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BMC Emerg MedBMC Emergency Medicine1471-227XBioMed Central London 1471-227X-4-31546268110.1186/1471-227X-4-3Research ArticleUnderweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: a retrospective study Finkielman Javier D [email protected] Ognjen [email protected] Bekele [email protected] Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA2004 5 10 2004 4 3 3 17 6 2004 5 10 2004 Copyright © 2004 Finkielman et al; licensee BioMed Central Ltd.2004Finkielman 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
Low and high body mass index (BMI) have been recently shown to be associated with increased and decreased mortality after ICU admission, respectively. The objective of this study was to determine the impact of BMI on mortality and length of stay in patients admitted to the intensive care unit (ICU).
Methods
In this retrospective cohort study, the Acute Physiology and Chronic Health Evaluation (APACHE) III database of patients admitted to the ICUs of a tertiary academic medical center, from January 1997 to September 2002, was crossed with a Hospital Rule-based Systems database to obtain the height and weight of the patients on admission to the ICU. The cohort was divided in post-operative and non-operative groups. We created the following five subgroups based on the BMI: <18.5, 18.5 to 24.9, 25 to 29.9, 30.0 to 39.9, ≥ 40.0 Kg/m2. A multiple logistic regression analysis was used to determine the independent impact of BMI on hospital mortality. The ICU length of stay ratio was defined as the ratio of the observed to the predicted LOS. P-value < 0.05 was considered significant. The 95% confidence interval (CI) was calculated for the odds ratio (OR).
Results
BMI was available in 19,669 of the 21,790 patients in the APACHE III database; 11,215 (57%) of the patients were admitted post-operatively. BMI < 18.5 was associated with increased mortality in both post-operative (OR = 2.14, 95% CI, 1.39 to 3.28) and non-operative (OR = 1.51, 95% CI, 1.13 to 2.01) patients. Post-operative patients with a BMI between 30.0 to 39.9 had a lower mortality rate (OR = 0.68, 95% CI, 0.49 to 0.94). Post-operative patients with BMI <18.5 or BMI ≥ 40 had an ICU length of stay ratio significantly higher than patients with BMI between 18.5 to 24.9. The addition of BMI < 18.5 did not improve significantly the accuracy of our prognostic model in predicting hospital mortality.
Conclusions
Low BMI is associated with higher mortality in both post- and non-operative patients admitted to the ICU. LOS is increased in post-operative patients with low and high BMIs.
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Background
The body mass index (BMI) is an anthropometric measure of nutritional status that is calculated as the weight in kilograms divided by the square of the height in meters[1]. The relationship between BMI and mortality has been shown to be J- or U-shaped in large population studies; the highest mortality was observed in persons with low and high BMIs [2-5]. Previous studies have shown that low BMI (but not high) is an independent predictor for mortality in patients admitted to the hospital[1,6,7]. Two recent studies have investigated the impact of BMI on ICU outcome. In a large retrospective study, Tremblay et al. found that low BMI (< 20 Kg/m2), but not high, was associated with increased mortality following admission to the ICU; the increased mortality was seen in medical and emergency surgical groups but not in the elective surgical group and the length of stay (LOS) was longer in severely obese and underweight patients[8]. In the prospective study by Garrouste-Orgeas et al., a low BMI (<18.5) was found to be associated with higher mortality and high BMI (>30 Kg/m2) with lower mortality[9].
Previous studies did not analyze the data from post-operative and non-operative patients separately when they looked at the impact of BMI on the outcome of critically ill patients. We undertook this study to determine the influence of BMI on mortality in post-operative and non-operative patients admitted to the ICU, in a single tertiary academic medical center. Based on the association of low BMI with increased mortality, a recent publication has highlighted the importance of looking at whether adding BMI would improve the predictive accuracy of the ICU prognostic models[9]. In the current study, we tested the hypothesis that adding BMI improves the predictive accuracy of the APACHE III prognostic system.
Methods
In this retrospective, cohort study, we crossed the prospectively collected Acute Physiology and Chronic Health Evaluation (APACHE) III database of adult patients consecutively admitted to the intensive care units of Mayo Medical Center, Rochester, Minnesota, between January 1997 and September 2002, with a Hospital Rule-based Systems database that records the height and weight on admission to the ICU. Patients were admitted to one medical, two surgical and one multi-specialty ICU. Those patients admitted to the neurological, cardiovascular surgery, and coronary care units were not included since they were not part of the APACHE III database. Only first admissions were included in this study. The Mayo Foundation Institutional Review Board approved the study, and a waiver of informed consent was granted. Patients who did not authorize their medical records to be reviewed for research, and those whose weight or height values were missing were excluded.
