After completion of the rest period, SPECT imaging of the brain was performed, with the subject's head supported by a headrest, using a dual detector gamma camera (Elscint, Helix, GE Medical Systems, USA) equipped with fan beam collimators.
Data were acquired in the step-and-shoot mode, using a 360 degree circular orbit, with the detectors of the gamma camera as close as possible to the subject's head. The height of the imaging table and radius of rotation were noted for each subject and the same measurements were used for the follow-up study. Data were acquired using a 128 × 128 image matrix in 3 degree steps of 15 seconds per step.
Data were reconstructed by filtered backprojection, using a Metz filter (power = 5, FWHM = 14 mm) and a zoom factor of 2.29. The Chang (1978) method was used for attenuation correction. Scatter correction was not performed. The final reconstructed pixel size was 3.87 mm by 3.87 mm. Image files were converted from interfile to analyze format using conversion software (Medcon, Erik Nolf, UZ Ghent).
Stastical analyses were conducted on a voxel-by-voxel basis using the Statistical Parametric Mapping (SPM99, Wellcome Department of Cognitive Neurology, UK) [26]. The realign function was used to co-register baseline and posttreatment SPECT images for each subject and to generate a mean image for each subject. Realigned images were then normalised to the Montreal Neurological Institute (MNI) standard anatomical space to a value of 50 using proportional scaling. For this the transform function from the mean image for each subject to the normalised image with 4 mm3 voxels using 12 affine transformations and 7 × 8 × 7 non-linear basis functions was used. Standardised images were then smoothed using a Gaussian kernel with a FWHM of 12 mm3.
A multi-group study design was performed using 2 groups (responders and non-responders) with 2 conditions each (pre- and post-treatment). Contrasts were applied to look for areas of significant change post-treatment compared to pre-treatment. Contrasts were also used to search for areas of relative change in treatment responders compared to non-responders. A second design was employed to compare the baseline scans of responders to SSRI pharmacotherapy with those of non-responders. Contrasts were used to search for regions of significant differences on the baseline scans of responders compared to non-responders.
In view of a priori knowledge suggesting involvement of the cingulate, hippocampus, inferior frontal cortex, and striatum in the anxiety disorders, an uncorrected p-value of p < 0.001 corresponding to a t value of 3.34, was chosen for the analysis of these regions in order to minimize type I errors. Given the relative paucity of data in this area, we chose this uncorrected p-value, based on work using a similar methodology [19]. In order to minimize type I errors a significance level of p < 0.05 corrected for Gaussian Random Field Theory was used for the remainder of the brain. A spatial extent threshold of 5 voxels was also used at all times. Masking using a threshold proportional to 0.4 times the mean voxel value was used to minimize the analysis of voxels not located in grey matter. Furthermore, clusters were ignored if co-registration with a SPECT template demonstrated that they were located outside of grey matter.
Results
Twenty-two males and fifteen females with a mean age of 33.5 years (SD 9.8) completed the study. Clinical changes with pharmacotherapy for each disorder are provided in Table 1. This shows that for each of the anxiety disorders being studied, citalopram was effective in significantly reducing clinical measures of severity as determined by a CGI change score of 2 or less (much or very much improved). As such, 20 of 37 patients (54%) were responders to citalopram.
Table 1 Clinical parameters for all the groups (mean ± SD), (paired t-test).
Baseline Endpoint p
OCD (n = 11) YBOCS 26.6 ± 4.7 23.7 ± 5.8 0.001
MADRS 13.64 ± 9.6 9.9 ± 6.4 0.119
CGI-severity 4.7 ± 0.647 4.18 ± 1.1 0.025
CGI-improvement 3.1 ± 0.7
SAD (n = 15) LSAS 79.2 ± 30.2 63.1 ± 28.5 0.003
MADRS 15 ± 4.9 9.1 ± 5.9 0.004
CGI-severity 4.6 ± 0.8 3.3 ± 1.1 0.001
CGI-improvement 2.7 ± 1.2
PTSD (n = 11) CAPS 78.1 ± 16.9 45.5 ± 23.9 <0.01
MADRS 25 ± 6.7 15.9 ± 8.0 <0.01
CGI -severity 4.5 ± 0.5 2.5 ± 0.7 <0.01
CGI-improvement 1.9 ± 0.7
YBOCS, Yale Brow Obsessive-compulsive scale; MADRS, Montgomery Asberg Depression Rating scale; CGI-s, Clinical global impressions severity; CGI-I, Clinical global impressions – improvement; LSAS, Liebowitz Social Anxiety Scale; CAPS, Clinician Administered PTSD scale.
Comparison of pre- and post-treatment scans for the whole group showed decreased activity in 4 significant clusters in grey matter (Figure 1): These included the superior cingulate, right thalamus, anterior cingulate, and the left hippocampus (Table 2). Comparison of pre- and post-medication scans showed no significant areas of activation.
Figure 1 Combined group deactivation following treatment with citalopram. Regions of deactivation for the combined group of OCD + SAD + PTSD following treatment with citalopram. Significant grey matter clusters are seen in the superior cingulate, anterior cingulate, left medial temporal region (hippocampus).
Table 2 Localisation of significant clusters of deactivation following treatment for the combined group of OCD, SAD, PTSD. Zmax set to threshold of t = 3.34 corresponding to p < 0.001
Cluster size (voxels) t MNI co-ordinates (x,y,z) Brain region
44 4.78 -4,12,36 Superior cingulate
19 4.66 24,-28,12 Right thalamus
10 4.04 0,48,8 Anterior cingualate
7 3.96 -24,-12,-20 Left hippocampus
Comparison of responders with non-responders demonstrated that responders had a significantly greater decrease of activity in 4 clusters (Figure 2). These clusters were localised to the left precentral, right middle frontal, right inferior frontal and, left prefrontal regions (Table 3). Comparison of baseline scans of responders and non-responders did not reveal any significant differences.
Figure 2 Regional deactivation (responders > non-responders). Grey matter clusters of greater deactivation in responders vs non-responders were detected in the left precentral, prefrontal and right mid - and inferior-frontal regions.
Table 3 Localisation of significant clusters of deactivation in responders vs non-responders to SSRI treatment for the combined group of OCD, SAD, PTSD. Zmax set to threshold of t = 3.34 corresponding to p < 0.001
Cluster size (voxels) t MNI co-ordinates (x,y,z) Brain region
21 4.26 -24,-20,56 Left precentral
33 4.03 12,64,-8 Right mid-frontal
17 3.99 36,32,-20 Right inferior frontal cortex
5 3.85 8,-48,16 Right precuneus
18 3.81 -28,60,-8 Left prefrontal
Discussion
The main finding of this paper was that citalopram pharmacotherapy resulted in significant deactivation within anterior and superior cingulate cortex, the left hippocampus and the right thalamus in a combined group of patients with different anxiety disorders (OCD, PTSD, and SAD). Furthermore, deactivation was significantly more apparent in responders than in non-responders to SSRI treatment within precentral, right inferior, middle frontal and left prefrontal regions. Interestingly, no pre-treatment differences in regional perfusion between subsequent treatment responders vs non-responders were found.
Although there are important differences in the symptomatology of the anxiety disorders, these conditions do share certain aspects of their phenomenology, including heightened anxiety and avoidance behaviour. Furthermore, previous functional brain imaging work has demonstrated overlapping neurocircuitry across different anxiety disorders with activation of paralimbic circuitry and right inferior frontal cortex in a combined group comprising subjects with OCD, PTSD, and specific phobia [1]. Results in the present study now also point to an overlap in the functional neuroanatomy, primarily implicating paralimbic neurocircuitry, in treatment response to the same SSRI, citalopram, across anxiety disorders. In citalopram responders, effects across disorders were most pronounced in the mid, inferior and prefrontal cortex. In other regions, such as the striatum, data on treatment response and symptom provocation seems to indicate less overlap across anxiety disorders, which may suggest only partial and regionally specific overlap between disorders [1,2].
Specific limbic regions are well-known to play a role in broadly mediating anxiety. Early observations of epileptogenic cingulate lesions support its role in regulating affect [27]. Furthermore, recent work has suggested a role for the anterior cingulate in integrating cognitive and motivational processes. These include evaluating environmental cues and monitoring performance [28] On the other hand, a central role for the hippocampus in contextual aspects of fear conditioning has been demonstrated [29,30].
The findings here complement previous studies of OCD, PTSD, and SAD that have demonstrated a specific role for the cingulate and hippocampus in these conditions. Studies in OCD have shown increased anterior cingulate activity at baseline, or deactivation during pharmacotherapy with serotonergic agents [31]. In PTSD, anterior cingulate activity is also increased in some, although not all, studies of PTSD [32,33] Further, the anterior cingulate is deactivated during citalopram treatment of SAD patients [17]. Dysfunction of the hippocampus, as indicated by smaller hippocampal volume and declarative memory deficits, may play an important role in PTSD [34].
The medial prefrontal cortex comprises several related areas including anterior cingulate cortex. Lesions of this area are associated with suboptimal responses to stress, and the area has important inhibitory inputs to the amygdala which mediate extinction of fear conditioning [29]. The middle and inferior frontal cortex, on the other hand, is involved in encoding and retrieval of verbal memories. Our finding that the right inferior frontal cortex was more deactivated in responders is perhaps consistent with previous findings showing increased activity pre-treatment in this region across different anxiety disorders [2] and in some, but not all, studies of PTSD [35].
Serotonergic circuits innervate the medial prefrontal cortex and other limbic structures, and chronic administration of a serotonin reuptake inhibitor may lead to an increase in their neurotransmission. It is possible that the medial prefrontal cortex deactivation during serotonergic pharmacotherapy indicates that a compensatory increase of activity in this region is no longer needed after symptom improvement. Along these lines, a number of functional and electrophysiological imaging studies of depression have found that anterior cingulate hyperactivity predicts a positive response to pharmacotherapy, a finding that has also been interpreted as indicating the baseline presence of an adaptive compensatory response [36]. In addition changes in cognitive processing of frontal cortex may be secondary to symptom reduction caused by primary drug-induced changes within the limbic system. We have previously demonstrated similarly higher pre-treatment prefrontal perfusion in subsequent responders relative to non-responders using inositol in OCD [37]. Interestingly, inositol responsive disorders overlap with those responsive to SSRI's which may suggest that it is serotonergic components of these disorders that account for at least some of the overlap in perfusion patterns demonstrated here.
In contrast, however, increased activity in anterior cingulate or orbitofrontal region in OCD has also been shown to predict a poorer response to pharmacotherapy [9].
Perhaps increased activity in particular limbic circuits plays a different functional role in different psychiatric disorders. Only limited functional imaging studies of pharmacotherapy effects have involved provocation paradigms [38] and such differences in design may account for certain inconsistencies across studies. Alternatively, it is feasible that different effects in different disorders may also help explain inconsistencies. In the current dataset, however, we were unable to demonstrate any associations between baseline activity and pharmacotherapy response for the combined group.
This study is limited by the slightly different inclusion criteria (inclusion of secondary depression in PTSD group) and pharmacotherapy duration for different disorders. While the absence of untreated controls may to some extent limit the conclusions we can draw, comparing non-responders to responders we believe serves as a reasonable evaluation of changes that result from treatment response. The lower spatial resolution of SPECT may be considered a limitation nevertheless this study usefully emphasizes the importance of limbic regions (amygdala, hippocampus) in mediating anxiety. Furthermore, deactivation within these regions as well as richly connected frontal regions following SSRI treatment, particularly in responders, is clearly demonstrated. Further research combining pharmacological interventions and functional methodologies, and using tracers tailored to specific neurotransmitter receptors, will undoubtedly lead to increased understanding of the pathogenesis of the anxiety disorders and the mechanisms of response to treatment in the future.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
The authors contributed to the work in the following ways: drafting of manuscript (PDC, JW, DJS), data analysis (JW, PDC), psychiatric evaluations (DJHN, GVDL, SS, DJS), SPECT imaging (JW, BBVH). All authors have read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by the Medical Research Council of South Africa. Medication was provided by Lundbeck South Africa.
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| 15482603 | PMC529251 | CC BY | 2021-01-04 16:33:00 | no | BMC Psychiatry. 2004 Oct 14; 4:30 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-30 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-391550069310.1186/1471-2202-5-39Research ArticleDevelopment of the time course for processing conflict: an event-related potentials study with 4 year olds and adults Rueda M Rosario [email protected] Michael I [email protected] Mary K [email protected] Clintin P [email protected] Psychology Dept. University of Oregon, Eugene, USA2 Sackler Institute, Weill Medical College, Cornell University, NY, USA2004 22 10 2004 5 39 39 17 6 2004 22 10 2004 Copyright © 2004 Rueda et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Tasks involving conflict are widely used to study executive attention. In the flanker task, a target stimulus is surrounded by distracting information that can be congruent or incongruent with the correct response. Developmental differences in the time course of brain activations involved in conflict processing were examined for 22 four year old children and 18 adults. Subjects performed a child-friendly flanker task while their brain activity was registered using a high-density electroencephalography system.
Results
General differences were found in the amplitude and time course of event-related potentials (ERPs) between children and adults that are consistent with their differences in reaction time. In addition, the congruency of flankers affected both the amplitude and latency of some of the ERP components. These effects were delayed and sustained for longer periods of time in the children compared to the adults.
Conclusions
These differences constitute neural correlates of children's greater difficulty in monitoring and resolving conflict in this and similar tasks.
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Background
Conflict tasks involve the selection of a sub-dominant object or response in the presence of a competing dominant object or response. One of the most common tasks used in the literature to measure conflict is the flanker task. In this task, a target surrounded by stimuli suggesting either the same (congruent) or the opposite (incongruent) response is presented. Conflict is induced by incongruent flankers which, compared to congruent ones, produce larger reaction times and reduced response accuracy [1].
Different cognitive operations appear to be involved in processing conflict [2]. First, conflict has to be detected. This involves not only the recognition of the presence of conflict in the display but also the evaluation of the degree of conflict and the realization that the situation calls for a particularly careful action, therefore we use the term conflict monitoring to describe these operations. Once conflict is detected, it is necessary to determine the appropriate action in a goal-directed manner. Depending on the task, the resolution of conflict might involve different processes (e.g. inhibition, rule-holding, set switching, planning, etc.).
Monitoring and resolving conflict is a function of executive attention [3]. A network of brain areas have been shown to be active in tasks that involve conflict between stimulus dimensions, including the Stroop, flanker and spatial conflict tasks. These three tasks have been shown to activate a common neural network including the anterior cingulate cortex (ACC) and lateral prefrontal areas [2].
Recent studies have dissociated the brain areas within the executive network that are responsible for monitoring and resolving of conflict [4]. In a fMRI study, [5] the ACC was shown to be involved in the detection and monitoring of conflict, while lateral prefrontal areas have been shown to be mainly related to processes required to resolve the conflict [6]. Conflict processing has also been anatomically dissociated from orienting to relevant information that involves areas of the superior parietal cortex and superior frontal gyrus [7].
Young children have more difficulty than older children and adults performing tasks that involve conflict. Using conflict tasks adapted to children, we have reported a considerable reduction in the amount of interference produced by distracting information in children from 2 to 3 years of age [8]. This reduction continues up to 7 years but we have found a striking stability after this age up to adulthood [9]. We have interpreted these data as indicating a greater difficulty in monitoring and resolving conflict from competing stimulation in young children compared to older children and adults. The greater susceptibility to interference from irrelevant stimulation for young children has been reported using many different tasks: Flanker [10-12], S-R compatibility [13], Stroop [14], negative priming [15], etc. Other tasks have been used to assess the ability to inhibit non-appropriated responses (e.g. Go-NoGo [6] and Stop-signal [16,17] tasks). In these studies, children also show greater difficulty than adults in controlling prepotent, but incorrect, responses. Depending on the difficulty of the task, developmental differences in the ability to resolve conflict between children and adults can be observed up to middle childhood and early adolescence, suggesting that full maturation of the executive control network does not take place until early adulthood.
Some developmental studies have been carried out using neuroimaging aimed at understanding the brain mechanisms that underlie the development of executive functions. For instance, Casey and her colleagues [6] conducted a fMRI study comparing 7 to 12 year old children and adults in a Go-NoGo task. Despite a very similar pattern of activations in the prefrontal cortex following No-Go trials, the average volume of activation was significantly greater for children than for adults. The same pattern of results was obtained in similar studies [18,13], suggesting that the brain circuitry underlying executive functions is more focal and refined as it becomes more efficient with development. However, due to the limited time resolution of MRI, these studies cannot analyze whether there are additional maturational differences in the time course of activation of these areas.
A number of studies have used the high temporal resolution of event related potentials (ERPs) to assay the timing of action-monitoring processes with adults. The N2 is one of the ERP indexes that have been associated with executive attention. The N2 is a pre-response negative deflection in the ERP at around 300 ms post-stimulus, which appears to be larger (more negative) for trials that involve more conflict. The N2 is observed over parietal and frontal leads and has been obtained with both flanker [19,20] and Go-NoGo tasks [21]. In both situations, the N2 has been associated with the withholding of a prepotent, but inappropriate, response. In a recent ERP study with a flanker task, van Veen and Carter [20] linked the scalp distribution of activity associated with the N2 to a source of activation originating at the caudal portion of the ACC, supporting a connection between this electrophysiological index and the executive attention network.
Only a few ERP studies have been conducted with children using conflict tasks. In one of these studies, a flanker task was used to compare conflict resolution in three groups of children aged 5 to 6, 7 to 9 and 10 to 12, and a group of adults [12]. As expected, the behavioral results showed a consistent reduction of the interference produced by the flanker with age. In addition, developmental differences were found in two ERP components, the lateralized readiness potential (LRP) and the P3. The LRP seems to be related to response preparation [22] while P3 is thought to be an index of stimulus evaluation [23]. Ridderinkhof & van der Molen [12] found differences between children and adults in the latency of the LRP, but not in the latency of the P3 peak, suggesting that developmental differences in the ability to resist interference are mainly related to response competition and inhibition, but not to stimulus evaluation.
Recently, Davis et al. [24] conducted an ERP study using a Go-NoGo task with a group of 6 year old children and a group of adults. In this study, differences between children and adults in the latency of the P3 peak were also reported. Both the amplitude and the latency of the P3 were greater for NoGo trials compared to Go trials, although this pattern was similar for children and adults. Nevertheless, in contrast with the literature, no differences in the amplitude of the N2 component were observed as a function of type of trial. Finally, a late positive component (LPC) was observed only for children over the frontal leads. The amplitude of this component was reduced for NoGo trials compared to Go trials. This difference started around 550 ms post stimulus and extended over a time window of 600 ms. The modulation of the amplitude of the LPC might result from the greater prefrontal activation observed in children when a response has to be inhibited, as shown by imaging studies that used the same type of task [6]. This would be consistent with the study by Ridderinkhof and van der Molen, in which developmental differences seem to appear in ERP components related to response selection to a greater extent than those associated with stimulus selection.
So far, the literature suggests that monitoring and resolution of conflict involve separate brain areas in adults, and that children activate similar, but somewhat larger areas. Moreover, the N2 component of the ERPs appears to be related to activation coming from the ACC and to be mainly associated with conflict monitoring, whereas later components (e.g. LPC) might result from prefrontal sources of activation and could be related to conflict resolution.
The flanker task is an appropriate experimental paradigm for assessing conflict processing. The aim of this study was to use a version of this task with children and adults to assess developmental differences in the time course of the different operations involved in conflict processing. We have recently developed a flanker task appropriated for use with children as young as 4 years [9]. In this task, a row of five fish appear in the center of the screen and the child's job is to help "feed" the middle fish by pressing the key that corresponds to the direction in which the middle fish is pointing. In the congruent trials, the fish surrounding the middle one point in the same direction as the middle fish, while in the incongruent trials, the flanker fish point in the opposite direction, suggesting an incorrect response (see Figure 1).
To study the time course of conflict processing, we examined the latency to significant differences between the ERPs for congruent and incongruent trials, and the sustainability over time of these differences. In the adult literature, the amplitude of the N2 component has been shown to be modulated by the congruency of distracting information in flanker tasks and has been related to conflict monitoring, but this component was not assessed in the study by Ridderinkhof and van der Molen. We aim to replicate the effect with adults using our child-friendly flanker task as well as analyzing this ERP component in 4 year olds. While there may be subcomponents of the N2 sensitive to other types of manipulations such as the degree to which the predicted identity of a display is violated [25], in our study we will focus on the effect of the congruency of flankers that are equally expected to ensure the activity we measure is related to conflict. In addition, differences between children and adults in stimulus selection processes as reflected by the P3 component could be playing a role in selecting a relevant stimulus among distractors, and these were examined. Finally, we explored whether the reduced amplitude of the LPC for NoGo situations reported by Davis et al. [24] for children, but not adults, is also observed with a flanker task. This outcome will rule out the possibility that the reduction of the LPC is associated with the withholding of a motor response, and will make more plausible the hypothesis of it being related to resolving situations that call for particularly careful actions as those in which conflict is induced by distracting flankers.
Results
Behavioral results
Means of the median RT and percentage of errors are shown in Table 1 for both children and adults. A mixed-designed ANOVA was performed with Group as a between subject factor and Flanker Type as a repeated measure with RT as the dependent measure. The ANOVA revealed significant main effects of Group (F(1,38) = 74.42; p < .001) and Flanker Type (F(1,38) = 6.15; p < .05) as well as a significant Group × Flanker Type interaction (F(1,38) = 4.65; p < .05). In addition, conflict effects were examined in the two groups. Conflict effects refer to the difference between congruent and incongruent conditions, and they can be measured using both RT and accuracy variables. Statistical significance of these effects was tested for each group independently using paired t-tests. The conflict effect was significant for both RT and accuracy for adults (t(17) = -6.2; p < .001 and t(17) = -2.54; p < .05 respectively) as well as for children (t(21) = -2.57; p < .05 and t(21) = -2.51; p < .05 respectively for RT and accuracy effects). Conflict effects were also examined for the subgroup of children (n = 14) with useful ERP data. For this group, the conflict RT was marginally significant (t(13) = -2.004; p = .066) while the conflict accuracy was significant (t(13) = -2.4; p < .05).
A similar 2 (Group) × 2 (Flanker Type) ANOVA was conducted using percentage of errors as the dependent measure. Again, the main effects of Group (F(1,38) = 22.33; p < .001) and Flanker Type (F(1,38) = 7.08; p < .05) were significant, whereas the Group × Flanker Type interaction was marginal (F(1,38) = 3.28; p = .078).
ERP snalysis
Figure 2 shows the ERPs of adults and children at leads located at the midline of frontal and parietal sites. Despite general differences in overall amplitude and latency, the waveforms for the two groups were strikingly similar. We observed N1 and N2 components over frontal leads and a P3 over parietal leads for both children and adults. In addition, children showed a pre-response late positive component (LPC) over frontal channels.
We hypothesized that particular ERP components would be sensitive to the presence of conflict in the display. To examine these predicted effects, we computed the latency and amplitude of the peaks of the N1, N2 and P3 components in both groups and of the LPC in the group of children separately for congruent and incongruent conditions in a selection of frontal and parietal leads (see Table 2). The selected leads had equivalent locations in the children's 128 and adults' 256 channels arrays and corresponded to particular 10-10 international system positions [26]. Overall amplitude refers to the maximum negative or positive voltage values (in microvolts, μVolts) within the ERP component. Latency was computed in milliseconds (ms) from the time the target was presented to the time of the maximum positive or negative peak within the ERP component. To calculate both peak amplitudes and peak latencies we selected time-windows in which the waveforms deflections defining each ERP component were included, and computed the latency and amplitude of the peak within those windows using the tool provided by the Net Station 3.0 (EGI software). These time-windows, specified in Table 2, were different for children and adults.
Because of the greater presence of artifacts in the children's ERPs, a significantly larger number of segments were used to compute the averaged ERPs in the adult data (see Method section). To control for possible influence of this difference on the amplitude of the ERPs [27], the number of segments used to compute the averaged ERPs was included as a covariate of the differences between children and adults in the dependent measures entered in the analysis. Separate 2 (Group) × 2 (Flanker Type) × 3 (Channel) ANCOVAs with the means of peak latency and amplitude as dependent measures were conducted separately for the N1, N2 and P3 components. In addition, we conducted a 2 (Flanker Type) × 3 (Channel) ANOVA with both the peak latency and amplitude of the LPC only for children. In these analysis we used the Huynh-Feldt correction for sphericity as needed. The results of these ANOVAs are summarized in Table 3.
For the peak latency, the main effect of Group was significant in all the ERP components. The main effect of Flanker Type was significant for the N1, P3 and LPC. No significant interactions were found for any of the ERP components for the latency data.
For the peak amplitude values, the main effect of Group was significant for the N1 and N2. The main effect of Flanker Type was not significant for any of the components although it was marginally significant for the N1 and for the LPC in the children. The main effect of Channel was marginal for the N2 and highly significant for the P3. Interestingly, there was a significant Group × Flanker interaction for the P3, indicating a significant effect of the type of flankers in the peak amplitude of this component for children (F(1,30) = 6.0; p < .05) but not for adults (F < 1).
Although the peak amplitude of ERP components is a widely used measure to look at effects of the variables of interest in the patterns of brain activation, these effects can certainly occur along the entire epoch and not only in the peaks of the components. To examine the effect of congruency in the amplitude of the registered activity in the entire epoch, we computed amplitude differences between congruent and incongruent conditions sample by sample along the ERP segment in all channels for children, and a selection of channels around the Fz, Fcz and Pz positions in the adult data. T-tests were carried out to assess the significance of these differences along the epoch. In Figure 3, the leads in which the congruent vs incongruent differences in amplitude were found significant are highlighted for both children and adults, as well as the time windows for these differences and the ERP components in which the differences appear. In addition, graphs displaying the ERPs for each flanker condition at Fz, Fcz and Pz positions are shown in Figure 4 for adults and children. The shadowed areas in these figures show the sections of the ERPs in which congruent vs. incongruent differences were significant.
Correlations between reaction time and electrophysiological measures
In order to explore possible associations between the amplitude and latency dimensions of the ERP components and the particular cognitive processes measured by reaction time (RT), we examined correlations between RT measures and patterns of brain activity at Fz, Fcz and Pz positions and their left and right equivalents. Correlations were computed independently for adults and children. The first set of correlations involved the conflict effect as measured by subtracting the RT for congruent trials from the RT for incongruent trials (conflict score) and the overall RT as behavioral measures, and the overall (across flanker conditions) latency and amplitude of the ERP components as electrophysiological measures. For the adults, the overall RT correlated negatively with the amplitude of the N1 at channel F3 (r = -0.53; p < .05), and positively with the latency of the N2 component at channel Fc4 (r = 0.47; p < .05). For the children, the overall RT correlated negatively with the amplitude of the P3 at channel P4 (r = 0.72; p < .01), and positively with the latency of the N1 at channel F4 (r = 0.60; p < .05) and the N2 at channel Fcz (r = 0.58; p < .05). No significant correlations were established between any of the overall ERP components and the conflict score in either adults nor children.
Finally, correlations were calculated between the behavioral measures of overall RT and conflict score, on the one hand, and the effect of flankers on the amplitude and latency of the peaks of the ERP components on the other. For the adults, overall RT correlated positively with the N2 latency effect at Fcz (r = 0.59; p < .01) and the P3 amplitude effect at P4 (r = 0.46; p = .05), whereas the correlation was negative with the N2 latency effect at Fc4 (r = -0.54; p < .05). On the other hand, the conflict score correlated positively with the amplitude effect on the N2 at Fc4 (r = 0.47; p < .05) and Fcz (r = 0.41; p = .09), although the last effect was only marginal. In the children, the overall RT correlated negatively with the N2 latency effect at Fc3 (r = -0.56; p < .05), and marginally with the N1 amplitude effect at Fz and F4 (r = -0.50; p = .07 and r = -0.46; p = .09 respectively). However, the conflict score correlated negatively with the P3 latency effect at channel P4 (r = -0.81; p < .001).
Discussion
As expected, young children showed increased difficulty compared to adults in both processing the target and dealing with distracting information incongruent with the correct response. The greater difficulty of the task for children was reflected in children's much longer overall RT and conflict scores. The main goal of the current study was to analyze the differences in brain activation between children and adults underpinning their behavioral differences. Our results show differences among children and adults in both the time course of brain activations overall and across flanker conditions.
Time course of target processing
Significantly larger N1 and N2 amplitudes were found for children than for adults, whereas the P3 showed equivalent amplitudes in the two groups. Children usually show larger event related potentials and often with delayed latency compared to adults [12,24]. These differences in general amplitude and latency relate to a variety of maturational factors as brain size, skull thickening and synaptic density [28]. It is not clear how the amplitude of the ERPs components relates to the effort to process the target. In both adults and children, the overall RT correlated negatively with the amplitude of some of the waveform components (N1 for adults, P3 for children), consistent with the idea that the amplitudes of the ERPs components are associated with cognitive operations that can facilitate the speed of processing the target.
Differences in latency of the ERPs components can be of special interest when it comes to accounting for differences in RT. Accordingly, children showed significant delays in the latency of all components compared to adults. The difference between children and adults was greater in the later components, suggesting that children's delay in target processing is more pronounced in later stages of processing. An objection to this conclusion is that latency and amplitude of the waveforms deflections are not independent, given the fact that greater peak latencies can be expected with more pronounced differences in amplitude. However, two pieces of information in our data point to the fact that differences in amplitude cannot account for all differences in latency. First, the P3 component shows a large difference in latency despite no overall differences in amplitude. Second, the overall RT appears to correlate negatively with the amplitude of some of the components in both children and adults, whereas it correlates positively with latency measures.
In addition, overall RT appear to correlate with the overall amplitude and latency of some ERP components, while no significant correlations are established between these and the conflict score, suggesting that the general speed of processing but not the ability to manage conflict might be related to the general form of the ERP.
Time course of conflict resolution
It should be borne in mind in comparing the adult data with previous studies conducted with other flanker tasks that the child-friendly version of this task used in our study was very easy for adults. This could account for the modest amplitude differences between congruent and incongruent trials in this study compared to what has been found with other versions of the flanker task [29]. From when children are first able to perform reaction time tasks, the time to respond appears to decline linearly to adulthood as do the conflict scores up to seven years of age [9]. The continuous nature of the two behavioural reductions suggest that although the flanker task might be easier for older children and adults the same mental processes are involved.
As shown in Table 2, the manipulation of congruence between relevant and distracting information in the display produced some effects on both the amplitude and latency of the ERP elicited by the target (see also Figure 4). In consonance with the literature, adults show an effect in the N2 component at frontal and parietal areas around the midline as well as an effect on the P3 observed at the left and mid parietal leads. For the P3, these effects are found on the latency of the peaks as well as the amplitude of particular time windows within the components (see Figure 3). For the N2, the effect is mainly observed on the amplitude of the component. In addition, adults show an effect on the amplitude and latency of the N1 that is localized at the frontal midline (Fz).
On the other hand, 4 year old children do not show differences in brain activity among the two flanker conditions until approximately 500 ms post target. Therefore, the effect of flankers is not observed at this age in the relatively early N1 component, and only very weakly at the N2 (see Figures 3 &4). However, as in the adults, children show robust frontal and parietal effects. The frontal effect is observed in the LPC, and consists of a less positive amplitude of the component during incongruent relative to congruent trials. The parietal effect in children is observed in a late P3 component that, as for adults, consists of a greater amplitude for incongruent trials than congruent ones. Although the frontal effect appears around 200 ms earlier than the parietal effect, in both cases, the amplitude effect lasts for over 500 ms. The amount of time the amplitude difference is sustained constitutes an important difference between children and adults, and may reflect the time course of brain mechanisms supporting the monitoring and resolution of conflict.
As mentioned in the introduction, the N2 effect has been consistently found in different versions of the flanker task, and has been related to action-monitoring processes implemented in the anterior cingulate [20]. Botvinick, et al [5] more precisely specified the role of ACC in detecting and signaling conflict. In consonance with these data, our results showed a modest positive correlation between the effect of flankers on the amplitude of the N2 and the conflict score in adults.
It is less clear what type of cognitive operation is underlying the P3 effect. In a review of mental chronometry, Coles et al. [30] distinguished between amplitude and latency effects in their analysis of the conditions that elicit the P3. According to these authors, amplitude differences in the P3 can be elicited by stimuli that differ in their probability (either objective or subjective) of occurrence, but also in the amount of goal-relevant information contained in the stimulus, whereas latency differences might be associated with time differences in stimulus evaluation or categorization. In our task, the two types of trials had equal probability of occurrence. Consequently, the greater P3 amplitude for incongruent trials is more likely to be associated with the need for a more careful evaluation of the stimulus to determine the correct response. If we only look at the amplitude effect on the P3 at the particular time window in which the effect is found significant in the adult data, a positive correlation between the amplitude effect on the P3 and the conflict score is found (r = 0.62; p < .01). This suggests that the greater the effort to select the correct response, the greater the relative P3 amplitude for incongruent trials, and therefore the greater the conflict score.
Our data fit quite well within the sequence of cognitive operations suggested by the literature. In the adults, conflict detection, as reflected by the frontal effects, is a few tens of milliseconds delayed for incongruent trials. Immediately after, we observe an effect over parietal leads apparently related to the effort to determine the correct response. This process is approximately 40 ms delayed when incongruent flankers are presented. Nonetheless, the delays in the N2 and P3 components under incongruent conditions may not be completely independent, as suggested by their quite overlapping topographies (see Figure 3).
In the children, we have also observed frontal and parietal effects occurring prior to the response. However, probably due to their generally slower capacity for processing information, the frontal effect is quite delayed in comparison with adults, and mostly observed on the LPC instead of the N2. Although determining whether the frontal effects observed in these two different ERP components in children and adults are equivalent will require further research, the fact that the effect on the LPC occurs over the frontal leads and prior to the parietal effect supports its involvement in conflict monitoring.
Likewise, a remarkable increase in the amplitude and delay of the latency of the P3 peak is observed for the children on incongruent trials. In consonance with the result of the study by Ridderinkhof & van der Molen [12], both children and adults showed the effect on the peak latency to the same degree. However, in their study, Ridderinkhof & van der Molen did not report amplitude effects. Interestingly, our data reveal a greater effect on the P3 amplitude for the 4 year old children compared to the adults. This suggests that children at this age take longer than adults in evaluating the display. This delay occurs in addition to the delay in response selection revealed by the adults vs. children differences in the LRP shown by Ridderinkhof & van der Molen.
At both the frontal and parietal components, the flanker effect is sustained for a longer period of time in the case of children. These differences in patterns of brain activation are likely to underlie the observed behavioral differences in conflict resolution between children and adults. Certainly, the brain processes underlying the detection of conflict and the selection of the appropriate response appear to take longer to be resolved into a correct action in the brains of 4 year old children.
The distribution of the flanker effects shown in Figure 3 appears to be another important difference between 4 year old children and adults. In adults, the frontal effects appear to be focalized in the mid line (Fz for N1, and Fcz for N2), while in children we observed the effects mostly at pre-frontal sites and in a broader number of channels, including the mid line (Fz) and leads on the left (F3) and right (F4) sites. In addition, the effect on the P3 appears to be left-lateralized in the adults data but lateralized to the right side in the children. The focalization of the signals in adults as compared to children is consistent with neuroimaging studies conducted on developmental populations in which children appear to activate a broader area of the brain compared to adults when exposed to the same task [13,18].
Conclusions
A major new finding of this study is the difference found between 4 years old children and adults in the longer latency and the sustained congruency effect on the ERPs. Consistent with their larger conflict scores in reaction time, these differences shed light on the brain mechanisms underpinning the much greater difficulty for children in monitoring and resolving conflict.
Methods
Participants
Eighteen young adults (12 women, 6 men; mean age: 23 years; SD: 6.45) and twenty-two children (11 girls, 11 boys; mean age: 4 years, 4 months; SD in months: 2.2) participated in the study. All participants were right-handed. The adult participants and the parents of children involved in the study gave written consent prior to the experimental session. Both children and adults were paid for participating in the study.
Procedure
The stimulus sequence for each trial was controlled using E-Prime (Psychological Software Tools, Pittsburgh, PA). Each trial began with a sound to alert participants about the start of the trial. One second after the sound, a line with five drawn fish was presented in the center of the screen (Figure 1). The central fish was the target, and the ones on the sides the flankers. Participants were instructed to press the mouse button that matched the direction toward which the middle fish was pointing while ignoring the flanker fish. Half of the trials were congruent and half incongruent. In the congruent trials, the five fish were pointing in the same direction; in the incongruent trials, the flanker and target fish were pointing in opposite directions. The experiment was presented to the children as a game in which they will be shown a hungry fish surrounded by other fish. The children were told the hungry fish is always the one in the middle and that they will make it happy by feeding it when they press the key corresponding to the direction it is swimming. The target display was presented until a response was made, or up to 1700 ms in the case of adults, or 5000 ms in the case of children. After the response was given, the display did not change for another second, after which feedback was provided. Feedback consisted of a 1500 ms long animation of the middle fish, showing it happy (bubbles coming up from his mouth) for the correct response, or sad (bubbles coming down the eye) for the incorrect or missed trials. The inter-trial interval was 1500 ms for adult participants. For the children, the experimenter initiated each trial once the child was focused on the computer monitor. A fixation cross was continuously displayed in the center of the screen except when targets and feedback were presented. All participants were instructed to be as fast and accurate as possible. Both children and adults completed five blocks of 20 trials each, preceded by 12 practice trials. Children could repeat the practice block as many times as needed until it was clear they understood the instructions. The experimental session was about 35 minutes long for adults and 45-to-60 minutes long for children.
EEG recording and data processing
EEG was recorded using a 128-channel Geodesic Sensor Net [31] for children, and a 256-channel net for adult participants. The GSN is a reliable method for acquiring high-density EEG data, and given its fast application, this method is specially convenient for children [32].
The EEG signal was digitized at 250 Hz. Impedances for each channel were measured prior to recording and kept below 80 kΩ during testing. Recording in every channel was vertex-referenced and the time-constant value was 0.01 Hz for both children and adults. Data were recorded using Net Station 2.0 (EGI Software) and processed using Net Station 3.0.
Once acquired, data were filtered using a FIR bandpass filter with 12 Hz low-pass and 1 Hz high-pass cutoffs. Continuous EEG data was segmented into target-locked epochs. The epochs were 1 sec. long for adults (-200 ms to 800 ms around target) and 1.7 sec. long for children (-200 ms to 1500 ms around target). Segmented files were scanned for artifacts with the Artifact Detection NS tool using a threshold of 70 μV (adults) or 100 μV (children) for eye blinks and eye movements. Segments containing eye blinks or movements as well as segments with more than 25 bad channels were rejected. Within each segment, channels with an average amplitude of more than 200 μV or a difference average amplitude of 100 μV were also discarded from further processing. Finally, particular channels were rejected if they contained artifacts of any kind in more than 50% of the segments. Children's data were also visually inspected trial by trial to make sure the parameters of the artifact detection tool were appropriate for each child. As a consequence of the artifact detection procedure, an average of 36% of the ERP segments in the children data and an average of 18.5% of the ERP segments in the adults were rejected. The larger number of rejected segments for the children was due to a higher frequency of blinks, mouth and/or head movements, speaking, and other behaviors that generate artifacts on the EEG signal during the experimental procedure. Thus, we decided to have a criterion of a minimum of 12 clean segments per flanker condition among the correctly responded trials for further processing individual data. All adults participants and a total of 14 children reached this selection criterion. The average number of segments included in the averaged ERPs was 53.2 (SD: 23.1) for the children (26.6 per flanker condition; SD: 11.41 and 11.48 respectively for congruent and incongruent conditions), and 80.3 (SD: 17.3) for adults (40.3, SD: 17.3 for congruent trials; and 40.0, SD: 9.8 for incongruent trials), and this children vs adults difference was significant (t(23.4) = -3.66; p < .001).
Artifact-free segments for correct responses were averaged across conditions and subjects and re-referenced against the average of all channels. The 200 ms preceding the target served as baseline.
Authors' contributions
MRR, MIP & MKR designed the study and participated in the theoretical elaboration of the paper. MRR was responsible for the data collection and data analysis processes. CPD designed and performed part of the statistical analysis on the EEG data.
Acknowledgements
This study was supported by a 21st Century Science award from the James S. McDonnell Foundation. Dr Rueda was partially supported by a grant from La Caixa – USA program. We would like to acknowledge Dr. Phan Luu and Gwen Frishkhof from Electrical Geodesic Inc for helpful comments and support on processing the EEG data with NS.
Figures and Tables
Figure 1 Representation of the flanker task used in the experiment
Figure 2 Comparison of children's and adults' ERPs. ERPs are the average of the artifact-free segments for correct responses.
Figure 3 Distribution of significant congruency effects in children and adults Each image represents the distribution of the 128 (children) or 256 (adults) channels on the scalp. Marked channels showed significant (p < .05) congruent vs. incongruent differences. The time windows of the differences are color-coded and exposed in the tables below the montages.
Figure 4 Comparison of flanker effects in adults and children ERPs. ERPs are the average of the artifact-free segments for correct response for each flanker condition. Cong: congruent trials; Incg: Incongruent trials.
Table 1 Children and adults RT (in ms) and percentage of errors in the flanker task
RT % Errors
Overall C I
Conflict
Overall C I
Conflict
Adults 431 (86) 415 (83) 445 (85) 30** (20) 1.4 (1.9) 0.2 (0.65) 2.6 (0.65) 2.3* (3.9)
Children (n = 22) 1614 (489) 1490 (476) 1913 (916) 424* (773) 16.7 (13.6) 10.6 (12.4) 22.87 (21.9) 12.98* (22.9)
Children (n = 14)
a
1385 (349) 1292 (327) 1525 (563) 233# (436) 10.4 (8.2) 8.4 (8.9) 12.38 (8.7) 3.95* (6.16)
SDs are shown in brackets. Conflict is measured by subtracting congruent (C) from incongruent (I) data. Significance of the conflict effects (using t-tests): ** p < .001; * p < .05; # p < .07. a Children with a minimum of 12 clean ERP segments
Table 2 Latency and amplitude of the peak of each ERP component by channel location, flanker condition and group
N1 N2 P3 LPC
Fz F3 F4 Fcz Fc3 Fc4 Pz P3 P4 Fz F3 F4
Peak Latency
Adults C 111 (13.5) 114 (15.8) 110 (17.8) 264 (42.6) 272 (47.7) 279 (47.2) 397 (55.7) 377 (60.0) 381 (59.3)
I 117 (15.6) 119 (17.3) 110 (24.2) 286 (46.3) 273 (54.0) 266 (50.6) 430 (69.4) 418 (87.8) 389 (72.4)
Children C 147 (14.4) 146 (14.4) 152 (18.9) 395 (54.8) 401 (49.8) 399 (55.7) 562 (70.9) 613 (158.5) 600 (156.9) 843 (179.6) 881 (153.5) 890 (141.7)
I 151 (12.2) 155 (16.0) 154 (14.9) 391 (46.2) 405 (49.9) 397 (63.0) 654 (168.2) 691 (193.6) 622 (132.9) 1014 (180.5) 1054 (169.4) 939 (192.9)
Peak Amplitude
Adults C -2.14 (1.4) -1.92 (1.41) -1.75 (1.3) -3.56 (3.42) -2.78 (1.83) -2.69 (2.06) 8.40 (3.88) 5.82 (2.55) 7.81 (4.97)
I -2.55 (1.32) -2.13 (1.39) -2.01 (1.21) -3.70 (3.03) -2.70 (1.55) -2.58 (1.78) 8.49 (3.40) 5.56 (2.19) 7.37 (4.85)
Children C -6.20 (4.59) -6.99 (3.34) -7.15 (3.97) -9.59 (4.54) -9.46 (4.03) -8.91 (3.45) 8.92 (4.79) 6.23 (3.33) 9.38 (5.11) 6.60 (3.69) 6.33 (3.8) 5.29 (2.61)
I -8.27 (5.24) -9.29 (5.56) -8.29 (3.93) -10.55 (4.84) -9.22 (4.83) -9.04 (3.32) 10.91 (3.53) 7.15 (3.35) 12.14 (4.95) 3.39 (3.51) 3.53 (3.51) 4.06 (2.91)
SDs are shown in brackets. The latency values are expressed in milliseconds and the amplitude values in μVolts. The time-windows used to compute the latency and amplitude values were the following respectively for adults and children: 50–150 ms and 100–200 ms for the N1, 200–400 ms and 300–550 for the N2, 250–650 ms and 400–1100 ms for the P3; and 550–1300 for the LPC in children. C: congruent; I: incongruent.
Table 3 Summary of results of the ANCOVAs performed for each ERP component using the peak latency and peak amplitude data
N1 N2 P3 LPC
F df F df F df F df
Peak Latency
Group (G) 31.7*** 1, 29 58.1*** 1, 29 57.53*** 1, 29
Flanker (F) 4.41* 1, 30 <1 1, 30 8.09** 1, 30 8.37* 1, 13
Channel (Ch) <1 1.5, 43.9 <1 1.5, 46.1 <1 1.8, 53.8 1.66 1.5, 19.2
G × F <1 1, 29 <1 1, 30 1.31 1, 30
G × Ch 3.17# 1.5, 43.9 <1 1.5, 46.1 1.22 1.8, 53.8
F × Ch 2.49 1.6, 48.6 1.37 1.5, 45 1.78 2, 60 3.13# 2, 26
G × F × Ch <1 1.6, 48.6 1.16 1.5, 45 <1 2, 60
Peak Amplitude
Group (G) 27.67*** 1, 29 24.49*** 1, 29 1.02 1, 29
Flanker (F) 3.33# 1, 30 <1 1, 30 2.69 1, 30 3.54# 1, 13
Channel (Ch) <1 1.7, 52.3 3.07# 2, 60 15.63*** 1.6, 47.6 <1 2, 26
G × F 1.75 1, 30 <1 1, 30 4.13* 1, 30
G × Ch 3.46* 1.7, 52.3 <1 2, 60 1.71 1.6, 47.6
F × Ch <1 2, 60 1.26 2, 60 1.31 1.6, 48.7 2.84 1.4, 18.5
G × F × Ch <1 2, 60 <1 2, 60 1.72 1.6, 48.7
The number of segments used to compute the averaged ERPs was included as a covariate to account for group differences. Significance of effects: *** p < .001; ** p < .01; * p < .05; # p < .10.
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| 15500693 | PMC529252 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Oct 22; 5:39 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-39 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-411551128810.1186/1471-2202-5-41Research ArticleNeural stem cells express melatonin receptors and neurotrophic factors: colocalization of the MT1 receptor with neuronal and glial markers Niles Lennard P [email protected] Kristen J [email protected]ón Castro Lyda M [email protected] Chung V [email protected] Rohita [email protected] Catherine R [email protected] Laurie C [email protected] David L [email protected] Department of Psychiatry and Behavioural Neurosciences, McMaster University 1200 Main Street West, Hamilton ON L8N 3Z5, Canada2 Department of Pathology and Molecular Medicine, McMaster University 1200 Main Street West, Hamilton ON L8N 3Z5, Canada2004 28 10 2004 5 41 41 11 3 2004 28 10 2004 Copyright © 2004 Niles et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In order to optimize the potential benefits of neural stem cell (NSC) transplantation for the treatment of neurodegenerative disorders, it is necessary to understand their biological characteristics. Although neurotrophin transduction strategies are promising, alternative approaches such as the modulation of intrinsic neurotrophin expression by NSCs, could also be beneficial. Therefore, utilizing the C17.2 neural stem cell line, we have examined the expression of selected neurotrophic factors under different in vitro conditions. In view of recent evidence suggesting a role for the pineal hormone melatonin in vertebrate development, it was also of interest to determine whether its G protein-coupled MT1 and MT2 receptors are expressed in NSCs.
Results
RT-PCR analysis revealed robust expression of glial cell-line derived neurotrophic factor (GDNF), brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in undifferentiated cells maintained for two days in culture. After one week, differentiating cells continued to exhibit high expression of BDNF and NGF, but GDNF expression was lower or absent, depending on the culture conditions utilized. Melatonin MT1 receptor mRNA was detected in NSCs maintained for two days in culture, but the MT2 receptor was not seen. An immature MT1 receptor of about 30 kDa was detected by western blotting in NSCs cultured for two days, whereas a mature receptor of about 40 – 45 kDa was present in cells maintained for longer periods. Immunocytochemical studies demonstrated that the MT1 receptor is expressed in both neural (β-tubulin III positive) and glial (GFAP positive) progenitor cells. An examination of the effects of melatonin on neurotrophin expression revealed that low physiological concentrations of this hormone caused a significant induction of GDNF mRNA expression in NSCs following treatment for 24 hours.
Conclusions
The phenotypic characteristics of C17.2 cells suggest that they are a heterogeneous population of NSCs including both neural and glial progenitors, as observed under the cell culture conditions used in this study. These NSCs have an intrinsic ability to express neurotrophic factors, with an apparent suppression of GDNF expression after several days in culture. The detection of melatonin receptors in neural stem/progenitor cells suggests involvement of this pleiotropic hormone in mammalian neurodevelopment. Moreover, the ability of melatonin to induce GDNF expression in C17.2 cells supports a functional role for the MT1 receptor expressed in these NSCs. In view of the potency of GDNF in promoting the survival of dopaminergic neurons, these novel findings have implications for the utilization of melatonin in neuroprotective strategies, especially in Parkinson's disease.
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Background
Neural stem cells are multipotent cells which are capable of self-replication and differentiation into neurons, astrocytes or oligodendrocytes in the central nervous system [1]. Because of their intrinsic plasticity and multipotency, there are great expectations that NSC transplantation will ultimately provide immense benefits in the treatment of neurodegeneration. However, it is essential to fully understand the cellular and molecular mechanisms involved in the differentiation and function of NSCs, in order to fully harness their therapeutic potential. Because of the very limited availability of NSCs in the central nervous system (CNS), neural stem cell lines are very useful for the study and characterization of NSC biology. For example, transplantation studies with the C17.2 neural stem cell line [2] have revealed that these cells express diverse neurotransmitter phenotypes, depending on the environment prevailing in the CNS area of engraftment [3,4]. Recently, transplanted C17.2 NSCs, genetically modified to express glial cell line-derived neurotrophic factor (GDNF), were found to engraft in the 6-hydroxydopamine-lesioned mouse striatum and to express therapeutic levels of this neurotrophin, with consequent protection of dopaminergic neurons in this model of Parkinson's disease [5]. Although this and other similar approaches are promising, limitations including the stability and regulation of transduced genes await resolution. Therefore, it was of interest to determine whether C17.2 cells have the intrinsic ability to express neurotrophins or neurotrophic factors, which would make them amenable to modulation by appropriate agents in vitro or in vivo. In addition, we examined whether these NSCs express receptors for the pineal hormone melatonin, which can induce GDNF mRNA and protein expression [6,7] and which has been implicated in the development of vertebrates including humans [8-10]. Initially, different concentrations and types of sera were used for cell culture in order to select optimal conditions for gene expression studies. We now report that C17.2 NSCs exhibit heterogeneous phenotypes and express neurotrophic factors and melatonin MT1 receptors.
Results
Effects of culture conditions on neurotrophic factor and cell-specific marker mRNA expression in C17.2 NSCs
Following two days in culture, C17.2 cells remain in an undifferentiated state, as indicated by their flat and rounded appearance (Fig. 1A) and high expression of the stem cell/progenitor cell marker, nestin (Fig. 1C,1E,1G). These cells also expressed the early neuronal marker, β-tubulin III, but there was little or no expression of the mRNA for the glial marker, glial fibrillary acidic protein (GFAP). After seven days in culture, differentiating C17.2 cells exhibit an elongated shape with an extension of neurite-like processes, as shown in Fig. 1B. However, as observed in undifferentiated cells after two days, there was still strong expression of nestin and β-tubulin III, with little or no detectable GFAP mRNA (Fig. 1D,1F,1H). An examination of neurotrophin mRNA expression in undifferentiated C17.2 cells, revealed a robust expression of GDNF, BDNF and NGF, regardless of the type or concentration of serum used for culturing (Fig. 1C,1E,1G). A similar strong expression of BDNF and NGF was observed in differentiating cells after seven days, but GDNF mRNA was relatively lower in cells maintained in 1% fetal bovine serum (FBS) or 10% FBS + 5% horse serum (Fig. 1F,1H). The mRNA levels of the control gene, GAPDH, did not change under the conditions examined (Fig. 1I).
Detection of MT1 receptor mRNA and protein in C17.2 NSCs
Melatonin MT1 receptor mRNA was detected by RT-PCR in NSCs maintained for two days, especially in cells cultured in 1% FBS, as shown in Fig. 2A. GAPDH mRNA expression did not change under the conditions examined (Fig. 2B). C17.2 NSCs maintained for indicated periods in 1% FBS or 10% FBS + 5% horse serum, expressed the MT1 receptor protein, as revealed by western analysis (Fig. 2C,2D). Interestingly, when cells were cultured for 2–3 days, the MT1 protein detected had a molecular weight of about 30 kDa, which is less than the predicted size of the mature receptor. However, when cells were cultured for 10–12 days, a mature MT1 receptor of about 40–45 kDa was present, as shown in Fig. 2D. The MT2 receptor transcript was not detected under any of the conditions used in this study.
Immunocytochemical detection of the MT1 receptor and cell-specific markers in C17.2 NSCs
MT1 receptor immunoreactivity was detected within C17.2 cells maintained in 1% FBS for two days, as shown in Figure 3A,3B. Omission of the primary antibody or its preincubation with a blocking peptide (CIDtech Research Inc., Cambridge, ON) abolished MT1 immunoreactivity (Fig. 3C), indicating the specificity of MT1 detection. In keeping with RT-PCR results, nestin (Fig. 3D,3E) and β-tubulin III (Fig. 3F), were detected by immunocytochemical analysis. Double- labeling studies indicated that the MT1 receptor is coexpressed with the stem /progenitor cell marker, nestin (Fig. 4A,4B,4C), the glial marker, GFAP (Fig. 4D,4E,4F) and the early neuronal marker, β-tubulin III (Fig. 4G,4H,4I).
Induction of GDNF mRNA expression by melatonin in C17.2 NSCs
In order to assess the potential functionality of the MT1 receptor detected in C17.2 NSCs, the effect of low physiological concentrations of melatonin on GDNF mRNA expression was examined. Cells were grown as described in Methods and treated with melatonin or vehicle (0.001% DMSO) for 24 hours. Following RT-PCR analysis, GDNF mRNA levels were converted to optical density (OD) values and normalized to GAPDH OD levels, as reported previously [6]. After conversion of GDNF/GAPDH OD ratios to percentage values, one-way ANOVA indicated a significant treatment effect (F3,7 = 7.03, p < 0.04). A Neuman-Keuls test indicated a significant increase in relative GDNF mRNA expression in cells treated with 0.05, 0.1 and 1 nM melatonin as shown in Figure 5.
Discussion
The expression of nestin in undifferentiated C17.2 cells is consistent with the presence of this intermediate filament protein in stem and progenitor cells in the mammalian CNS [11]. However, as noted above, nestin mRNA was also readily detected in cells exhibiting morphological changes characteristic of differentiation, after one week in culture. Similarly, mRNA for the early neuronal marker, β-tubulin III, was found under all conditions examined, whereas GFAP mRNA was detected only in some cultures. These observations suggest that the C17.2 cells examined in this study are an heterogeneous population of stem and progenitor cells in keeping with evidence that NSCs exhibit morphological and phenotypic heterogeneity [12,13].
The expression of diverse neurotrophins by NSCs is consistent with the role of these factors in the differentiation and development of the CNS. Presumably, the robust mRNA expression observed, particularly in cells maintained in 10% FBS + 5% HS, is driven by the serum-enriched milieu of potential inducers including neurotransmitters, hormones and growth factors, such as basic fibroblast growth factor and epidermal growth factor, which can stimulate C17.2 cell growth in vitro [14]. In contrast to BDNF and NGF, which exhibited strong mRNA expression under all conditions examined, GDNF expression was weaker or not detectable in differentiating cells after seven days in culture. The suppression of GDNF expression might have been due to the prolonged exposure of NSCs to regulatory factors in the serum, as its decline appears to be inversely correlated with the concentration or enrichment of serum used for cell culture. Thus, moderate, weak or no expression of GDNF was observed in cells cultured for 1 week in 1% CS, 1% FBS or 10% FBS + 5% HS, respectively (see Fig. 1D,1F,1H). Various biological agents or pathways have been implicated in the regulation of GDNF expression. For example, fibroblast growth factor-2 and proinflammatory cytokines such as interleukin(IL)-1β, IL-6 and tumor necrosis factor-α stimulate GDNF synthesis and secretion [15]. Activation of protein kinase C by phorbol esters increases GDNF expression [15,16], whereas the adenylate cyclase activator, forskolin, inhibits GDNF production in cultured cells, suggesting an inhibitory role for the cyclic AMP- protein kinase A pathway [15]. The cAMP pathway and its transcriptional factor cAMP response element binding protein (CREB) have been shown to induce differentiation in neuronal progenitor cells [17,18]. Therefore, it is possible that activation of this pathway was involved in both the initiation of differentiation and the inhibition of GDNF expression observed in C17.2 cells after seven days.
While this work was in progress, it was reported that C17.2 neural stem cells constitutively secrete BDNF, GDNF and NGF, but do not label for GFAP or neuronal markers like β-tubulin III [19]. Our findings are in agreement with these observations with regard to neurotrophin expression. However, in contrast to their findings, β-tubulin III mRNA and immunoreactivity were readily detected in our study. In addition, although GFAP mRNA was weakly expressed or not detectable in some cultures, immunoreactivity for this glial cell marker is present in C17.2 cells, as shown in Figure 4. These differences may be due to our examination of β-tubulin III and GFAP expression in cells maintained for 2–12 days in culture, whereas their C17.2 cells were examined after 2–3 weeks [19]. Other factors, such as our use of low serum concentrations, as compared with the enriched culture medium used by Lu et al. [19], may also be involved. The detection of melatonin MT1 receptor mRNA in C17.2 cells after 2 days but not after 7 days, presumably involves downregulation of this receptor. There is considerable evidence that many G protein-coupled receptors are downregulated by their agonists [20]. More importantly, melatonin, which is present in serum, has been found to suppress MT1 transcription in vitro [21]. Interestingly, our immunocytochemical studies revealed MT1 immunoreactivity within C17.2 cell bodies and extensions, as shown in Figure 3A,3B. Although an intracellular localization could result from internalization of receptors [20], it is also possible that the immunoreactivity detected within these neural stem/progenitor cells is due to the presence of newly synthesized MT1 receptors. In accordance with this view, the MT1 protein detected in short-term (2-day) cultures is about 30 kDa, which is less than the approximately 37–45 kDa molecular weight observed in various mammalian tissues [22-24]. Moreover, when cells were cultured for 10–12 days, a MT1 receptor of about 40–45 kDa was detected, as shown in Fig. 2D. The mammalian MT1 contains two glycosylation sites in its N-terminal [24] and it may exist in more than one glycosylated form, as has been reported for other G protein-coupled receptors [25,26]. Thus, the above cytochemical observations suggest that newly synthesized immature MT1 receptors,which have yet to undergo posttranslational modification and translocation to the plasma membrane, were detected in cells cultured for 2 days in 1% FBS, whereas a mature glycosylated receptor was present in cells grown for longer periods. Although the MT2 receptor transcript was not detected under any of the conditions used in this study, additional studies are required before the possibility of its expression in these cells can be ruled out. It is possible that MT2 mRNA may undergo rapid turnover/degradation, while a functional protein may still be present. This is the first evidence that melatonin receptors are expressed in neural stem or progenitor cells and raises the obvious question of whether this hormone plays a role in neuronal development. Although studies in this field are limited, there is increasing evidence that melatonin is involved in the early development of vertebrates. For example, melatonin is produced in chick embryos as early as the 7th day of embryonic development [27], and a physiological concentration of this hormone has been shown to significantly enhance mouse embryogenesis in vitro [8]. Similarly, when sheep blastocysts were treated with melatonin for 24 hr in vitro, there was a significant increase in the percentage of embryonic survival [28]. Other studies have shown that functional Gi protein-coupled melatonin receptors, which mediate inhibition of the adenylate-cyclase-cAMP pathway, are present in the embryonic (day 8) neural retina [29]. Melatonin receptor transcripts for all the known Gi protein-coupled receptor subtypes have been found in 24 hr-old embryos from Japanese quail [30]. Various studies have detected melatonin receptors in human fetal brain [31,32] and peripheral tissues [33]. Moreover, recent autoradiographic and in situ hybridization studies indicate that the melatonin MT1 receptor is expressed in diverse areas of the human fetal brain [9]. Thus, the presence of MT1 receptors in NSCs is in keeping with the foregoing, and supports the view that melatonin is involved in neurodevelopment. Colocalization evidence that the MT1 receptor is present in both neural and glial progenitor cells is consistent with a neurodevelopmental role for melatonin, and suggests that in addition to the presence of the MT1 in mammalian neurons [34], it may also be expressed in astrocytes, as observed in similar cells from rat [6] and chick brain [35]. The detection of nestin in some cells expressing the MT1 receptor is consistent with its presence not only in neural progenitor cells but also in GFAP positive glial progenitors [36]. Preliminary evidence that melatonin induces GDNF mRNA expression in C17.2 NSCs, as we have observed previously in C6 glioma cells [6], supports the foregoing as this neurotrophic factor plays a critical role in both central and peripheral neurodevelopment [37,38]. GDNF also exerts neuroprotective effects in the CNS, including a potent role in the survival of dopaminergic neurons in the midbrain [39,40]. Therefore, modulation of GDNF expression may be one of the mechanisms underlying physiological neuroprotection by melatonin in the CNS [6].
Conclusions
In summary, the NSCs utilized in this study exhibited an intrinsic ability to express neurotrophins under various cell culture conditions. This ability was not affected by their morphological state, except in the case of GDNF mRNA expression which was lower in cells undergoing differentiation in FBS-supplemented media. Novel evidence that neural stem/progenitor cells express MT1 receptors adds to the increasing evidence that NSCs can respond to diverse modulators [41], and suggests an early role for melatonin in CNS development. Moreover, since melatonin induces GDNF expression in NSCs, its potential in vivo modulation of this and/or other neurotrophic factors, via its G protein-coupled receptors in the brain or on transplanted NSCs, could have important implications for optimizing therapeutic strategies in neurodegenerative disorders such as Parkinson's disease.
Methods
Cell culture
The C17.2 cell line was derived by retrovirus-mediated oncogene (v-myc) transduction of cells from the external germinal layer of neonatal mouse cerebellum [2]. C17.2 cells were grown on 10 cm Corning culture dishes (Fisher Scientific Ltd., Nepean, ON, Canada) in DMEM supplemented with 2 mM glutamine and calf serum, fetal bovine serum or horse serum (Invitrogen Canada Inc., Burlington, ON) in the concentrations indicated. Cells were maintained in a humidified 5% CO2 – 95% air incubator at 37°C and routinely split at approximately 90% confluency [3].
RT-PCR
Total RNA was isolated from C17.2 cells with TRIzol as described by the supplier (Invitrogen Canada Inc., Burlington, ON). After DNase treatment, cDNA was synthesized from 1–2 μg of total RNA using the Omniscript reverse transcriptase kit (Qiagen Inc., Mississauga, ON) and oligo dT primers. PCR was carried out using 1.5 μl (or 3 μl for melatonin MT1 and MT2 receptors) of the RT product and the HotStarTaq master mix kit (Qiagen Inc., Mississauga, ON), together with appropriate primers (Table 1). Following a hot start at 95°C for 15 min, samples were amplified for 36 cycles (or 38 cycles for MT1 and MT2) at 94°C for 30 s, 57°C for 30 s and 72°C for 1 min, followed by a final incubation at 72°C for 10 min.
Treatment of C17.2 NSCs with melatonin
For semi-quantitative examination of the effects of melatonin on GDNF mRNA expression, cells were grown in 10% FBS + 5% horse serum (HS) for 1 week. After subculture, cells were kept in 10% FBS + 5% HS for 2 days followed by another subculture to 1% FBS for 2 or 3 days. The cells were then treated with vehicle (0.001% DMSO) or melatonin (0.05, 0.1, and 1 nM) for 24 hours. Following RNA extraction, RT-PCR was performed as described above, except that an annealing Tm of 55°C was used and samples were amplified for 30 cycles. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), was amplified with intron-spanning primers [42], in order to control for DNA contamination.
Immunocytochemistry
After fixation for 15 minutes in 4% paraformaldehyde on poly L-ornithine-coated glass cover slips, cells were incubated overnight at 4°C with anti-melatonin MT1 receptor serum (1:100; CIDtech Research Inc., Cambridge, ON). Cells were washed three times with PBS and then incubated with a fluorescein (FITC)-conjugated donkey anti- rabbit IgG (1:100 dilution; Jackson ImmunoResearch Labs. Inc.,West Grove, PA). In some experiments, the primary antibody was omitted or it was preincubated with the corresponding peptide immunogen (CIDtech Research Inc., Cambridge, ON), before use. In order to examine cell marker expression, mouse monoclonal antibodies against nestin (1: 500), β-tubulin III (1:200) or GFAP (1:400; Chemicon International, Temecula, CA) were used together with a FITC-conjugated donkey anti-mouse IgG (1:100; Jackson ImmunoResearch Labs.Inc.,West Grove, PA). For double- labeling studies of the MT1 and cell markers, a rhodamine (TRITC)-conjugated donkey anti-mouse IgG (1: 100; Jackson ImmunoResearch Labs. Inc.,West Grove, PA) was used to detect nestin, GFAP and β-tubulin III. Digital images were recorded on a Zeiss confocal microscope.
Western analysis
C17.2 NSCs were grown as described in Figure 2, and proteins were extracted in a modified RIPA buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 0.25% sodium deoxycholate) supplemented with PMSF (1 mM), aprotinin (2 μg/ml), leupeptin (2 μg/ml), and sodium orthovanadate (2 mM). Extracted proteins (80 μg per lane) were separated by SDS- polyacrylamide gel electrophoresis and transblotted to nitrocellulose membranes. The blots were blocked with 5% nonfat dry milk in TBS-T buffer (50 mM Tris-HCl, 150 mM NaCl, 0.1% Tween 20; pH 8.5) for 1 hour at room temperature, and then incubated overnight with a 1:100 dilution of rabbit anti-MT1 antibody (CIDTech Research Inc., Cambridge, ON) at 4°C. After washing, membranes were incubated with a horseradish peroxidase-conjugated second antibody (1:1000; Santa Cruz Biotechnology, Inc., Santa Cruz, CA) for 1 hour. Following washing and exposure to enhanced chemiluminescence (ECL) reagents (Amersham Biosciences, Inc., Baie d'Urfé, Québec) for about 5 minutes, proteins were detected by autoradiography, as described previously [43]. Buffer reagents and protease inhibitors were obtained from Sigma- Aldrich Canada Ltd. (Oakville, ON).
Authors' contributions
LPN conceived and planned the study, designed PCR primers, supervised all aspects of the study and wrote the manuscript. KJA cultured cells and performed initial RT-PCR experiments. LMRC treated cultured cells and carried out immunocytochemistry experiments. CVD cultured cells and carried out western blotting. RS treated cultured cells and performed RT-PCR experiments. CRM performed double- labeling of cultured cells. LCD provided the C17.2 cells, and collaborated on immunocytochemical studies. DLK assisted with initial cell culture. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by grants from CIHR and NSERC Canada to LPN.
Figures and Tables
Figure 1 Morphology and expression of neurotrophins and phenotypic markers in C17.2 NSCs. (A) Undifferentiated cells exhibit a flat and rounded structure after 2 days in culture, as revealed by phase contrast microscopy. (B) After 7 days in culture, differentiating cells appear elongated with processes. Lanes:1–6: GDNF, BDNF, NGF, Nestin, GFAP and β-tubulin III mRNA expression in neural stem cells maintained in 1% calf serum (C,D), 1% fetal bovine serum (E,F), or 10% fetal bovine serum + 5% horse serum (G,H) for 2 or 7 days as indicated. (I) GAPDH mRNA from cells cultured in 1% calf serum, 1% fetal bovine serum (FBS) or 10% FBS + 5% horse serum (HS)-Lanes: 1–3 (2 days), 4–6 (7 days).
Figure 2 Melatonin MT1 receptor mRNA and protein expression in C17.2 NSCs. (A) RT-PCR detection of the MT1 transcript (397 bp) in neural stem cells (NSCs) maintained for 2 days in 1% FBS: lane 2 or 10% FBS + 5%HS: lane 3, but not in 1% calf serum : lane 1. (B) Expression of glyceraldehyde- 3-phosphate dehydrogenase (GAPDH, 237 bp), lanes 1–3. (C) Cells were kept in culture for the number of days indicated and extracted proteins were examined by western analysis. Lane 1: 1 day in 1% FBS; lane 2: 1 day in 10% FBS + 5% HS; lane 3: 3 days in 1% FBS. (D) Cells were grown for 1 week in 10% FBS + 5% HS and then subcultured in 1% FBS for 5 days before western analysis. Molecular weight (kDa) markers are indicated on the left.
Figure 3 Melatonin MT1 receptor, nestin and β-tubulin III immunoreactivity in C17.2 NSCs. Cells were maintained in 1% FBS for the number of days indicated, except for those shown in panel E, which were cultured in 10% FBS + 5% HS. (A,B) MT1 in cells maintained for 2 days. (C) Peptide (immunogen) blockade of MT1 immunoreactivity after 2 days. (D,E) Nestin immunoreactivity after 2 and 3 days, respectively. (F) β-tubulin III immunoreactivity after 3 days. Scale bars: (A,D,F) 50 μm. (B,E) 20 μm.
Figure 4 Colocalization of the MT1 receptor with phenotypic markers in C17.2 NSCs. Cells were maintained in 10% FBS + 5% HS for 9 days and then in 1% FBS for 3 days before seeding on coverslips in 1% FBS for 2 days. (A,B,C) Confocal images of MT1 (green), Nestin (red), and MT1 & nestin. (D,E,F) MT1 (green), GFAP (red) and MT1 & GFAP. (G,H,I) MT1 (green), β-tubulin III (red) and MT1 & β-tubulin III. Double-labeled cells exhibit yellow-orange fluorescence. Scale bars: A-F: 50 μm; G-I: 20 μm.
Figure 5 Induction of GDNF mRNA by melatonin in C17.2 NSCs. (A,B) Gel images of RT-PCR amplification of GDNF (643 bp) and GAPDH (237 bp). Lanes 1–4: Control, 0.05, 0.1, and 1 nM melatonin. M = markers. (C) Percentage values of GDNF/GAPDH optical density ratios as a function of melatonin treatment. Data shown are the means ± s.e.m. from 2 separate experiments. *p < 0.05 vs control.
Table 1 Forward and reverse primers used for PCR amplification
Gene Primers (5'→3') Nucleotides Size(bp)
GDNF atgggatgtcgtggctgtctg 58–98 643
tctctggagccagggtcagat 700–680
BDNF ggatgaggaccagaaggttgc 2342–2362 390
ttgtctatgcccctgcagcct 2731-2711
NGF gcagacccgcaacatcactgt 484–504 517
agccttcctgctgagcacaca 1000-980
Nestin aggaaccaaaagagacaggtg 4141–4161 653
ttcctcagatgagaggtcaga 4793-4773
GFAP cctcaagaggaacatcgtggt 1119–1139 592
acactggagtcatcacctgga 1710-1690
β-Tubulin III tagtggagaacacagacgaga 600–620 442
ctgctgttcttactctggatg 1041-1021
MT1 tgagtgtcatcggctcgatat 1–21 397
tagtaactagccacgaacagc 397-377
MT2 tgctgcatctgtcatagtacc 4–24 297
acatggttaggaaactgcgca 346-326
GAPDH ttcaccaccatggagaaggc 1147–1166 237
ggcatggactgtggtcatga 1383-1364
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| 15511288 | PMC529253 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Oct 28; 5:41 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-41 | oa_comm |
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BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-261549809910.1186/1471-2210-4-26Research ArticlePeroxynitrite-mediated inactivation of heme oxygenases Kinobe Robert [email protected] Yanbin [email protected] Kanji [email protected] Department of Pharmacology and Toxicology, Faculty of Health Sciences, Queen's University, Kingston, Ontario, K7L 3N6, CANADA2004 21 10 2004 4 26 26 14 7 2004 21 10 2004 Copyright © 2004 Kinobe et al; licensee BioMed Central Ltd.2004Kinobe et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Endogenous nitric oxide (NO) and carbon monoxide (CO) are generated by nitric oxide synthase and heme oxygenase, respectively. Like NO, CO has been accepted as an important cellular signaling molecule in biological systems. An up-regulation in both gene and protein expression of heme oxygenase-1 (HO-1) under oxidative/nitrosative stress has been well documented, and the protective role of HO-1 and HO-2 against oxidative damage is proposed. However, data on the direct effect of reactive oxygen/nitrogen species (ROS/RNS) on HO function is incomplete. Using gas chromatography to quantify carbon monoxide (CO) formation from heme oxidation, we investigated the effects of peroxynitrite (ONOO-) on the in vitro catalytic activity of rat spleen (HO-1) and brain (HO-2) microsomal heme oxygenases.
Results
Exposure to ONOO- led to concentration-dependent but reversible decreases in the activity of microsomal rat spleen and brain HO activity. Spleen HO activity was 100-fold more sensitive to ONOO--dependent inactivation compared to that of the brain, with IC50 values of 0.015 ± 0.005 mM and 1.25 ± 0.25 mM respectively. Inhibition of both rat spleen and brain microsomal HO activity was also observed with tetra-nitromethane, a tyrosine nitrating agent, as well as two NO donors, S-nitrosoglutathione (GSNO) and diethylamine NONOate (DEA-NONOate). However, no additive effect was found following the application of NO donors and ONOO- together.
Conclusion
These results indicate that ONOO- may regulate HO-1 and HO-2 activities by mechanisms that involve different interactions with these proteins. It is suggested that while nitration of tyrosine residues and oxidation of sulfhydryl groups may be involved, consideration should be given to other facets of ONOO- chemistry. This inhibition of HO activity offers a mechanism for cross talk between the nitric oxide synthase and HO systems.
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Background
Heme oxygenases (HO, EC 1.14.99.3) are a highly conserved family of proteins that catalyse the oxidative cleavage of heme at the α-meso carbon to yield equimolar amounts of iron, carbon monoxide (CO) and biliverdin. Biliverdin is subsequently reduced to bilirubin by biliverdin reductase. Three distinct isozymes of HO (HO-1, HO-2 and HO-3) have been identified. HO-1 (the inducible isoform) is predominantly expressed in the spleen, the primary site of heme catabolism but has been detected in many different tissues including the liver and the kidney. Several substances and conditions may induce the expression of HO-1. The involvement of HO and products of heme catabolism have been studied extensively with respect to oxidative stress, ischemia, hypoxia and protection against transplant rejection [1-4]. HO-2 (the constitutive isoform) is predominantly expressed in the testes and the brain where HO-2 dependent CO production is thought to aid neuronal function [5-7]. HO-3 is also a constitutive isoform, which shares 90% homology with HO-2 but has very limited catalytic function [8].
Increasing amounts of evidence suggest that products of heme catabolism have cytoprotective roles such as anti-inflammation, anti-apoptosis and anti-proliferation. Biliverdin and bilirubin have anti-oxidant and anti-inflammatory properties [9-11], while iron is known to regulate transferrin, ferritin and nitric oxide synthase gene expression [12,13]. An up regulation in the synthesis of transferrin and ferritin enhances the binding, transport and storage of iron thus serving as an important control mechanism against the oxidative effects of iron. CO, like NO, has been accepted as an important cellular signalling molecule in biological systems. For example, both NO and CO are known to activate soluble guanylyl cyclase, resulting in elevated cGMP and the cGMP-mediated dilatation of blood vessels. CO also mediates vasodilation by directly activating the calcium-dependent potassium channels in vascular smooth muscle cells [14,15]. In addition, CO inhibits platelet aggregation and proliferation of vascular smooth muscle cells, inhibits apoptosis, and stimulates angiogenesis [16-19]. Because of the diversity in the effects of heme catabolism, several studies have suggested that induction of HO-1 expression by oxidising agents may serve as a defense mechanism against oxidative stress in vivo. For example, an induction in the expression of HO-1 prevents superoxide associated endothelial cell sloughing in diabetic rats [20]. Similarly, it has been demonstrated that ONOO-, a potent oxidizing agent generated by the interaction of NO and superoxide radical [21], causes a concentration-dependent increase in HO-1 protein expression and enzyme activity in rat aortic endothelial cells [22-24], as well as human colorectal adenocarcinoma cells [25]. These studies indicate that ONOO- regulates the expression of HO-1 and that the heme oxygenase pathway contributes to protection against the cytotoxic effects of ONOO- which are due to its reactivity with cellular macromolecules such as the covalent modification of tyrosine, cysteine, methionine or tryptophan residues, oxidation of nucleic bases or the scavenging of cellular antioxidants such ascorbate and urate [26]. It is proposed that the ONOO--mediated HO-1 induction might occur via an interactive signalling mechanism that modulates oxidative stress responses but direct effects of ONOO- on HO activity have not been studied. In this work, we examined the effect of ONOO- on HO catalytic activity in rat spleen and brain microsomes respectively.
Results
Effect of ONOO- and TNM on Microsomal HO activity
Exposure of either rat spleen or brain microsomes to ONOO- resulted in decreases of HO activity in a concentration dependent manner (Figure 1A and Figure 1B). HO-1 (spleen microsomes) was more sensitive towards ONOO--mediated inactivation compared to HO-2 (brain microsomes) (Figure 1A and Figure 1B). The IC50 values for inhibition of HO activity in rat spleen and brain microsomes were 0.015 ± 0.005 mM and 1.25 ± 0.25 mM, respectively. The same concentration of degraded ONOO- did not show any effects on HO activity in rat spleen or brain microsomal fractions. Maximal inhibition for microsomal HO-1 and HO-2 activity was approximately 70%, beyond which there was no further inhibitory effect with increasing concentrations of ONOO-. To investigate whether effects of ONOO- were due to nitration of tyrosine residues, spleen and brain microsomal fractions were treated with TNM, a known tyrosine-nitrating agent. It is shown that HO activity in rat spleen as well as brain microsomal fractions was significantly reduced by TNM albeit at higher concentrations compared to ONOO- (Figure 2).
Figure 1 ONOO--mediated inactivation of HO-1 and HO-2. Rat spleen (Figure 1A) and brain microsomes (Figure 1B) (50–100 μg protein) were treated with indicated concentrations of ONOO- or degraded ONOO- in 100 mM potassium phosphate, pH 7.4 at room temperature for 10 seconds. The reaction was stopped by dilution of the reaction mixture to a protein concentration of (0.5 mg/mL) spleen microsomes and (1 mg/mL) brain microsomes in 100 mM phosphate buffer containing 1 mM NADPH and 50 μM methemalbumin. Incubations of the pretreated microsomal fractions were performed for 15 min and enzyme activity was determined by the quantitation of CO formed in the reaction mixture. Data are presented as the mean ± SD of triplicate experiments. The rates of CO formation in the control reactions were 12 ± 1 and 5 ± 1 pmoles CO/min/mg protein for spleen and brain microsomes respectively. IC50 values for HO-1 and HO-2 were 0.015 ± 0.005 mM and 1.25 ± 0.25 mM respectively.
Figure 2 Effect of TNM-mediated nitration on the catalytic activity of HO. HO-1 (rat spleen microsomes and HO-2 (rat brain microsomes). Microsomal protein (50–100 μg) was incubated with 0.2 mM or 2 mM (final concentration) of TNM in 100 mM potassium phosphate buffer, pH 7.4, at 37°C for 20 minutes. The reaction was stopped by dilution of the reaction mixture to a protein concentration of (0.5 mg/mL) in 100 mM phosphate buffer containing 1 mM NADPH and 50 μM methemalbumin. Incubations of the pretreated microsomal protein were done for 15 min and enzyme activity was determined by the quantitation of CO formed in the reaction mixture. Data are presented as the mean ± SD of triplicate experiments. The rate of CO formation in control reactions was 11.4 ± 0.6 and 4.0 ± 0.14 pmoles CO/min/mg protein for HO-1 and HO-2 respectively. The asterisk denotes significant inhibition of the respective HO activity using a one-way ANOVA, P ≤ 0.05.
Effects of sulfhydryl modifying reagents on HO activity
Since ONOO- may affect protein targets by the oxidation of sulfhydryl groups as well as the nitration of tyrosine residues, we examined and compared the effect of other sulfhydryl modifying reagents on HO activity. Rat spleen or brain microsomal protein was treated with different sulfhydryl modifying reagents such as GSNO, DEA-NONOate, NEM and H2O2, and a combination of equimolar concentrations of ONOO- and GSNO or ONOO- and DEA-NONOate. We found that GSNO caused a concentration dependent inactivation of both HO-1 and HO-2. Consistent with the effect of ONOO-, GSNO was more active against HO-1 than HO-2 (Table 1). In contrast, DEA-NONOate and NEM caused a significant decrease in the catalytic activity of HO-1 at the two concentrations tested (0.2 mM and 2 mM), but did not inhibit HO-2 (Table 1). Pre-treatment with equimolar concentrations of ONOO- and DEA-NONOate or GSNO did not result in further inactivation of HO-1 or HO-2 than ONOO- alone. H2O2 at concentrations as high as 2 mM had no significant effect on the catalytic activity of either isozymes.
Table 1 Effect of NO donors and sulfhydryl modifying reagents on the catalytic activity of HO-1 (spleen microsomes) and HO-2 (brain microsomes)
Heme oxygenase activity (pmoles CO/min/mg protein)
Experimental conditions HO-1 (Spleen microsomes) HO-2 (Brain microsomes)
Control 11.9 ± 0.6 3.1 ± 0.5
ONOO-
0.2 mM 5.4 ± 0.9* 2.7 ± 0.6
2 mM 3.8 ± 0.9* 1.4 ± 0.3*
GSNO
0.2 mM 6.5 ± 0.3* 2.9 ± 0.5
2 mM 4.2 ± 0.2* 1.4 ± 0.1*
DEA-NONOate
0.2 mM 4.7 ± 0.4* 2.9 ± 0.7
2 mM 3.6 ± 0.4* 2.4 ± 0.5
NEM
0.2 mM 8.7 ± 0.8 2.9 ± 0.3
2 mM 3.4 ± 0.5* 2.6 ± 0.4
H2O2
0.2 mM 11.3 ± 0.2 3.0 ± 0.4
2 mM 11.8 ± 1.5 3.2 ± 0.6
ONOO- and GSNO
0.2 mM 4.3 ± 0.6* 2.8 ± 0.4
2 mM 4.0 ± 0.2* 1.3 ± 0.2*
(ONOO- and DEA-NONOate)
0.2 mM 4.7 ± 0.1* 2.6 ± 0.2
2 mM 3.9 ± 0.6* 1.4 ± 0.3*
Microsomal protein (50–100 μg) was incubated with 0.2 mM or 2 mM (final concentration) of ONOO-, GSNO, DEA-NONOate, NEM, H2O2, equimolar concentrations of ONOO- and GSNO or ONOO- and DEA-NONOate in 100 mM potassium phosphate buffer, pH 7.4, at 37°C for 20 minutes. The reaction was stopped by dilution of the reaction mixture to a protein concentration of (0.5 mg/mL) in 100 mM phosphate buffer containing 1 mM NADPH and 50 μM methemalbumin. Incubations of the pretreated microsomal fractions were done for 15 min and enzyme activity was determined by the quantitation of CO formed in the reaction mixture. Data are presented as the mean ± SD of triplicate experiments. Asterisks denote significant inhibition of the respective HO activity using a one-way ANOVA, P ≤ 0.05.