Mayo Medical Center includes two hospitals with a total of approximately 1900 beds. The medical ICU is a 15-bed closed unit in Saint Mary's Hospital. The surgical ICUs consist of a general surgical/trauma 24-bed unit and a 20-bed surgical unit mainly for thoracic and vascular surgery patients, both located at Saint Mary's Hospital. The 12-bed (increased to 17 beds in March 2000) multi-specialty ICU is located in Rochester Methodist Hospital. The patient population in the multi-specialty ICU included liver, kidney, pancreas and bone marrow transplant recipients; and hematology, oncology, general surgery and orthopedic patients.
Data were obtained from the APACHE III database using the software provided by Cerner Corporation (Kansas City, MO). Data collected included age, ethnicity, gender, ICU admission source, admission type (postoperative or non-operative), intensity of treatment (low-risk monitor, high-risk monitor, active treatment), ICU admission diagnosis group, ICU length of stay (LOS), APACHE III score, APACHE III-predicted hospital mortality, APACHE III predicted-ICU LOS and hospital discharge status. The admission source was classified as operating room/recovery room (OR/RR), emergency room/direct admission from outpatient clinic (ER/direct), transfer from other floors of the same hospital and transfer from other institutions. All ICU admissions were categorized into three groups based on the intensity of treatment: "active treatment" if a patient received one or more of 33 items of the Therapeutic Intervention Scoring System (TISS) defined as ICU specific therapy on the first ICU day; "high-risk monitor" if a patient who did not receive active treatment on the first ICU day had greater than 10% probability of receiving active treatment during the ICU stay; and "low-risk monitor" if a patient who did not receive active treatment on the first ICU day had less than 10% probability of receiving active treatment during the ICU stay [10-12]. The ICU admission diagnosis groups included cardiovascular, genitourinary, gastrointestinal, hematologic, metabolic/endocrine, musculoskeletal/skin, neurologic, respiratory, transplant and trauma. The APACHE III score and predicted hospital mortality rate for each patient were calculated as described by Knaus and colleagues[13]. The type of ICU (medical, surgical or multi-specialty) to which each patient was admitted was recorded.
The body mass index for each patient (BMI) was calculated as the weight (in kilograms) divided by the square of the height (in meters). All BMIs are presented in Kg/m2. Four BMI subgroups were created using the cut-off points of the World Health Organization (WHO): 18.5 to 24.9 (normal range), 25.0 to 29.9 (grade 1 overweight), 30.0 to 39.9 (grade 2 overweight), ≥ 40 (grade 3 overweight)[14]. A fifth subgroup for BMI < 18.5 (underweight) was added as in the study of Calle et al[3].
Descriptive data were summarized as mean (standard deviation), median (interquartile range) (IQR) or percentages. The cohort was divided into post-operative and non-operative patients. The primary analysis consisted of a comparison of hospital mortality for each BMI subgroup. A multiple logistic regression model consisting of hospital mortality as a dependent variable, BMI subgroup, APACHE III predicted mortality, admission source, and intensity of treatment as independent variables was created to adjust for potentially confounding variables that could affect hospital mortality. In the post-operative group the admission source was not included in the model since the source in all the patients was either the recovery room or the operating room. This logistic regression model was based on a previous analysis that identified the variables independently associated with hospital mortality[15]. We performed another logistic regression analysis by entering into the model BMI < 18.5, APACHE III predicted mortality, admission source, and intensity of treatment as independent variables and hospital mortality as dependent variable. We calculated the area under the receiver operating characteristic curve (AUC) to determine the performance of the logistic regression models with and without BMI < 18.5 in discriminating survivors from non-survivors[16]. Differences in mortality rates were expressed as odds' ratios (OR) for death with 95 % confidence intervals (CI) and corresponding P-values.
The ICU LOS ratio was defined as the ratio of the observed to the predicted LOS. LOS ratios less than one indicate stays shorter than predicted[17]. In comparing differences in ICU LOS ratios among the BMI subgroups, we performed the Kruskal-Wallis test first. If the Kruskal-Wallis test showed statistically significant difference among groups, we subjected the data to further analysis by the Mann-Whitney U test to identify the BMI subgroup that was significantly different from the normal BMI subgroup.
StatView 5.0 (SAS Institute, Cary, NC) and MedCalc 7.3 (Mariakerke, Belgium) computer softwares were used for statistical analyses. Patients with missing data were excluded from analysis involving the missing elements. A P-value < 0.05 was considered statistically significant.