To test whether effects of ONOO- and the sulfhydryl modifying reagents on HO-1 and HO-2 are reversible, ONOO- and DEA-NONOate were pre-incubated with rat spleen and brain microsomal protein for 20, 60 and 120 minutes prior to evaluation of HO activity. The inactivation of HO-1 by ONOO- and DEA-NONOate and HO-2 by ONOO- was reversible following prolonged pre-incubation time (60–120 minutes) Table 2. However, total HO activity in brain and spleen microsomal protein was not affected by the prolonged pre-incubation for 60–120 minutes in the absence of ONOO- and DEA-NONOate.
Table 2 Time dependent reversibility of the effect of ONOO- and DEA-NONOate on the catalytic activity of HO-1 (spleen microsomes) and HO-2 (brain microsomes)
Heme oxygenase activity (pmoles CO/min/mg protein) at different pre-incubation times
Experimental conditions 20 min 60 min 120 min
Spleen microsomes (HO-1) ONOO-
Control 10.4 ± 1.0 10.8 ± 0.8 8.7 ± 0.9
0.2 mM 3.6 ± 1.4* 10.5 ± 0.8 8.3 ± 1.1
DEA-NONOate
Control 10.6 ± 2.0 11.5 ± 1.3 9.4 ± 0.2
0.2 mM 6.6 ± 0.7* 11.1 ± 1.4 8.9 ± 0.3
Brain microsomes (HO-2) ONOO-
Control 3.6 ± 0.5 4.2 ± 0.4 4.4 ± 0.3
2 mM 1.9 ± 0.1* 4.4 ± 0.3 3.8 ± 0.3
DEA-NONOate
Control 3.6 ± 0.3 4.1 ± 0.3 4.0 ± 0.3
2 mM 3.0 ± 0.3 3.9 ± 0.1 3.6 ± 0.3
Microsomal protein (50–100 μg) was incubated with 0.2 mM or 2 mM (final concentration) of ONOO- and DEA-NONOate in 100 mM potassium phosphate buffer, pH 7.4, at 37°C for 20, 60 and 120 minutes. The reaction was stopped by dilution of the reaction mixture to a protein concentration of (0.5 mg/mL) in 100 mM phosphate buffer containing 1 mM NADPH and 50 μM methemalbumin. Incubations of the pretreated microsomal fractions were done as described in materials and methods. Data are presented as the mean ± SD of triplicate experiments. Asterisks denote significant inhibition of the respective HO activity using a one-way ANOVA, P ≤ 0.05.
Discussion
Production of ONOO- in vivo is a consequence of oxidative and nitrosative stress, and ONOO--mediated tissue injury is thought to be involved in the pathogenesis of many conditions including atherosclerosis, ischaemia/reperfusion, shock, Alzheimer's disease, diabetes and multiple sclerosis [27-30]. Continuous challenge and exposure to oxidative/nitrosative stressors has led to the evolution of numerous defense mechanisms in biological systems and one such mechanism that has been elucidated by many researchers is the HO/CO system [4,9-11]. ONOO- causes a concentration-dependent increase in HO-1 protein expression suggesting that the HO pathway contributes to protection against the cytotoxic effects of ONOO- [22-24]. Most studies in this field, however, have focussed on the induction of HO-1 mRNA and/or protein expression under different conditions of oxidative/nitrosative stress rather than enzyme activity. Considering the cellular toxicity of ONOO- and the inhibitory effect of ONOO- on numerous enzyme systems, we sought to investigate the direct effect of ONOO- on the catalytic activity of two microsomal HO isozymes.
Rat spleen and brain microsomal fractions were used because of the predominant expression of HO-1 in the spleen and HO-2 in the brain. We have shown that ONOO- inhibits the activity of both HO-1 and HO-2 in a concentration-dependent manner (Figure 1). HO-1 was found to be more sensitive to ONOO- treatment compared to HO-2. Lower concentrations of ONOO- (15 μM, final concentration) decreased HO-1 activity by 50% while a much higher concentration (1.25 mM) was required to decrease HO-2 activity by the same magnitude. Generally, HO catalytic function is dependent on an accessory enzyme NADPH-cytochrome P450 reductase (CPR), which serves as a redox partner during the oxidative break down of heme and the conversion of NADPH to NADP+. It is possible therefore, that ONOO- may have altered HO activity indirectly by inactivating CPR. The ONOO- dependent inactivation of recombinant CPR in bacterial membranes has been noted [32], but our attempt to supplement rat microsomal HO-1 and HO-2 with recombinant rat CPR in the presence of a detergent did not attenuate the effect of ONOO- on microsomal HO-1 and HO-2 (data not shown). This suggests that the effects of ONOO- on HO activity may be mediated by mechanisms that are independent of the effect of ONOO- on microsomal CPR activity. The differential effect of ONOO- on the catalytic activity of HO-1 and HO-2 may suggest a selective mechanism on the catalytic function of the inducible enzyme. This may have direct implications in biological systems where ONOO- is generated at high concentrations. The ONOO--mediated increase in HO-1 gene and/or protein expression may be a cellular response at the acute phase. Subsequently, total HO activity may then be maintained in certain ranges in order to retain intracellular oxidative responses and the balance of redox states for normal cellular function. This idea is consistent with other studies, which have shown that induction of HO-1 mRNA and protein expression is not always followed by a proportionate increase in catalytic function. For example, by using quantitative RT-PCR it was found that a 3.8-fold increase in sarcoma-induced HO-1 mRNA expression yields only 2.1-fold increase in total HO activity [31]. Overall, the ONOO--mediated regulation of the catalytic function of HO-1 and differences in the sensitivity of HO-1 and HO-2 may be attributed to disparity in the amino acid compositions and the structure of the two proteins.
Mechanistically, the effects of ONOO- on protein function are mainly due to its reactivity and covalent modification of tyrosine and/or cysteine residues [26]. This often leads to impaired protein function with very few exceptions such as the recently observed nitration and stimulation of the enzymatic activity of microsomal glutathione S-transferase (MGST) [33]. To probe mechanisms of ONOO--mediated inhibition of HO activity observed here, we examined the effects of TNM, another tyrosine-nitrating chemical compound on rat spleen and brain microsomal HO activity. Significant inhibition of HO catalytic activity by TNM was observed for spleen as well as brain microsomal fractions albeit at higher concentrations compared to ONOO- (Figure 2). This suggests that both TNM and ONOO- may inactivate HO activity by nitration of tyrosine residue(s) that is/are important for conservation of HO-1 and HO-2 function. This is consistent with data from the analysis of the complete amino acid sequences of rat and human HO-1 [34,35] and HO-2 [36,37]. However, there was no difference in the effects of TNM on HO-1 and HO-2 (Figure 2), suggesting that the differential effects observed with ONOO- could be due to modification of amino acid residues other than tyrosine. Oxidation of cysteine residues as a putative mechanism underlying ONOO--mediated inactivation of HO was investigated. Effects of GSNO, DEA-NONOate, NEM and H2O2, or a combination of equimolar concentrations of ONOO- and GSNO or DEA-NONOate on HO activity were tested. GSNO showed similar inhibitory effects to ONOO- but a combination of equimolar concentrations of ONOO- and DEA-NONOate or GSNO did not have any additive effect on the ONOO--mediated inactivation of HO-1 or HO-2. In addition, the inactivation of HO-1 by ONOO- and DEA-NONOate and HO-2 by ONOO- were reversible following prolonged pre-incubation time (60–120 minutes) Table 2. This differential effect between HO-1 and HO-2 following exposure to ONOO-, GSNO, DEA-NONOate or NEM is suggestive of qualitative as well as quantitative differences in the distribution of target amino acids such as critical tyrosine and/or cysteine residues in the tertiary structures of these proteins. The amino acid sequences of human and rat HO-2 reveals a conserved core of cysteine residues (Cys 264-Pro 265, Cys 281-Pro 282) [38,39], but there are no cysteine residues in the HO-1 amino acid sequence. Despite this fact, results from our experiments show that HO-1 is more sensitive to inactivation by the NO donors GSNO and DEA-NONOate, and NEM (Table 1). We considered the possibility that ONOO- might exert its effects by interaction with the substrate as has been reported for the interaction of NO with heme [40]. This possibility seems unlikely as such a mechanism would be expected to affect the catalytic rate of HO-1 and HO-2 equally. Another consideration is that the chemistry of ONOO- is richer than is usually discussed; in their review Alvarez and Radi [41] describe the interactions of ONOO- with a number of functional groups that are not often mentioned in the consideration of the interactions of ONOO- with various proteins. Thus, ONOO- inhibition of HO activity could be a result of an interaction with amino acids other than cysteine or tyrosine. This possibility of ONOO- interactions with other amino acids may also explain the reversibility of the inhibition of HO activity by ONOO-. Furthermore, it is possible that ONOO- inhibition of HO-1 and HO-2 occurs courtesy of different facets of ONOO- chemistry. Differences in the relative potency of DEA-NONOate, NEM, GSNO and ONOO- may also be attributed to differences in the chemical properties and the electrophilic potentials of these reagents. For instance, the nitric oxide radical (NO•) generated from DEA-NONOate is less reactive and unstable compared to NO+ produced from GSNO.
Collectively, our data indicate that ONOO- may be important in the cross-talk between HO and NO systems. While tyrosine residues for HO-1 and/or sulfhydryl groups for HO-2 should be considered as potential targets for ONOO- interactions, other amino acids should be studied in the elucidation of mechanism(s) of ONOO- action on HO isozymes.
Conclusion
This study documented for the first time the ONOO--mediated inactivation of HO-1 and HO-2. The IC50 for HO-1 was approximately 80-fold less than that for HO-2. While conserved tyrosine residues (HO-1) and/or sulfhydryl group(s) (HO-2) may play a critical role in maintaining the functional capacity of heme oxygenases, the consideration of other amino acids is suggested. In addition, the higher sensitivity of HO-1 to ONOO--mediated inactivation may indicate dual regulatory mechanisms on the catalytic function of the inducible enzyme under the conditions of oxidative/nitrosative stress.
Methods
Materials
Hemin, ethanolamine, bovine serum albumin (BSA), β-NADPH, N-ethylmaleimide (NEM), tetra-nitromethane (TNM) and reduced glutathione (GSH) were purchased from Sigma Chemical Co. (St. Louis Mo.). Diethylamine NONOate (DEA-NONOate) was obtained from Calbiochem Inc. (Darmstadt, Germany). All other chemicals were reagent grade from a variety of commercial sources.
Preparation of rat spleen and brain microsomes
Adult male Sprague-Dawley rats (250–300 g) were obtained from Charles River Canada Inc. (St-Constant, Que). The animals were cared for in accordance with the principles and guidelines of the Canadian Council on Animal Care. Microsomal fractions were prepared according to previously described procedures [42,43]. Spleen microsomes were used as a source of HO-1 while brain microsomes were used as a source of HO-2.
Preparation of ONOO- and treatment of microsomal protein
ONOO- was synthesised from acidified nitrite and H2O2 according to the method of Beckman [44] and microsomal HO-1 (spleen microsomes) and HO-2 (brain microsomes) were treated with ONOO- following the procedures described by Ji and Bennett [33]. Briefly, microsomes (50 –100 μg protein in 100 mM potassium phosphate, pH 7.4) were exposed to ONOO- at room temperature at indicated concentrations for 10 seconds by adding a small volume during vigorous mixing. HO activity was initiated by diluting the reaction mixture to a protein concentration of (0.5–1 mg/ml) in 100 mM phosphate buffer containing substrates (50 μM methemalbumin, and 1 mM NADPH). To control for the potential effect of nitrite and nitrate that may be formed during the incubation of ONOO-, ONOO- was allowed to decompose in phosphate buffer prior to incubation with microsomal protein and determination of enzyme activity for some of the reactions.
Treatment of microsomes with NO donors, TNM and sulfhydryl modifying reagents
GSNO was prepared by reacting equimolar amounts of sodium nitrite and GSH according to the method described by Ji et al., [43]. Microsomal protein (50 – 100 μg) was incubated with 0.2–2 mM GSNO, DEA-NONOate, NEM or H2O2 in 100 mM potassium phosphate buffer, pH 7.4, at 37°C for 20 minutes. The reaction was stopped by dilution of the reaction mixture to a protein concentration of (0.5–1 mg/mL) in 100 mM phosphate buffer containing (50 μM methemalbumin, and 1 mM NADPH).
In vitro Assay for HO activity
HO activity in rat spleen and brain microsomal fractions was determined by the quantitation of CO formed from the oxygen, CPR and NADPH-dependent degradation of methemalbumin as previously described [45,46]. The reaction mixture contained, (0.5–1 mg/mL microsomal protein, 50 μM methemalbumin, and 1 mM NADPH) in 100 mM phosphate buffer. All incubations for the assay of HO activity were performed under the conditions for which the rate of CO formation (pmol CO. mg-1 protein. min-1) was linear with respect time and microsomal protein concentration. CO formation was monitored by gas chromatography according to the method described by Vreman and Stevenson [46].
Statistical analysis
Data are presented as the mean ± SD from triplicate experiments and statistical analyses were performed by one-way ANOVA. P values ≤ 0.05 was considered to be significant.
Abbreviations
HO, heme oxygenases; CPR, NADPH-cytochrome P450 reductase; CO, carbon monoxide; ONOO-, peroxynitrite; GSNO, S-nitrosoglutathione; DEA-NONOate, diethylamine NONOate; NEM, N-ethylmaleimide; TNM, tetra-nitromethane; ROS/RNS, reactive oxygen/nitrogen species; RT-PCR; reverse transcriptase polymerase chain reaction; H2O2, hydrogen peroxide.
Authors' contributions
RK, YJ and KN were involved in the experimental design and data collection. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by grant MOP 64305 from the Canadian Institutes of Health Research.
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| 15498099 | PMC529254 | CC BY | 2021-01-04 16:33:05 | no | BMC Pharmacol. 2004 Oct 21; 4:26 | utf-8 | BMC Pharmacol | 2,004 | 10.1186/1471-2210-4-26 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-271550714110.1186/1471-230X-4-27Research ArticleThe diagnostic value of endoscopy and Helicobacter pylori tests for peptic ulcer patients in late post-treatment setting Maaroos Heidi-Ingrid [email protected] Helena [email protected]õivukene Krista [email protected]ütt Pirje [email protected] Helgi [email protected] Ingrid [email protected] Katrin [email protected] Marika [email protected] Department of Family Medicine, Faculty of Medicine, University of Tartu, Estonia2 Department of Microbiology, Faculty of Medicine, University of Tartu, Estonia3 Department of Internal Medicine, Faculty of Medicine, University of Tartu, Estonia2004 26 10 2004 4 27 27 18 6 2004 26 10 2004 Copyright © 2004 Maaroos et al; licensee BioMed Central Ltd.2004Maaroos et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Guidelines for management of peptic ulcer patients after the treatment are largely directed to detection of H. pylori infection using only non-invasive tests. We compared the diagnostic value of non-invasive and endoscopy based H. pylori tests in a late post-treatment setting.
Methods
Altogether 34 patients with dyspeptic complaints were referred for gastroscopy 5 years after the treatment of peptic ulcer using a one-week triple therapy scheme. The endoscopic and histologic findings were evaluated according to the Sydney classification. Bacteriological, PCR and cytological investigations and 13C-UBT tests were performed.
Results
Seventeen patients were defined H. pylori positive by 13C-UBT test, PCR and histological examination. On endoscopy, peptic ulcer persisted in 4 H. pylori positive cases. Among the 6 cases with erosions of the gastric mucosa, only two patients were H. pylori positive. Mucosal atrophy and intestinal metaplasia were revealed both in the H. pylori positive and H. pylori negative cases. Bacteriological examination revealed three clarithromycin resistant H. pylori strains. Cytology failed to prove validity for diagnosing H. pylori in a post-treatment setting.
Conclusions
In a late post-treatment setting, patients with dyspepsia should not be monitored only by non-invasive investigation methods; it is also justified to use the classical histological evaluation of H. pylori colonisation, PCR and bacteriology as they have shown good concordance with 13C-UBT. Moreover, endoscopy and histological investigation of a gastric biopsy have proved to be the methods with an additional diagnostic value, providing the physician with information about inflammatory, atrophic and metaplastic lesions of the stomach in dyspeptic H. pylori positive and negative patients. Bacteriological methods are suggested for detecting the putative antimicrobial resistance of H. pylori, aimed at successful eradication of infection in persistent peptic ulcer cases.
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Background
Treatment of peptic ulcer in accordance with relevant guidelines is becoming a common task for general practitioners [1-6]. In a post-treatment setting, in accordance with guidelines, prompt check-up of treatment results is recommended only in gastric ulcer cases with the use of 13C-urea breath test (13C-UBT) [2-5]. In a situation where patients have clinical symptoms after H. pylori eradication therapy, endoscopy is favoured in all peptic ulcer cases [6]. The aim of endoscopy is to establish the reason for clinical symptoms and to prove presence of peptic ulcer or malignancies, but also to support physicians and patients in the understanding of complaints [7]. Moreover, endoscopy allows determination of persistent H. pylori infection using endoscopy-based tests. Endoscopic biopsies alone are not considered adequate for confirming eradication of bacteria, although they might provide additional information about gastritis and dysplasia [8]. Use of more than one method in testing gastric specimens definitely enhances the diagnostic value when assessing the post-treatment H. pylori status [9].
Our aim was to assess the diagnostic value of different non-invasive (13C-UBT) and endoscopy-based diagnostic methods (visual endoscopy, classical cytological and histological examination of mucosal specimens, PCR and bacteriological methods) for monitoring patients after eradication therapy in a late post-treatment setting.
Methods
Patients
The study group was formed of 134 consecutive peptic ulcer outpatients who had been treated by 7-day triple therapy with metronidazole, amoxicillin and omeprazole in 1996. The group was observed at the outpatient department of Tartu University Hospital at 4 weeks, at 1 year (1997) and at 5 (2001) years after treatment [10]. Five years after treatment, 108 patients (81% of the initial group) were available for the follow-up of the clinical course of peptic ulcer. During the 5-year follow-up period only 11 (10 %) patients had relapses of peptic ulcer. For comparison of the diagnostic value of different diagnostic methods in a post-treatment setting, 34 patients were recruited. The inclusion criteria for this study group were resistant upper abdominal pain as the predominant complaint and compliance with all investigations (clinical symptoms, 13C-UBT, endoscopy, biopsy, bacteriology, PCR and cytology). The studied patients were not NSAID users.
Methods
The patients passed the Gastrointestinal Symptoms Rating Scale (GSRS) test [11] in a validated Estonian translation. Dyspeptic syndrome (abdominal pain, heartburn, acid regurgitation, sucking sensation, nausea and vomiting) was registered on the 7-grade Likert scale for assessing severity of symptoms. The mean score of dyspeptic syndrome was calculated for each patient.
13C-UBT
The subjects passed 13C-UBT drinking 100 mg 13C-urea; the test meal was citric acid and the time of specimen collection was 30 min. The test was provided, according to a standard protocol, from the Helsinki Keskuskatu Laboratory, Finland. The ratio of 13CO2 to 12CO2 in expired breath was measured by mass spectrometry and expressed in ml/mmol/kg (δ). An automated breath 13C analyser (ABCA) with chromatographic purification and a single inlet isotope ratio mass spectrometer (IRMS) were used. A difference of 5‰ in the content (δ13C) was considered positive for H. pylori infection.
Endoscopy of the upper gastrointestinal tract
The procedure was performed with the gastroscope Olympus-GIF 21. All mucosal defects were registered according to the Sydney classification for endoscopic evaluation [12]. Gastric ulcer was diagnosed if the ulcer was located at the angulus or above it. Duodenal ulcer was diagnosed if the ulcer was found in the duodenal bulb area.
Gastrobiopsy and histological examination
Five specimens from the antrum mucosa and five from the corpus mucosa were taken with medium-sized forceps. Two specimens were embedded in paraffin and the paraffin sections were stained using haematoxylin-eosin and Giemsa methods. The mucosal specimens were evaluated histologically according to the Sydney classification: presence of neutrophil infiltration, chronic lymphocytic inflammation, surface epithelial damage, atrophy, intestinal metaplasia, lymphoid follicles and H. pylori colonisation were evaluated on a three-grade scale both for the antrum and the corpus [12-14].
Bacteriological examination
One specimen from the antrum and one from the corpus were placed in the Stuart Transport Medium (Oxoid) and taken to the laboratory within two hours for bacteriological examination. The biopsy samples were homogenised with sterile glass powder and under a stream of CO2 and diluted in the Brucella broth (Oxoid). H. pylori was isolated on the Columbia Agar Base supplemented with 7% horse blood and 1% Vitox (Oxoid) or Isovitalex (BBL). The plates were incubated for 3–7 days at 37°C under microaerobic conditions (CampyBak, BBL or CampyGen, Oxoid). H. pylori was identified by Gram staining and by oxidase, catalase and urease reactions [15]. The sensitivity of the isolated H. pylori strains to clarithromycin was estimated by E-test. The antibiotic cut-off points employed for the E-test were 1.0 mg/l (NCCLS, 2002).
Cytological examination
One specimen was used for imprinting the cytology slides from the antrum and corpus mucosa, fixed with 96% ethanol and stained by Acridine Orange (Difco, BBL) [16]. The cytological specimens were studied under a fluorescence microscope (AXI Phot 2) where the morphotypes and the density of bacterial colonisation were evaluated [17]. A positive cytological diagnosis was based on the presence of typical helical H. pylori cells on the gastric mucosa and in the mucus layer.
PCR
For DNA extraction of H. pylori from a frozen gastric biopsy specimen, a previously described procedure was used [18]. The presence of the glmM gene in each strain was established by PCR using primers, the reaction mixture, and thermal cycling [19,20]. DNA from H. pylori NCTC 11637 (National Collection of Type Cultures, Central Public Health Laboratory, Colindale Ave., London NW9 5HT, England, United Kingdom) and the DNA-free reaction mixture were assayed in separate tubes in each PCR and were run as the positive and negative controls of the reaction, respectively. The PCR products were identified by electrophoresis on 2% agarose gels.
Criteria for evaluation
H. pylori was assessed positive if at least two tests were positive according to golden standard [21].
Statistical analysis
The data were analysed by Fisher's exact tests using the Jandel SigmaStat 2.0 program. Measurements from the GSRS were expressed as the mean values for dyspeptic syndrome.
Ethics
The study was carried out in accordance with the Helsinki Declaration and was approved by the Ethics Committee of the University of Tartu.
Results
Dyspeptic syndrome was found in all 34 cases. The mean GSRS score for the patients varied from 1.2 to 4.3.
The applied non-invasive test revealed H. pylori infection in half of the investigated patients: positive 13C-UBT was found in 17 out of 34 cases. There was no difference between the mean GSRS score values for the H. pylori positive and negative cases (2.8 ± 1.8 vs. 2.9 ± 1.7, p > 0.05).
On endoscopy, among the 34 patients, no ulcer or other mucosal defects were observed in 24 cases; erosions in the duodenal bulb were revealed in 6 cases and peptic ulcer was found in 4 cases (2 duodenal ulcers and 2 gastric ulcers). The data of H. pylori status and of the endoscopic finding are presented in Table 1.
Table 1 Comparison of the findings in H. pylori positive and negative cases in a late post-treatment setting
Patients (n = 34) Non-invasive method
13C-UBT (+) n = 17 13C-UBT (-) n = 17
Invasive methods Endoscopy: Normal 11 13
Duodenal ulcer 2 0
Gastric ulcer 2 0
Erosions 2 4
Cytology: H. pylori (+) 4* Diverse forms of bacteria
Histology: H. pylori (+) 17 0
Bacteriology: H. pylori (+) 16 1
PCR: H. pylori (+) 17 0
* typical morphology of H. pylori (the other cases showing diverse forms of bacteria)
A poor concordance was found between the visual examination of the gastric and duodenal mucosa on endoscopy and the applied non-invasive and invasive tests of H. pylori (accepting 13C-UBT, histological examination and PCR as the reference tests). The gastric and duodenal mucosa was visually normal in 11 H. pylori positive cases out of 17. On the contrary, only in 4 H. pylori positive cases did the endoscopic examination reveal the above mentioned peptic ulcers. Among the 6 cases with erosions of the duodenal mucosa, only two patients were H. pylori positive.
Comparison of the different diagnostic methods used for the detection of H. pylori is shown in Table 1. The results of 13C-UBT and PCR were consistent with the data of histological examination both in 17H. pylori positive and 17 negative cases. On bacteriological examination, only one case, which was H. pylori positive both by PCR and the histological tests, was H. pylori negative. In contrast, cytological examination assessed typical H. pylori bacterial cells in only 4 of the 17H. pylori positive cases (24%), while all other cases (both positive and negative for H. pylori by the other methods) displayed abundant bacteria of different morphotypes.
The data of the histological findings are presented in Table 2. Colonisation of the gastric mucosa by H. pylori was detected in 17 patients out of 34. Neutrophil infiltration, chronic inflammation, and surface epithelial damage both in the antrum and corpus mucosa were significantly expressed in the H. pylori positive cases (p < 0.001). Glandular atrophy and intestinal metaplasia were rarely observed both in the antrum and corpus mucosa of the H. pylori negative cases in comparison with the H. pylori positive cases, but the difference was not statistically significant (p > 0.05). Lymphoid follicles were more frequent in the antrum colonised with H. pylori (p < 0.05).
Table 2 Gastric mucosal findings (by the Sydney system) in H. pylori positive and negative cases
Gastric mucosal findings (Sydney system) H. pylori (+) n = 17 H. pylori (-) n = 17 p values
Activity of neutrophil polymorphs
Antrum 11/17 0/17 <0.001
Corpus 7/16 0/17 <0.05
Chronic inflammation
Antrum 16/17 1/17 <0.001
Corpus 13/16 0/17 <0.001
Surface epithelial damage
Antrum 13/17 0/17 <0.001
Corpus 8/16 0/17 <0.001
Glandular atrophy
Antrum 7/17 2/17 NS*
Corpus 4/16 3/17 NS
Intestinal metaplasia
Antrum 1/17 2/17 NS
Corpus 0/16 2/17 NS
Lymphoid follicles
Antrum 6/17 0/17 <0.05
Corpus 5/16 2/17 NS
* NS, not significant (p > 0.05).
Bacteriological investigation revealed H. pylori in 16 biopsy samples of the antral mucosa, while highly (> 256 mg/l) clarithromycin resistant H. pylori strains were found in 3 cases.
Discussion
Proper diagnostic and therapeutic management of patients with dyspeptic syndrome after H. pylori eradication therapy is of utmost importance for physicians as well for patients [7]. Several studies [22,23] have demonstrated the reliability of H. pylori tests used before treatment, while post-treatment testing is not yet adequately studied. However, in the case of long-lasting recurrent dyspepsia after H. pylori eradication therapy, endoscopy has been strongly recommended [4]. Our study shows that endoscopy gives useful information for the general practitioner both in the cases where peptic ulcer is found and in the cases where it is not found. In the case of a normal endoscopic finding, further management depends on the histological finding and on H. pylori status. Since persistent H. pylori positivity is always associated with possible peptic ulcer recurrence, the second line treatment according to bacterial susceptibility should be recommended. In the remaining cases where H. pylori is absent, the gastric mucosa is normal and no ulcer is detected, management of such patients should be aimed at establishment of other possible reasons for their complaints. Usually, a normal endoscopic finding reassures both the doctor and the patient [7].
A recent study of Ohkusa et al. [24] showed that even simple careful visual evaluation of the mucosa and the diagnoses of erythema and oedema correlated well with H. pylori infection. On the contrary, our results demonstrate that although all patients with recurrent peptic ulcer were H. pylori positive, the minor visual findings in the other cases were not in concordance with H. pylori colonisation. Usually, the mucosa was visually normal even when H. pylori was found, and, on the contrary, most duodenal erosions occurred in H. pylori negative patients. The clinical data of our patients did not suggest earlier use of NSAID, which would have been one of the main reasons for H. pylori negative erosions. Therefore, after treatment, in presence of complaints, it is important to obtain samples for the investigation of gastric mucosa specimens to enhance the value of endoscopic examination. We completely agree with the opinion that the value of using mucosal specimens for histological evaluation of late post-treatment H. pylori eradication is sometimes underestimated [9]. The non-invasive H. pylori test alone cannot solve the clinical problem of these patients. In our study, H. pylori negative patients had dyspeptic syndrome as well as gastric mucosal erosions, glandular atrophy and intestinal metaplasia. The last two lesions can presumably be associated with previous H. pylori infection and the follow-up of severe mucosal changes is recommended [25]. Hence it is evident that follow-up strategy should be considered also in H. pylori negative cases in accordance with endoscopic and histological findings.
Our study demonstrates that evaluation of the gastric mucosa with a focus on neutrophil and lymphocyte infiltration and epithelial damage is specific and sensitive for diagnosing H. pylori infection even after treatment, and that the diagnostic value of a histology-based decision is high. Today, the value of mucosal specimens for the post-treatment histological diagnosis of H. pylori is considered low assuming that H. pylori colonisation may be patchy, or coccoid forms are difficult to detect [25]. We have excluded patchy damage by using 13C-UBT in parallel with histological investigation.
Next, for detecting the coccoid forms of the bacteria, we used additionally PCR method. Our results show that the histological finding of H. pylori completely correlates with the results of 13C-UBT and PCR both in H. pylori positive and negative cases. This confirms the validity of the histological evaluation of mucosal specimens in the case of recurrent peptic ulcer or erosions. Moreover, in countries with a high rate of H. pylori infection and gastric cancer, it is especially important to follow up patients for detecting dysplasia and malignancies [26-29].
Surprisingly, brush cytology from the mucosa failed to detect H. pylori in cases where it was found by other methods. Cytology is highly evaluated for detection of H. pylori infection, as its agreement with histology is considered to be 100% [30]. Our results show that when patients had been treated with antibacterial drugs and still had dyspeptic complaints, cytological examination was not suitable for H. pylori detection, as different forms of the bacteria were found. The morphology of the helicobacters could have been modified for coccoid or otherwise non-typical forms. It is possible that some other bacteria might have colonised the mucosa due to reduced colonisation resistance after antibacterial treatment, failure of some intestinal functions or usage of medicines administered to relieve the feeling of discomfort [31-33].
Bacteriological investigation enabled to find a few clarithromycin resistant H. pylori strains, which may result in the failure of repeat triple therapy. As the macrolide clarithromycin is chemically stable and well tolerated [34], physicians often choose it for treatment of different infections. Therefore, if the physician plans to use macrolides, endoscopy and histological testing should be accompanied by bacteriological investigation. Regarding PCR, its main value, obtaining of fast results, is evidently not so important in post-treatment settings.
Conclusions
In a late post-treatment setting, patients with dyspepsia should not be monitored only by non-invasive investigation methods; it is also justified to use the classical histological evaluation of H. pylori colonisation, PCR and bacteriology as they have shown good concordance with 13C-UBT. Moreover, endoscopy and histological investigation of a gastric biopsy have proved to be the methods with an additional diagnostic value, providing the physician with information about inflammatory, atrophic and metaplastic lesions of the stomach in dyspeptic H. pylori positive and negative patients. Bacteriological methods are suggested for detecting the putative antimicrobial resistance of H. pylori, aimed at successful eradication of infection in persistent peptic ulcer cases.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contribution
HIM, IK and KLa carried out GSRS, endoscopy and gastrobiopsy. HK recruited patients, collected 13C-urea breath tests, and performed GSRS. KLõ carried out bacteriological examination. PH performed cytological examination. HA carried out molecular analysis and participated in the writing of the manuscript. HIM performed histological examination and statistical analysis, and participated in the design of the study and in the writing of the manuscript. MM coordinated the study and participated in the completion of the manuscript. All authors have read and approved the final version of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by the Estonian Science Foundation grants No 4383, 4898 and by the Estonian Target Funding for topic No 0418.
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| 15507141 | PMC529255 | CC BY | 2021-01-04 16:29:55 | no | BMC Gastroenterol. 2004 Oct 26; 4:27 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-27 | oa_comm |
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BMC GeriatrBMC Geriatrics1471-2318BioMed Central London 1471-2318-4-91548814310.1186/1471-2318-4-9Research ArticlePotentially inappropriate prescriptions for older patients in long-term care Rancourt Carol [email protected] Jocelyne [email protected] Lucie [email protected] René [email protected] Danielle [email protected]égoire Jean-Pierre [email protected] Health Economics and Outcomes Research, Merck Frosst Canada Ltd, Montreal, Qc, H9H 3L1, Canada2 Population Health Research Unit and Faculty of Pharmacy, Université Laval, Hôpital St-Sacrement, 1050 Chemin Ste-Foy, Québec, Qc, G1S 4L8, Canada3 Family Medicine Unit, Centre hospitalier universitaire de Québec, 2701 boul. Laurier, Québec, Qc, G1V 4G2, Canada4 Geriatric Research Unit, and Faculty of Medicine, Université Laval, Hôpital St-Sacrement, 1050 Chemin Ste-Foy Québec, Qc, G1S 4L8, Canada5 Geriatric Research Unit and Faculty of Pharmacy, Université Laval, Hôpital St-Sacrement, 1050 Chemin Ste-Foy Québec, Qc, G1S 4L8, Canada2004 15 10 2004 4 9 9 27 2 2004 15 10 2004 Copyright © 2004 Rancourt et al; licensee BioMed Central Ltd.2004Rancourt et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Inappropriate medication use is a major healthcare issue for the elderly population. This study explored the prevalence of potentially inappropriate prescriptions (PIPs) in long-term care in metropolitan Quebec.
Methods
A cross sectional chart review of 2,633 long-term care older patients of the Quebec City area was performed. An explicit criteria list for PIPs was developed based on the literature and validated by a modified Delphi method. Medication orders were reviewed to describe prescribing patterns and to determine the prevalence of PIPs. A multivariate analysis was performed to identify predictors of PIPs.
Results
Almost all residents (94.0%) were receiving one or more prescribed medication; on average patients had 4.8 prescribed medications. A majority (54.7%) of treated patients had a potentially inappropriate prescription (PIP). Most common PIPs were drug interactions (33.9% of treated patients), followed by potentially inappropriate duration (23.6%), potentially inappropriate medication (14.7%) and potentially inappropriate dosage (9.6%). PIPs were most frequent for medications of the central nervous system (10.8% of prescribed medication). The likelihood of PIP increased significantly as the number of drugs prescribed increased (odds ratio [OR]: 1.38, 95% confidence interval [CI]: 1.33 – 1.43) and with the length of stay (OR: 1.78, CI: 1.43 – 2.20). On the other hand, the risk of receiving a PIP decreased with age.
Conclusion
Potentially inappropriate prescribing is a serious problem in the highly medicated long-term care population in metropolitan Quebec. Use of explicit criteria lists may help identify the most critical issues and prioritize interventions to improve quality of care and patient safety.
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Background
Inappropriate medication use is a major health care issue for the elderly population [1-3]. Older patients are more at risk for adverse medication outcomes because they often have complex drug regimens and because of the age-related changes in drug pharmacokinetics and pharmacodynamics [1]. Potentially inappropriate prescriptions (PIPs), defined as prescriptions in which risks outweigh benefits, have been assessed in various settings using lists of explicit criteria most often based on that developed by Beers [4]. PIPs have been estimated to affect 4.8% to 45.6% of the elderly population [5-12].
Prevalence estimates of PIPs are likely to vary with the criteria that are applied. Some authors have based their assessment on the Beers criteria [5-7,9-12]. However, in all these studies but one [7], criteria applied were a subset only of Beers criteria as dosage and duration was not evaluated. Despite controversy about which explicit criteria should be used, there is a strong body of evidence showing that suboptimal prescribing is disturbingly common in older patients.
In Canada, a list of explicit criteria was developed by a panel of experts in 1997 [13]. The Canadian criteria required diagnostic information which is not easily accessible in the long-term care setting [6,14]. Using various methodologies, several studies have investigated the extent of the problem in Canada. A 1995 study of community-dwelling and institutionalized older patients reported large variations in PIPs among provinces, ranging from 4.8% in the prairies to 12.8% in Quebec [9]. More recently, the prevalence of PIPs in long-term care patients in Ontario was reported to range between 14.9% and 20.8% [15-17]. In Quebec, a 1990 retrospective database survey of 63,268 older Medicare patients reported that 45.6% of non-institutionalized patients received high-risk prescriptions of questionable appropriateness [8], while a recent survey of 3,400 elderly patients in the Quebec general population reported that 6.5% had a potentially inappropriate prescription (PIP) [18]. A 1995 physician survey reported that 77.1% of nursing home patients in Quebec had been taking benzodiazepine for over one year [19].
The long-term care elderly population is particularly vulnerable to inappropriate medication use; it is composed of frail older patients who typically have functional disabilities and acute and chronic medical histories that require complex medication regimens [20,21]. Assessing PIPs using the data available in long-term care, in particular data on dosage and duration of use, may help designing efficient interventions to improve prescribing practices in one of the frailest populations. The objectives of this study were (1) to describe prescribing patterns in elderly patients residing in long-term care facilities in the Quebec metropolitan area, (2) to assess the prevalence of PIPs in this long-term care setting using published explicit criteria [4,13,22] adapted for this study, and (3) to identify patient-related predictors of PIPs.
Methods
Design and data sources
A cross-sectional chart review of long-term care patients aged 65 years and over living in the Quebec City area was performed in the period between April 1995 and December 1996. All long-term care facilities located in the Quebec City area were contacted and the majority (29 out of 33) agreed to participate in the study. Within the 29 participating facilities, there were a total of 71 long-term care units. Numbers of beds in these units averaged 41 (10 to 190). Units were visited once during the study period. Data on drugs currently being prescribed the day of the visit was collected using medication charts. Demographic data included age, gender and length of stay. This study was approved by the ethics committees at Université Laval, Hôpital Saint-François d'Assise and Hôpital de l'Enfant-Jésus.
For each medication order, the name, dosage, frequency of dosing and nature of prescription (scheduled or given on an as-needed basis) were collected. To capture the fullest possible extent of potentially inappropriate prescribing, it was assumed that all medications prescribed on an as-needed basis were taken. The total daily dose of an as-needed prescription was calculated by multiplying the prescribed unit dose with the indicated daily frequency of administration. Prescriptions for creams, ointments and drops were not included. Each medication was classified using the Anatomical Therapeutic Chemical (ATC) classification system [23]. The maximal prescribed daily dose was calculated for each medication order.
Classification of potentially inappropriate prescribing
A list of explicit criteria for PIP in older patients was developed based on a review of the literature [4,6,10,11,13,14,22]. Criteria referring to medications unavailable in Canada were excluded. Because diagnostic information is difficult to obtain in the long-term care setting [6,14], criteria involving clinical information were also excluded. The list of criteria was elaborated using a modified Delphi method [24]. A consensus panel of four local experts was convened including a general practitioner with a geriatric practice (RV), a family physician (LB), a clinical pharmacist and a pharmacoepidemiologist (JPG), all involved in practice or research on medication issues in the elderly population. In the first step, experts were asked to review and comment independently on the preliminary list of published criteria. Responses from the experts were used to revise this list. In the second step, the panel discussed each criterion until a consensus was reached. A total of 111 explicit criteria were included in the list to assess the quality of prescribing (Appendix).
Medication charts were reviewed and compared with the list of explicit criteria. PIPs were categorized as:
• Potentially inappropriate medication;
• Potentially inappropriate duration;
• Potentially inappropriate dosage; and
• Potentially inappropriate drug-drug interaction.
Data analyses
Drug prescribed and PIP data were stratified by age and gender. Chi-square and Student t tests were used to compare proportions and means, respectively. Association between age and drug utilization was evaluated by analysis of variance. Factors predicting PIP were identified by logistic regression analyses. Independent variables were age, sex, number of prescribed drugs and length of stay. An initial bivariate analysis allowed calculation of crude odds ratios, identification of variables individually associated with the risk of PIP, and determination of the appropriate scale for each variable. A multivariate analysis with a significance threshold of 0.10 for the inclusion of variables subsequently yielded adjusted odds ratios for the number of prescribed medications, age and length of stay. Data were analyzed for collinearity and overdispersion. Data analyses were performed using SAS version 6.12 (SAS Institute Inc. Cary, NC).
Results
Study population
The study population included 2,633 individuals, aged 65 years and older, residing in long-term care facilities for a mean duration of 8.5 years. Mean age was 82 ± 8 years and the majority of individuals were women (74.2%). Women were older than men (84 ± 8 years versus 79 ± 8 years, p = .0001).
Drug utilization
Most residents (94%, n = 2,481) had one or more prescribed medications and 48% (n = 1,266) of the total population had five or more medications. Residents had on average 4.8 prescribed medications. Proportions of patients by number of prescribed medications were similar for men and women, but varied according to age. The oldest patients, aged 85 years and more, received significantly less medications than their youngest counterparts aged between 65 and 74 years; 43.8% of patients aged over 85 years received five medications or more, compared to 59.4% of those aged 65 to 74 years. Of the 12,707 medications prescribed, 86% were scheduled administrations and 82% were prescribed for more than three months.
A majority of patients (85.5%, n = 2,251 patients) had a prescription for medications of the central nervous system (CNS). Cardiovascular medications (46.4%, n = 1,221 patients) and medications of the alimentary tract and metabolism (29.3%, n = 772 patients) were the following most frequently prescribed anatomical groups of medications. Most commonly prescribed therapeutic classes included analgesics (48.0%), anxiolytics (41.4%), antipsychotics (35.0%) and loop (high-ceiling) diuretics (18.6%) (Table 1). There were differences in therapeutic classes prescribed to men and women. Acetaminophen (36.7% of patients), haloperidol (20.5% of patients) and lorazepam (20.2% of patients) were the three most frequently prescribed drugs (Table 2).
Table 1 Proportion (in %) of elderly patients on medication by therapeutic class and sex*
Proportion of patients (%)
Therapeutic class All (n = 2,633) Men (n = 680) Women (n = 1,953) p value
Analgesics & antipyretic 48.0 44.6 49.2 0.037
Anxyolitics 41.4 41.4 41.4 0.983
Antipsychotics 35.0 39.5 33.5 0.004
Loop (high ceiling) diuretics 18.6 16.2 19.4 0.062
Antiepileptics 14.9 21.6 12.6 <0.001
Thyroid preparations 14.6 8.4 16.8 <0.001
Vasodilators 14.6 11.0 15.8 0.002
Antidepressants 13.7 11.0 14.7 0.017
Cardiac glycosides 12.4 11.6 12.7 0.462
Drugs for peptic ulcer 11.0 11.3 10.9 0.765
Hypnotics & sedatives 10.9 9.8 11.3 0.293
Anticholinergics 10.9 11.2 10.8 0.788
Selective calcium channel blockers 10.6 7.2 11.7 0.001
Angiotensin converting enzyme inhibitors 10.0 10.0 10.0 0.991
* Only therapeutic classes prescribed to 10% or more of the elderly are displayed
Table 2 Most frequently prescribed medications among the elderly in long-term care
Proportion of patients (%)
ATC code Medication Men (n = 680) Women (n = 1,953) All (n = 2,633)
N02BE01 Acetaminophen 30.5 38.9 36.7
N05AD01 Haloperidol 21.9 20.1 20.5
N05BA06 Lorazepam 20.2 20.2 20.2
C03CA01 Furosemide 16.2 19.2 18.6
N02BA01 Acetyl salicylic acid 19.1 16.7 17.3
N05BA04 Oxazepam 16.3 17.1 16.9
H03AA01 Levothyroxin sodium 8.4 16.8 14.6
C01DA02 Nitroglycerin 10.0 14.6 13.4
C01AA05 Digoxin 11.6 12.7 12.4
ATC: Anatomical Therapeutic Classification
Potentially inappropriate prescribing
Overall, 51.5% of the population under study had one or more PIPs. Of the 2,481 patients with at least one prescribed drug, more than half (54.7%) had one or more PIPs; 29.5% had one PIP, 12.5% had two PIPs, 7.5% had three PIPs and 5.3% had four or more PIPs.
A total of 12,707 drugs were prescribed of which 1807 were given on an as-needed basis. The proportion of PIPs among scheduled and as-needed prescriptions were 9.2% and 11.5%, respectively. If we exclude as-needed prescriptions, 46.4% of all residents had one or more PIPs.
The most common type of PIP was drug-drug interaction, affecting 33.9% of patients treated with drugs, followed by potentially inappropriate duration (23.6%), potentially inappropriate medication (14.7%), and potentially inappropriate dosage (9.6%) (Figure 1). The proportion of patients receiving any type of PIP decreased with age, from 66.7% for patients aged 65 to 74 years to 56.4% for those aged 75 to 84 years and 47.7% for patients aged 85 years and more. PIPs were the most frequent for CNS medications, representing 9.3% of prescribed medications.
Figure 1 Potentially inappropriate prescribing including inappropriate medication, dosage, duration and potential drug-drug interaction among three age-groupsof long-term care elderly (N = 2,481) ♂ = male ♀ = female
The most common PIP was a potentially inappropriate duration for intermediate and short-acting benzodiazepines for more than one month (22.9%); more than half of those PIPs were for the anxiolytic oxazepam (Table 3). A substantial number of patients treated with pharmacotherapy were receiving repeat (dual) prescriptions of antipsychotics (16.5%) or benzodiazepines (14.9%). Almost 6% of patients treated with pharmacotherapy were prescribed potentially inappropriate long-acting benzodiazepines and 5.2% were receiving haloperidol at a potentially inappropriate dosage. The most common PIP among cardiovascular drugs was repeat prescription of calcium channel blockers, affecting 3.1% of treated patients.
Table 3 Most common potentially inappropriate prescriptions (PIPs) among older patients receiving medication in long-term care
Criteria Number of patients Proportion of all patients prescribed a medication (%) (N = 2,481)
Potentially inappropriate medication 365 14.7
Long-acting benzodiazepines 138 5.6
Preparations including an antihistaminic 112 4.5
Flurazepam 54 2.2
Doxepin 31 1.3
Amitryptiline 27 1.1
Propanolol 27 1.1
Chloral hydrate 22 0.9
Potentially inappropriate duration 585 23.6
Intermediate and short-acting benzodiazepines at bedtime for more than one month 567 22.9
Oxazepam at bedtime for more than one month 313 12.6
Potentially inappropriate dosage 239 9.6
Haloperidol > 3 mg daily 129 5.2
Thioridazine > 30 mg daily 53 2.1
Lorazepam > 3 mg daily 34 1.4
Potential drug-drug interaction 842 33.9
Repeat* prescription of antipsychotics 409 16.5
Repeat* prescription of benzodiazepine 369 14.9
Clonazepam and other benzodiazepine 46 1.9
Benzodiazepine and hypnotic or sedative 93 3.8
Repeat* prescription of calcium channel blockers 77 3.1
Repeat* prescription of tricyclic antidepressants 37 1.5
Repeat* prescription of angiotensin converting enzyme inhibitors 19 0.8
Repeat* prescription of β-blockers 11 0.4
Repeat* prescription of non-steroidal anti-inflammatory drugs (except acetylsalicylic acid) 10 0.4
Repeat* prescription of barbiturate 10 0.4
Total potential inappropriate prescriptions** 1,358 54.7
*Repeat prescription indicates that two agents of the same drug class are being prescribed
**Numbers do not add up since one prescription may be linked to more than one PIP (e.g., duration and dosage)
Predictors
Multivariate analysis indicated that patients with a length of stay 10 years or over were 1.78 times at greater risk of being prescribed a PIP than those with less than 10 years of stay (adjusted odds ratio [OR]: 1.78, 95% confidence interval [CI]: 1.43–2.20) (Table 4). The risk of PIP also increased significantly as the number of drugs prescribed increased (OR: 1.36, CI: 1.32–1.41) whereas it decreased with age. Gender was not a significant predictor of PIP. No problems of collinearity or overdispersion were observed in the multivariate model.
Table 4 Predictors of potentially inappropriate prescription among elderly patients in long-term care (N = 2,481)
Predictor Proportion of patients with PIP (%) Crude odds ratio (95% CI) Adjusted odds ratio (95% CI)*
Number of prescribed drugs (increments of one drug) 54.7 1.38 (1.33–1.43) 1.36 (1.32–1.41)
Gender
Women 54.5 1.00 -
Men 55.5 1.04 (0.87–1.25) -
Age
65 to 74 years 66.7 1.00 1.00
75 to 84 years 56.4 0.65 (0.51–0.81) 0.74 (0.58–0.96)
85 years or more 47.7 0.46 (0.36–0.57) 0.60 (0.47–0.77)
Length of stay
<10 years 51.1 1.00 1.00
≥10 years 67.4 1.98 (1.62–2.41) 1.78 (1.43–2.20)
CI: confidence interval; *Adjusted for number of prescriptions, age, and length of stay
Discussion
The long-term care elderly population evaluated in this study was highly medicated and a majority of patients receiving medication had a PIP. These results indicate that potentially inappropriate prescribing was significant at the time of the study in institutionalized older patients in the Quebec metropolitan area.
A total of 94% of residents in this long-term care population were prescribed at least one drug, compared to 60% in community-dwelling elderly patients in Quebec [25]. The mean number of medications was also higher (4.8) than in community-dwelling individuals in Quebec (2.9) [25], but lower than in American long-term care (7.2) [7].
The total prevalence of PIPs among the population under study was high (51.5%). Estimates of PIP prevalence in the literature vary between 4.8% [6] and 45.6% [8] for both institutionalized and community-dwelling older patients. Caution must be used when comparing these results, as the delivery of care may vary from one setting and one region to another [9]. The current lack of consensus when defining lists of criteria and variations with respect to methodologies also contribute to the observed differences [26]. For example, Zhan and colleagues [5] estimated the proportion of potentially inappropriate medication use in the community-dwelling elderly in the United States. Applying criteria on the indication for the use of 33 drugs, they observed a prevalence of 21.3% for 1996. In our study, PIPs were identified using an explicit criteria list that was primarily based on Beers and McLeod criteria [4,13,22] and that was updated and validated by local experts to apply to the long-term care context in Quebec. As we had access to dosage and duration information, we were able to apply a broader set of criteria which can explain the higher prevalence of PIPs we have observed. Explicit criteria lists, such as those developed by Beers and McLeod, define inappropriate prescription according to the drug overall risk-benefit profile for elderly patients. These lists were previously used in studies examining inappropriate prescribing in elderly populations [3,5,6,11,15,27-30] and undergo a continuous process of revision and updating to reflect the most current clinical information on the risks and benefits of medications [31].
A large number of patients were receiving CNS medication (85%) and the most common PIPs were related to that category of drugs. Thirty-five percent of patients were prescribed antipsychotics and 22.9% had benzodiazepine for potentially inappropriate duration, defined as more than a month [32]. A number of studies have reported the inappropriate use of CNS drugs [5,8,33,34], particularly benzodiazepines [18,19]. Many factors may contribute to the continued use of inappropriate CNS medications, including prescriber attitudes, patient demands and the design of the health care system [34]. A survey of physicians in Quebec reported that the psychological distress of aging patients and the quasi-absence of reported side-effects justified the long-term use of psychotropic medication, which was seen as the most effective way of helping the patient [35]. Moreover, side effects of psychoactive medication are often believed to be a consequence of the aging process [34]. Almost three quarters of potentially inappropriate psychoactive medications can produce a physical dependence [34]. Psychoactive pharmacotherapy increases risk of hip fractures and is advocated for use with caution to prevent falls in elderly populations [36,37]. Anticonvulsants, antidepressants and short- and long-acting benzodiazepines were reported to increase risk of falls in older women [38].
The length of stay was positively associated with PIPs, while the prevalence of PIPs decreased with age. Although the association between length of stay and the likelihood of receiving a PIP in nursing homes was studied in the past [6], to our knowledge, this is the first time it is being shown to be a predictor of PIPs. On the other hand, the risk of receiving a PIP was previously reported to decrease with age in nursing home patients over 65 years [7,12]. Data on clinical status was not considered in these studies and it can be hypothesized that either the oldest residents were less ill or that physicians were more cautious when prescribing to very old patients. As reported in previous studies [12,26], the number of medications was also a predictor of PIP in older patients. Patients in long-term care frequently have multiple diseases resulting in complex medication regimens, which makes assessment of the risks versus benefits of treatments often difficult. Female gender was previously reported as a predictor of PIP [7,12]. Although we observed gender differences in the prescribed therapeutic classes, female gender was not a predictor of PIPs in our study.
The results presented here should be viewed in light of potential limitations. As in previous studies [15], we did not abstract information on diagnoses from the patients charts and drug prescriptions were considered as surrogates for disease conditions. Thus, the explicit criteria used in this study apply to general circumstances, but may not be applicable to specific cases, since they do not consider clinical information. For example, lipid-lowering drugs may be potentially inappropriate in patients aged 75 and over, but evidence from clinical trials suggests that statins may be of benefit if the patient's life expectancy exceeds two years [39]. Thus, misidentification of potential cases of appropriate or inappropriate prescribing may have occurred, since complex medical conditions can alter the risk-benefit profile of medications. However, due to the frail condition of most patients, it is unlikely that such misidentifications have occurred frequently. Since access to clinical data is often difficult in the nursing home setting, a list of explicit criteria that does not require that type of information may be easier to apply on a larger scale.
This study evaluated prescription patterns rather than the actual consumption of medication. The low prevalence of as-needed medication (14%) and the long-term care setting, in which medication is administered to patients by a health caregiver, suggest that this limitation did not have a significant impact on the results. As-needed prescriptions may have accounted for repeat prescriptions, which may in turn have led to overestimation of the number of drug-drug interactions. However, even after excluding as-needed prescriptions from the analysis, the proportion of residents with a PIP remains high.
Predictors of PIPs were assessed using a multivariate analysis. It allowed us to adjust for potential confounding variables. However, we were not able to adjust for facility variables as those were not available.
This study is the first to describe and qualify prescribing practices in long-term care facilities in urban Quebec. In particular, it highlights the extent of potentially inappropriate prescribing in elderly long-term care patients, which are among the frailest of society [4,21]. Inappropriate prescribing is one component of the major health care problem of suboptimal prescribing that also includes underuse of effective agents, drug-disease interactions and prescription errors. Substantial morbidity, mortality and cost are attributed to suboptimal prescribing [1,2]. Although a decline in the prevalence of PIPs was reported in community-dwelling older patients in the United States between 1987 and 1996 [40], the continued use of inappropriate medications is a major concern. A growing body of evidence suggests that clinical pharmacy and multidisciplinary team interventions can modify suboptimal prescribing in older patients. Modern data management [15,41] and use of the best clinical evidence could help practitioners improve the management of complex cases [40,42]. Recent studies in long-term care settings showed that physician or pharmacist interventions reduce PIPs [1,12,16,43], while a clinical review program of prescriptions for community-dwelling patients conducted by a team of physicians, pharmacists and nurses did not seem to improve prescribing practices [44].
Conclusions
Inappropriate prescribing is highly prevalent in the elderly long-term care population in metropolitan Quebec. The use of a explicit criteria list to identify PIPs is a first step towards identifying most critical issues and implementing strategies to improve quality of care and patient safety. Identifying predictors of PIPs may help to target problems and prioritize interventions that are most needed in the rapidly expanding older population.
Competing interests
Carol Rancourt and Jean-Pierre Grégoire were employed by Merck Frosst Canada at the time of the preparation of this article.
Authors' contributions
CR, in partial fulfillment for the grade of M.Sc., lead the protocol development, expert panel consultation, data analyses, discussion of results, and manuscript preparation. JM contributed to all steps of this research project and manuscript preparation. LB contributed to protocol development, presentation and discussion of results and manuscript preparation and participated in the expert panel to define the explicit criteria. RV is the principal investigator for the initial research project which generated the drug prescription data used for this study. He contributed to protocol development, presentation and discussion of results, manuscript preparation and participated in the expert panel to define the explicit criteria. DL was a co-investigator for the initial research project, which generated the drug prescription data used for this study, and contributed to protocol development, data analyses and manuscript preparation. All authors read and approved the final manuscript. JPG contributed to protocol development presentation and discussion of results, manuscript preparation and participated in the expert panel to define the explicit criteria.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Appendix: List of explicit criteria used to assess the quality of prescribing in long-term care for elderly patients provide as additional file
Click here for file
Acknowledgements
The authors wish to thank Carmen Vezina MSc for her contribution as a clinical pharmacist to the expert panel. This study was made possible by grants from the Fonds de la Recherche en Santé du Québec (project #940984-104) and the Laval University Chair for Geriatric Research (project #95-14). This article was prepared with the assistance of BioMedCom Consultants Inc.
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| 15488143 | PMC529256 | CC BY | 2021-01-04 16:30:12 | no | BMC Geriatr. 2004 Oct 15; 4:9 | utf-8 | BMC Geriatr | 2,004 | 10.1186/1471-2318-4-9 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-441550069110.1186/1471-2334-4-44Research ArticleAdjuvant interferon gamma in patients with drug – resistant pulmonary tuberculosis: a pilot study Suárez-Méndez Roberto [email protected]ía-García Idrian [email protected]ández-Olivera Norma [email protected]és-Quintana Magalys [email protected]és-Virelles María T [email protected] Dalia [email protected] Delfina [email protected] Carmen M [email protected]ópez-Saura Pedro A [email protected] "Benéfico Jurídico" Hospital, Havana, Cuba2 Center for Biological Research, Clinical Trials Division, Havana, Cuba2004 22 10 2004 4 44 44 27 2 2004 22 10 2004 Copyright © 2004 Suárez-Méndez et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Tuberculosis (TB) is increasing in the world and drug-resistant (DR) disease beckons new treatments.
Methods
To evaluate the action of interferon (IFN) gamma as immunoadjuvant to chemotherapy on pulmonary DR-TB patients, a pilot, open label clinical trial was carried out in the Cuban reference ward for the management of this disease. The eight subjects existing in the country at the moment received, as in-patients, 1 × 106 IU of recombinant human IFN gamma intramuscularly, daily for one month and then three times per week up to 6 months as adjuvant to the indicated chemotherapy, according to their antibiograms and WHO guidelines. Sputum samples collection for direct smear observation and culture as well as routine clinical and thorax radiography assessments were done monthly.
Results
Sputum smears and cultures became negative for acid-fast-bacilli before three months of treatment in all patients. Lesion size was reduced at the end of 6 months treatment; the lesions disappeared in one case. Clinical improvement was also evident; body mass index increased in general. Interferon gamma was well tolerated. Few adverse events were registered, mostly mild; fever and arthralgias prevailed.
Conclusions
These data suggest that IFN gamma is useful and well tolerated as adjunctive therapy in patients with DR-TB. Further controlled clinical trials are encouraged.
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Background
Tuberculosis (TB) is not yet a defeated affection. Although controllable at a community level and curable in individuals, its eradication seems distant. At present, at least one third of the World's population, more than 1 500 million individuals are infected with the Mycobacterium tuberculosis. Every year, around 8 – 10 million new cases occur [1]. Two million people die annually due to non-AIDS related TB, which is the highest number of deaths attributable to a single infectious agent [2], and corresponds to the 7th cause of death worldwide [3]. TB represents 26 % of avoidable deaths in developing countries [4]. The emergency of drug resistant (DR) or multidrug-resistant (MDR) strains has increased this global problem, leading to a high morbidity and mortality [5]. MDR-TB affected patients' mean survival ranges from 2 to 14 months [6]. According to the World Health Organization's (WHO) data, MDR patients global proportion is 2.2 % [7].
The infection is mainly transmitted by inhalation of the bacilli coming from respiratory airways infected secretions. Once inhaled, the bacilli are subjected to phagocytosis within the alveolar macrophages, where they can be destroyed. Nevertheless, Mycobacteria have developed mechanisms to adapt to the noxious intracellular environment. Thus, it can persist, replicate and disseminate, leading to new infectious foci. Resistance emergence depends on several factors such as initial bacillary load, inadequate or incomplete chemotherapy administration, and the patient's immune condition.
Chemotherapy is successful in most cases, given that the treatment schedule is thoroughly followed. It is prolonged, costly, and needs to be directly observed. Otherwise it is inadequate to kill all the bacilli and drug resistance emerges. Toxicities are frequent as well [8].
The immunologic approach to TB treatment can be promising since only 10 – 20% of infected people develop the disease and many of them have spontaneous remission [9]. Interferon (IFN) gamma plays a main role in the immunity to TB. It is a glycoprotein, secreted by CD4+, CD8+ and NK cells. Nevertheless, CD4+ Th1 lymphocytes are the main producers in response to a stimulus [10].
Enough evidences exist related to IFN gamma action on the macrophages immunoregulatory activity [11-14]]. Lack of production of this cytokine [15] or expression of its receptor [16,17] is associated to the infection's most lethal forms. Interferon gamma has also a potent antifibrotic effect [18-20].
Therefore a pilot clinical study was done with the aim to evaluate IFN gamma effect on drug resistant pulmonary TB patients regarding their clinical, bacteriological and radiological evolutions. The results show that the use of this protein in eight DR-TB patients (four of them MDR) as adjuvant to antibiotics had short and middle-term beneficial effects.
Methods
An open-label, non-randomized, non-controlled, pilot trial was carried out at the "Benéfico -Jurídico" Hospital, Havana, which is the national reference unit for TB and other respiratory diseases. According to the national TB program, all patients with unfavorable response to treatment are remitted to this center.
The study population was constituted by eight Cuban patients, both sexes, more than eighteen years old, with diagnosis of TB without a favorable response to the usual therapy, who gave their written, informed consent to participate. The diagnosis comprised clinical findings such as cough and expectoration, pulmonary lesions at thorax radiography, and positive DR-TB sputum-smear and culture. To confirm drug resistance the Canetti's multiple proportions method [21] was used as antibiogram. Exclusion criteria were another chronic disease, pregnancy or nursing, severe psychiatric dysfunction, multiple sclerosis or another autoimmune disorder, other pulmonary infections, HIV co-infection, and treatment with glucocorticoids or any other immunosuppressor medication.
The previous drug therapy received by the patients included 4 drugs (isoniazid, rifampin, streptomycin, and pyrazinamide) daily during 2 months, then isoniazid and rifampin twice per week for 2 additional months. Since their sputum tests had not become negative at this point they were returned to the 4 drugs regime plus ethambutol daily for 3 months, and finally isoniazid, rifampin, and ethambutol three times per week for 5 months.
The trial was done in compliance with the Helsinki Declaration. The protocol was approved by the hospital's Ethics Committee and by the Cuban Regulatory Authority. Data from nineteen historical control cases were obtained from the hospital's archives.
Patients received 1 000 000 IU of human recombinant IFN gamma (Heberon Gamma R®, Heber Biotec, Havana), intramuscularly, daily during 4 weeks and then 3 times per week for the next 20 weeks. Participants stayed as in-patients during the study period. They received anti-TB drugs (WHO schemes) [22], according to the resistance detected in each case by the antibiogram (Table 1). Drugs were given as follows: rifampin 10 mg/Kg (maximum 600 mg), ethambutol 20 mg/Kg (max. 1500 mg), ethionamide 10 mg/Kg (max. 750 mg), pyrazinamide 15–30 mg/Kg (max. 2000 mg), ciprofloxacin 15–20 mg/Kg (max. 1500 mg), amikacin 15 mg/Kg (max. 1000 mg), and kanamycin 15 mg/Kg (max. 1000 mg) daily. After the end of the 6-months IFN gamma treatment period, chemotherapy continued up to 9 months if the scheme included rifampin and 18 months otherwise.
Evaluations were carried out at entry and monthly during IFN gamma treatment. A complete physical examination was done. Sputum samples were taken for acid-fast-bacilli smear and culture, as well as blood samples for hematological counts, globular sedimentation rate, alanine aminotransferase, and creatinin determinations. Thorax radiographies were also recorded. Afterwards, patients were followed up with half-yearly evaluations during one year.
Treatment efficacy evaluation included clinical, bacteriological and radiological outcomes. Complete response was defined as total disappearance of all signs and symptoms, negative sputum acid-fast-bacilli smear and culture, and pulmonary lesions improvement at X-ray. Partial response included signs and symptoms decrease, negative sputum smear and culture and stable X-ray lesions. No response consisted in signs and symptoms persistence, positive bacteriological examinations, and lesions stabilization or progression. Safety and tolerability of the IFN gamma treatment were monitored by means of a rigorous control of the adverse events that could be presented.
Results
Eight patients were enrolled in the study. Those were all the pulmonary DR-TB cases in the country during the inclusion period, from December 1999 to February 2002. They had not responded to the usual Directly Observed Treatment Short-course (DOTS) chemotherapy regime. Strain resistance was acquired in all cases. This was well determined since in Cuba all detected cases are screened for resistance on their first isolate. The patients did not have any extra-pulmonary manifestation of the disease. Their demographic and baseline characteristics are shown in Table 1. Five of them were men, six of them non-white. The age ranged between 23 and 54 years old, and body mass index (BMI) between 13.2 and 22.0 Kg/m2. Their main symptoms were cough, expectorations, dyspnea, stertors, distal cyanosis, and finger clubbing. Bacteriological tests codification was mostly high and all patients showed active lesions at thorax radiography. Most of them had accelerated globular sedimentation rates (GSR). Anemia or other hematological alterations were not recorded.
A rapid favorable evolution was obtained after treatment with IFN gamma (Table 2). Clinical improvement was evident since the first month of treatment, when all signs and symptoms (except for finger clubbing) had disappeared in all patients and BMI increased in all but one of them. Sputum acid-fast-bacilli smears and cultures were negative since the 1 – 3 months of treatment. The eight patients had radiological improvement, with lesions size reduction (total disappearance in one case) (Figure 1). GSR decreased in five out of 6 patients who had abnormal values at inclusion. After six additional months follow-up, patients # 3 and 4 normalized it to 10 and 15 mm/h, respectively. At the end of the IFN gamma treatment all the patients were evaluated as complete responders.
The treatment with IFN gamma was safe and well tolerated. Four patients presented at least one adverse event. These events were arthralgias, fever, headache and asthenia. All adverse events were mild, except for one moderate fever, which was efficiently controlled with acetaminophen. Significant differences were not detected in other clinical laboratory tests.
After completion or the IFN gamma 6-months treatment the patients continued with the corresponding chemotherapy schedule. Seven of the eight patients remained bacteriologically, clinically and radiologically negative at least twelve months after the treatment with IFN gamma concluded. However, patient number five relapsed six months after the end of IFN therapy. He developed additional resistance to rifampin and ethionamide and a chronic obstructive respiratory disease that contributed negatively to his evolution.
Table 3 shows the results obtained at the same hospital with the 19 DR-TB cases during the five years prior to the present study. These patients had also failed to the standard DOTS regime. They were all resistant to isoniazid and rifampin, 13 were resistant to streptomycin, 7 to kanamycin, 4 to ethambutol, and one to pyrazinamide. Resistance was primary in 2 cases and acquired in the rest. Their average age was 58 years. Management was essentially the same as for the patients included in the study, except for IFN gamma treatment. None of these DR-TB cases reached culture conversion at three months of treatment with chemotherapy and less than half had converted at six months. Their clinical outcome was also worse.
Discussion
In spite of the reduced size of the population studied, the results suggest the efficacy of IFN gamma on DR-TB, when used as adjuvant to chemotherapy. All eight patients were considered as complete responders at the end of IFN treatment with disappearance of the disease signs and symptoms, sputum tests conversion, and pulmonary lesions improvement. Bacteriological and radiological improvement correlated with the clinical evolution; BMI increased and manifestations as cough and expectoration did not recur after IFN gamma treatment. Clinical practice demonstrates that these results are very difficult to obtain in such a short period of time with the chemotherapy alone.
Any conclusion from this study is also limited by the fact that it was not a controlled trial. This was not possible due to the low incidence of TB (7.6/100,000 inhabitants in 2002) [23] and DR-TB in Cuba [7]. Therefore a historical control with the results obtained at the same hospital by the same investigators in patients treated only with chemotherapy is used for comparison. Despite the fact that this kind of control is not fully comparable since they were not in the original design of the trial, a clear difference is shown regarding patients' performance. Literature reports on chemotherapy-treated DR-TB patients show similar unfavorable outcome [6].
The fact that four patients received rifampin, according to the strain sensitivity, which is one of the election drugs for the treatment of TB with excellent results in patients treated with DOTS regime, does not diminish the consideration regarding the possible benefit exerted by IFN gamma since these same patients had already not responded to this antibiotic as part of the DOTS. Moreover, one patient recurred after he had finished IFN gamma treatment for 6 months, despite still being under chemotherapy regime. This suggests that in his case a new IFN gamma cycle combined to the antibiotics would have been necessary, but this was not previewed in the protocol. However, only a controlled trial can definitely clarify the role of IFN gamma and second line antibiotics in this kind of patient's improvement.
The radiological results demonstrated IFN gamma antifibrotic properties as well. All patients had a reduction in the pulmonary lesions size, while one showed a complete resolution. This effect cannot be attributable to the antibiotics, since it is well known that DR-TB patients only develop radiological improvement long time after sputum smears and culture become negative. In many cases extensive fibrotic lesions never improve, and stay stable for life. This antifibrotic action agrees with that obtained with IFN gamma in idiopathic lung fibrosis [24] and suggests that IFN gamma can have future indications in other pulmonary diseases where fibrosis is present.
Therapy was well tolerated. It was not necessary to suspend the combined treatment due to adverse events; mostly mild. Adverse events such as arthralgias, fever and headache coincide with those reported for interferons [25].
Immunity to TB depends on the development of CD4+ cells- and macrophages-mediated Th1 response. The role of IFN gamma as the main macrophage-activator Th1 cytokine has been clearly established in animal models infected with M. tuberculosis [15,26,27]. IFN gamma action on the macrophages leads to kill intracellular Mycobacteria. It stimulates macrophages to produce tumor necrosis factor alpha (TNF α), oxygen free radicals and nitric oxide, increase surface display of MHC antigens and Fc receptors, decrease lysosomal pH, and increase the intracellular concentration of some antibiotics [11-14,28].
Regarding its antifibrotic effect, IFN gamma inhibits lung fibroblast proliferation and chemotaxis in a dose dependent manner, and reduces collagen synthesis [18,19,29]. Furthermore, this protein is a potent inhibitor of the transforming growth factor β (TGF-β)[20], involved in the pathogenesis of many fibrotic lung diseases [30-32].
On the other hand, mutations in the IFN gamma gene [15], or in the IFN gamma receptor alpha chain gene [16,17], increase susceptibility to develop the disease. Patients with disseminated BCG and other infections present defects in IFN gamma and other cytokines secretion and action [33-35].
IFN gamma therapy has been shown effective for cerebral tuberculosis caused by a multidrug-resistant strain [36]. The aerosol route of administration has been proposed as organ specific delivery method, obtaining a high release to infected alveoli [37]. Condos et al. reported clinical and bacteriological improvement and tolerability with aerosolized IFN gamma in five patients with MDR-TB [38]. In addition, other previous trials demonstrated promising results in patients infected with other Mycobacteria [10,39-42].
Conclusions
These results can suggest a beneficial effect of IFN gamma when it is used as adjuvant in the treatment of tuberculosis patients that have resistance to standard chemotherapy, and encourage carrying out more extensive, controlled studies. Combination with second-line drugs can reduce the time of treatment, diminishing toxicities and possible relapses; in many cases could reduce the application of recessional surgery. Further controlled clinical trials are needed to confirm these results.
Competing interests
Authors IGG, CMVS, and PALS are employees of the Center for Biological Research, which is part of the Center for Genetic Engineering and Biotechnology, Havana network, where IFN gamma is produced. The rest of the authors have no competing interests at all.
Authors' contributions
RSM conceived the study and carried out the bacteriological determinations. IGG participated in the study design and coordinating, and wrote the manuscript draft. NFO, MVQ, MTM, DC, and DMM took care of patient recruitment, management, and follow-up. CMVS participated in the study design and result analysis. PALS took part in the design, results analysis and manuscript writing. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
The authors wish to thank Dr. Carmen Rodríguez and technician Helia Herrera for their participation in bacteriological testing. They also thank the technicians Nancy Silva, Leovaldo Álvarez and Yuselis Domínguez for their assistance. The authors received free drug (IFN gamma) from Heber Biotec, Havana, Cuba. The Ministry of Public Health of Cuba took care of hospital facilities and medical attention of the patients, including diagnostic procedures and the rest of the medicaments.
Figures and Tables
Figure 1 Radiological improvement with IFN gamma treatment (ray-x of two patients are shown). Patient 4 (A), left-lung fibroexudative lesions, and (B) complete resolution after IFN gamma treatment. Patient 2 (C), bilateral moderate exudative lesions before IFN gamma treatment, and (D) important improvement of the lesions afterwards.
Table 1 Demographic and baseline characteristics of patients with DR-TB treated with IFN gamma
Patient 1 2 3 4 5 6 7 8
Age (years) 28 50 54 27 44 50 23 37
Sex Male Male Female Female Male Male Female Male
Race Mestizo White Mestizo Mestizo White Mestizo Black Black
BMI (Kg/m2) 19.6 18.9 13.2 18.3 14.6 21.6 22.0 17.1
Sputum smear status* 0 9 9 9 8 9 8 9
Sputum culture status** 7 9 7 9 6 9 8 9
Drug Resistance INH
RIF
STR INH
STR INH
RIF
STR INH
RIF INH, STR INH
RIF
STR INH INH
Initial thorax X-ray R1 R2 R3 R4 R5 R6 R7 R8
GSR (mm/h) 5 66 53 53 68 101 10 60
INH: Isoniazid; RIF: Rifampin; STR: Streptomycin; ETB: Ethambutol; ETN: Ethionamide; PRZ: Pyrazinamide; CPF: Ciprofloxacin; KAN: Kanamycin; AMK: Amikacin.
* Number of bacilli; 0: 0 in 4 lines; 8: 25 or more in 1 line; 9: bacilli in most of the fields.
** Number of colonies; 6: 6–24 colonies; 7: 25–100 colonies; 9: confluent growth.
R1 and R3. Extensive bilateral exudative lesions
R2: Moderate exudative lesions
R4: Left fibro-exudative lesions
R5: Moderate Bilateral fibro-exudative lesions.
R6: Cavitary lesions at superior lobes; fibrous lesions at right superior lobe; alveolar infiltrate at left superior lobe.
R7 and R8: Very diffuse fibromatous lesions at superior lobes.
Table 2 Six months follow-up data of DR-TB patients treated with IFN gamma
Patient 1 2 3 4 5 6 7 8
Drug regimen ETB ETB ETB ETB
RIF RIF RIF RIF
ETN ETN ETN ETN
ETB ETB ETB ETB
PRZ PRZ PRZ PRZ
PRZ PRZ PRZ PRZ
CPF CPF CPF CPF
KAN KAN KAN KAN
KAN KAN AMK KAN
Gain BMI (Kg/m2) 1.8 0.4 0.4 0.4 0.3 2.2 1.8 - 2.1!
Sputum smear status Negative Negative Negative Negative Negative Negative Negative Negative
Sputum culture status Negative Negative Negative Negative Negative Negative Negative Negative
Conversion time 2 mo. 3 mo. 3 mo. 1 mo. 3 mo. 2 mo. 2 mo. 3 mo.
Thorax X-ray Residual fibrosis Reabsorption and residual fibrosis Residual bilateral fibrosis. Lesions resolution Residual fibrosis Lesions size reduction Residual fibrosis Lesions size reduction
GSR (mm/h) 5 48 26 40 42 20 23 77
Legend: see Table 1
! Lost body weight
Table 3 Bacteriological evolution of DR-TB historical controls at "Benéfico Jurídico" Hospital (1994–1999) under specific chemotherapy. These patients had failed to respond to the standard DOTS regime and were then further treated with chemotherapy according to the sensitivity of their strains.
Sputum acid-fast-bacillus smear status Sputum culture status
Positive Negative Positive Negative
Trimester N % N % N % N %
1 17 89.5 2 10.5 19 100 - -
2 5 26.3 10 52.6 8 42.1 7 36.8
3 2 10.5 9 47.4 2 10.5 9 47.4
4 2 10.5 9 47.4 2 10.5 9 47.4
Five patients died and other three interrupted the chemotherapy before concluding it. Percents are calculated considering initial DR-TB population (19 patients)
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| 15500691 | PMC529257 | CC BY | 2021-01-04 16:03:31 | no | BMC Infect Dis. 2004 Oct 22; 4:44 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-44 | oa_comm |
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BMC Med GenetBMC Medical Genetics1471-2350BioMed Central London 1471-2350-5-251549810010.1186/1471-2350-5-25Research ArticleHLA haplotypes associated with hemochromatosis mutations in the Spanish population Pacho Arantza [email protected] Esther [email protected] Rey Manuel J [email protected] Maria J [email protected] Desamparados [email protected]ía-Berciano Miguel [email protected]ález Luis [email protected] Pablo [email protected] Immunology. Hospital Universitario "12 de Octubre". Carretera de Andalucia. 28041. Madrid, Spain2004 21 10 2004 5 25 25 12 5 2004 21 10 2004 Copyright © 2004 Pacho et al; licensee BioMed Central Ltd.2004Pacho et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The present study is an analysis of the frequencies of HLA-A and -B antigens and HLA haplotypes in two groups of individuals homozygous for the two main HFE mutations (C282Y and H63D) and a group heterozygous for the S65C mutation.
Methods
The study population includes: 1123 healthy individuals, 100 homozygous for the C282Y mutation, 138 homozygous for the H63D mutation and 17 heterozygous for the S65C mutation. HFE and HLA alleles were detected using DNA-based and microlymphocytotoxicity techniques respectively.
Results
An expected significant association between C282Y and the HLA-A3/B7 haplotype was found, but other HLA haplotypes carrying the -A3 antigen were found: HLA-A3/B62 and HLA-A3/B44. Also, a significant association between H63D mutation and HLA-A29/B44 haplotype was found, and again other HLA haplotypes carrying the HLA-A29 antigen were also found: HLA-A29/B14 and HLA-A29/B62. In addition, the S65C mutation seems to be associated with a HLA haplotype carrying the HLA-A26 antigen.
Conclusion
These findings clearly suggest that HLA-A3/B7 and HLA-A29/B44 are the ancestral haplotypes from which the C282Y and H63D mutations originated, respectively. The frequencies of these mutations in different populations, their geographical distribution, and the degree of the statistical association to the ancestral haplotypes, suggest that the H63D mutation must have occurred earlier than the C282Y mutation.
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Background
Hereditary hemochromatosis (HH) is an autosomal recessive disease common in northern European populations. HH is characterized by increased iron absorption and deposition in the liver, pancreas, heart, joints and pituitary gland. Without treatment, death may occur from cirrhosis, primary liver cancer, diabetes or cardiomyopathy. In 1996, the HH gene (HFE) was cloned and located on the short arm of chromosome 6 (6p21.3) [1], 4 megabases (Mb) telomeric to the major histocompatibility complex (MHC). A single point mutation 845 G>A (exon 4), changing cysteine at position 282 to tyrosine (C282Y) is identified in 85 to 100% of patients with HH in populations of northern European descent, but it is found in only 60% of cases from Mediterranean populations [2]. Two other mutations, 187 C>G (exon 2), a histidine to aspartate change at amino acid 63 (H63D) and 193 A>T (exon 2), a serine to cysteine change at amino acid 65 (S65C) appear to be associated with milder forms of HH and may increase risk of disease in persons heterozygous for C282Y mutation [3,4].
C282Y lies within a Celtic ancestral haplotype which includes the human MHC (HLA) haplotype HLA-A3/B7 [5]. The HLA-A3/B7 haplotype was reported in HH patients in many European and populations of European descent [5-8] but HLA-A3/B14, HLA-A3/B35 and others were also reported [9-11]. The predominance of the HLA-A3 associated haplotypes on hemochromatosis chromosomes, and the pattern of their distribution in the world, led Simon et al [5] to propose the founder hypothesis, postulating that the hemochromatosis mutation was a rare event that occurred once on a particular chromosome which was subsequently modified by recombinations involving both HLA-B and HLA-A alleles and population migrations, producing the varied haplotype associations that were described.
In contrast, the H63D substitution is not restricted to European populations: allele frequencies greater than 5% are found in countries bordering the Mediterranean, in the Middle East, and in the Indian subcontinent. H63D seems to be associated with HLA-A29 and HLA-B44 [3,12].
A few studies have been performed on the distribution of the S65C mutation in Europe and other countries. In 1999, Barton et al [13] identified the S65C mutation in Alabama hemochromatosis probands and found that this mutation was linked to a haplotype characterized by HLA-A32; and recently, Couto et al [14] found that this mutation is in linkage disequilibrium with the HLA-A29/B44 haplotype.
The aim of this study is to find the HLA antigens and haplotypes associated with the three main mutations in the HFE gene in a sample of the Spanish population.
Methods
Individuals
A total of 100 unrelated individuals homozygous for the C282Y mutation, 138 unrelated individuals homozygous for the H63D mutation and 17 individuals heterozygous for the S65C mutation were selected for this study. These were subjects in whom HFE genotyping has been previously performed on a medical care basis because of a presumptive diagnosis of hemochromatosis. In addition, 1113 unrelated, apparently healthy subjects were used as controls for the study. In addition, HLA typing was performed in 230 individuals whom HFE genotyping was negative for the three mutations.
HLA-A and -B typing
HLA-class I typing was performed on freshly collected venous blood samples by the standard complement-dependent microlymphocytotoxicity assay using commercially available alloantisera.
DNA extraction and HFE amplification
Genomic DNA from whole blood samples was extracted by standard protocols. Polymerase chain reaction (PCR) with the pair of primers HEMEx2-5' (5'-CTT TGG GCT ACG TGG ATG ACC) and HEMEx2-3' (5'-CTG GCT TGA AAT TCT ACT GGA AAC C) was used to amplify exon 2 of the HFE gene. To amplify exon 4, a second set of oligonucleotides was used: HEMEx4-5' (5'-GGT GTC GGG CCT TGA ACT ACT ACC) and HEMEx4-3' (5'-A CAT ACC CCA GAT CAC AAT GAG G).
The following conditions were used for the PCR reactions: five minutes denaturation at 94°C, 40 cycles of 15 seconds denaturation at 95°C, 15 seconds annealing at 57°C and 30 seconds extension at 72°C. PCR products coming from exons 2 and 4 were 101 and 228 base pairs (bp), respectively.
Digestion with mutation-specific restriction endonuclease
Following the PCR amplifications, aliquots (17 μl) of the reaction mixture were digested with the restriction endonucleases Bcl I (exon 2), Hinf I (exon 2) and Rsa I (exon 4) for 3 hours following the protocol recommended by the manufacturer (Promega, Madison, WI). The H63D mutation destroys the Bcl I site in the 101 bp PCR product, so while normal DNA is cut into two fragments of 38 and 63 bp, the mutated DNA is not cut. The S65C mutation destroys the Hinf I site in the 101 bp PCR product, so while normal DNA is cut into two fragments of 47 and 54 bp, the mutated DNA is not cut. The C282Y mutation creates a new Rsa I site, the 228 bp DNA product digested with this enzyme is cut into two fragments of 145 and 83 bp in the normal allele, while in the mutated DNA three fragments of 145, 29 and 54 bp are generated after digestion. The digested products were size resolved in 10% acrylamide gel and detected by staining with ethidium bromide.
Statistical analysis
Allele and haplotype frequencies were estimated using Arlequin V2.0 software [15]. The haplotype frequencies were computed using the Expectation-Maximization algorithm [16]; this procedure is an interactive process aimed at obtaining maximum-likelihood estimates of haplotype frequencies from multi-locus genotype data when the gametic phase is unknown. The existence of association between HFE mutations and HLA-A and -B alleles and haplotypes was calculated by 2 × 2 comparison tables and p values were corrected according to the number of alleles or haplotypes compared [17] and using Yates corrected Chi2 and Fisher's tests. Odds ratios were calculated as previously described [18].
Results
The allele frequencies of the HLA-A and -B antigens found in the C282Y carriers group, in comparison with the frequencies in the control population are listed in Table 1. As expected, significant associations were found for HLA-A3 and -B7, but HLA-B62 also shows significant association. The association of HLA-A3 is stronger than that of HLA-B7 or -B62. The frequencies of the HLA-A/B haplotypes in the homozygous group in comparison with the haplotype frequencies in the control population are listed in Table 2. Three haplotypes are significantly associated with the C282Y mutation: HLA-A3/B7, HLA-A3/B62 and HLA-A3/B44.
Table 1 Allele frequencies of HLA-A and HLA-B antigens in the C282Y homozygous group in comparison with the control group.
C282Y HOMOZYGOTES CONTROLS C282Y HOMOZYGOTES CONTROLS
HLA Freq (n = 200) OR p Freq (n = 2226) HLA Freq (n = 200) OR p Freq (n = 2226)
A1 0.070 0.69 N.S. 0.097 B8 0.035 0.81 N.S. 0.044
A3 0.425 6.23 <10-7 0.106 B7 0.260 3.67 <10-7 0.089
B62 0.075 2.74 0.030 0.028
A29 0.035 0.54 N.S. 0.062 B44 0.170 1.19 N.S. 0.146
A30 0.010 0.16 0.093 0.060 B18 0.015 0.27 0.120 0.088
A2 0.195 0.67 N.S. 0.264 B35 0.095 0.77 N.S. 0.119
A11 0.060 0.75 N.S. 0.078 B51 0.055 0.60 N.S. 0.088
A26 0.020 0.42 N.S. 0.046 B49 0.025 0.67 N.S. 0.036
A28 0.025 0.59 N.S. 0.041 B60 0.015 0.42 N.S. 0.035
A32 0.020 0.57 N.S. 0.034 B14 0.010 0.28 N.S. 0.035
A23 0.030 0.95 N.S. 0.031 B38 0.010 0.33 N.S. 0.029
A33 0.010 0.36 N.S. 0.027 B27 0.025 0.88 N.S. 0.028
A25 0.020 0.97 N.S. 0.020 B50 0.005 0.21 N.S. 0.023
A31 0.025 1.56 N.S. 0.016 B65 0.015 0.64 N.S. 0.023
Freq: Frequency n: Total number of alleles in each group OR: Odds ratio p: Significance level N.S.: Not significant
Table 2 Frequencies of HLA-A/B haplotypes in the two groups homozygous for H63D and C282Y in comparison with the control group.
HAPLOTYPE H63D HAPLOTYPES Freq (n = 276) p OR C282Y HAPLOTYPES Freq (n = 200) p OR CONTROLS Freq (n = 2226)
A1/B8 0.02513 N.S. 0.01500 N.S. 0.0246
A2/B7 0.03612 N.S. 0.02859 N.S. 0.0239
A2/B44 0.08254 N.S. 0.04037 N.S. 0.0443
A2/B51 0.04839 N.S. 0.00000 0.0404
A2/B35 0.01545 N.S. 0.03543 N.S. 0.0258
A3/B14 0.00000 0.00000 0.0005
A3/B7 0.00725 N.S. 0.20275 <10-7 7.29 0.0343
A3/B62 0.00000 0.03379 10-5 16.11 0.0024
A3/B44 0.00409 N.S. 0.08989 <10- 12.13 0.0079
A11/B27 0.01812 N.S. 0.00000 0.0064
A11/B35 0.00362 N.S. 0.00862 N.S. 0.0204
A24/B35 0.00794 N.S. 0.01478 N.S. 0.0233
A29/B44 0.13261 <10-7 3.93 0.02999 N.S. 0.0378
A29/B14 0.01449 <0.01 32.7 0.00000 0.0005
A29/B62 0.01591 <0.01 32.7 0.00000 0.0005
A30/B18 0.01087 N.S. 0.00000 0.0278
A33/B14 0.00000 0.00500 N.S. 0.0121
Freq: Frequency n: Total number of haplotypes in each group OR: Odds ratio p: Significance level N.S.: Not significant
The allele frequencies of the HLA-A and -B antigens in the H63D homozygous group in comparison with the frequencies in the control population are listed in Table 3. Significant associations were found for HLA-A29 and -B44. Again, the association of the HLA-A antigen (HLA-A29) is stronger than HLA-B44. The frequencies of the HLA-A/B haplotypes are also listed in Table 2, and three HLA-A/B haplotypes (with the same HLA-A antigen, HLA-A29) are significantly associated with the H63D mutation: HLA-A29/B44, HLA-A29/B14 and HLA-A29/B62.
Table 3 Allele frequencies of HLA-A and HLA-B antigens in the H63D homozygous group in comparison with the control group.
H63D HOMOZYGOTES CONTROLS H63D HOMOZYGOTES CONTROLS
HLA Freq (n = 276) OR p Freq (n = 2226) HLA Freq (n = 276) OR p Freq (n = 2226)
A1 0.061 0.60 N.S. 0.097 B8 0.050 1.14 N.S. 0.044
A3 0.086 0.80 N.S. 0.106 B7 0.068 0.75 N.S. 0.089
B62 0.028 1.00 N.S. 0.028
A29 0.199 3.65 <10-7 0.062 B44 0.264 2.10 2.10-5 0.146
A30 0.036 0.58 N.S. 0.060 B18 0.086 0.98 N.S. 0.088
A2 0.286 1.12 N.S. 0.264 B35 0.076 0.60 N.S. 0.119
A11 0.036 0.44 N.S. 0.078 B51 0.097 1.12 N.S. 0.088
A26 0.029 0.61 N.S. 0.046 B49 0.032 0.89 N.S. 0.036
A28 0.021 0.52 N.S. 0.041 B60 0.011 0.30 N.S. 0.035
A32 0.039 1.16 N.S. 0.034 B14 0.014 0.40 N.S. 0.035
A23 0.047 1.52 N.S. 0.031 B38 0.032 1.10 N.S. 0.029
A33 0.007 0.26 N.S. 0.027 B27 0.036 1.29 N.S. 0.028
A25 0.032 1.60 N.S. 0.020 B50 0.018 0.76 N.S. 0.023
A31 0.011 0.67 N.S. 0.016 B65 0.039 1.74 N.S. 0.023
Freq: Frequency n: Total number of alleles in each group OR: Odds ratio p: Significance level N.S.: Not significant
From the 17 individuals heterozygous for the S65C mutation, 7 (20%) were HLA-A26 versus 4.6% found in the control population (p = 0.02, OR = 5.29). No association with HLA-B has been found.
The significant associations did not change if we used a control group of 230 individuals without HFE mutations.
Discussion
Populations of homozygous individuals for C282Y and H63D are optimal groups to study the HLA haplotypes in which these mutations preferentially appear. To our knowledge, Barton [19] and the present work are the only studies of associations between HFE mutations and HLA antigens and haplotypes in homozygous probands. The paper by Barton and Acton [19] presents haplotype frequencies assessed by family studies where phase could be set; in our paper, the haplotype frequences are estimated because the probands and controls are unrelated individuals. The low frequency of the S65C mutation makes the sampling of homozygous probands difficult and imposes the use of heterozygous individuals for the analysis.
C282Y and HLA
The strong association between the HLA-A3/B7 haplotype and the C282Y mutation indicates that this haplotype is the main one associated with this mutation in the Spanish population. However, other haplotypes are also associated: HLA-A3/B62 and HLA-A3/B44. This finding supports the founder hypothesis of Simon et al [5]: the ancestral haplotype where the C282Y mutation occurred on the ancestral haplotype HLA-A3/B7 and subsequent recombinations involving both HLA-B and HLA-A alleles produced the varied haplotype associations that have been found. Thus, we found many HLA-A/B haplotypes in our C282Y group, but only three HLA-A3 bearing haplotypes are statistically associated with this mutation. The two less frequent haplotypes (HLA-A3/B44 and HLA-A3/B62) have been observed in other populations in association with HH [20,21].
In addition, the high frequency of the HLA-A3/B7 haplotype makes other HLA antigens and haplotypes have reduced frequencies in respect to the controls. It is interesting to see that haplotypes with high-frequency in the Spanish population, such as HLA-A30/B18 and HLA-A2/B51 are absent in the C282Y homozygous group (Table 2). These HLA haplotypes are not contaminated by the C282Y mutation, and up until now, these haplotypes may be considered as protector haplotypes.
H63D and HLA
Porto et al [3] and Cardoso et al [22] found individual associations between HLA-A29 and non-classical forms of iron overload in linkage disequilibrium with H63D, and a strong linkage disequilibrium between H63D and all A29 containing haplotypes assigned in a large population of normal portuguese families.
In the present work we found a strong association between the HLA-A29/B44 haplotype and the H63D mutation. Our finding agrees with the association of HLA-A29/B12(44) and hemochromatosis described in the Danish population [21].
H63D and HLA-A29-bearing haplotypes follow a pattern of associations similar to that described for C282Y and HLA-A3-bearing haplotypes. This promotes speculation that HLA-A29/B44 is the ancestral haplotype from which the H63D mutation emerged, since other HLA-A29 carrying haplotypes are also statistically associated with the mutation (HLA-A29/B14 and HLA-A29/B62, see Table 2), confirming the results reported by Cardoso et al [22] in the normal Portuguese population. Studies in other populations might lend support to whether HLA-A29/B44 is the ancestral haplotype of the H63D mutation, and HLA-A29/B14 and HLA-A29/B62 are specific Spanish haplotypes associated with H63D mutation.
C282Y and H63D mutations: which one is older?
In an attempt to establish the relative age of C282Y and H63D mutations, we have analysed the geographical distribution, allele frequencies and HLA haplotype associations for each mutation, assuming that the mutations ocurred once and that its age is directly proportional to its geographical spread, its frequency in the population, and the number of HLA haplotypes to which they are linked. On the other hand, a strong association of a particular HFE mutation to a particular HLA haplotype could mean that the mutation arose more recently, since the lower number of ruptures and recombinations of the original haplotype would reflect that a shorter time has passed.
Merryweather-Clarke et al [23] analysed 2978 samples from probands distributed world-wide and showed that the C282Y mutation was most prevalent in northern European populations and absent from samples of non-European subjects (Africans, Asians, Australasians and Americans). In contrast, the H63D mutation is not restricted to European populations, being found in countries bordering the Mediterranean, in the Middle East and in the Indian subcontinent, and its allele frequency is higher than that of the C282Y mutation [23]. Our analysis of C282Y and H63D homozygous groups yielded a higher number of HLA haplotypes in association with the H63D mutation; and the frequency of HLA-A3/B7 in the C282Y homozygous group is 20%, while the frequency of HLA-A29/B44 in the H63D homozygous group is 13% (see table 2), reflecting that the HLA-A29/B44/H63D haplotype has suffered more recombinations than HLA-A3/B7/C282Y, and therefore, that HLA-A29/B44/H63D is older [24].
Altogether, these features suggest that the H63D mutation may have occurred earlier than the C282Y mutation, as has been previously proposed in studies from Italian populations [25,26].
S65C and HLA
Few studies have been performed on the distribution, frequency and HLA association of the S65C mutation in Europe and other continents. Barton et al [13] described the linkage of the S65C mutation to a HLA-A32 haplotype in hemochromatosis probands from Alabama. Surprisingly, Couto et al [14] found the linkage of S65C (and not H63D) to the HLA-A29/B44 haplotype in a population from the Azores, although only 5 H63D homozygous and 9 S65C heterozygous individuals were studied in that work.
In the present work we find that the S65C mutation seems to be linked to HLA-A26 in the Spanish population. Further studies in other populations and with more S65C-bearing haplotypes are necessary to shed light on the generation of the S65C mutation.
Conclusions
We have found that, in the Spanish population, the three main HFE mutations: C282Y, H63D and S65C, are in linkage disequilibrium with HLA haplotypes carrying the HLA-A3, -A29 and -A26 alleles, respectively. In addition, the ancestral HLA haplotypes from which C282Y and H63D mutations were originated are HLA-A3/B7 and HLA-A29/B44, respectively, and H63D is older than C282Y. Further studies in other populations using homozygous individuals for HFE mutations will help to identify the associated ancestral and specific haplotypes.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Authors AP and PM conceived the study, contributed to proband characterisation, performed statistical comparisons and edited the manuscript. Authors EM, MJR, MJC, DO, MGB and LG contributed at different times to the characterisation of probands and HLA typing.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank the people who worked with the samples for several years: Belén Suárez and Mario González. Also, to Estela Paz-Artal for the critical review of the manuscript.
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| 15498100 | PMC529258 | CC BY | 2021-01-04 16:31:07 | no | BMC Med Genet. 2004 Oct 21; 5:25 | utf-8 | BMC Med Genet | 2,004 | 10.1186/1471-2350-5-25 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-141548557410.1186/1471-2369-5-14Study ProtocolBIOKID: Randomized controlled trial comparing bicarbonate and lactate buffer in biocompatible peritoneal dialysis solutions in children [ISRCTN81137991] Nau Barbara [email protected] Claus P [email protected] Margarida [email protected] Klaus [email protected] Gianluigi [email protected] Klaus E [email protected] Alberto [email protected] Michel [email protected] Karin [email protected] Joachim [email protected] Markus J [email protected]önnholm Kai [email protected] Simone [email protected] Franz [email protected] Pediatric Peritoneal Dialysis Study Group (EPPS) [email protected] University Children's Hospital Heidelberg, Im Neuenheimer Feld 150, 69120 Heidelberg, Germany2 Hospital de Santa Maria, Serviço de Nefrologica Pediátrica, Av. Prof. Egas Moniz, 1649-035 Lisbon, Portugal3 Dept. of Pediatrics, AKH Wien, Währinger Gürtel 18–20, 1090 Vienna, Austria4 Division of Pediatric Nephrology, Istituti Clinici di Perfezionamento, Via della Commenda 9, 20122 Milan, Italy5 University Children's Hospital Essen, Hufelandstr. 55, 45122 Essen, Germany6 Nephrology Dialysis Transplantation Children's Unit, Hopitaux Universitaires de Strasbourg, Avenue Moliere, 67098 Strasbourg, France7 Urban Hospital St. Georg, Delitzscher Str. 141, 04129 Leipzig, Germany8 University Children's Hospital Jena, Kochstr. 2, 07740 Jena, Germany9 University Children's Hospital Hamburg, Martinistr. 52, 20246 Hamburg, Germany10 Hospital for Children and Adolescents, University of Helsinki, Stenbäckinkatu 11, 00290 Helsinki, Finland2004 14 10 2004 5 14 14 26 8 2004 14 10 2004 Copyright © 2004 Nau et al; licensee BioMed Central Ltd.2004Nau et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Peritoneal dialysis (PD) is the preferred dialysis modality in children. Its major drawback is the limited technique survival due to infections and progressive ultrafiltration failure. Conventional PD solutions exert marked acute and chronic toxicity to local tissues. Prolonged exposure is associated with severe histopathological alterations including vasculopathy, neoangiogenesis, submesothelial fibrosis and a gradual loss of the mesothelial cell layer. Recently, more biocompatible PD solutions containing reduced amounts of toxic glucose degradation products (GDPs) and buffered at neutral pH have been introduced into clinical practice. These solutions contain lactate, bicarbonate or a combination of both as buffer substance. Increasing evidence from clinical trials in adults and children suggests that the new PD fluids may allow for better long-term preservation of peritoneal morphology and function. However, the relative importance of the buffer in neutral-pH, low-GDP fluids is still unclear. In vitro, lactate is cytotoxic and vasoactive at the concentrations used in PD fluids. The BIOKID trial is designed to clarify the clinical significance of the buffer choice in biocompatible PD fluids.
Methods/design
The objective of the study is to test the hypothesis that bicarbonate based PD solutions may allow for a better preservation of peritoneal transport characteristics in children than solutions containing lactate buffer. Secondary objectives are to assess any impact of the buffer system on acid-base status, peritoneal tissue integrity and the incidence and severity of peritonitis.
After a run-in period of 2 months during which a targeted cohort of 60 patients is treated with a conventional, lactate buffered, acidic, GDP containing PD fluid, patients will be stratified according to residual renal function and type of phosphate binding medication and randomized to receive either the lactate-containing Balance solution or the bicarbonate-buffered Bicavera® solution for a period of 10 months. Patients will be monitored by monthly physical and laboratory examinations. Peritoneal equilibration tests, 24-h dialysate and urine collections will be performed 4 times. Peritoneal biopsies will be obtained on occasion of intraabdominal surgery. Changes in small solute transport rates, markers of peritoneal tissue turnover in the effluent, acid-base status and peritonitis rates and severity will be analyzed.
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Background
Peritoneal dialysis (PD) is the preferred dialysis modality in children. Advantages of PD over hemodialysis relevant to pediatric patients include its compatibility with a normal lifestyle and full psychosocial integration, the continuous mode of blood purification without dysequilibrium conditions, the absence of vascular access issues and the avoidance of puncture pain. However, the major drawback of PD is its limited technique survival. Almost fifty percent of adult as well as pediatric PD patients must switch to hemodialysis within 4 to 5 years of treatment [1,2]. While the incidence of PD failure due to infectious complications is steadily decreasing, loss of ultrafiltration due to degenerative changes of the peritoneal tissue is becoming the leading cause of non-elective termination of PD [1]. Histopathological alterations induced by exposure to PD solutions include a severe vasculopathy, neoangiogenesis, submesothelial fibrosis and a progressive loss of the mesothelial cell layer [3-5]. Acute and chronic toxicity of standard PD fluids to mesothelial cells, affecting cell turnover and the pattern of growth factor and cytokine release, is considered a key mechanism underlying the progressive transformation of the peritoneum. Conventional PD fluids contain large doses of glucose, are lactate-buffered at acidic pH and contaminated with toxic glucose degradation products (GDP) formed during heat sterilization. Low pH, lactate and hyperosmolar glucose independently impair mesothelial cell functions [6-9]. GDPs impair the viability and functional integrity of mesothelial cells upon extended exposure [10], and stimulate VEGF and TGF-β release by mesothelial cells [11,12].
In recent years, a new generation of more biocompatible PD fluids has been introduced into clinical practice. The separation of alkaline and acidic fluid compartments in pluri-chamber bags permits to sterilize glucose at very low pH with greatly reduced GDP formation and yet produce pH-neutral final dialysis solutions, using lactate and/or bicarbonate as a buffer. We recently compared the safety and efficacy of Bicavera® (Fresenius), a purely bicarbonate-buffered biocompatible PD solution, with that of a conventional acidic, lactate buffered solution by a three-month crossover trial in children on automated PD [13]. We observed a marked increase of the mesothelial cell marker CA125 in the effluent during Bicavera® treatment, which was readily reversible when patients returned to conventional solution. This effect was also observed with lactate- or lactate/bicarbonate-buffered biocompatible PD solutions [14,15], and is interpreted as a functional and/or numeric recovery of mesothelial cells exposed to these fluids. Moreover, in agreement with previous studies [7,16] we observed a trend towards increasing small solute permeability with the standard solution; this trend was absent when Bicavera® was used. Two studies observed a slightly lower initial increase of the functional peritoneal surface area during a single PD dwell with pH-neutral compared to acidic solutions compatible with reduced peritoneal capillary recruitment; this trend was significant in one study [17-19]. Fischbach et al. also demonstrated lower intraperitoneal pressure and less inflow pain in children receiving a low-GDP, neutral-pH PD solution [19]. Finally, we noted a more effective compensation of metabolic acidosis with Bicavera® than with lactate buffered conventional fluid despite identical content of base equivalents.
While these results are encouraging with respect to the long term preservation of the peritoneal membrane and strongly favour the primary use of low-GDP, neutral-pH biocompatible PD fluids, the relative importance of the buffer system is still unclear. In vitro data suggest that lactate per se may compromise local cell functions independently of pH by affecting the cellular redox state and reducing cellular energy sources [6,20-22]. By intravital microcopy of rat peritoneum, lactate-based neutral-pH PD solution caused mesenteric vasodilation whereas bicarbonate buffered PD fluid had no hemodynamic effects [23]. All previous clinical trials comparing conventional and biocompatible PD fluids were unsuitable by design to identify any role of the buffer for peritoneal tissue integrity, perfusion and the acute or chronic regulation of peritoneal solute transport, since the solutions tested differed not only by the buffer used, but also by pH and GDP content. To clarify the role of the buffer the BIOKID trial has been designed. Patients participating in this trial will be exposed to two solutions which are both pH neutral and of low GDP content, but contain either pure bicarbonate or pure lactate as buffer compound.
Methods/design
Objectives of the study
The European Pediatric Peritoneal Dialysis Study Group (EPPS) plans a prospective, randomized study with administration of pH neutral, low-GDP PD solutions containing either lactate or bicarbonate buffer over a period of 10 months.
The primary objective is to evaluate the effect of lactate vs. bicarbonate buffer on peritoneal transport capacity in children. The hypothesis to be tested is that bicarbonate based PD solutions may allow for a significantly better preservation of peritoneal transport characteristics (D/PCrea) in children compared to a solution containing lactate buffer. Secondary objectives will be to assess differential effects of lactate and bicarbonate buffered PD fluids on acid-base status, surrogate parameters of peritoneal biocompatibility and local and systemic carbonyl stress, peritoneal morphology, the incidence and severity of peritonitis, statural growth and nutritional status. Moreover, this study will be used to assess genetic determinants of the peritoneal transporter status and the evolution of peritoneal morphology over time.
Study design
This is a multicenter open-labelled, controlled, randomized clinical trial, designed to test the effects of the buffer substance in biocompatible PD fluids on peritoneal small solute transport capacity. All subjects will undergo a 2-month run-in period, in which they receive conventional lactate buffered, acidic, GDP containing PD fluid. During this period, patient eligibility for the trial will be verified and the dialysis dose will be optimized if necessary to ensure appropriate PD adequacy. At the end of this period, the patients will be stratified according to residual renal function (greater or less than 100 ml urine output/day/1.73 m2) and the type of phosphate binder therapy (Sevelamer vs. calcium-containing phosphate binders), since these variables may affect the overall efficacy of metabolic acidosis control. Following stratification, subjects will be randomized centrally to receive either the lactate-containing Balance solution or the bicarbonate buffered Bicavera® solution for a period of 10 months. Both during the run-in period and during the intervention phase, patients will be monitored by monthly clinical and laboratory examinations, including capillary blood gas analyses. In addition, peritoneal equilibration tests (PETs), 24-hour dialysate and urine collections and intraabdominal pressure assessments will be performed at time of randomization (with conventional PD fluid) and after 3, 6 and 10 months of treatment (with the study solutions). Also, peritoneal biopsies will be performed on occasion of intraabdominal sugery or laparoscopy prior to start and after termination of the study (usually at time of catheter insertion and renal transplantation).
Primary outcome measure
The primary outcome measure will be the longitudinal change in 4h-D/Pcreatinine in the sequential PET examinations. Differential changes in this parameter will indicate differences in the development of the peritoneal solute transport status over time.
Secondary outcome measures
Secondary outcome measures will be surrogate parameters of mesothelial cell viability (CA-125), peritoneal neoangiogenesis (VEGF), fibrotic activity (TGF-β) and local inflammation (IL-6). With the same intention, the evolution of peritoneal histomorphology will be assessed in all patients available for sequential biopsies. Moreover, possible differential effects of lactate and bicarbonate buffer on the control of metabolic acidosis will be assessed by monthly blood gas analyses. Finally, the incidence and clinical course of peritonitis will be recorded as a possible indirect marker of local peritoneal macrophage function.
Inclusion criteria
Criteria for inclusion in the study are 1) patients above 1 month and less than 19 years of age, 2) end-stage renal disease with manual or automated continuous peritoneal dialysis as maintenance treatment modality, 3) a fill volume approximately of 1100 ml/1.73 m2 body surface area, 4) the most recent episode of PD-associated peritonitis, if any, occurred more than 3 weeks ago, 5) signed informed consent by parent/guardian, with a subject aged > 7 years also signing an age-appropriate assent form.
Exclusion criteria
Criteria for exclusion from the study are 1) reduced efficiency of peritoneal dialysis due to anatomic anomalies or intraperitoneal adhesions, 2) uncontrolled hyperphosphatemia, 3) severe pulmonary, cardiac, hepatic or systemic disease including any kind of malignancy, and 4) current or recent (within 30 days) exposure to any investigational drug.
Exit criteria
Reasons for permanently discontinuing the study medication are 1) renal transplantation, 2) switch to hemodialysis due to PD technique failure, 3) patient/parent withdrawal of consent of participate, 4) patient moving out of the area to a location with no participating center within reasonable distance, and 5) a severe adverse event.
Study medications
The composition of the study fluids is given in Table 1. Both Bicavera® and Balance will be available in three different glucose concentrations to meet individual ultrafiltration requirements. The fluids will be administered at a dose of approximately 1,100 ml/m2 body surface area per dwell. Both in patients on CAPD and CCPD, the number of cycles and dwell times can be varied according to clinical needs. The dose of dialysis will be tailored individually in order to ascertain a minimum total weekly Kt/VUrea of ≥ 2.0.
Table 1 Balance 1.5% Balance 2.3% Balance 4.25% Bicavera® 1.5% Bicavera® 2.3% Bicavera® 4.25%
Sodium (mmol/l) 134 134 134 134 134 134
Calcium (mmol/l) 1.75 1.75 1.75 1.75 1.75 1.75
Magnesium (mmol/l) 0.5 0.5 0.5 0.5 0.5 0.5
Chloride (mmol/l) 101.5 101.5 101.5 104.5 104.5 104.5
L-Lactate (mmol/l) 35 35 35 - - -
Bicarbonate (mmol/l) - - - 34 34 34
Glucose-monohydrate (g/l) 15 23 42.5 15 23 42.5
Osmolarity (mosmol/l) 358 401 511 358 399 509
PH 7.4 7.4 7.4 7.4 7.4 7.4
Any kind of concomitant medication during the run-in period and the study period will be documented in the case report form with respect to type, dosage and mode of delivery. In case of peritonitis (defined by cloudy effluent, white blood cell count greater than 100/mm3 with more than 50% polymorphonuclear leukocytes), treatment will be given intraperitoneally according to international pediatric guidelines [24] using cefazoline and ceftazidime in patients with mild peritonitis and a glycopeptide/ceftazidime combination in patients with defined risk factors for severe course and poor outcome.
Bicarbonate supplementation will be discontinued at start of the run-in period and only re-instituted if blood bicarbonate levels drop below 17 mmol/l despite sufficient dialysis efficacy. The recommended dosage is 0.5 mmol/kg/day divided into 3 doses.
Clinical safety monitoring
An adverse event is defined as any untoward medical occurence in a patient who takes the study medication. It does not necessarily have a causal relationship with this treatment. This may be an unfavourable and unintended sign, symptom or disease, which is observed after exposure to the study medication, whether or not considered related to the treatment. Moreover, the participating investigators will report all treatment-emergent adverse events that are observed on the online adverse event form. This applies regardless of the clinical significance or the assessment of study drug causality. In this trial, such adverse events may include inflow pain, severe changes of the state of hydration, abnormal electrolyte and glucose blood levels, peritonitis, abdominal hernia, and allergic reactions.
A severe adverse event or reaction is any untoward medical occurrence that a) results in death, b) is life-threatening, c) requires inpatient hospitalization or prolonges an existing hospitalization, or d) results in persistent or significant disability/incapacity.
Any serious adverse event, whether or not considered related to the study medication, and any unexpected drug reactions with significant hazard to the patient population will be reported to the responsible safety assessor at Fresenius Medical Care by phone or by fax within 24 hours following first knowledge of the event. Alternatively the clinical monitor may be informed. This information will be forwarded to and evaluated by the safety monitoring committee, and reports of serious adverse reactions will be disseminated to all participating centers for submission to their respective institutional review boards. Fresenius Medical Care is responsible for passing on the information to relevant supervisory authorities.
Data management
Data acquisition will be entirely through the internet. The case report form menus will be used to record the following: 1) baseline clinical patient information, 2) physical and biochemical examination variables, 3) study and concurrent medications, and 4) adverse events.
Periodic computerized audit reports will be run to monitor data quality and completeness. The data base is stored on a server drive that is backed up to tape daily by Tel-A-Vision, Media Networking GmbH.
In order to insure confidentiality, data from each patient will be recorded in the computer data base with a unique contributing center code, study code, sequence number and patient initials. Patient names are never entered online or forwarded in any other form to the coordinating office.
Sample size estimation
The primary study outcome is the change in 4h-D/PCr from the time of randomization to the conclusion of the 10-month study period. In a previous trial we demonstrated a 6% increase of D/PCr in children on lactate-buffered PD fluid in contrast to a 4% decrease in D/PCr using bicarbonate buffered fluid within 3 months of exposure, resulting in a statistically significant 10% difference in the evolution of peritoneal creatinine transport rate [13]. Assuming that this effect was at least in part due to the different buffer substances applied, a difference in D/PCr at least 7 ± 10% can be expected when Balance and Bicavera® are applied for 10 months. To detect this difference with a sensitivity of 80% and an error probability of 5%, at least 15 patients per randomization group will be required. Assuming a 50% drop-out rate due to renal transplantation and other reasons in the course of the study, 60 patients will have to be enrolled.
Statistical approach
A computer based randomization protocol will be generated and applied centrally at the end of the run-in period. Baseline comparability between the two treatment groups will be evaluated with respect to entry criteria. Chi-square and t-tests will be used to assess differences between the two groups on the baseline variables. Any variables that are found to be discrepant between the two groups and that are related to the outcome variables will be treated as co-variates in later analyses.
In order to evaluate the patients' change in D/PCr ratios, two strategies will be employed:
1. Repeated measure ANOVA will be performed on those patients who complete the 10-month observation period on the study medications.
2. The Kaplan-Meier method will be used to estimate the time in which patients are likely to display a ≥ 7.5 % increase in the D/PCrea ratio during the 10-month period. All patients started on study medication will be available for this analytical approach.
The same strategies will be used to analyze secondary outcome variables.
Publication of study results
All publications will be authored by members of the scientific advisory committee. Co-authors will have contributed to the design, analysis, execution and actual reporting of the study.
Study investigators
Steering committee: C.P. Schmitt, F. Schaefer, K.E. Bonzel, K. Rönnholm
Scientific advisory committee: M. Almeida, K. Arbeiter, G. Ardissino, K.E. Bonzel, A. Edefonti, M. Fischbach, K. Haluany, J. Misselwitz, Markus J. Kemper, K. Rönnholm, F. Schaefer, C.P. Schmitt, S. Wygoda
Safety monitoring committee: V. Schwenger, U. Querfeld, G. Offner
Discussion
We report the protocol of a randomized clinical trial designed to test the effect of the buffer type on the evolution of peritoneal tissue integrity and transport function in children treated with 'biocompatible', i.e. neutral-pH, low-GDP PD solutions. The study utilizes the availability of two novel biocompatible PD solutions manufactured by the same company which differ selectively in the buffer employed, namely either pure lactate or pure bicarbonate. The comparative administration of these solutions will provide information on differences in peritoneal cell viability, tissue morphology, local host defense and solute transfer capacity potentially inferred by cytotoxic and/or vasocative effects of lactate administered to the abdominal cavity in unphysiological concentrations.
Competing interests
This investigator-initiated trial was designed exclusively by the members of the EPPS scientific advisory committee. None of the investigators have any financial relationship with the manufacturer of the study medication. Financial support is received from Fresenius Medical Care to cover coordination costs and investigator meetings.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The helpful input of Dr. Jutta Passlick-Deetjen and Dr. Thomas Schaub in the development of the study protocol is kindly appreciated.
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| 15485574 | PMC529259 | CC BY | 2021-01-04 16:32:51 | no | BMC Nephrol. 2004 Oct 14; 5:14 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-14 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-131546961510.1186/1471-2369-5-13Research ArticleThe role of hemodialysis machines dedication in reducing Hepatitis C transmission in the dialysis setting in Iran: A multicenter prospective interventional study Shamshirsaz Alireza Abdollah [email protected] Mohammad [email protected] Mir Reza [email protected] Farzam [email protected] Seyed Reza [email protected] Navid [email protected] Mohammad Reza [email protected] Nima [email protected] Varshasb [email protected] Amirhooshang Abdollah [email protected] Maziyar [email protected] Mehrdad [email protected] Niloofar Nobakht [email protected] Behrooz [email protected] Nephrology, Hazrat-e-Rasoul hospital, Tehran, Iran2 Nephrology, Pars Hospital, Tehran, Iran3 Academy of Medical Sciences, P.O.Box: 19395/4655, Tehran, Iran4 Research and Education Department, Charity Foundation for Special Diseases Tehran, Iran2004 7 10 2004 5 13 13 25 4 2004 7 10 2004 Copyright © 2004 Shamshirsaz et al; licensee BioMed Central Ltd.2004Shamshirsaz et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Hepatitis C virus (HCV) infection is a significant problem among patients undergoing maintenance hemodialysis (HD). We conducted a prospective multi-center study to evaluate the effect of dialysis machine separation on the spread of HCV infection.
Methods
Twelve randomly selected dialysis centers in Tehran, Iran were randomly divided into two groups; those using dedicated machines (D) for HCV infected individuals and those using non-dedicated HD machines (ND). 593 HD cases including 51 HCV positive (RT-PCR) cases and 542 HCV negative patients were enrolled in this study. The prevalence of HCV infection in the D group was 10.1% (range: 4.6%– 13.2%) and it was 7.1% (range: 4.2%–16.8%) in the ND group. During the study conduction 5 new HCV positive cases and 169 new HCV negative cases were added. In the D group, PCR positive patients were dialyzed on dedicated machines. In the ND group all patients shared the same machines.
Results
In the first follow-up period, the incidence of HCV infection was 1.6% and 4.7% in the D and ND group respectively (p = 0.05). In the second follow-up period, the incidence of HCV infection was 1.3% in the D group and 5.7% in the ND group (p < 0.05).
Conclusions
In this study the incidence of HCV in HD patients decreased by the use of dedicated HD machines for HCV infected patients. Additional studies may help to clarify the role of machine dedication in conjunction with application of universal precautions in reducing HCV transmission.
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Background
Hepatitis C virus (HCV) transmission occurs mainly through large or repeated direct percutaneous punctures to blood vessels; for example repeated injections for drug abuse [1]. Less frequent routes are sexual transmission [2], perinatal transmission [3], acquisition from mucous membrane exposure [4,5], body fluids [6] and colonoscopy [7]. However, in up to 40% of infected individuals, the route of transmission remains unknown [8]. Since the introduction of blood and organ donor screening by antibody testing in 1991, HCV has rarely been transmitted by transfusion of blood products[1], but there remains a relatively high incidence of new infections in hemodialysis (HD) units [9,10]. Several reports around the world indicate that the frequency of HCV is higher in patients undergoing maintenance HD than in the general population. The reported prevalence of HCV infection in maintenance HD patients varies markedly from country to country and from one center to another [11] ranging between 8% and 39% in North America, 1% and 54% in Europe, 17% and 51% in Asia, and 1% and 10% in Australia [12]. In Iran the prevalence of HCV varies from 5.5%–24%. [13,15].
Molecular virological studies have clearly shown the nosocomial transmission of HCV to hemodialysis patients,[16,17] but the exact modes of transmission remain unclear. Studies suggest several risk factors, including transmission through blood components [18]; patient-to-patient transmission through shared equipment [19], devices [20], or multidose vials [21]; and between patients treated on the same shift but not sharing equipment [16]. Basic hygienic precautions, for instance hand washing, the use of protective gloves when patients and HD equipment is touched are observed worldwide but only a few centers have isolated their HCV-positive patients or dialyzed them during dedicated shift or using dedicated dialysis machines. At the present time, the Center for Disease Control and Prevention (CDC) does not recommend isolation of patients with HCV [1]. The evaluation of this problem is difficult because of the paucity of prospective studies and the scarce data about patient-to-patient transmission in settings other than HD centers [6] and therefore the benefit of isolation of HCV infected dialysis patients remains controversial. The prevalence of HCV in hemodialysis units is higher than normal population in Iran (5–24% [13,15] versus 0.3 [22]) and most other countries. Considering the added expense of patient isolation we conducted a prospective study in hemodialysis units in Tehran, Iran, to evaluate the role of HD machine separation in reducing HCV transmission to HD patients.
Methods
Among 40 HD centers in Tehran, we randomly selected centers one by one to reach a total number of 593 patients (12 centers) to enroll in this study. Selected centers were randomly divided in to dedicated (D) and non-dedicated (ND) HD machine groups, including 297 patients in D (4 centers) and 296 patients in ND group (8 centers). ELISA III checked all patients for HCV antibody detection before enrolling in the study. Positive cases were confirmed by RT-PCR. Only patients who were HCV positive by RT-PCR were considered to be HCV infected. Out of 593 HD cases 51 were RT-PCR positive (30 in the D groups and 21 in the ND group), and 542 were HCV negative (267 in the D group and 275 in the ND group). The prevalence of HCV infection in the D group was 10.1% (range: 4.6%– 13.2%) and was 7.1% (range: 4.2%–16.8%) in ND group. During the study conduction, 5 new HCV positive cases (1 in D group and 4 in ND group) and 169 new HCV negative cases entered the study. Information regarding age, sex, occupation (health care personnel, surgeons and dentists), HCV infected relatives, previous peritoneal dialysis, surgery during last 2 months, duration of hemodialysis, number of blood product transfusions, history of organ transplantation, and the causes of ESRD was collected. The obtained history of IV drug abuse, tattooing, and multiple sex partners was not reliable.
442 patients (254 cases in the D and 192 in the ND group) were followed for 9 months (first follow up population). 281 patients (160 cases in the D and 121 cases in the ND group) who remained within our study were followed for an additional 9 months (Second follow up population). Histories of surgeries or blood product transfusion were obtained at each follow-up; with no significant difference found between the D and the ND groups. There were no significant differences between the D and the ND groups in the number of patients lost to follow-up due to death, renal transplantation or transfer to a different hospital change (data not shown).
Patients were dialyzed for 4 or 4.5 hours, 2 or 3 times weekly, using standard HD techniques by Cuprophane and Polysulfone dialyzers. All included HD patients were HIV and HBs-Ag negative. Dialysis membranes were low-pressure and used only once and HD machines were bleached and rinsed between dialysis sessions according to the manufacturers' instruction. Socioeconomic level was essentially similar between D and ND groups. The only difference between two groups of HD centers was that in-group D, HCV positive patients were assigned to a dedicated HD machine, but in-group ND, HD HCV positive and negative patients were not assigned to dedicated machines. All machines were located in dialysis wards and not in separate rooms in both groups.
Patient to staff ratio in the D and the ND groups was not statistically different (3.1 and 3.4 respectively) and all staff members were negative for anti-HCV. To prevent HCV transmission, educational courses were held for the staff to reemphasize the CDC hygienic guidelines; however, an interview of all nurses directly involved in patient care disclosed some deviation from CDC hygienic guidelines. The minority of nurses remembered situations when they had failed to change their gloves due to an urgent adjustment of a hemodialysis machine. A checklist was used respecting hemodialysis-specific infection control practices and new gloves were applied for each individual patient. Nevertheless, masks, aprons and protective glasses were not universally used. In all centers, all patients had specific dialysis stations assigned to them, and chairs and beds were cleaned after each use. Handling and storage of medications and hand washing were not done in the same or adjacent areas to those where used equipment or blood samples were handled. One of the ND centers was excluded from the study due to non-adherence to CDC hygienic guidelines in the first months of the study.
Statistical analysis was performed using SPSS 10.5 software. Comparisons between groups were made by the chi-square test method for categorical variables and by the t-test for quantitative variables.
Results
The mean age was 49.5 years (range from 12–84), 58.7% were male, and the mean HD duration was 21.6 months. The etiology of end-stage renal disease was hypertension in 36% followed by diabetes in 28% and glomerulonephritis in 10.5%. 15.5% of patients in the dialysis centers had a one-time history of kidney transplantation, and 2.2% had undergone transplantation twice. The demographic data for two groups is illustrated in table 1.
Table 1 Demographic characteristics of dedicated and non-dedicated groups.
Cases included at the beginning the study Cases included during the study (new cases)
Dedicated Non-dedicated Dedicated Non-dedicated
Total count of included cases 267 275 85 84
Age [Mean (SE)] 48.5 (0.9) 50.6 (1.0) 47.9(3.1) 51.9(1.8)
Male proportion (%) 59.9 54.2 62.2 61.9
At-risk occupation (%) 0.4* 2.6 5.1 6.1
Duration of HD [Mean (SE)] 24.9(2.8) 25.2 (4.9) 12.6(4.8) 11.8(5.8)
Previous peritoneal dialysis (%) 6.9** 2.2 2.4 2.4
IV drug abuse (%)*** 0.0 1.3 3.9 1.2
Surgery during the last 2 months (%) 1.9 1.5 12.2 8.3
Transfusion during the last 2 months (%) 27.0 21.0 22.0 19.0
Previous transplantation (%) 17.7 18.2 9.5 8.3
* P = 0.04 (Significant difference with the control group)
** P = 0.009 (Significant difference with the control group)
*** History of IV drug abuse was not ascertained.
All other differences between the D and the ND group were not significant.
In the first follow-up period, the incidence of HCV infection was 1.6% and 4.7% in the dedicated and the non-dedicated groups (p = 0.05). In the second follow-up period, the incidence was 1.3% in the dedicated and 5.7% in the non-dedicated groups (p < 0.05) (table 2).
Table 2 Incidence of HCV positive (PCR) cases in dedicated and non-dedicated groups in the first and second follow-up.
First follow-up Second follow-up
Positive No (%) Negative No (%) Positive No (%) Negative No (%)
Dedicated 4(1.6) 250(98.4) 2 (1.3) 158 (98.7)
Non-dedicated 9(4.7) 183(95.3) 7 (5.8) 114 (94.2)
P value = 0.05 <0.05
Discussion
The possibility of an intradialytic spread of HCV appeared to be very low and the treatment of HCV infected patients with dedicated machines was not strictly required [23-26]. Although there is no consensus regarding machine dedication between HCV non-infected and HCV infected patients, we found that using dedicated HD machines in both follow-up periods has an important role in reducing HCV transmission. Similar results have been shown previously. Low prevalence of HCV infection (HCV antibodies) in a HD unit in Istanbul (4.7%) showed that patient isolation and use of dedicated dialysis machines for seropositive patients decrease the transmission of HCV infection in HD centers [27]. Data derived from another study in Turkey demonstrated that nosocomial spread of HCV in HD units in which both seropositive and seronegative patients were treated together were higher than that of units with dedicated machines [28]. A study in Lebanon has shown that infection by HCV may be dialysis machine-related, rather than transfusion-related [29]. Another study from Portugal also demonstrated that the incidence of HCV infection was lowest in units that used dedicated machines or dedicated rooms for anti-HCV-positive patients [30]. Genotyping analysis in a molecular study confirmed that implementation of rigorous hygienic routines and introduction of dedicated rooms and machines for HCV-infected patients are important measures for effective control of HCV infection in a hemodialysis environment [31]. Findings from a study conducted in Shiraz, Iran, where 5.5% of patients were anti-HCV positive indicate that cross-infection by dialysis machines was mainly responsible for HCV infection. This study also reemphasized that cross infection through dialysis machines, rather than transfusion of blood products was the priming mode of transmission of hepatitis C virus among HD patients [15].
Some authors recommended that it is sufficient to treat every dialysis patient as potentially infectious, strictly adhering to the "universal precautions for prevention of HCV transmission", to prevent the spread of HCV in dialysis units [32,33] and isolation of HCV-infected dialysis patients and use of dedicated machines are unjustified [34]. P. Gilli et al demonstrated that machine separation in the presence of strict application of hygienic precautions did not reduce HCV transmission [35]. In agreement to this report, simpler measures such as the observance of Universal Precautions (UP), continuous training of the care staff and the use of anti-HCV positive patients personal instruments which can stop the diffusion of HCV infection in HD centers [36] have been mentioned.
In our study population, the prevalence of HCV infection was approximately the same in both groups at the beginning of the study, but significantly lower incidence of HCV infection in D group may show that machine dedication strategies can be effective to reduce HCV transmission at least in our HD centers.
Conclusions
Considering the prevalence of HCV infection and adherence to adequate infection control measures, HD machine dedication may help to decrease transmission of HCV infection in our dialysis units.
However rigorous implementation of precaution measures remains a cornerstone for prevention of HCV transmission among patients undergoing maintenance hemodialysis, but as unpredictable accidents can always take place in hemodialysis units; machine dedication may play a more important role in prevention of HCV transmission. Further studies are needed to evaluate the possible roles of machine dedication in the presence of strict adherence to hygienic precautions.
Authors' contributions
Dr AA Shamshirsaz designed the draft questionnaires and study protocol, managed the coordination of the surveys and drafted the manuscript. . Dr. Kamgar participated in drafting the manuscript and data collection, coordinated the study and designed the protocol. Dr Bekheirnia conceived of and designed the study, performed the statistical analysis, drafted the manuscript and helped design the protocol. Dr Bouzari helped draft the manuscript, participated in statistical analysis and managed paraclinical surveys. Dr Habibzadeh drafts the manuscript, participated in statistical analysis and helped in data collection and physical examination. Dr Pourzahedgilani participated in physical exam, filled out questionnaires and searched scientific sources. Dr V Broumand participated in drafting the manuscript. Dr Moradi participated in statistical analysis. Dr. Ayazi, Dr. Hashemi, Dr. A.H Shamshirsaz, Dr. Borghei and Dr. Haghighi took part in physical exams and filling questionnaires. Dr. B Broumand facilitated the study design and progress.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
This study is funded by Charity Foundation for Special Diseases (CFSD). The authors would like to gratefully acknowledge the kind assistance of Dr. Seyed Mahmoud Tabatabaie (research and education deputy of CFSD) and his help, support, suggestions, encouragement and devotion to this project. We would not be able to finish this project without his supports and comments. We also would like to thank Dr. Bagher Larijani, Dr. Nobakht Haghighi, Dr. Mooraki, Dr. Ataiepour, Dr. Nejadgashti, Dr. Atabak, Dr. Najafi, Dr. Nasrollahi, Dr. Hakemi, and Dr. H. Najmabadi.
The authors also wish to thank Pasteur Institute and Dr. Kariminejad lab for performing the laboratory assays.
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| 15469615 | PMC529260 | CC BY | 2021-01-04 16:32:50 | no | BMC Nephrol. 2004 Oct 7; 5:13 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-13 | oa_comm |
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BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-161549810810.1186/1471-2369-5-16Research ArticleActive collaboration with primary care providers increases specialist referral in chronic renal disease Mondry Adrian [email protected] Ai-Ling [email protected] Marie [email protected] Thuy D [email protected] Kai [email protected] Bioinformatics Institute, Singapore, Republic of Singapore2 Klinikum Solingen, Solingen, Germany3 Dialysegemeinschaftspraxis Karl- Harr- Strasse, Dortmund, Germany2004 22 10 2004 5 16 16 27 7 2004 22 10 2004 Copyright © 2004 Mondry et al; licensee BioMed Central Ltd.2004Mondry et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Late referral to specialist nephrological care is associated with increased morbidity, mortality, and cost. Consequently, nephrologists' associations recommend early referral. The recommendations' effectiveness remains questionable: 22–51% of referrals need renal replacement therapy (RRT) within 3–4 months. This may be due to these recommendations addressing the specialist, rather than the primary care providers (PCP).
The potential of specialist intervention aiming at slowing progression of chronic renal failure was introduced individually to some 250 local PCPs, and referral strategies were discussed. To overcome the PCPs' most often expressed fears, every referred patient was asked to report back to his PCP immediately after the initial specialist examination, and new medications were prescribed directly, and thus allotted to the nephrologist's budget.
Methods
In retrospective analysis, the stage of renal disease in patients referred within three months before the introductory round (group A, n = 18), was compared to referrals two years later (group B, n = 50).
Results
Relative number of patients remained stable (28%) for mild/ moderate chronic kidney disease (MMCKD), while there was a noticeable shift from patients referred severe chronic kidney disease (SCKD) (group A: 44%, group B: 20%) to patients referred in moderate chronic kidney disease (MCKD) (group A: 28%, group B: 52%).
Conclusion
Individually addressing PCPs' ignorance and concerns noticeably decreased late referral. This stresses the importance of enhancing the PCPs' problem awareness and knowledge of available resources in order to ensure timely specialist referral.
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Background
Late referral to specialist care for renal failure is associated with increased morbidity, mortality, and cost (review in[1,2]). Consequently, nephrologists' associations recommend early referral[3,4]. The recently published ERA/ EDTA guideline states: "Referral to nephrology should be considered when the GFR is <60 ml/min and is mandatory when the GFR is <30 ml/min"[3]. The recommendations' effectiveness remains questionable: 22–51% of patients need renal replacement therapy (RRT) within 3–4 months [5,6] after first referral to specialist nephrological care.
Chronic renal disease may be asymptomatic for a very long time. This stresses the importance of primary care providers in ensuring timely referral to a renal care center. Levin[1] reviewed aspects of the referral process and noted that the consequences of late referral were usually and most specifically described in specialist nephrological journals, thus not reaching the necessary target audience, the primary care providers (PCPs).
The problem of late referral seems to be ubiquitous, if discussions with colleagues world- wide may be believed, and has led to numerous local, regional and national initiatives aiming at ensuring timely referral into specialist care. Some initiatives[7,8] successfully bypass the PCPs by introducing population screening programs, while others opt for the alternative of "managed care"[9].
This article summarizes findings from a local initiative started by one of the authors (K.H.) in 1997 in the city of Dortmund, Germany.
In Germany, about 90% of the population are covered by the mandatory health insurance system ("Gesetzliche Krankenversicherung"). All physicians who wish to treat these patients are organized in associations, which negotiate a budget with the insurance companies. Depending on the specialty, a typical per capita budget is then assigned per patient for a period of three months. This budget covers consultation fees, and the cost of medication prescribed. If a physicians exceeds the total budget thus calculated for his practice, the insurance companies can demand restoration of funds. Typically, a GP's per patient budget would be much lower than a nephrologist's. During the time period described here, the nephrologist's budget per patient was about ten times as much than a general practitioner's. While this budget strategy is intended as a safeguard against excess prescriptions, it has been criticized by many physicians for inducing sub- optimal treatment as practice owners comply rather with budget demands than with best practice guidelines.
Dortmund, a town of 589000, has a physician to patient ratio of 141/ 100000 (German average: 156/ 100000). There are five nephrological centers, including two hospitals. Currently, 172 general practioners ("Allgemeinärzte und Praktische Ärzte"), 123 internists ("Ärzte für Innere Medizin") and 21 urologists are listed in Dortmund. One of the internist practices supervises a dialysis center, but does not provide specialized nephrological consultancy. GPs, internists and urologists were considered primary care providers, as all referrals for treatment of chronic renal failures came from one of these specialties. There were no major fluctuations of physician numbers between 1997 and 1999, the period for which data was analyzed here.
Joint initiatives by all five nephrology centres in Dortmund to give PCPs a series of lectures on treatment of chronic renal disease met with little response, as the presenters regularly outnumbered the audience. In the spirit of the Declaration of Helsinki, part II.1.: ("In the treatment of the sick persons, the doctor must be free to use a new therapeutic measure, if in the doctor's judgment it offers hope of saving life, reestablishing health, or alleviating suffering")[10], K.H. decided to take active measures to induce PCPs to refer patients at an earlier stage.
In order to do so, education was brought to the primary care providers, as opposed to bringing the PCPs to education. Over a period of 18 months, K.H. introduced himself and the potential of timely nephrological care in delaying or even halting the progression of chronic renal disease to PCPs- General Practitioners, and specialists in Internal Medicine and Urology. This introductory round included participation in local PCPs' round tables and PCP organized continuous medical education events, but also individually meeting some 250 PCPs for discussion. During these sessions, the topic of renal disease was subtly introduced during discussions on the more common diseases and risk factors, such as cardiovascular disease, hypertension and diabetes mellitus.
During these teaching sessions and discussions, three arguments were repeatedly brought forward by the PCPs:
1. Mild- moderate renal failure should be treated by the primary care providers, while the nephrologists' task was seen in providing renal replacement therapy.
2. Referral to specialist care meant risking the loss of the patient to the specialist's practice (In Germany, direct specialist access is possible, and patients tend to stay with one doctor if satisfied).
3. If patients did return from specialist consultation, they were usually carrying recommendations for costly permanent medication, such as ACE inhibitors, that put a heavy burden on the PCP's budget.
The first of these arguments was addressed by the "teaching module" of the visits. PCPs were given an executive summary of the complexities of diagnostic and therapeutic procedures used in state- of- the- art nephrology, and the potential of intensified treatment by specialists aiming at slowing the progression of renal failure was explained in detail.
Secondly, all patients referred were asked to report back to the referring PCP within one week of the consultation, by which time a summary of findings and appropriate counsel had been sent ahead.
Thirdly, instead of drug recommendations, drug prescriptions were handed out directly and so allotted to the much higher nephrologist's budget, thus easing the financial pressure on the primary care providers. More importantly, also the follow- up prescriptions were done in the renal care center, so that at no time the PCPs budget became endangered.
The present study investigates the question whether improving the PCP's knowledge about the potential of timely treatment of chronic renal failure, in combination with addressing their economic concerns, succeeded in encouraging timely referral to specialist care.
Methods
Retrospective analysis of patients' records. Analysis of patient records was carried out by a final year medical student (TDV). All patients had agreed to have their data used for quality control measures at the time of referral. Specifically informed consent was obtained from all patients still alive at the time of data collection. Two groups were selected by date of first contact with the nephrologist: Group A, third quarter 1997, immediately before the start of the initiative, and Group B, third quarter 1999, six months after the last visit to a primary care provider had taken place. All new patients whose records indicated referral for nephrological specialist treatment were included in the study. Criteria were subnormal creatinine clearance (ECC) or elevated serum creatinine, elevated blood pressure, proteinuria, or erythrocyturia. The patients in each of the two groups were divided into three subgroups according to their renal function: mild/moderate chronic kidney disease (MMCKD), ECC > 60 ml/min/1.73 m2, moderate chronic kidney disease (MCKD), 60 ml/min/1.73 m2 > ECC > 20 ml/min/1.73 m2, and severe chronic renal disease (SCKD), ECC < 20 ml/min/1.73 m2. Due to small proband numbers in the subgroups, the null hypothesis ("no inter- group differences") was tested by the non- parametric, two sided Chi- square test. Survival was estimated by Kaplan- Meier analysis. Statistical analysis was carried out using the SPSS v11.5 package.
Results
Table 1 and 2 show the descriptive statistics of two groups, including gender and age distribution, diagnosis, prevalence of diabetes, and biopsy frequency. Individually addressing PCPs ignorance and concerns decreased late referral, from the high (SCKD: 44%) to the low (SCKD: 20%) end of the spectrum in published data[5,6] (Chi-square test: 2-sided p = 0.09), as detailed in Figure 1. The relative share of patients seen at the stage of moderate CKD increased, while the relative percentage of patients seen in mild/moderate CKD remained stable. The health outcome (survival in MMCKD and MCKD groups) of patients was insignificantly improved: the mean survival time in group A is 1.71 yrs (1.30–2.12 yrs) compared to a mean survival of 1.90 yrs (1.78–2.02 yrs) in group B.
Table 1 Age and gender distribution in the two cohorts.
Group A: n= 18
MMCKD: n= 5 (28%) MCKD: n= 5 (28%) SCKD: n= 8 (44%)
M F M F M F
Gender (n/ %) 3/ (60%) 2/ (40%) 4/ (80%) 1/ (20%) 4/ (50%) 4/ (50%)
Age (yrs ± SD) 59.7 ± 10.7 44.0 ± 11.3 59.5 ± 20.7 26.0 ± 0.0 55.3 ± 27.2 72.5 ± 8.3
Group B: n= 50
MMCKD: n= 14 (28%) MCKD: n= 26 (52%) SCKD: n= 10 (20%)
M F M F M F
Gender (n/ %) 11/ (78.6%) 3/ (21.4%) 17/ (65.4%) 9/ (34.6%) 1/ (10.0%) 9/ (90%)
Age (yrs ± SD) 32.7 ± 16.1 25.0 ± 17.6 60.9 ± 12.6 54.8 ± 11.8 75.0 ± 0.0 65.4 ± 13.2
Table 2 Diagnosis of patients referred for specialist nephrological evaluation.
Group A, n = 18 count (% within group) Group B, n = 50 count (% within group)
Diagnosis No renal failure 0 (0%) 13 (26%)
Glomerulonephritis 4 (22.2%) 5 (10%)
Diabetes 3 (16.7%) 8 (16%)
Nephrosklerosis 2 (11.1%) 4 (8%)
Lupus 1 (5.6%) 0 (0%)
Nephrectomy 2 (11.1%) 4 (8%)
Renal cirrhosis 2 (11.1%) 4 (8%)
Reflux 0 (.0%) 1 (2%)
Polycystic disease 1 (5.6%) 1 (2%)
Others 1 (5.6%) 3 (6%)
Unknown cause 2 (11.1%) 7 (14%)
Diabetes No 12 (66.7%) 30 (62.5%)
Yes 6 (33.3%) 18 (37.5%)
Biopsy No 15 (83.3%) 40 (83.3%)
Yes 3 (16.7%) 8 (16.7%)
Figure 1 Relative distribution (% of total) of patients into the three subgroups of MMCKD, MCKD and SCKD as defined under "methods". Distribution into MCKD and SCKD is inversed after intervention while MMCKD remains stable. Group A: patients referred in 1997; Group B: patients referred in 1999.
Discussion
The present study is a retrospective analysis of patient data from a single nephrological referral centre, and statistics are carried out on a data set of limited size (n = 18 only in the pre- intervention group A). As such, the statistical significance of the findings is doubtful, and the results might be interpreted as akin to a case report, or a medical anecdote.
As such, however, it may have a value quite different from, but equal to that of a randomized, controlled study.
Aronson recently discussed the value of medical anecdotes[11]; of the eight reasons listed there, the present study meets four: it generates a hypothesis (i.e., close communication with PCPs leads to earlier referral to specialist nephrological care), it suggests a method of management (i.e., individualized teaching sessions and adoption of financial incentives to increase early referral), it reminds and educates (i.e., of the benefit of early referral), and it hopes to stimulate a systematic review (i.e., the authors hope that the results will entice larger dialysis providers to design and carry out a prospective, randomized study).
More often than not, observational studies give similar results to controlled randomized trials[12,13], and critical appraisal of this notion leads to the conclusion that, while good controlled randomized trials do provide the highest level of evidence, a flexible approach may be taken "in which randomised controlled trials and observational studies have complementary roles. High quality observational studies may extend evidence over a wider population and are likely to be dominant in the identification of harms and when randomised controlled trials would be unethical or impractical"[14].
The positive finding of the present study is that active recruiting strategies may improve the referral pattern. Following the discussions with individual PCPs, more patients were referred to specialist care at a stage where appropriate treatment may slow or even halt the progression of chronic renal failure. This should benefit first the patient, and then in the long run society as a whole. Recent studies[2,15] have shown that early referral causes a substantial decrease in hospitalization costs in the first year after referral. It is very probable that adequate treatment and therefore prolongation of the pre- dialysis moderate CKD stage will incur reduced overall spending; the increase in quality of life for the patients is immeasurable. Due to the relatively small number of patients available in this single centre study, combined with the short follow- up interval of just two years, only a slight trend towards prolonged "survival" as defined above could be demonstrated. An American multi- centre study[16] has recently shown, however, that late referral is clearly associated with higher morbidity. To what extend the timing of dialysis initiation influences survival remains as yet questionable. By contrast, Traynor[17] recently demonstrated that early initiation of dialysis may even be detrimental. There is currently one controlled, randomized study addressing this question[18], and the perspective may change once high level evidence is obtained.
Late referral to specialist care in chronic renal disease remains a problem world- wide. As early as 1984, focusing on the question under which circumstances renal replacement therapy should be initiated or not, questionnaires showed significant differences in the attitudes of nephrologists and non- nephrologists towards referral[19] in the United Kingdom, and this difference in behavior has decreased, but not been eradicated over time[20]. Similar observations were made in Canada[1], and the United States [21].
It has been speculated that this may be because these recommendations address the specialist, rather than the primary care providers (PCP)[1,2].
At present, a medline search using the keywords "referral" and "chronic renal failure" listed a total of 81 references since 1974, with only 48 in specialist, not necessarily nephrological journals, while adding "guideline" reduced the reference list to a disappointing three references from the years 1998 and 1999, which included, however, a fully accessible online guideline in the Canadian Medical Association's journal. Neither the American, nor the European Renal Associations' guidelines[3,4] were found by these obvious search strategies. Only the former was found by the search terms "guideline" and "kidney disease", leading to the relevant article in the American Journal of Kidney Disease, accessible only to subscribers.
Better results were obtained using the same search terms on a general search engine (Google), leading at first hit to the DOQI webpage, which includes the free access to full text guidelines. An extensive search using the same terms in German, however, showed no relevant results but an abundance of lay articles of dubious quality. This scarcity of qualified information might exclude those physicians not fluent in English from gathering relevant and up- to- date information. National renal care associations should therefore consider extending their educational efforts beyond their specialist members and selectively target PCPs and their respective professional associations.
In the present study, two arguments brought forward by PCPs to explain their reluctance to refer early focused on the economic burden this presented to their own practice, rather than society as a whole. These two arguments concerned budget penalties if too much money was spent on the (costly) pre- dialytic patients with chronic kidney disease, and loss of patients after referral. This is an unusually frank statement.
The majority of 22 medline hits using the search terms "economy" and "renal disease" address the question of best cost- efficiency of treatment strategies on a macro- economic level. Micro- economic considerations in the distribution of health care are only hinted at[2]. Health care planners increasingly stress economic factors. While their laudable aim is to provide affordable treatment options to the general population, the drawback of this approach becomes obvious in situations as these, where a conflict of interest arises between best practice and best revenue.
Due to the retrospective design of this study, one cannot analyze to what extent the PCPs' economic considerations rather than their educational state were responsible for their referral pattern. The fact that financial concerns were the tenor of the individual discussions, however, indicates that health care planners may profit from taking this into account.
The authors hope that this single- centre study will entice multi- centre RRT providers to conduct a prospective, large scale study to further investigate the relative contribution of the factors discussed here.
Conclusions
The initiative presented here shows that timely specialist referral in renal care can be achieved. National and regional differences in the organization of health care provision may lead to variant strategies to implement optimal health care, but close collaboration with the primary care providers is essential.
List of abbreviations
ECC: endogenous creatinine clearance
MCKD: moderate chronic renal disease
MMCKD: mild/moderate chronic renal disease
PCP: primary care provider
RRT: renal replacement therapy
SCKD: severe chronic renal disease
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
K.H. started the initiative, carried out all the teaching modules and gave access to the data.
T.D.V. retrieved the data from archived files
Z.A.L. did statistical analysis
M.L. did statistical analysis
A.M. did statistical analysis and wrote the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15498108 | PMC529261 | CC BY | 2021-01-04 16:32:51 | no | BMC Nephrol. 2004 Oct 22; 5:16 | utf-8 | BMC Nephrol | 2,004 | 10.1186/1471-2369-5-16 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-161549810710.1186/1471-2377-4-16Research ArticleDifferential diagnosis of tuberculous meningitis from partially-treated pyogenic meningitis by cell ELISA Kashyap Rajpal S [email protected] Rani P [email protected] Ravindra M [email protected] Neha P [email protected] Nitin H [email protected] Hemant J [email protected] Girdhar M [email protected] Hatim F [email protected] Biochemistry Research Laboratory, Central India Institute of Medical Sciences, 88/2 Bajaj Nagar, Nagpur-440010, India2 Environmental Modeling and Genomic division, NEERI, Nehru Marg, Nagpur-440020, India2004 22 10 2004 4 16 16 16 3 2004 22 10 2004 Copyright © 2004 Kashyap et al; licensee BioMed Central Ltd.2004Kashyap et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Tuberculous meningitis (TBM) is a major global health problem, and it is sometimes difficult to perform a differential diagnosis of this disease from other diseases, particularly partially-treated pyogenic meningitis (PTPM). In an earlier study, we demonstrated the presence of a 30-kD protein antigen in cerebrospinal fluid (CSF) of TBM patients. We have also shown that lymphocytes from CSF of TBM patients respond differently to this antigen than do those from PTPM patients. The purpose of this study was to develop an assay that can discriminate between TBM and PTPM.
Methods
We developed a cell enzyme-linked immunosorbant assay (Cell ELISA) to quantitatively measure production of antibodies against the 30-kD protein in B cells from CSF of TBM and PTPM patients.
Results
The cell ELISA yielded 92% (11/12) sensitivity and 92% (11/12) specificity for the differential diagnosis of TBM from PTPM.
Conclusion
When induced with the 30-kD protein antigen, B cells derived from CSF of TBM patients respond to IgG production within 24 h while those derived from PTPM patients do not respond.
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Background
Tuberculous meningitis (TBM) is an infection of the central nervous system (CNS) that is prevalent in both under-developed and developing countries. An increased incidence of TBM has occurred in recent years due to the growing number of people infected with human immunodeficiency virus (HIV). Diagnosis of TBM remains problematic despite many new, advanced diagnostic methods [1,2]. Previous clinical studies have clearly demonstrated that the timing of TBM treatment is the most critical factor in determining the ultimate outcome, which underscores the importance of early diagnosis [3]. The laboratory confirmation for the diagnosis of TBM is based on the detection of acid-fast bacilli (AFB) in the cerebrospinal fluid (CSF) and by culturing CSF for Mycobacterium tuberculosis bacilli (MTB) [4]. However, the sensitivity of direct AFB smears from CSF ranges from 5–10% and culturing techniques take 4–6 weeks. It has been recently reported that the staining efficiency of the AFB smear test can be increased to detect up to 50% of TBM cases, but this technique requires a very large amount of CSF [5].
Clinical as well as CSF features are helpful for diagnosing TBM, but they cannot be used to differentiate TBM from other infectious and non-infectious disorders [6,7]. In particular, clinicians often encounter difficulty when performing a differential diagnosis of TBM from partially-treated pyogenic meningitis (PTPM) cases. Both the results from biochemical and pathological analysis of CSF and the clinical presentation of TBM are often similar to those of PTPM, which results in frequent misdiagnosis.
In an earlier study, we reported the presence of a diagnostic 30-kD protein antigen in CSF of confirmed and suspected TBM patients [8]. Immunological methods such as antibody-capture enzyme-linked immunosorbant assay (ELISA) have been previously used for diagnosing TBM [9]. The cell ELISA method allows further confirmation of the results obtained by antibody-capture ELISA.
Cellular immune function is characterized by the existence of various types of lymphoid cells. As lymphocytes participate in the production of humoral immunity, they may respond to the 30-kD protein antigen in TBM and PTPM patients. We have developed a cell ELISA to study the response of B cells derived from CSF of TBM and PTPM cases following challenge with the 30-kD protein antigen. The purpose of the present study was to evaluate the antibody response to the 30-kD protein antigen in CSF of TBM and PTPM patients by cell ELISA and to determine whether this method may be used in differential diagnosis of TBM from PTPM.
Methods
Patients and sample collection
The Central India Institute of Medical Sciences (CIIMS), Nagpur, is a tertiary referral center. CSF was collected from patients who were suspected of having TBM or other infections before they received any treatment. For patients undergoing cranial surgery, analysis of CSF was performed if they were suspected of having meningitis. These patients were already on broad-spectrum antibiotics, such as third-generation cephalosporins and aminoglycosides.
To establish a diagnosis of meningitis, 2–5 ml CSF was withdrawn from patients using a lumbar puncture. CSF was then subjected to routine biochemical analysis and pathological analysis including Gram staining, India ink staining, and AFB staining and culturing. One milliliter of CSF was used for the cell ELISA study, and 1 ml was used for detection of the 30-kD protein band by SDS-PAGE analysis in 12 randomly selected TBM and PTPM patients. Diagnosis of TBM and PTPM was based on the criteria described below.
Diagnostic criteria
1. Tuberculous Meningitis (TBM)
Presence of Mycobacterium tuberculosis in CSF by staining and/or culture, OR
Clinical meningitis with the following observations:
A. Sub-acute or chronic fever with features of meningeal irritation such as headache, neck stiffness, and vomiting with or without other features of CNS involvement
B. CSF findings showing increased proteins, decreased glucose (CSF:blood glucose ratio <0.5), and/or pleocytosis with lymphocytic predominance
C. Presence of the 30-kD protein band in CSF on SDS-PAGE analysis
D. Good clinical response to antituberculous drugs
None of the 12 TBM patients had positive AFB staining.
2. Partially-treated pyogenic meningitis (PTPM)
Presence of pathogenic bacteria in CSF by staining and/or culture, OR
Clinical meningitis with the following observations:
A. Fever and/or signs of meningeal irritation (patients who have undergone cranial surgery to treat tumor(s), stroke, or head injury and who have received antibiotics), OR High fever and/or signs of meningeal irritation with or without CNS manifestations (patients who received broad-spectrum antibiotics)
B. CSF findings showing increased proteins, decreased glucose (CSF:blood glucose ratio <0.2), and/or pleocytosis with a predominance of polymorphonuclear cells; CSF may resemble that of chronic meningitis patients
C. Absence of the 30-kD protein band in CSF on SDS-PAGE analysis
D. Good clinical response to broad-spectrum antibiotics
3. Control group
Peripheral blood samples from six healthy volunteers were also analyzed and included as negative controls.
Laboratory studies
Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE
CSF samples obtained from confirmed and suspected TBM cases were subjected to SDS-PAGE. SDS-PAGE was performed with a vertical slab gel electrophoresis system (Broviga, India) using the standard Laemmali method (10). A 4% stacking gel and 10% running gel were used. Electrophoresis was carried out at 250 volts/50 mAmps. Gels were developed by staining with Coomassie brilliant blue GR-250 and the protein profiles were then studied. Band size (i.e., molecular weight) was estimated using molecular weight markers (Genei, Bangalore, India) in a parallel lane.
Antigen (30-kD) preparation
Following separation of proteins from CSF of confirmed TBM patients (AFB-positive) by SDS-PAGE, the 30-kD protein band was sliced out of the gel and pre-equilibrated in elution buffer (0.15 M phosphate-buffered saline [PBS], pH 7.4) and then electro-eluted in a whole gel eluter system (Biotech, India) for 90 min at 30 volts (11). The sample was then harvested from the unit and dialyzed against PBS and the protein content was measured using a Bio Lab KIT. Protein purity was checked using native PAGE and was then used to evaluate the antibody response of B cells derived from CSF of TBM and PTPM patients.
Preparation of CSF Cells
One milliliter of CSF collected from TBM and PTPM patients was centrifuged at 400 rpm for approximately 20 min. The supernatant was then discarded and the cell pellet was washed two times with PBS and then diluted in RPMI 11640 tissue culture medium containing 10% fetal calf serum.
Preparation of Blood Cells
Heparinized blood samples were obtained from six healthy volunteers. Peripheral blood mononuclear cells (PBMC) were isolated from heparinized blood by standard Ficoll-Hypaque gradient centrifugation. PBMCs were dissolved in PBS and centrifuged at 400 rpm for approximately 15–20 min, and the PBMCs were then diluted in RPMI 11640 tissue culture medium containing 10% fetal calf serum.
Cell ELISA
Flat-bottomed, 96-well ELISA plates were coated with 10 μg 30-kD antigen/ml diluted in PBS (pH 7.2). Following overnight incubation, the plates were washed with PBS and then coated with 5% BSA-PBS for 4 h. The plates were then washed five times with PBS. Two-hundred μl of the cell preparation derived from CSF of patients with TBM or PTPM were then added to the wells and coated. Each sample was prepared in duplicate. Plates were maintained overnight at 37°C in 5% CO2 in a carbon dioxide incubator. The following day, the plates were washed with PBS and horseradish peroxidase (HRP)-conjugated rabbit anti-human IgG (1:10,000) was then added to the plates. After a 2-hr incubation at 37°C, the plates were washed again with PBS and 100 μl tetramethylbenzidine (TMB)/H2O2 were added. The TMB/H2O2 served as a substrate for HRP. After a 15-min incubation, 100 μl stop solution (2.5 N sulphuric acid) were added and the plates were then read with an ELISA reader at 450 nm (12).
Results
Detailed clinical data for TBM and PTPM patients are presented in Table 1. Out of the 12 PTPM patients, two cases harbored microorganisms, which were cultured (gram-positive cocci in one case and gram-negative bacilli in the other case). Among the 12 patients who fulfilled the criteria for TBM (shown in Table 1), CSF of all these patients was positive for the 30-kD protein antigen and was negative for AFB. None of the patients had a previous history of extra-CNS tuberculosis. In addition to the patients described in Table 1, we also tested an additional 700 CSF samples, including 150 from TBM patients. The 30-kD protein antigen was observed in >90% of these TBM patients (data not shown). Figure 1 shows the presence of the 30-kD protein band in the CSF of suspected TBM cases. This band was markedly absent from the CSF of PTPM patients.
Table 1 Clinical and CSF Findings for TBM and PTPM Patients
CSF Analysis
Case No. Age (years)/Sex TLC P % L % Protein (mg/dl) Sugar (mg/dl) CSF:blood sugar ratio Neck Stiffness Duration of fever (weeks) Headache
TBM
1* 16/f 30 - 100 83 27 0.32 Present 16 Present
2 8/m 90 5 95 105 30 0.31 Absent 8 Present
3 54/f 25 2 98 47 67 0.54 Absent 3 Present
4 16/f 450 18 82 535 21 0.24 Absent 12 Present
5* 26/m 120 60 38 143 43 0.66 Present 12 Present
6 58/m 14 - 100 68 37 0.44 Present - Absent
7 31/f 180 12 88 203 86 0.53 Absent 1 Present
8 55/f 112 10 90 231 22 0.38 Present 4 Present
9 65/m 150 1 99 131 23 0.33 Present 4 Present
10 56/m 121 12 88 217 41 0.47 Present 2 Present
11 7/f 32 35 65 68 31 0.32 Present 4 Present
12 43/f 60 - 100 97 39 0.48 Present 2 Present
PTPM
1# 38/m 220 90 10 96 26 0.20 Present 1 Present
2# 63/f 1600 61 38 401 15 0.14 Present 1 Present
3**# 23/m 61 95 5 270 33 0.20 Absent 6 Present
4+π 25/m 180 88 12 203 86 0.53 Present 1 Present
5# 48/m 450 77 32 868 32 0.21 Present 8 Present
6π 14/f 150 84 11 61 38 0.31 Absent 1 Present
7ψ 52/m 140 92 8 471 12 0.14 Absent - Absent
8# 56/m 430 80 20 518 17 0.22 Absent - Present
9++ψ 6/m 36 82 9 61 107 0.33 Absent - Absent
10# 62/f 40 73 27 142 25 0.25 Absent - Present
11# 4/m 50 90 5 71 24 0.19 Absent - Absent
12π 27/m 200 78 22 131 21 0.18 Absent 2 Present
%P- Polymorphs, % L- Lymphocytes.
*Pulmonary tuberculosis (on chest x-ray) **Consolidation (on chest x-ray)
#Cranial surgery post operative. ψ Post Head injury, π Presented to CIIMS as PTPM.
+ gram-negative bacilli observed, ++ gram-positive& non-capsulated cocci pairs observed.
Figure 1 SDS-PAGE electrophoretogram of CSF from control (lanes B, C, E) and suspected (lane D) TBM subjects. Molecular weight marker is shown in lane A. The arrow indicates the 30-kD band, which represents the 30-kD protein antigen
The ELISA absorbance values of IgG to the 30-kD protein antigen in CSF from TBM and PTPM patients are presented in Figure 2. The cut-off value (OD at 450 nm) for positivity to the 30-kD protein antigen IgG in the control CSF is 0.6. High-titer values for IgG antibody production against the 30-kD protein antigen were observed in 11 out of 12 TBM patients. However, the titer in PTPM patients was much lower than that observed in TBM patients. IgG antibody production (expressed as ELISA absorbance value) ranged from 0.7 to 2.0 for cells derived from CSF of TBM patients, with the exception of case no. 5 (ELISA absorbance value, 0.59), and from 0.05 to 0.38 for cells derived from CSF of pyogenic meningitis cases, with the exception of case no. 4 (ELISA absorbance value, 0.79). The sensitivity of the cell ELISA was 92% and the specificity was 92% for differential diagnosis of TBM from PTPM. No IgG antibodies to the 30-kD protein antigen were produced by PBMCs from six healthy individuals within 48 h of exposure to the 30-kD protein antigen.
Figure 2 B Cell response (IgG reactivity) to the 30-kD protein antigen in CSF cells derived from tuberculous meningitis (TBM) and partially-treated pyogenic meningitis (PTPM) patients and peripheral blood cells from control subjects
Discussion
During the past decade, several conventional immunoassays including ELISA, dot immunobinding assays, immunoblot assays, and various molecular methods such as the polymerase chain reaction (PCR) have been reported as adjuncts in the diagnosis of TBM [4,13-15]. However, difficulties have been encountered when using many of the aforementioned techniques to differentiate TBM from PTPM. CSF TLC, DLC (total and differential leukocyte count), protein, and glucose estimation are helpful parameters for establishing a TBM diagnosis and for differentiating other infectious and non-infectious neurological disorders, but these tests are non-specific and often cannot differentiate TBM from PTPM in patients in whom organisms are not observed. Delays in diagnosis and treatment are regarded as major contributing factors to the high mortality and morbidity of TBM, and any delay in starting appropriate medication for TBM and PTPM worsens the outcome.
We previously used SDS-PAGE to demonstrate the presence of a 30-kD protein antigen in the CSF of TBM patients that is specific to M. tuberculosis and may be considered to be a diagnostic marker for TBM. In this study, we used this 30-kD protein antigen to evaluate the IgG antibody response of B cells derived from CSF of TBM and PTPM patients and from peripheral blood samples from six healthy volunteers. A cell ELISA was developed for the quantitative measurement of antibody production against the 30-kD protein antigen by these cells. Higher titers of IgG antibody production were observed in TBM patients compared to PTPM patients. The cells obtained from CSF of TBM patients gave an early response, presumably because they were already sensitized against the TBM antigen. However, when challenged with the 30-kD protein antigen, the cells obtained from PTPM patients and healthy volunteers gave a delayed response since they are not sensitized against this antigen. Therefore, an early response on this time scale is indicative of TBM.
We have thus shown that cell ELISA is a sensitive technique for the differential diagnosis of TBM from PTPM. This method involves the demonstration of active antibody production by cells, particularly those derived from the affected site [16]. Previously, we standardized cell ELISA methodology in our laboratory using standard culture filtrate protein of M. tuberculosis (H37Rv strain) received from Colorado State University, Fort Collins USA (data not shown). The only limitation of this method is the time period (24–30 h) involved. However, the sensitivity of the test overcomes this drawback since it the only reported method that can discriminate TBM from PTPM.
The sensitivity and specificity of IgG antibody in differential diagnosis of TBM from PTPM using the 30-kD protein antigen by cell ELISA was found to be 92% (11/12). We have also demonstrated that antibody production against the 30-kD protein antigen is higher in cells derived from CSF of TBM patients compared to PTPM patients.
Various methods have been developed in our laboratory that yield a high specificity and sensitivity for diagnosis of TBM, but a small number of false positive results have been observed in pyogenic meningitis cases, particularly PTPM cases [17,18]. The cell ELISA method developed in our laboratory using the 30-kD protein antigen marker can potentially provide additional information to the treating physician that may enable a differential diagnosis of TBM from PTPM.
The cell ELISA method for diagnosing TBM is based on the assumption that local synthesis of humoral antibodies against MTB antigen occurs. Various researchers have shown that CSF-derived cells have a significantly higher proliferation response to purified protein derivative (PPD) in patients with TBM, which is suggestive of an intrathecal immune response [11,19].
Our data can be summarized by the following observations: first, cell ELISA is a useful method for differentiating TBM from PTPM using the 30-kD protein antigen; second, the method of challenging B cells from CSF of suspected TBM patients with the 30-kD protein antigen can be helpful in confirming a TBM diagnosis; and third, the cell ELISA allows several samples to be analyzed simultaneously. Hence, the cell ELISA should be a very useful tool for the differential diagnosis of TBM from PTPM.
Conclusion
The presence of a 30-kD protein antigen in CSF of TBM patients indicates that this protein carries the candidate marker antigen which is specific to M. tuberculosis. We have demonstrated that by using cell ELISA, we can differentiate TBM patients from PTPM patients, which should be helpful for diagnosing TBM. Additionally our results suggest that lymphocytes from CSF of TBM patients when challenged with 30 kD protein give a quick response by producing IgG antibodies when compared with that of PTPM and healthy volunteers. This may be because lymphocytes from TBM patients have already been exposed to 30 kD MTB antigens.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
RSK carried out the study design, data collection, statistical analysis, data interpretation, literature search, and manuscript preparation; NPA, RPK, and RMS assisted in data analysis collection; NHC assisted in data collection, statistical analysis, and data interpretation; HJP participated in the preparation of the manuscript, data interpretation, and study design; GMT provided assistance in preparation of the manuscript, data interpretation, study design, and funds collection; and HFD supervised the study design, statistical analysis, data interpretation, manuscript preparation, and literature search.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to acknowledge the help of Colorado State University, Fort Collins, USA and the US NIH, NIAID Contract No. 1 AI-75320, entitled "Tuberculosis Research Materials and Vaccine Testing."
==== Refs
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| 15498107 | PMC529262 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Oct 22; 4:16 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-16 | oa_comm |
==== Front
BMC NeurolBMC NeurolBMC Neurology1471-2377BioMed Central 1471-2377-4-171551626310.1186/1471-2377-4-17Research ArticleTraumatic-event headaches Haas David C [email protected] Department of Neurology, SUNY Upstate Medical University, University Health Care Center, 90 Presidential Plaza, Syracuse, NY 13202, USA2004 29 10 2004 4 17 17 2 4 2004 29 10 2004 Copyright ©2004 Haas; licensee BioMed Central Ltd.2004Haas; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background
Chronic headaches from head trauma and whiplash injury are well-known and common, but chronic headaches from other sorts of physical traumas are not recognized.
Methods
Specific information was obtained from the medical records of 15 consecutive patients with chronic headaches related to physically injurious traumatic events that did not include either head trauma or whiplash injury. The events and the physical injuries produced by them were noted. The headaches' development, characteristics, duration, frequency, and accompaniments were recorded, as were the patients' use of pain-alleviative drugs. From this latter information, the headaches were classified by the diagnostic criteria of the International Headache Society as though they were naturally-occurring headaches. The presence of other post-traumatic symptoms and litigation were also recorded.
Results
The intervals between the events and the onset of the headaches resembled those between head traumas or whiplash injuries and their subsequent headaches. The headaches themselves were, as a group, similar to those after head trauma and whiplash injury. Thirteen of the patients had chronic tension-type headache, two had migraine. The sustained bodily injuries were trivial or unidentifiable in nine patients. Fabrication of symptoms for financial remuneration was not evident in these patients of whom seven were not even seeking payments of any kind.
Conclusions
This study suggests that these hitherto unrecognized post-traumatic headaches constitute a class of headaches characterized by a relation to traumatic events affecting the body but not including head or whiplash traumas. The bodily injuries per se can be discounted as the cause of the headaches. So can fabrication of symptoms for financial remuneration. Altered mental states, not systematically evaluated here, were a possible cause of the headaches. The overall resemblance of these headaches to the headaches after head or whiplash traumas implies that these latter two headache types may likewise not be products of structural injuries.
==== Body
Background
Chronic headaches from head trauma [1-4] and whiplash injury [5-8] are well-known and common. Together with their accompanying symptoms they are usually referred to as "postconcussive (or post-traumatic) syndrome" and "whiplash syndrome," respectively. Chronic headaches from other sorts of physical traumas are not recognized, but a few authors have mentioned them [9-11]. Parker [9] reported that, among 750 consecutive litigants seen by him for industrial and motor-vehicle accidents, 53% of those who had sustained neither a head nor whiplash injury complained of headache. Duckro et al. [10] were perplexed about their patients with such headaches: "Even more puzzling, from a diagnostic standpoint, are those patients who suffer persistent exacerbation of headache following physical trauma not involving the head or neck." They also noted that "...in our experience at a university-based clinic for chronic head and neck pain, the problem is not uncommon." The present paper details a series of chronic headaches that began soon after and in apparent relation to various physical traumas that did not include head trauma or whiplash injury. It describes the traumatic events, their temporal relation to the headaches, the features of the headaches, and the symptoms associated with the headaches. It also analyses the relation of the headaches to the events and compares the headaches with those following head trauma and whiplash injuries.
Methods
The 15 patients in this series were all those seen by the author from February 1997 through December 2001 for chronic headaches apparently related to traumatic events that involved the body but did not cause either head trauma or whiplash injury (a term denoting a painful cervical injury, typically occurring during a motor-vehicle collision and generally considered to be a cervical sprain) [7,12,13]. The patients themselves attributed their headaches to the events and so did most of their referring physicians. Detailed accounts of the events and the headaches had been obtained from the patients, family members sometimes, and medical records sent by the referring physicians. Before their events, none of the patients had more than minor occasional headaches.
Detailed information had been systematically collected on the headaches' characteristics (intensity, location, quality, and response to physical activities), duration and frequency (when episodic), and accompaniments (nausea, vomiting, hypersensitivity to light and noise), and on the patients' use of pain-alleviative drugs. With this information, the headaches were classified by the 1988 diagnostic criteria of the International Headache Society (IHS) [14], extant during this review, as though they were naturally-occurring headaches [15]. Information on the presence of other post-traumatic symptoms and litigation had also been recorded, but less systematically than that for the headaches.
Results
The physical traumas
The physical traumas were very diverse (Table 1). None occurred in motor-vehicle accidents. One patient had his face cut by a metal sign as he fell, but he was not stunned by this contact. Two patients lost consciousness briefly, one from syncope after giving blood, and the other from a high-voltage electrical current. Six events produced identifiable damage to a part of the body (patients 1, 2, 9, 10, 13, 15). All but two of the events (patients 8, 11) were sudden and unexpected.
Table 1 Patients with headaches from diverse traumatic events.
Patient G/Age Traumatic event Headache onset Headache class*
1 M/38 He slid down a roof into a wall and broke a foot and back bone. 4 weeks 2.2
2 F/50 A falling mass of snow buried her and fractured 3 back vertebrae. 2 weeks 2.2
3 F/36 She fainted after donating blood. 1 day 2.2
4 M/41 He was knocked into his car when another car hit his shopping cart. Immediate 2.2
5 M/46 He hurt his neck while yanking a wrench on a rusted bolt. 5 days 2.2
6 M/52 He heard his neck "pop" while lifting a cargo door. Hours 2.2
7 M/28 He slipped off a plank while carrying buckets but landed on his feet. Days 2.2
8 M/43 He had a bone-marrow transplant for a lymphoma. Days 2.2
9 M/48 His face was badly cut on a metal sign when he fell while walking. Hours 2.2
10 F/56 Her scalp was burned by a hair-curling chemical at a beauty parlor. 1 day 1.1
11 M/40 He became angry at the urologist right after his cystoscopy. 1 hour 2.2
12 F/47 Her snowmobile was mistakenly backed up 5 feet into a tree. Days 2.2
13 M/53 He fell sideways in a beachchair and fractured his ribs on a rock. 2 days 2.2
14 F/33 She was exposed to an acute Freon-gas leak at work. Minutes 1.1
15 M/43 A high-voltage electrical injury led to amputations of both arms. 4 weeks 2.2
*IHS codes: 2.2 = chronic tension-type headache, 1.1 = migraine without aura
Intervals between traumatic events and headaches
The reported intervals between the traumatic events and the headaches' onsets are listed in Table 1. These intervals can be placed into four groups: within minutes (patients 4, 14), within hours (patients 6, 9, 11), within days (patients 3, 5, 7, 8, 10, 12, 13), and within weeks (patients 1, 2, 15). The interval for the one patient who did not have an abrupt traumatic event (patient 8) was arbitrarily set as the interval between his hospital discharge and headache onset.
The headaches
Thirteen of the 15 patients had serious continuous headaches of varying intensity. Among these, 11 patients had headaches that met the IHS's diagnostic criteria for chronic tension-type headache [14] fully, while the other 2 patients' headaches met all but one of the criteria. The 2 patients without continuous headaches had frequent headaches that met the criteria for migraine without aura, and one of them also had headaches fitting the criteria for migraine with aura [14]. Table 1 lists each patient's headache class.
After the completion of this study, the IHS added a new primary headache class called new daily-persistent headache, which has the same features as chronic tension-type headache, but is distinguished from it by becoming daily and unremitting within three days of its onset [16]. Many, if not all, of the 13 patients in this study with continuous headaches may have had their headaches develop in this way, but as information about this was not specifically sought and as this distinction is of unproven merit, chronic tension-type headache is used herein.
The IHS's criteria for chronic tension-type headache and migraine without aura [14] are listed below in abbreviated form.
Chronic tension-type headache
Headache frequency more than 15 days/month.
At least 2 of the following pain characteristics:
1. Pressing quality
2. Mild or moderate severity
3. Bilateral location
4. No aggravation by routine physical activity
Both of the following:
1. No vomiting
2. No more than one of the following: Nausea, photophobia or phonophobia
Migraine without aura
Headaches last 4 to 72 hours.
Headache has at least 2 of the following characteristics:
1. Unilateral location
2. Pulsating quality
3. Moderate or severe intensity
4. Aggravation by routine physical activity
During headache at least one of the following:
1. Nausea and/or vomiting
2. Photophobia and phonophobia
Examples of chronic tension-type headache after traumatic events
Patient 5
This 46-year-old man was seen in 1999 for headache that began five days after an "injury" at work five months earlier. Ten minutes after forcefully yanking a wrench to loosen a rusted bolt, he felt pain in his neck and right shoulder. An urgent-care facility prescribed analgesics after taking (unremarkable) cervical radiographs. This pain disappeared before I saw him. The headache was a continuous dull to moderate ache mostly in the right cranium. It was unaffected by physical activities or neck movements, and was not nauseating. Neurological examinations were normal. Pressure on his posterolateral neck was not painful. A cranial CT was normal. His only other symptom was insomnia. Analgesics, taken just a few days per week, had little effect. Amitriptyline lessened the headache's intensity enough for him to return to work.
Patient 9
This 48-year-old man with a neurologic impairment of gait was seen in 1999 for continuous headache that began a few hours after he fell while walking two months earlier and cut his face on the edge of a metal sign. His head was not struck and he was not stunned. He bled profusely from the laceration, which was closed with 26 stitches in the emergency room. His neurologist detected no new neurologic findings and a cranial CT was normal. His headache was a non-nauseating steady ache of mild to moderate intensity in his forehead, unaffected by exercises, brightness, or noise. Non-prescription analgesics and opioids had been ineffective and discontinued.
Patient 11
This 40-year-old man was seen in 2000 for a continuous headache that began 10 months earlier soon after he awoke from anesthesia for a cystoscopy. When the urologist did not report the (negative) result of the procedure to him in the recovery suite, the patient became visibly angry and soon complained about his treatment to the health-care facility. His anger persisted and was expressed at his consultation. The headache was a steady non-nauseating pain that fluctuated from dull to moderate intensity at the vertex, temples, and posterior neck, and was unaffected by physical activities, brightness, or noise. He worked despite it and no longer took analgesics. A cranial MRI had been normal. A trial of amitriptyline had been unsuccessful. At his last report a month later he reported improvement on buspirone.
Patient 12
This 47-year-old woman was seen in 2001 for symptoms that she attributed to a snowmobile accident seven weeks earlier. She was seated behind the driver when he mistakenly shifted into reverse sending the machine backwards five feet into a tree. At impact, he fell on top of her without hurting her or himself. She recalled no impact of her helmet against the tree or the snowmobile. She felt no pain, but was upset and asked to be taken home. On the next day, her neck ached. Cervical radiographs taken eight days after the accident were normal. Two days later, she developed severe headache, nausea, dizziness, and confusion. A cranial CT taken later that day was normal. Subsequent MRIs of her head and neck were normal.
Headache soon became her most prominent pain. It was a continuous pressing ache of mild to moderate intensity in her temples and orbits, unaffected by physical activities. It was sometimes nauseating, without emesis. Brightness and noise bothered her. She also complained of dizziness and impaired thinking and memory. Infrequent doses of analgesics were not beneficial. She was unable to work. Neurologic examinations were normal. Preventive medications were refused. When seen next, by a colleague, two months after her visit with me, the headache and dizziness had lessened considerably, but her thinking difficulties remained disabling. Four months later, she reported continuing improvement.
Patient 13
This 53-year-old man was seen in 2001 for a headache of six-months duration that began two days after he struck the right side of his chest against a rock without striking his head when he toppled over in a beach chair. His chest pain was extreme. He obtained a prescription for hydrocodone/acetaminophen tablets that day, but discontinued taking them after several doses because of side effects. Coughing, sneezing, and lying on his right side were excruciating. His physician diagnosed a fractured rib. Two days after the injury, he returned to work despite his chest pain and new headache. The chest pain disappeared in a few weeks, but the headache persisted. It was a continuous, non-nauseating, bifrontal "tightness" of dull to moderate intensity, unaffected by mild physical activities, brightness or noise. Amitriptyline and propranolol had been ineffective and produced side effects. When I saw him, he was taking only occasional doses of non-prescription analgesics. Neurological examinations and cranial MRI were normal. He declined other medications. Seven months later, he reported that his headache persisted.
Patient 15
This 43-year-old man was seen in 2001 for a continuous headache that he first became aware of soon after discharge from hospital, in 1997, where he had undergone 25 days of intensive treatment for a high-voltage electrical injury that had necessitated amputation of his arms. Unconsciousness had been instantaneous, but brief, and post-traumatic amnesia lasted about ten minutes.
His headache was a bi-occipital, non-pressing ache, usually of mild to moderate intensity, and only occasionally severe enough to force him to cease physical activities. Then it was nauseating, without emesis. Some loud noises, but not brightness, seemed to intensify it. He had been getting slight relief from a few doses of ibuprofen per week, but had received no preventive medications.
His cognitive and emotional states and cranial MRI were unremarkable. He had been provided with prosthetic upper limbs with grasping hands. Amitriptyline decreased the headache slightly. The addition of progressively larger doses of dextroamphetamine limited the headache to only a few days per month.
Other post-traumatic symptoms
Thirteen of the fifteen patients had, besides headache, at least one other post-traumatic symptom of the type commonly seen after head trauma [3,17,18]. Seven patients had either three or four symptoms. The number of patients having each of the symptoms is listed in Table 2. Only patient 2 had any of the symptoms included in the syndrome of post-traumatic stress disorder [19].
Table 2 Other post-traumatic symptoms.
Symptoms Number of patients with symptom
Insomnia 14
Decreased concentration 7
Decreased memory 5
Dizziness 5
Mild depression 3
Anxiety 3
Tiredness 3
Litigation/compensation
Seven patients (numbers 3, 5, 9, 10, 11, 12, 13) were not seeking and could not seek financial compensation for their headaches. Three patients (numbers 6, 7, 14) were receiving Workers' Compensation (WC) payments for their headaches and other symptoms. One patient (number 15) was receiving both WC and Social Security (SS) disability payments. One other receiving WC payments was also seeking SS disability payments (number 1). One patient (number 8) was seeking SS payments only. One patient (number 2) was litigating for payment of medical bills. Only one patient (number 4) had pursued a lawsuit for monetary compensation for the symptoms, but he continued to experience headaches even after receiving the award.
Discussion
This report has presented evidence suggesting that traumatic accidents and other events without head trauma or whiplash injury but with other physical effects can induce chronic headaches. Such headaches have barely been alluded to before (see Background). The 15 patients presented here blamed their headaches and other symptoms on their traumas and are supported in this by the juxtaposition of their headaches to the traumas. Twelve of the 15 patients developed their headaches minutes, hours, or a few days afterwards (Table 1). These intervals are well within those deemed necessary by the IHS for connecting both head trauma and whiplash injury to subsequent headaches. Their 1988 classification [14] lists "less than 14 days" but their 2004 revision [16] lists "within 7 days" as the maximum interval acceptable for relating chronic headaches to these injuries. The other 3 patients reported headache onsets of two weeks, four weeks, and four weeks. Nevertheless, they are included in this series because the apparent connection of their headaches to their serious accidents outweighs the still non-evidential latent-period requirement chosen by the IHS. In support of this inclusion is evidence suggesting that genuine post-traumatic symptoms can develop as late as three months after head trauma [11,20] and that chronic headaches after whiplash injuries may not appear for weeks or even months [21].
Although the headaches in this study were temporally related to events involving the body, they were unlikely to have been caused by the bodily injuries themselves, for there is no conceptual link between injuries such as a facial laceration or a fractured rib and chronic headaches. Moreover, nine patients had either trivial or unidentifiable injuries (Table 1, patients 3, 4, 5, 6, 7, 8, 11, 12, 14). Fabrication of symptoms for financial remuneration was not evident in this series, in which seven patients were not even seeking payments of any kind. If these headaches can not be attributed to bodily injury, then the symptoms would appear to be of psychological origin, as they are after some other sorts of traumatic events, such as those that set off chronic "post-traumatic stress disorder" (PTSD) [19]. Traumatic psychological symptoms can include headache. It has been reported, for example, to accompany the characteristic symptoms of PTSD [22], and it was the most prominent post-traumatic symptom in a "mass psychogenic illness" induced by false perceptions of exposure to toxic fumes at a school [23]. In this study the psychological states of the patients were not investigated (though they were not ignored), because the study's purpose was to analyze the traumatic events, the headaches following them, and the relationship between the two. Thus, this study can not present positive evidence for a psychological basis of the headaches. The headaches were not, however, related to PTSD, since only one patient had (some) symptoms of this condition.
Both head-trauma and whiplash headaches form clinical classes based on their preceding traumas, but the headaches of the present series have no single trauma to join them. They were, however, all related to acute and unexpected (with one exception) traumatic events that affected the patients physically. Hence, they could be designated traumatic-event headaches. Recognition of this class of post-traumatic headaches would link heretofore puzzling individual phenomena (see Background) and thereby foster their investigation.
The headaches themselves were serious chronic headaches. They had the features of continuous chronic tension-type headaches in 13 patients and frequent migraines in the other two [14,16]. This distribution of headache types is similar to that of the chronic post-traumatic headaches after head-trauma and whiplash injury (excepting the advocated cervical syndrome from some whiplash injuries [24]). In studies using the 1988 IHS diagnostic criteria, head-trauma headaches were 75% chronic tension-type, 21% migraine, and 4% unclassifiable [15], whiplash headaches were 74% tension-type, 15% migraine, and 11% cervicogenic [6], and "cranio-cervical acceleration/deceleration trauma" headaches (22% with head trauma, 78% with whiplash) were 37% tension-type, 27% migraine, 18% cervicogenic, and 18% unclassifiable [25]. In addition to the headaches, the present series of patients suffered other symptoms like those in the post-concussion [3,17,18] and whiplash syndromes [5,26] (Table 2).
The apparent overall likeness of the traumatic-event headaches to those after head and whiplash traumas suggests a pathogenic link between them. Such a link would be discredited if, as many believe, headaches after head and whiplash traumas are due to structural injuries to the brain and neck, respectively [21,24,27-29]. But such injuries have not been seen in cranial or cervical MRIs in the great majority of cases and whatever injuries may be present have an uncertain relation to the chronic headaches [7,28,30,31]. Moreover, certain evidence seems incompatible with the presence of symptomatic injuries. Firstly, the incidence of chronic symptoms after head trauma [2,3,28] or car crashes [32,33] does not increase in step with increasing degrees of trauma. Secondly, the development of chronic symptoms is dependent on the circumstances in which the trauma occurs. For example, whereas chronic headaches are common after head traumas at work, they are rare after head blows during sports [34-36]. Likewise, chronic whiplash symptoms have not been produced in volunteers subjected to rear-end car crashes [31,33] and they do not occur in demolition-derby drivers [37] and drivers or passengers subjected to rear-end crashes in certain countries where whiplash is not common knowledge [8]. This and additional evidence has led some authors to discount a role for cranial or cervical injuries in the development of chronic post-traumatic symptoms, offering instead explanations based on altered mental states [2,8,31,38,39]. These include, among others, neurotic reactions and culturally-related expectations of symptom development. The present study supports a mental origin from another perspective by showing that chronic post-traumatic headaches can be sequelae of traumatic events having neither head trauma nor whiplash injury.
Conclusions
This study indicates that chronic headaches similar to those after head trauma and whiplash injury can follow other types of acute and unexpected physically injurious traumatic events. The evidence suggests that these traumatic-event headaches are psychogenic. The occurrence of such headaches supports the concept that the chronic headaches after head trauma or whiplash injury may likewise not be products of structural injuries.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DCH was the sole investigator and author.
Pre-publication history
The pre-publication history for this paper can be accessed here:
http://www.biomedcentral.com/1471-2377/4/17/prepub
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| 15516263 | PMC529263 | CC BY | 2021-01-04 22:25:48 | no | BMC Neurol. 2004 Oct 29; 4:17 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-17 | oa_comm |
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BMC PediatrBMC Pediatrics1471-2431BioMed Central London 1471-2431-4-211549150010.1186/1471-2431-4-21Research ArticleParents' reported preference scores for childhood atopic dermatitis disease states Friedman Joëlle Y [email protected] Shelby D [email protected] Kevin P [email protected] Kristijan H [email protected] Emmanuel B [email protected] Kevin A [email protected] Center for Clinical and Genetic Economics, Duke Clinical Research Institute, Duke University Medical Center, PO Box 17969, Durham, NC 27715 USA2 Novartis Pharmaceuticals Corporation, East Hanover, NJ 07936 USA3 Duke Children's Primary Care, Department of Pediatrics, Box 3675, Duke University Medical Center, Durham, NC 27710 USA2004 18 10 2004 4 21 21 5 2 2004 18 10 2004 Copyright © 2004 Friedman et al; licensee BioMed Central Ltd.2004Friedman et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
We sought to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. We also evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.
Methods
Subjects were parents of children aged 3 months to 18 years. The sample was derived from the General Panel, Mommies Sub-Panel, and Chronic Illness Sub-Panel of Harris Interactive. Participants rated health scenarios for atopic dermatitis, asthma, and eyeglasses on a visual analog scale, imagining a child was experiencing the described state.
Results
A total of 3539 parents completed the survey. Twenty-nine percent had a child with a history of atopic dermatitis. Mean preference scores for atopic dermatitis were as follows: mild, 91 (95% confidence interval [CI], 90.7 to 91.5); mild/moderate, 84 (95%CI, 83.5 to 84.4); moderate, 73 (95%CI, 72.5 to 73.6); moderate/severe, 61 (95%CI, 60.6 to 61.8); severe, 49 (95% CI, 48.7 to 50.1); asthma, 58 (95%CI, 57.4 to 58.8); and eyeglasses, 87(95%CI, 86.3 to 87.4).
Conclusions
Parents perceive that atopic dermatitis has a negative effect on quality of life that increases with disease severity. Estimates of parents' preferences can provide physicians with insight into the value that parents place on their children's treatment and can be used to evaluate new medical therapies for atopic dermatitis.
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Background
Atopic dermatitis is the most common of childhood skin diseases, with a lifetime prevalence in children of 10% to 20% [1]. The disease is most common in industrialized countries and among Caucasians and Asians [2]. Annual total costs of treatment are estimated to range from $0.9 to 3.8 billion in the United States [3].
Atopic dermatitis can have a negative impact on quality of life by affecting psychosocial adjustment in children. Lapidus and Kerr [4] report that atopic dermatitis can cause embarrassment, disrupt sporting activities in older children, and interfere with employment opportunities among adults. The disease can also have a negative impact on families. Parents report feelings of guilt, exhaustion, frustration, and helplessness [4-7]. Atopic dermatitis can disrupt sleep in patients and their family members, and parents can miss work or avoid outside work altogether to care for a child with the disease [4,8]. Fivenson et al [9] found that 50% of the total burden of illness of atopic dermatitis is associated with lost productivity. Specifically, they found that days lost from work and nights of sleep lost were high among parents of young children with atopic dermatitis [9]. These stresses take additional tolls on familial relationships and are confounded in low-income families, who often have minimal access to social support mechanisms [4].
As the need to control increasing medical expenditures continues to mount, formal economic evaluations are taking on a prominent role in assessing the value of new medical therapies. To properly evaluate the impact of new therapies for atopic dermatitis, patients' health-related quality of life (HRQOL) must be considered. Although a number of quality-of-life evaluations have been conducted for adults and children affected with atopic dermatitis [4,7,9-12], quality-of-life adjustments in cost-utility analyses must be performed using preference weights. Preference weights represent summary measures of HRQOL associated with individual health states and are necessary to calculate quality-adjusted life-years (QALYs) for use in cost-utility analyses.
Although the prevalence of atopic dermatitis is highest in children, the existing literature on patient preferences for atopic dermatitis is limited to the adult population [13,14]. However, eliciting preferences from children may not be possible, because they lack the necessary language skills and cognitive abilities to interpret and respond to questions used to evaluate preferences. Evidence suggests that proxy reports by parents may provide valid estimates of HRQOL in children [15]. Therefore, our primary objective was to elicit preference weights from parents for health states corresponding to children with various levels of severity of atopic dermatitis. In addition, we evaluated the hypothesis that parents with children who had been diagnosed with atopic dermatitis would assign different preferences to the health state scenarios compared with parents who did not have a child with atopic dermatitis.
Methods
Preference assessment instrument
Patient preferences can be elicited using standard gamble or time-tradeoff or direct rating methods such as a visual analog scale. Because both the standard gamble and the time-tradeoff exercises involve choices between two alternatives that involve a chance of death or longevity, we believed it was unethical to ask parents to participate in such exercises when children were the subjects. Therefore, our choice for eliciting preferences was the rating scale.
We developed case scenarios for 5 levels of atopic dermatitis severity – mild, mild/moderate, moderate, moderate/severe, and severe. These severity levels were created by combining the characteristics of an Investigator Global Assessment (IGA) and the Eczema Area and Severity Index (EASI) score [16]. Each scenario included descriptions of erythema, infiltration and/or papulation, excoriation, and lichenification, as well as location of body area affected (Table 1). Efforts were made to ensure that the scenarios were descriptive, explicit, nonjudgmental, and targeted to an eighth-grade reading level. A medical artist developed images to depict the descriptions of atopic dermatitis. We also included 2 additional scenarios – one that described wearing glasses and another that described suffering from asthma – to compare the preferences for atopic dermatitis health states to nondermatological health states. A pediatrician and a pediatric dermatologist reviewed the descriptions and illustrations to ensure their validity and realism. The scenarios were revised based on their comments.
Table 1 Scenario descriptions
Severity Scenario
Mild • The area looks like a light pink or white, dusty rash.
• It is affecting the cheeks.
• It is rarely itchy and your child scratches it only a few (about 3) times a day.
• There are only a few (about 3) slightly bumpy areas.
• There is no oozing or crusting.
• The skin is not dry or leathery.
• Sleep is rarely disrupted by itching.
Mild/Moderate • The area looks like a light pink or white, dusty rash.
• It is affecting the cheeks and the chin.
• It is somewhat itchy and your child scratches it about 5 times a day.
• There are about 5 slightly bumpy areas.
• There is no oozing or crusting.
• The skin is not dry or leathery.
• Sleep is somewhat disrupted by itching. Your child loses about 15 minutes of sleep each night because of scratching.
Moderate • The area looks like a dark pink rash.
• It is affecting the cheeks, the chin and the inside of the elbows.
• It is moderately itchy and your child scratches it often (about 15 times) during the day.
• There are about 7 moderately bumpy areas.
• There is no oozing or crusting.
• The skin is not dry or leathery.
• Sleep is disrupted by itching. Your child loses about 1 hour of sleep each night because of scratching.
Moderate/Severe • The area looks like a dark pink rash.
• It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees.
• It is itchy and your child scratches it often (about 30 times) during the day.
• There are about 10 moderately bumpy areas.
• There is some light oozing or crusting in one area.
• The skin is not dry or leathery.
• Sleep is disrupted by itching. Your child loses about 2 hours of sleep each night because of scratching.
Severe • The area looks like a red rash.
• It is affecting the cheeks, the chin and the inside of the elbows, and the back of the knees, and the trunk of the body.
• It is very itchy and your child scratches it scratching continuously throughout the day.
• There are numerous bumpy areas.
• There is oozing or crusting in some areas.
• The skin is dry and leathery in some areas.
• Sleep is disrupted by itching. Your child loses about 3 hours of sleep each night because of scratching.
Using cognitive interview techniques, we pilot-tested the preference assessment instrument in a convenience sample of 20 parents of children who were being evaluated at a local children's primary care clinic to assess patients' understanding of the instrument and its instructions. The instrument was further revised based on the results of the pilot test.
Survey administration
A health state preference assessment asks subjects to make judgments regarding the value of particular health states [9]. Preferences for health states can be elicited from patients with disease (or their family members), from patients at risk for disease, or from the general public [17,18]. We elected to develop preferences from family members of children who currently had atopic dermatitis or were at risk for the disease. To obtain responses from a broad range of respondents in an efficient manner, we recruited participants over the Internet. The sample was derived from the General Panel, the Mommies Sub-Panel, and the Chronic Illness Sub-Panel of Harris Interactive (Rochester, NY), an international market research and consulting firm. The General Panel is a multimillion-member panel of respondents who register to participate in The Harris Poll online panel. The Mommies Sub-Panel is a subpanel of respondents with children aged up to 2 years. The Chronic Illness Sub-Panel identifies respondents (or household members) who have been diagnosed with at least 1 of more than 44 chronic medical conditions, including skin conditions. (The Mommies and Chronic Illness Sub-Panels are part of The Harris Poll online panel. Aside from parental and health status, their members do not differ systematically from members of the General Panel.).
Subjects were invited to participate in the survey from February 12 through 14, 2002, and were asked to register at a specific survey site. After consent was obtained online, subjects completed the survey. Respondents were offered the incentive of a chance to win one of five $100 prizes. Respondents had to be adults with children aged 3 months to 18 years in order to be included in the study. The study was approved by the institutional review board of Duke University Medical Center.
Clinical data were based on self-report and included information on diagnosis history and severity level. Specifically, respondents were asked if they had a child between the ages of 3 months and 18 years who had ever been diagnosed by a medical professional with atopic dermatitis. If they responded "yes," they were then asked to describe the child's atopic dermatitis at its worst point by selecting from the following response options: mild, mild/moderate, moderate, moderate/severe, severe.
For the preference assessment, each respondent was given the scenarios in the same order – mild through severe atopic dermatitis, asthma, and glasses. Subjects were instructed to indicate on a scale ranging from 100 (perfect health) to 0 (death) how good or bad they believed it would be to be a child experiencing the scenario depicted. Respondents recorded their values using a movable pointer on the scale. Respondents whose Internet browser software did not support the movable pointer entered their numerical responses manually into a required field. All 7 values were summarized at the end of the survey to allow respondents to review and, if desired, revise their ratings.
Data analysis
Descriptive statistics were used to describe the sample. Because subjects provided responses across severity levels, a repeated-measures analysis of variance was conducted using polynomial contrasts for the within-subjects (severity) effect. A P value of ≤ .05 was used as the criterion for statistical significance.
Results
Of the 28105 subjects contacted, 6131 (22%) responded. Of the 6131 respondents, 3539 (57.7%) met the inclusion criteria, consented to participate in the study, and completed the survey (Table 2). The mean age was 41 years; 93% of the subjects were women; and 90% were white. Nonresponders were similar to responders with respect to age, sex, and race/ethnicity. More than 98% of the sample had at least a high school education, with 83% of respondents completing at least some college courses. Overall, the sample reflected moderate- to high-income families, with 74% having an annual household income of at least $35000. Thirty percent of the parents had a child with atopic dermatitis, 55% had a child with asthma, and 46% had a child who wore glasses.
Table 2 Subject characteristics
Characteristic Responders (n = 3539) Nonresponders (n = 21974)
Age
Mean (SD) 40.6 (6.2) 41.0 (6.4)
Range 18–64 19–76
Female sex 3298 (93.2) 20263 (92.2)
Race/ethnicity*
White 3045 (89.7) 18507 (84.2)
Black/African-American 136 (4.0) 1098 (5.0)
Hispanic 79 (2.3) 703 (3.2)
Asian/Pacific Islander 21 (0.6) 171 (0.8)
Native American 38 (1.1) 316 (1.4)
Mixed/other 77 (2.3) 520 (2.4)
Unknown 0 559 (2.5)
Declined to answer 0 100 (0.5)
Education level†
Did not complete high school 55 (1.6) 706 (3.2)
High school degree 555 (15.7) 4366 (19.9)
Some college 1391 (39.3) 9074 (41.3)
College degree 991 (28.0) 5179 (23.6)
Some graduate school or degree 543 (15.4) 2568 (11.7)
Unknown 0 81 (0.4)
Annual household income‡
<$15000 107 (3.5) 868 (3.9)
$15000$24999 262 (8.6) 1983 (9.0)
$25000–$34999 410 (13.4) 2945 (13.4)
$35000–$49999 665 (21.7) 4177 (19.0)
$50000–$74999 806 (26.3) 5065 (23.0)
$75000–$99999 406 (13.3) 2003 (9.1)
$100000–$149999 220 (7.2) 1442 (6.6)
$150000–$199999 85 (2.8) 273 (1.2)
$200000–$249999 61 (2.0) 117 (0.5)
≥ $250000 16 (0.5) 103 (0.5)
Declined to answer 24 (0.8) 1 (0.0)
Unknown 0 2997 (13.6)
Employment status §
Employed full-time 1766 (80.0) 9070 (42.2)
Employed part-time 554 (15.7) 2355 (11.0)
Self-employed 281 (7.9) 1588 (7.4)
Not employed but looking for work 119 (3.4) 852 (4.0)
Not employed and not looking for work 70 (2.0) 588 (2.7)
Retired 40 (1.1) 258 (1.2)
Student 149 (4.2) 3071 (14.3)
Homemaker 974 (27.5) 3566 (16.6)
Disabled 0 138 (0.6)
Not sure 4 (0.1) 0
Declined to answer 4 (0.1) 0
Not applicable 0 0
Number of children in household
Mean (SD) 1.9 (1.0) 1.9 (1.2)
Range 0–9 0–15
Country of residence
Australia 2 (0.1) --
Canada 2 (0.1) --
United States 3535 (99.9) --
Values are reported as number (percentage) unless otherwise indicated.
*There were 143 missing responses for this variable.
† There were 4 missing responses for this variable.
‡ There were 477 missing responses for this variable.
§ Responses sum to more than 100% because respondents could select more than one answer. Nonresponders did not have this option. There were 488 missing responses for this variable.
Table 3 displays the characteristics of the children's atopic dermatitis (n = 1017). Seventy-eight percent (n = 806) of children were diagnosed more than a year ago. Sixteen percent (n = 160) of the sample described their child's atopic dermatitis as mild and 8% (n = 78) described their child's atopic dermatitis as severe. Thirty-five percent (n = 357) of the sample reported their child's atopic dermatitis under limited control or uncontrolled.
Table 3 Disease characteristics for children with atopic dermatitis
Characteristic Subjects (n = 1017)
Time of diagnosis
≤ 1 month ago 119 (11.7)
7 months to <12 months ago 95 (9.3)
1 year to 5 years ago 467 (45.9)
> 5 years ago 336 (33.0)
Disease severity
Mild 160 (15.7)
Mild to Moderate 236 (23.2)
Moderate 277 (27.2)
Moderate to Severe 266 (26.2)
Severe 78 (7.7)
How well controlled?
Complete 206 (20.3)
Good control 409 (40.2)
Limited control 330 (32.4)
Uncontrolled 27 (2.6)
No treatment 45 (4.4)
Values are reported as number (percentage) unless otherwise indicated.
The mean values for all participants are presented in Table 4. Among the atopic dermatitis health states, there was a progressive decline in respondents' preferences, with the mildest state receiving the highest mean preference score and the severe state receiving the lowest mean preference score. On average, preferences for asthma were higher than for severe atopic dermatitis but lower than moderate/severe atopic dermatitis. Not surprisingly, wearing glasses received a higher preference value than suffering from asthma. Average preference values for the glasses health state were ranked between the mild and mild/moderate atopic dermatitis health states.
Table 4 Average health state preference values
Health State Mean Median 95% Confidence Interval
Mild atopic dermatitis 91.1 95.0 90.7–91.5
Mild/moderate atopic dermatitis 83.9 88.0 83.5–84.4
Moderate atopic dermatitis 73.1 76.0 72.5–73.6
Moderate/severe atopic dermatitis 61.2 63.0 60.6–61.8
Severe atopic dermatitis 49.4 50.0 48.7–50.1
Asthma 58.1 60.0 57.4–58.8
Wearing eyeglasses 86.8 94.0 86.3–87.4
There was a significant effect of severity (F4,3391 = 3065.66; P = .0001). The linear effect of severity (F1,3394 = 11454.90; P < .0001) indicated that preference ratings significantly decreased as the severity of the health states increased (Figure 1). Furthermore, there was a significant main effect for preferences reported by parents of children with atopic dermatitis as compared to parents of children without atopic dermatitis (F1,3394 = 8.10; P = .0045). Across all health states, parents of children with atopic dermatitis gave a slightly higher mean preference (72.85 [SD, 13.50]) compared to parents whose children did not have atopic dermatitis (71.34 [SD, 14.19]) (Figure 1).
Figure 1 Comparison of overall ratings, stratified by children with atopic dermatitis and children without atopic dermatitis
There was no significant severity by parent group interaction (F4,3391 = 1.21; P = .31), indicating that the differences across health states were the same for both parent groups.
Discussion
Our study evaluated preferences for 5 health states for atopic dermatitis. The aggregate values for each health state may be used for computing QALYs for new therapies that treat atopic dermatitis or can be used to help physicians make more informed decisions by considering parents' perceptions of atopic dermatitis measured on a continuum from perfect health to death. The differences in average values across the 5 health states were generally consistent, with mild at 91, mild/moderate at 84, moderate at 73, moderate/severe at 61, and severe at 49.
Lundberg and colleagues [13] found that the mean health-state utility using a rating scale for patients with atopic dermatitis was 77, a value only slightly higher than our average health-state utility of 73. This small discrepancy could be explained by several factors. First, Lundberg et al [13] asked adult patients to provide ratings for themselves, whereas our study asked parents to provide ratings for children. Parents might feel that a given health state is worse for their children than it would be for themselves. Second, Lundberg et al [13] asked patients to provide a rating for their current health state, whereas our study asked parents to assign utilities to 5 varying levels of severity of atopic dermatitis. The average severity level of Lundberg et al's [13] sample might have been slightly lower than the average severity among our 5 health states, making the mean preference value slightly higher. By obtaining preferences for varying levels of severity, our results have greater applicability in various types of models for decision making.
Although preference ratings were systematically higher among parents of children with atopic dermatitis than among parents whose children did not have atopic dermatitis, the magnitude of the difference was small (difference = 1.5). Research on adults who rate the health states of other adults has suggested that people who have experienced a particular health state are more likely to assign a higher value to it than others who are asked to imagine the health state [19-21]. The lack of a larger difference between the parent groups in our study could indicate that parents evaluate health states of children the same, regardless of whose children they are, because of a general concern for all children. Regardless of the reason, these results have important implications for the use of community-based preference weights, as opposed to patient-based (or parent-based) weights, in preference-weighted decision analyses. For this limited therapeutic domain, our study shows that parents in the general community would supply approximately the same preferences as parents whose children suffer the condition under study. Future research should consider whether this consistency holds for other serious childhood diseases, such as pediatric cancer.
Our study has several limitations. First, by using the Internet, respondents did not have an opportunity to ask questions if they did not understand what they were being asked to do. However, we feel that this limitation was negligible, because the instrument was pilot-tested using in-person interviews, and the very large majority of responses were ranked appropriately (eg, mild ranked higher than severe). Secondly, there may be a sample bias in using the Harris Interactive database. Our sample reflected a predominately white, female cohort from a high socioeconomic class and may present generalizability issues. However, we received responses from 351 (14%) nonwhite respondents and 779 (22%) responses from participants with an annual household income of less than $35000, providing a sufficient number of responses to test for differences by race/ethnicity and income level. Further research will be needed to corroborate our findings using a population-based sample. Third, since these data are self-reported, some parents may have misclassified their children as having or not having atopic dermatitis, potentially biasing our results. Fourth, it is unclear whether physician assessment of severity would correspond with the severity levels that we assigned to the health states. However, physicians could evaluate the descriptions provided to judge whether their assessments are consistent. Finally, people enrolled in the Harris Interactive database are computer users who may be more motivated to participate in a survey than the general population. It is unclear how or in what direction these sample biases might affect the results of our analyses.
We had an apparent response rate of 22%. Harris Interactive generally achieves a 15% to 20% response rate when using the Chronic Illness Sub-Panel. While our response rate was higher than Harris' average response rate, possible reasons for why the response was low could be attributed to the nature of the study design. First, because subjects were contacted by e-mail, it is possible that some subjects did not open the e-mail message until after the survey deadline. Second, Harris Interactive panel members agree to be notified about survey opportunities, but do not agree to participate in each survey. Since the characteristics of responders and nonresponders did not differ, we have no reason to believe that nonresponse bias is exerting a substantial influence on our results.
Conclusions
The results of this analysis clarify the values that parents of children with atopic dermatitis assign to different atopic dermatitis health states. These assigned values, relative to the comparison states, clearly demonstrate the perceived burden of atopic dermatitis by parents of children suffering from the disease. Understanding the preferences for atopic dermatitis can provide physicians insight into the value that parents place on treatments for their child's disease and in evaluating the cost-effectiveness of therapies for atopic dermatitis.
Competing interests
This study was supported by a research agreement between Duke University Medical Center and the Novartis Pharmaceuticals Corporation, East Hanover, NJ, which manufactures a cytokine inhibitor for the treatment of atopic dermatitis.
KPW and KAS have received monetary compensation for consultancies, and EBW and KAS have received research grants, from Novartis. KHK is an employee of Novartis.
Authors' contributions
JYF conceived of and designed the study, performed the statistical analysis, interpreted the data, and drafted the manuscript. SDR conceived of and designed the study and assisted in interpretation of the data and drafting of the manuscript. KPW assisted in interpretation of the data and drafting of the manuscript. EBW and KHK conceived of and designed the study and assisted in drafting of the manuscript. KAS conceived of and designed the study, assisted in drafting of the manuscript, and obtained funding. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors thank Damon Seils for editorial assistance and manuscript preparation.
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Whalley D McKenna SP Dewar AL Erdman RA Kohlmann T Niero M Cook SA Crickx B Herdman MJ Frech F Van Assche D A new instrument for assessing quality of life in atopic dermatitis: international development of the Quality of Life Index for Atopic Dermatitis (QoLIAD) Br J Dermatol 2004 150 274 283 14996098 10.1111/j.1365-2133.2004.05783.x
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| 15491500 | PMC529264 | CC BY | 2021-01-04 16:31:00 | no | BMC Pediatr. 2004 Oct 18; 4:21 | utf-8 | BMC Pediatr | 2,004 | 10.1186/1471-2431-4-21 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-341550423310.1186/1471-244X-4-34Research ArticlePsychological trauma and evidence for enhanced vulnerability for posttraumatic stress disorder through previous trauma among West Nile refugees Neuner Frank [email protected] Maggie [email protected] Unni [email protected] Christine [email protected] Christina [email protected] Thomas [email protected] Department of Psychology, University of Konstanz and Center for Psychiatry Reichenau, D-78457 Konstanz, Germany2 vivo, Casella Postale no.17, Castelplanio Stazione, I-60032 Ancona, Italy3 Médecins sans Frontières, PO Box 10014, 1001 EA Amsterdam, The Netherlands4 Department of Family Social Science, University of Minnesota, St. Paul, MN 55108, USA2004 25 10 2004 4 34 34 4 2 2004 25 10 2004 Copyright © 2004 Neuner et al; licensee BioMed Central Ltd.2004Neuner et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Political instability and the civil war in Southern Sudan have resulted in numerous atrocities, mass violence, and forced migration for vast parts of the civilian population in the West Nile region. High exposure to traumatic experiences has been particularly prominent in the Ugandan and Sudanese of the West Nile Region, representing an indication of the psychological strain posed by years of armed conflict.
Methods
In this study the impact of traumatic events on the prevalence and severity of posttraumatic stress disorder (PTSD) in a random sample of 3.339 Ugandan nationals, Sudanese nationals, and Sudanese refugees (1.831 households) of the West Nile region is assessed.
Results
Results show a positive correlation between the number of traumatic events and the number of endorsed PTSD symptoms. Of the 58 respondents who experienced the greatest number of traumatizing experiences, all reported symptoms which met the DSM-IV criteria for PTSD.
Conclusions
There is a clear dose-effect relationship between traumatic exposure and PTSD in the studied populations with high levels of traumatic events. In this context, it is probable that any individual could develop PTSD regardless of other risk-factors once the trauma load reaches a certain threshold.
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Background
The debate about the impact of traumatic life events on psychiatric disorders has a long tradition in psychiatry. The introduction of posttraumatic stress disorder (PTSD) into the Diagnostic and Statistical Manual of Mental Disorders (DSM-III [1]) manifested the general recognition that a chronic condition consisting of characteristic symptoms including involuntary intrusions of the past, avoidance behavior and a condition of general hyperarrousal can be caused by traumatic exposure and must be viewed as mental disorder. Consequently, the original conceptualization of PTSD was based on the implicit assumption that the traumatic event is the main agent for the development of PTSD [2]. The initial idea was that traumatic events could cause PTSD in anyone regardless of pre-trauma vulnerability.
Contrary to this assumption, the following research showed that the development of chronic PTSD is rather the exception than the rule after the experience of a traumatic event. Community studies in the US showed that whereas more than 50% of the population reported the experience of a traumatic event, the prevalence of PTSD was not higher than 7.8% [3]. Among the different events studied, rape seemed to be the most adverse experience, with about 50% of victims developing chronic PTSD. But even studies that researched PTSD in those who experienced events considered to be most adverse, like torture in prison, found PTSD prevalence rates under 50% [4]. The realization that traumatic exposure is not a sufficient determinant of PTSD has stimulated vast research into risk and protection factors for the development of PTSD [5,6]. These studies show that pre-trauma developmental vulnerability (adverse childhood, psychiatric history, etc.), peri-traumatic factors (like peri-traumatic dissociation) [6], posttraumatic factors (like social support) as well as genetic factors [7], mediate the development of PTSD, although effect sizes were generally small.
A popular and intuitively plausible assumption in this context is the dose-response model of PTSD. This hypothesis predicts that the probability for the development of PTSD after the experience of a traumatic event mainly depends on the severity of trauma exposure. Some studies tried to test this hypothesis by relating the objective severity of the traumatic event to symptoms of PTSD. However, the empirical evidence for this model is scarce, with some findings supporting this hypothesis but many failing to confirm a relationship with meaningful effect sizes [8,9].
The probability of detecting a relationship between trauma exposure and PTSD depends on the range and variance of traumatic exposure that is present in the population studied. Studies investigating the relationship between the objective severity of single events and PTSD are restricted to a narrow variance of traumatic exposure. Community studies that assess trauma exposure across different types of traumatic events should be more adequate to examine a dose-effect hypothesis. From a worldwide perspective, even community studies in industrialized countries are restricted to a relatively narrow range of trauma exposure. In contrast, community studies in civil populations affected by war enable the examination of a much wider range of traumatic exposure. These populations present a continuum of subjects ranging from individuals without any history of traumatic events to victims with a history of high numbers of severe events that are rarely to be found in communities without a history of war. Studying a community sample of Cambodian refugees who had fled the Pol Pot regime, Mollica [10] actually confirmed a clear linear relationship between the number of traumatic events and symptoms of PTSD and depression. Other studies with refugee populations are in line with this result [11-14]. These studies suggests a specification of the dose-response model; i.e., that it is not the severity of a single traumatic event that is linearly related to symptoms of PTSD, but the severity of previous cumulative trauma exposure.
Consequently, it can be hypothesized that each individual who has experienced or is experiencing traumatic events will develop PTSD after reaching a certain threshold of traumatic exposure. As this threshold is probably very high, a large number of subjects exposed to a large variance of traumatic events is necessary to test this hypothesis. We examined the dose-response relationship in the context of a large survey in the West-Nile regions of Sudan and Uganda. The study included Ugandan nationals with a quite peaceful development in the last decade, as well as Sudanese nationals living in the Southern Sudan war region and Sudanese refugees who had fled to Uganda. Among these groups we expected a sufficient variance of traumatic exposure to test for the specified dose-response hypothesis, including an adequate number of subjects who had to experience a series of extremely severe traumatic events. Cumulative trauma exposure was estimated by assessing the number of different traumatic event types experienced or witnessed so far. We considered this measurement to be more reliable than assessing the frequency of traumatic events as many survivors of civil wars reported countless exposures to specific traumatic events. To examine the impact of recent traumatic exposure, we also assessed the traumatic event types experienced or witnessed in the last year.
Methods
As part of a study designed to better understand the impact of forced migration on fertility, mortality, violence and traumatic stress among Sudanese nationals living in southern Sudan and Ugandan nationals and Sudanese refugees living in northern Uganda, we interviewed 3371 individuals from 1842 households in the Ugandan and Sudanese populations in the West Nile. Interviews were structured and were administered in the native languages of Lugbara or Juba Arabic. The study's design involved a multi-stage sampling design.
The full training of the interviewers took two months. The project objectives and the rationale behind the structure of the survey instrument as well as that of each question in the questionnaire were discussed in detail. Great attention was also paid to issues such as initial contacts, maintaining a professional attitude while in the field, avoiding influencing the respondent, and reducing interviewer and courtesy biases. The importance of collecting information by means of standardized questions so that the same question was asked to all respondents is stressed and questioning and probing skills were developed. Supervisors were instructed separately on data collection guidelines, their roles and their responsibility to ensure data quality. Keeping in mind the sensitive nature of some of the questions regarding violence and trauma and the fact that the team members were from the study population and probably had experiences similar to the respondents, a workshop on sexual and gender-based-violence was conducted by a consultant to the UNICEF office in Kampala, before the survey. The aim of this workshop was to increase awareness and sensitivity of the team towards respondents and their experiences. Another consultant to the project reviewed the team's interviewing skills and the project's data quality control measures just before the start of the survey. Problem areas were identified and remedied.
Data were complete and analyzed for N = 3179 respondents: 2,540 (75 %) of the respondents were women (15–50 years of age) and 831 (25%) were men (20–55 years of age). Details of the sampling, translation and assessment procedures, as well as the socio-demographic characteristics of the populations, have been described elsewhere [15].
Traumatic events were assessed using a checklist consisting of possible war and non-war related traumatic event types (i.e. witnessing or experiencing injury by a weapon or gun, beatings/torture, harassment by armed personnel, robbery/extortion, imprisonment, poisoning, rape or sexual abuse, beatings, abduction, child marriage, forced prostitution/sexual slavery, forced circumcision, etc.). The checklist was compiled after interviews with key informants (security personnel, doctors, community leaders, women's representatives) and 30 respondents from all three populations about their personal history of stressful events. Following these interviews, the single events obtained in these studies were rated as being potentially traumatic by experts. The following pilot checklist was pre-tested among further 44 Ugandans and Sudanese in areas not selected for the survey and modified according to the suggestions of the respondents. A primary item analysis based on inter-item correlations led to the exclusion of some events that were obviously not directly related to traumatic histories, e.g. the experiencing of witchcraft. Events included 19 experienced events and 12 witnessed events. Respondents were asked for each event type if they had experienced or witnessed such an event ever (i.e., lifetime experience) and if it happened in the past year. PTSD in respondents was assessed using the Posttraumatic Stress Diagnostic Scale (PDS), modified for assessment by trained lay interviewers [16]. The PDS is a self-report measure widely-used in industrialized countries as a screening instrument for the diagnosis and severity of PTSD based on DSM-IV Criteria.
Confidentiality was assured and it was explained that researchers were not working for any UN or Ugandan government organization. Informed consent was obtained using a standardized form explaining the potential risks of participation and explaining that no compensation would be provided. Informed consent forms were signed by the respondent and a witness; fingerprints were taken from illiterate respondents. No financial incentives were provided and respondents were informed that no improvements in living conditions were to be expected as a result of participating in the survey. Respondents were provided with referrals to counseling services provided by NGOs where available.
Results
As no major clustering effects were expected in this large sample, statistical analyses were carried out on unweighted data. To examine the relationship between continuous PTSD symptoms and the number of event types reported, we correlated the PDS score and its subscales, intrusion, avoidance and arousal with the number of event types. The number of event types in life correlated with the frequency of intrusions (r = .49), hyperarousal (r = .41) and avoidance (r = .47), all P < 0.001. The PDS sumscore correlated significantly (P < 0.001) with the number of traumatic events in the past year (r = .45) and for the whole life (r = .49; see figure1).
Overall, 31.6% of the male and 40.1% of the female respondents (N = 3179) fulfilled the DSM criteria for a PTSD-diagnosis. We divided the whole population studied in the survey into different groups based on the number of traumatic event types reported, separately for the events reported for last year and in life. The initial division was made as follows: the first group consisted of respondents endorsing 0–3 event types, the second group consisted of individuals endorsing 4–7 event types. Each following group endorsed an additional four or more event types. Because the number of individuals in the groups of 12–15, 16–19, 20–23 and 24–27 event types was very small for the analyses of events reported last year (n = 38, 14, 8, 13, respectively), these groups were merged to two groups of 12–19 and 20–27 event types. Figure 2 shows the number of individuals and the prevalence of PTSD in these groups, separately for the groups based on the events reported for the whole life and for last year. The presentation indicates a near linear rise for increasing psychological strain with the number of traumatic event types ranging from 23% in respondents who reported three or fewer traumatizing experiences to 100% prevalence of PTSD in those who report 28 or more traumatic event types in their past. Figures related to traumatic event types in the past year display and even more pronounced increase of PTSD symptoms with significantly higher prevalence rates for the first three categories of numbers of events (Figure 2).
Figure 2 Prevalence of PTSD and number of individuals in groups of respondents. In this figure respondents are pooled on the basis of number of traumatic event types reported for whole life and last year.
Discussion
High prevalence rates of PTSD have been reported for three different population groups in the West Nile: Sudanese nationals (44.6%), Sudanese refugees (50.5%) and Ugandan residents (23.2%) [15]. Here we show that the exposure to traumatic events and the number of different types of traumatic experiences in particular can account for the different proportion of PTSD cases. The prediction of increased PTSD prevalence with increasing number of traumatic events is consistent with other studies investigating victims of organized violence [11-14].
As demonstrated, the number of traumatic events correlated equally strong with avoidance and with re-experiencing symptoms but coefficients were weaker, although still significant, for the hyperarousal cluster. These results are in agreement with [17], who also found a strong correlation between cumulative trauma and symptoms of re-experiencing and avoidance. Contrary to these findings, Mollica [10] could not find a correlation with avoidance symptoms. Problems in the translation of the avoidance items in the PTSD instruments might be responsible for this difference, as subtle modification in the translation process may turn PTSD avoidance criteria (like "less interest in important activities" or "feeling as if future plans will not come true") into unspecific depressive items that are unrelated to a traumatic experiences.
Typically, even severe single traumatic event produce PTSD in not more than half of those affected. Therefore, PTSD is not an inevitable consequence of potentially traumatizing events. Results from this study, however, suggest that there may be no ultimate resilience to ward off PTSD or that a psychobiological breaking point exists for even the most resistant individual. In the three population groups that were surveyed, each respondent experiencing 28 or more different traumatic event types developed the full set of symptoms of PTSD. This cumulative trauma threshold identified in this study is very high and affected only a small minority of persons even in a war-torn population. Nevertheless, if the cumulative exposure to traumatic events is high enough, these results indicate that anybody will develop chronic PTSD. We conclude that there is no ultimate resilience to traumatic stress and that the repeated occurrence of traumatic stress has a cumulative damaging effect on the mental health of the victim. In these conditions, the effect of pre-trauma factors is reduced to the modulation of the probability of exposure to traumatic events itself. The factors that determine who is exposed to many traumatic events and who manages to flee to secure places may depend on pre-trauma psychological factors. Further studies with war-populations should examine whether the exposure to traumatic events only depends on uncontrollable external factors or whether individual factors contribute to a person's ability to seek safe places.
Conclusions
High levels of trauma exposure is found in populations affected by civil war. We show that PTSD, the major psychological consequence of war events, is linearly correlated with traumatic exposure, thus explaining the high prevalence rates of PTSD generally found in war-torn societies. These findings highlight the need for reducing the frequent exposure to traumatic events by preventing wars, controlling the violence in wars, and providing safe and stable living environments for refugees. At the same time, the presence of high numbers of PTSD cases requires the implementation of individual and community based treatment programs. Given very limited resources in refugee communities, these centers must be created to provide short-term care and must be manageable by local personnel [18,19]. The provision of appropriate mental health assistance is necessary to break the vicious cycle of violence and psychological morbidity.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
FN, MS, UK & TE designed the study. FN, MS and UK composed the set of instruments. UK was responsible for original instrument translation and data collection, FN, MS, CK and TE for the validation part. FN and CK performed the data analysis. CR, TE and FN drafted the original manuscript and all authors revised and approved the final manuscript.
Figure 1 Scatterplot of number of traumatic event types for whole life and severity of PTSD symptoms. A number randomly chosen in the interval between -.05 and +0.5 was added to both the abscissa and the ordinate to visualize overlapping points.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was supported by the Deutsche Forschungsgemeinschaft, a USAID Grant, and the Andrew W Mellon Foundation.
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| 15504233 | PMC529265 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Oct 25; 4:34 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-34 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-461547656310.1186/1471-2458-4-46Research ArticleLength of sick leave – Why not ask the sick-listed? Sick-listed individuals predict their length of sick leave more accurately than professionals Fleten Nils [email protected] Roar [email protected]ørde Olav Helge [email protected] Department of Community Medicine, University of Tromsø, Tromsø, N-9037, Norway2 Department of Community Medicine and General Practice, Norwegian University of Science and Technology, Trondheim, Norway2004 12 10 2004 4 46 46 24 5 2004 12 10 2004 Copyright © 2004 Fleten et al; licensee BioMed Central Ltd.2004Fleten et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The knowledge of factors accurately predicting the long lasting sick leaves is sparse, but information on medical condition is believed to be necessary to identify persons at risk. Based on the current practice, with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire the diagnostic accuracy of length of sick leaves predicted in the Norwegian National Insurance Offices, and to compare their predictions with the self-predictions of the sick-listed.
Methods
Based on medical certificates, two National Insurance medical consultants and two National Insurance officers predicted, at day 14, the length of sick leave in 993 consecutive cases of sick leave, resulting from musculoskeletal or mental disorders, in this 1-year follow-up study. Two months later they reassessed 322 cases based on extended medical certificates. Self-predictions were obtained in 152 sick-listed subjects when their sick leave passed 14 days. Diagnostic accuracy of the predictions was analysed by ROC area, sensitivity, specificity, likelihood ratio, and positive predictive value was included in the analyses of predictive validity.
Results
The sick-listed identified sick leave lasting 12 weeks or longer with an ROC area of 80.9% (95% CI 73.7–86.8), while the corresponding estimates for medical consultants and officers had ROC areas of 55.6% (95% CI 45.6–65.6%) and 56.0% (95% CI 46.6–65.4%), respectively. The predictions of sick-listed males were significantly better than those of female subjects, and older subjects predicted somewhat better than younger subjects. Neither formal medical competence, nor additional medical information, noticeably improved the diagnostic accuracy based on medical certificates.
Conclusion
This study demonstrates that the accuracy of a prognosis based on medical documentation in sickness absence forms, is lower than that of one based on direct communication with the sick-listed themselves.
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Background
The increasing rate of sick leave experienced in most Western countries challenges insurance companies, employers, and public authorities to identify measures to reduce burdens at the individual, workplace and societal levels.
To reduce the expenses of sick leave and the risk of expulsion from work, the Norwegian government introduced legislation in 1993 that anticipated early and more vigorous interventions of the Norwegian National Insurance Scheme [1]. The Norwegian Public Report no. 27 [2], 2000, underscored the importance of early intervention by the National Insurance Offices (NIOs). A major challenge for the NIOs is to identify newly sick-listed individuals at risk of prolonged sick leave, and who are therefore potential candidates for rehabilitating interventions.
The selection process is currently based on information in medical sickness certificates supplied by access to the register of previous sickness benefits. A medical sickness certificate (Sickness Certificate 1; SC1) is required if sick leave exceeds 3 days, and after 8 weeks an extended medical certificate is mandatory (Sickness Certificate 2; SC2) [3]. In addition to diagnosis and certified period, the majority of SC1s contain information on the occupation and employee, whereas information on chronic disease, previous sick leave episodes, prognosis and comments are more scattered. SC2s include updated medical information on work ability, planned diagnostics and treatments, and on the prognosis. The value of this information as a guideline for selective intervention has, however, never been established, either as an indicator of potential prolonged absence, or as an indicator of the need for occupational or vocational rehabilitation [4].
Based on the current practice with identifying sick-listed individuals at risk of long-lasting sick leaves, the objectives of this study were to inquire diagnostic accuracy of predictions within the NIOs, and to compare their predictions with the self-predictions of the sick-listed.
Methods
In October and November 1997 and March and April 1998, newly sick-listed persons with musculoskeletal or mental disorders (ICPC, L- and P- diagnoses) [5] were included consecutively if they were certified sick for longer than 2 weeks (Figure 1). Five hundred persons were included in each period. The study took place in the cities of Tromsø and Harstad in Northern Norway. The total length of sickness benefits was registered during the following year in the National Sickness Benefit Register. Missing data on the length of sick leave reduced the number of included subjects to 993. The mean ages of these 391 men and 602 women were 41.4 and 39.7 years, respectively. Musculoskeletal disorders were the main reason for sick leaves (83% of the cases).
Figure 1 Flow-chart. Flow-chart of inclusion, and the different assessments of expected length, of the included sick leaves after 2 and 8 weeks of sick leave.
A total of 495 randomly selected persons received a questionnaire on the expected length of their ongoing sick leave period. The answer categories were: less than 4 weeks, 4 to 7 weeks, 8 to 11 weeks, 12 to 15 weeks, 16 to 25 weeks, 26 to 51 weeks, and at least 1 year. Some 152 persons (30.7%), called the responder group, returned the questionnaire with this question filled in.
Based on SC1s available after 14 days of sick leave, two NIO officers without formal medical competence, but experienced in working with sick-listed persons, and two experienced physicians working part time as insurance medical officers (NIO medical consultants), assessed the expected length in each of the 993 ongoing sick leave cases. In 496 randomly chosen cases, the NIO assessors had additional access to information on sick leave periods during the previous 3 years. Of potentially 1986 assessments in each profession, the officers and medical consultants had 18 and 25 missing assessments, respectively.
SC2s became available in 322 of the 459 cases where sick leave exceeded 8 weeks, and the NIO assessors reassessed these cases.
Reproducibility of assessments by medical consultants were analysed in 20 cases reassessed by the two NIO medical consultants, and assessed by another eight of their colleagues.
Observed length of sick leaves
The reference standard lengths of individual sick leaves within 1 year were collected from the National Sickness Benefit Register. Sick leaves interrupted by only 1–2 days without sickness benefits, typically on weekends, were registered as a single period. The observed length of sick leave thus comprised the total period of continuous full-time or part-time absence due to sickness within 1 year.
Statistics
The diagnostic accuracy of predicted lengths was compared on the basis of sensitivity, specificity, likelihood ratio and the area under the receiver operating characteristics curves (ROC area) [6,7]. The non-parametric standard error and 95% CI for the ROC area were calculated in SPSS-11. The ROC curve represents plots of the true-positive rate (sensitivity) and the false positive rate (1 – specificity) at the average of two consecutive categories of the assessments (>= 0 weeks, >= 4 weeks, >= 8 weeks etc). The ROC curves of the mean assessment by NIO officers and medical consultants include even intermediate points representing half categories.
The predictive validity is presented as sensitivity, specificity, positive predictive value (PPV) and likelihood ratio at different thresholds, cut-offs, in predicted length [8]. Reliability of predicted length was analysed with agreement between assessors, the kappa value [9,10].
Approval
The Regional Ethical Committee approved the protocol, and the Norwegian Data Inspectorate licensed the necessary register of sick-listed subjects.
Results
The mean observed continuous sickness absence was 100.8 days (median 48 days). Sick leaves in females lasted a mean of 105.1 days, compared to 94.6 days in men (medians 55 and 43 days, respectively). The mean length among persons with musculoskeletal disorders was 90.2 days in 335 males and 108.6 days in 489 females. The mean length among persons with mental disorders was 120.6 days in 56 males and 90.0 days in 113 females.
The mean length of the sick leave in the responder group was 107.4 days (95% confidence interval, CI, 88.7–126.1 days), compared to 92.4 days in the 343 non-responders. Stratified analysis revealed longer mean sick leaves among responders 40 years and younger, of 109.3 days (95% CI 81.4–134.5 days), compared to the 79.3 days (95% CI 65.6–93.1 days) in non-responders. Stratification on gender or musculoskeletal or mental disorders did not reveal any significant differences in the length of sick leave between responders and non-responders.
All assessors, including the sick-listed themselves, systematically overestimated the length of short sick leaves (lasting 4–11 weeks) and underestimated the length of long sick leaves (exceeding 16 weeks; Table 1). The proportions of sick leaves lasting longer than 8, 12 or 26 weeks did not differ significantly between the responder group and the rest.
Table 1 Categorical distribution of observed and predicted length of sick leave. Observed and predicted length of sick leaves in seven categories for all participants (n = 993) compared to the responder group (n= 152). The assessments of National Insurance medical consultants and officers are grouped according to proportions of persons predicted in each category.
All participants Proportion according to Responder group Proportion according to
Length of sick leave categories Observed length % Assessed by medical consultants %
95% CI Assessed by officers %
95% CI Observed length %
95% CI Assessed by medical consultants %
95% CI Assessed by officers %
95% CI Assessed by sick-listed %
95% CI
< 4 weeks 31.7 27.6
25.7–29.7 18.9
17.2–20.7 29.6
22.5–37.5 32.2
27.0–37.8 20.9
16.4–25.9 25.0
18.3–32.7
4–7 weeks 22.0 41.8
39.6–44.0 36.8
34.7–39.0 25.0
18.3–32.7 40.9
35.3–46.7 33.4
28.1–39.1 36.2
28.6–44.4
8–11 weeks 12.9 20.3
18.6–22.2 25.4
23.5–27.4 7.2
3.7–12.6 18.3
14.1–23.1 26.8
21.9–32.2 15.1
9.8–21.8
12–15 weeks 6.2 7.0
5.9–8.3 13.7
12.2–15.3 3.9
1.5–8.4 6.3
3.8–9.7 13.6
9.9–18.0 10.5
6.1–16.5
16–25 weeks 9.3 1.0
0.6–1.6 1.9
1.3–2.6 13.2
8.2–19.6 0.7
0.1–2.4 2.6
1.2–5.2 5.9
2.7–10.9
26–51 weeks 6.8 0.7
0.4–1.2 0.7
0.4–1.2 9.9
5.6–15.8 0.3
0.0–1.8 0.0
0.0–1.2 1.3
0.2–4.7
>= 52 weeks 11.1 1.5
1.0–2.1 2.5
1.9–3.3 11.2
6.7–17.3 1.3
0.4–3.2 2.6
1.2–5.2 5.9
2.7–10.9
Receiver operating characteristics of prediction
The sick-listed subjects predicted sick leaves equal to or longer than 12 weeks more accurately than the NIO medical consultants and officers, as shown by the ROC curve in Figure 2. The differences in ROC area between responders and non-responders were most marked among younger subjects and in females (Table 2). Generally, the length of sick leave was predicted more accurately in older subjects than in younger subjects, and better in males than in females. Access to past history of sick leaves improved the ROC area of NIO consultants from 60.6% (95% CI 51.3–69.9%) to 75.4% (95% CI 68.2–82.6%) in male sick-listed, but did not improve the ROC area in assessments of female sick-listed.
Figure 2 ROC curves of identifying sick leaves lasting at least 12 weeks. The ROC curve of ability to identify sick leaves lasting at least 12 weeks, plotted at the average of two consecutive categories, in length predicted by sick-listed (n = 152), and mean length predicted by National Insurance officers and medical consultants in the responder group (n = 149, 150) and for all the data (n= 972, 975). The points representing cut-offs in predicted length >= 4 weeks (red), >= 8 weeks (pink) and >= 12 weeks (blue) are identified.
Table 2 ROC area of identifying sick leaves lasting at least 12 weeks. The ability to identify sick leaves lasting at least 12 weeks in the responder group (n = 152) and in all participants (N = 993), presented as ROC area, calculated from length of sick leave predicted by sick-listed, and mean length predicted by National Insurance medical consultants and officers. The range of the individual National Insurance ROC areas is presented for all participants.
Medical consultants Officers
Self-assessed Responders n = 152 Responders n = 149 All participants n = 972 Responders n = 150 All participants n = 975
Sick-listed n ROC area ROC area ROC area Range individual ROC area ROC area Range individual
N 95% CI 95% CI 95% CI ROC area 95% CI 95% CI ROC area
All 152 80.9 55.6 64.6 59.6–64.2 56.0 61.4 55.6–65.6
993 73.7–86.8 45.6–65.6 60.8–68.3 46.6–65.4 57.7–65.1
17–40
years of age 78
508 76.4
65.6–87.2 43.0
28.8–57.2 57.2
51.7–62.8 54.5–57.8 48.9
35.4–62.5 57.4
52.0–62.9 51.9–57.8
41–67
years of age 74
485 85.7
77.2–94.2 68.3
54.8–81.8 70.7
65.8–75.6 63.4–70.1 62.5
49.4–75.6 65.1
60.1–70.1 56.1–73.4
Males 56 90.9 63.0 68.7 62.8–68.3 59.6 63.6 56.5–71.8
391 83.4–98.4 47.3–78.7 62.8–74.6 44.3–74.9 57.5–69.8
Females 96 74.7 50.9 62.0 57.7–61.5 54.0 60.3 52.9–61.8
602 64.8–84.7 38.1–63.8 57.2–66.8 42.0–65.9 55.6–64.9
Changing the observed length to be identified from 12 weeks to 8 or 26 weeks did not significantly change the diagnostic accuracy as assessed by the ROC area. The sick-listed identified sick leaves lasting 8 weeks or longer with a ROC area of 79.5% (95% CI 72.2–85.6%), and sick leaves lasting 26 weeks or longer with a ROC area of 75.5% (95% CI 67.9–82.1%). Sick-listed persons with mental disorders or with neck, or shoulder and arm disorders, were most accurate in their assessment (Figure 3). This was in contrast to NIO assessors, who demonstrated the lowest predictive ability in these diagnostic groups, particularly in responders. The impact on diagnostic accuracy of knowing the occupation was small.
Figure 3 ROC area in different diagnostic groups. ROC area representing ability to identify sick leaves 12 weeks or longer in different diagnostic groups, calculated on length predicted by sick-listed, and mean of lengths predicted by NIO assessors. The ROC area are presented with blue bars of 95% CI in the responder group (n = 152/), and red bars without horizontal lines between upper and lower individual ROC area of the NIO assessors for all sick leaves (n = /958).
Sensitivity, specificity, predictive value and likelihood ratio
The sick-listed subjects predicted their sick leaves with higher sensitivity and PPV than the NIO assessors (Tables 3, 4). Male sick-listed predicted sick leaves lasting at least 12 weeks with a sensitivity of 0.82% (95% CI 0.60–0.95) and a PPV of 0.78 (95% CI 0.56–0.93) using predicted length of at least 8 weeks. The corresponding sensitivity and PPV of female sick-listed were both 0.61 (95% CI 0.44–0.77).
Table 3 Predictive validity – identifying sick leaves lasting at least 12 weeks. Predictive validity of identifying sick leaves that lasted at least 12 weeks, using 8 weeks as the cut-off in length as predicted by the sick-listed, medical consultants and officers. The prediction based on the Sickness Certificate 2 (SC2) used a cut-off in predicted length of at least 12 weeks. Sensitivity, specificity, PPV, and likelihood ratio data for NIO assessors are presented as means with 95% CI.
Predicted length n assessments Sensitivity (95% CI) Specificity (95% CI) Likelihood ratio (95% CI) PPV1 (95% CI) PPV adjusted to prevalence 33.4% (95% CI)
Sick-listed 152 0.69
(0.56–0.84) 0.80
(0.70–0.87) 3.4
(1.9–6.2) 0.68
(0.54–0.79) 0.63
(0.49–0.76)
Medical consultants Responder group 301 0.35
(0.26–0.44) 0.78
(0.71–0.84) 1.6
(1.0–2.5) 0.49
(0.38–0.61) 0.44
(0.33–0.56)
Medical consultants All participants 1961 0.42
(0.38–0.45) 0.75
(0.73–0.77) 1.7
(1.4–1.9) 0.45
(0.41–0.49)
Officers Responder group 302 0.53
(0.44–0.62) 0.59
(0.51–0.66) 1.3
(0.9–1.8) 0.44
(0.36–0.53) 0.39
(0.31–0.48)
Officers All participants 1968 0.53
(0.49–0.57) 0.60
(0.58–0.63) 1.3
(1.2–1.5) 0.40
(0.37–0.43)
Medical consultants SC2 637 0.85
(0.82–0.88) 0.44
(0.36–0.52) 1.5
(1.2–1.9) 0.82
(0.79–0.86) 0.43
(0.39–0.48)
Officers SC2 636 0.88
(0.86–0.91) 0.33
(0.26–0.41) 1.3
(1.1–1.7) 0.80
(0.77–0.84) 0.40
(0.35–0.44)
1The prevalence of sick leaves lasting at least 12 weeks was 38.2% in the responder group (n = 152), 33.4% for all participants (n = 993), and 72.1% in the SC2 group (n= 322).
Table 4 Predictive validity – identifying sick leaves lasting at least 26 weeks. Predictive validity of the ability to identify sick leaves lasting at least 26 weeks, using 8, 12 or 26 weeks, as cut-offs in length as predicted by the sick-listed, medical consultants or officers. Sensitivity, specificity, PPV and likelihood ratio data for NIO assessors are presented as means for length predicted on Sickness Certificates 1 and Sickness Certificates 2 (SC2).
Predicted length n assess-ments Sensitivity (95% CI) Specificity (95% CI) Likelihood ratio (95% CI) PPV1 (95% CI) PPV adjusted to prevalence 17.9%(95% CI)
Sick-listed >= 8 weeks 152 0.69
(0.50–0.84) 0.69
(0.60–0.77) 2.2
(1.3–3.9) 0.37
(0.25–0.51) 0.33
(0.21–0.47)
Sick-listed >= 12 weeks 152 0.50
(0.32–0.68) 0.83
(0.75–0.90) 3.0
(1.5–6.1) 0.44
(0.28–0.62) 0.40
(0.24–0.58)
Sick-listed >= 26 weeks 152 0.28
(0.14–0.47) 0.98
(0.94–1.00) 16.9
(3.5–160) 0.82
(0. 48–0.98) 0.78
(0.44–0.95)
Consultants >= 8 weeks 1961 0.44
(0.39–0.49) 0.72
(0.70–0.74) 1.6
(1.3–1.9) 0.25
(0.22–0.29)
Consultants >= 12 weeks 1961 0.20
(0.16–0.24) 0.92
(0.90–0.93) 2.4
(1.8–3.2) 0.35
(0.28–0.41)
Consultants >= 26 weeks 1961 0.07
(0.04–0.10) 0.99
(0.98–0.99) 2.8
(1.5–5.4) 0.54
(0.38–0.69)
Officers >= 8 weeks 1968 0.55
(0.50–0.60) 0.58
(0.56–0.61) 1.3
(1.1–1.5) 0.22
19.3–24.9
Officers >= 12 weeks 1968 0.26
(0.21–0.31) 0.83
(0.81–0.85) 1.6
(1.3–2.0) 0.25
(0.20–0.29)
Officers >= 26 weeks 1968 0.06
(0.04–0.09) 0.98
(0.97–0.98) 1.5
(0.9–2.6) 0.34
(0.23–0.48)
SC2
Consultants >= 12 weeks 637 0.89
(0.86–0.93) 0.29
(0.25–0.34) 1.3
(1.1–1.5) 0.44
(0.40–0.48) 0.22
(0.18–0.25)
Consultants >= 26 weeks 637 0.24
(0.19–0.29) 0.96
(0.93–0.98) 5.9
(3.4–11.0) 0.79
(0.68–0.87) 0.56
(0.41–0.70)
Officers >= 12 weeks 636 0.89
(0.85–0.93) 0.21
(0.17–0.25) 1.1
(0.9–1.3) 0.41
(0.37–0.45) 0.20
(0.16–0.23)
Officers >= 26 weeks 636 0.28
(0.22–0.34) 0.90
(0.87–0.93) 2.8
(1.9–4.3) 0.64
(0.54–0.73) 0.38
(0.28–0.49)
1The prevalence of sick leaves lasting at least 26 weeks was 21.1% in the responder group (n = 152), 17.9% for all the data (n = 993), and 38.5% in the SC2 group (n = 322).
Duration of at least 8 weeks was the preferable cut-off in predicted length, to identify sick leaves lasting at least 12 weeks (Table 3). A predicted length of at least 12 weeks reduced the sensitivity in all the data to 0.17 in medical consultants and 0.25 in officers. The corresponding improvement in PPV was modest, reaching 0.54 in medical consultants and 0.45 in officers. Using a predicted length of at least 4 weeks would have markedly reduced the specificity (Figure 2).
The sensitivity of identifying sick leaves lasting at least 26 weeks was generally low when medical consultants and officers predicted on the basis of SC1s. (Table 4). The sensitivity was improved somewhat by introducing SC2 information, but the effects on likelihood ratio and PPV if prevalence corrected, were minor.
According to the results, the effects of the different predictive strategies can be illustrated by considering a program designed to intervene in all cases where the subject is expected to be sick-listed for more than 12 weeks at 14 days of sick leave. Out of every 1000 sick-listed persons, 333 will be sick-listed for more than 12 weeks according to the prevalence in this study. The random selection of 333 persons will include 111 true positives, while 333 persons selected by officers will include 133 of the 333 persons that will be sick-listed at least 12 weeks. The evaluation of 1000 sick-listed individuals thus increases the number of true positives by 22 in a selection of 333 sick-listed persons. The alternative strategy of asking the sick-listed themselves will include 210 true positives in a selection of 333 persons.
Reliability and reproducibility of the predicted length
Agreement between medical consultants in their initial prediction of sick leaves lasting at least 12 weeks, was fair, with a kappa of 0.31 (95% CI 0.20–0.43). The corresponding kappa value between officers was 0.05 (95% CI -0.05–0.14).
In the prediction of sick leaves lasting at least 12 weeks based on the SC2, agreement was moderate between medical consultants (kappa = 0.42, 95% CI 0.29–0.54) and fair between officers (kappa = 0.26, 95% CI 0.10–0.42). The corresponding agreements in the prediction of sick leaves lasting at least 26 weeks were moderate between medical consultants (kappa = 0.55, 95% CI 0.40–0.70) and fair between insurance officers (kappa = 0.31, 95% CI 0.17–0.47).
The differences in diagnostic accuracy, between the two participating medical consultants and their eight colleagues in the reproducibility group, were not significant.
Discussion
The results of the present study question any practical value of using information in medical sickness certificates in predicting the length of sick leave, as is the current practice in Norwegian NIOs. Instead, the sick-listed themselves predicted their length of sick leaves far more accurately, but this information is not routinely sought.
Representativeness
The officers in the present study were selected from experienced officers who had shown an interest in the field of sick leave. This might introduce a bias of overestimating the officers' general ability to predict the length of sick leaves. The performances of the two medical consultants were representative of eight of their colleagues who participated in the reproducibility part of the study. We therefore consider the diagnostic accuracy of the assessors to be representative of their professional groups, or at least not underestimated due to bias. Although the diagnostic accuracy varied within each group, the main conclusion of better predictive ability among the sick-listed, was challenged neither by comparing with the mean length predicted by assessors, nor by comparing with the best-performing NIO assessor.
The distributions of gender and diagnosis among the 993 persons included in the study were comparable with those in the National Sickness Benefits Register. The findings of longer sick leaves in women with musculoskeletal disorders, and longer sick leaves in men with mental disorders, are consistent with the Register and other studies [11-13].
The low responder rate among the sick-listed introduced a possible selection bias, although we could not identify any selection bias in gender, age, diagnosis or occupation [14]. If there was a selection towards more predictable sick leaves, this should have been reflected in the assessments of officers and medical consultants. The general trend of lower diagnostic accuracy of NIO assessors in the responder group indicates that if any selection bias contributes to the results, it is an underestimate of the self-predictive ability.
Why did the sick-listed make better predictions?
If the lengths of sick leaves were predominantly related to loss of function caused by sickness, in line with the legislation, we would expect that the medical consultants' professional competence would favour them in predictions of the lengths of sick leaves. The differences we observed between medical consultants and officers in mean ROC area, were however minor. Furthermore, we could not demonstrate any significant differences in diagnostic accuracy between medical consultants and officers when aggregate information on disease, treatment, function related to work, and prognoses were available in the SC2. The improvement in ROC area with this aggregated information was minor, with the area just reaching 70%, which is considered borderline useful for some purposes [7]. The result is in line with Bjørndal's findings of low prognostic impact of the SC2 [15], and is supported by findings of a low predictive power of symptoms and signs in neck and shoulder disorders [16]. The better prediction of the length of sick leave by the sick-listed themselves, is supported by studies that have identified different non-disease determinants of sick leave, such as job satisfaction [17], attitudes towards pain [18], irreplaceability [19] and psychosocial work environment [20-22]. Studies identifying that at least the initial sickness certification is predominantly patient controlled [23,24] indicate the competence of the sick-listed. Self-rated health seems to be an independent predictor of return to work [17], disability pension [25] and early retirement [26]. Our findings can be interpreted as indicating that the subjective perception of sickness and work ability is more predictive of the length of sick leave, than the apparently more objective description in medical terms. The differences in predictive ability were especially significant in persons with mental and neck disorders, while the NIO assessors performed equal to the sick-listed in the more clear-cut injuries with more standardised treatment and prognosis. Mental disorders, with high prevalence in the population, and an increasing cause of absence [27], are of special interest [13]. This increasing prevalence of sick leaves indicates the presence of factors separate from the diagnosis criteria. It seems that the more clear-cut the disease and the recommended treatment, the lesser the gain in predictive ability achieved by asking the sick-listed, and vice versa. The modest gain in predictive ability caused by introducing more medical information by the inclusion of the SC2 supports this interpretation. A more complete description of symptoms and treatment does not necessarily give better prognostic information when this includes little knowledge of the consequences related to occupation, and the effects of treatment are undocumented or, at best, marginal.
Diagnostic accuracy – practical implication
The Norwegian NIO is obliged by legislation to perform early intervention on the sick-listed in an effort to reduce the length of sick leave and the risk of expulsions from work. Limited resources and the large number of sick-listed individuals make selection desirable before any intervention is initiated. An alternative to selection on the basis of medical certificates is to communicate directly with the sick-listed themselves. This selection for intervention by NIOs might be seen as screening. The aim is to reach – at an acceptable cost – as many as possible of those that might profit from intervention. The potential individual gain by intervention will be greater when longer lasting sick leaves can be anticipated, and greater the sooner individual intervention programs are established.
The marginal predictive ability and modest agreement between NIO assessors questions the use of resources in selection based on information from medical certificates. The predictions of medical consultants tend to be better than those of officers, but not to an extent that makes it more meaningful to use medical consultants in the selection process, rather than officers.
With limited resources for intervention, it might be more cost effective to identify those whose sick listing will last longer than 26 weeks instead of 12 weeks. Based on self-reporting, eight out of ten would be true positives, and one fourth of the individuals would be reached. To reach the same number of true positives at 14 days of sick leave, the ratio of true positives would be reversed from eight out of ten, to two or three out of ten, if the selection were based on medical certificates.
In the search for tests predicting long-lasting sick leaves, such as The Örebro Musculoskeletal Pain Questionnaire [28], the present study indicates that the results of any such tests should be compared with the results of crude self-estimated length.
Conclusions
Sick-listed individuals predicted their length of sick leave far more accurately than did NIO medical consultants and officers based on information from sickness certificates and the history of past sick leaves. The predictions of sick-listed males were better than those of females, and older persons predicted better than younger persons. The availability of more information, as through the SC2, had only a minor effect on the predictive ability of the medical consultants and officers. Neither reliability nor validity of their predictions was satisfactory.
This study demonstrates the need to re-consider the diagnostic usefulness of documentation on sickness absences, and supports a change in strategy from collecting more medical information to more direct communication with the sick-listed themselves, for effective and early interventions to prevent long sick leaves and expulsions from work.
Competing interests
The author N.F. is part-time employed as National Insurance medical consultant.
Authors' contributions
NF was in charge of designing and running the study, and performed most of the analyses and the writing of this manuscript. RJ actively supervised all parts of the study, and OHF contributed to planning and writing. All authors read and approved the final version of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
The authors want to thank the Norwegian Ministry Of Health and Social Affairs for funding the study from July 1997 to December 1999, canalized through the National Insurance Administration (project no. 13345).
This study could not have been performed without the support and contribution of the county and local National Insurance Offices in Troms.
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