Results
Of the 21,790 patients with first ICU admissions during the study period, 19,669 had height and weight data. Their baseline characteristics are listed in Table 1. Fifty seven percent of the patients were post-operative. Most of the patients in both post- and non-operative groups were males, whites, and received active treatment during their first ICU day. The most common admission diagnoses were cardiovascular and respiratory for post- and non-operative groups, respectively. The most common BMI subgroup was 25.0–29.9 in post-operative patients and 18.5–24.9 in non-operative patients (Table 2).
Table 1 Characteristics of 19,669 patients admitted to the intensive care unit
Variables Post-Operative Non-Operative
N 11,215 8,454
BMI (Kg/m2) (SD) 28.4 (7.9) 27.5 (7.2)
Mean age (SD) 64.2 (15.9) 60.9 (19.1)
Male sex (%) 59.2 54.1
White ethnicity (%) 96.1 94.6
Median APACHE III (IQR) 39.0 (29.0–51.0) 48 (32.0–67.0)
Median predicted mortality % (IQR) 2.5 (1.2–5.7) 7.5 (2.3–22.7)
Admission source (%)
RR/OR 99.9 0.0
ER/Direct 0.0 49.0
Same hospital transfer 0.1 45.2
Other hospital transfer 0.0 5.8
ICU type (%)
Surgical 78.8 26.9
Medical 0.4 53.4
Multi-specialty 20.8 19.7
Treatment intensity (%)
Active 51.6 56.0
High-risk monitor 1.4 19.1
Low-risk monitor 46.9 24.9
Admission diagnosis group (%)
Cardiovascular 31.4 24.7
Gastrointestinal 24.3 20.1
Respiratory 14.8 28.9
Musculoskeletal 11.3 1.2
Genitourinary 8.1 3.1
Trauma 4.1 6.9
Neurology 1.2 9.8
Metabolic/endocrine 1.3 3.0
Transplant 3.3 0.1
Hematology 0.2 2.2
SD = Standard deviation; IQR = Interquartile range; ICU = Intensive care unit
Table 2 The body mass index subgroups of 19,669 patients admitted to the intensive care unit
BMI Subgroup Number of patients (%)
Post-Operative N = 11,215 Non-Operative N = 8,454
< 18.5 384 (3.4) 428 (5.1)
18.5–24.9 3,461 (30.9) 2,945 (34.8)
25.0–29.9 3,878 (34.6) 2,692 (31.8)
30.0–39.9 2,718 (24.2) 1,947 (23.0)
≥ 40.0 774 (6.9) 442 (5.2)
In the overall study population, BMI < 18.5 was independently associated with increased mortality (OR = 1.71, 95% CI, 1.34 to 2.17; P < 0.0001). Among the ICU admission diagnoses, BMI < 18.5 was associated with increased adjusted mortality in cardiovascular, genitourinary and musculoskeletal groups (Table 3). BMI of 30 to 39.9 was associated with increased and decreased mortality in the neurology and respiratory groups respectively (Table 3).
Table 3 The BMI subgroups that are independently associated with hospital mortality in each admission diagnosis group using BMI of 18.5 to 24.9 as reference
Admission diagnosis BMI subgroup Odds Ratio (95%CI) P-value
Cardiovascular < 18.5 2.84 (1.70–4.74) < 0.001
Genitourinary < 18.5 4.15 (1.21–14.25) 0.0236
Gastrointestinal None
Hematology None
Metabolism/endocrine None
Musculoskeletal < 18.5 3.70 (1.20–11.38) 0.0227
Neurology 30–39.9 2.74 (1.29–5.81) 0.0086
Respiratory 30–39.9 0.72 (0.56–0.94) 0.0138
Transplant None
Trauma None
For post-operative patients, the crude hospital mortality rate was 3.3 %, and the median (IQR) ICU LOS and ICU LOS ratio were 1.04 (0.82–2.15) and 0.42 (0.25–0.77) days, respectively. After adjusting for confounding variables, postoperative patients with a BMI < 18.5 had a higher hospital mortality, and those with BMI of 30.0–39.9 had a lower hospital mortality rate (Table 4). The median observed and predicted ICU LOS and the ICU LOS ratios for each BMI subgroup of the post-operative patients are listed in Table 5. There were statistically significant differences in the ICU LOS ratio between the various BMI subgroups (P < 0.0001 by Kruskal-Wallis test). Compared to the normal BMI subgroup, the ICU LOS ratio was higher in the BMI < 18.5 (P = 0.0411 by Mann-Whitney U test) and BMI ≥ 40 (P < 0.0001 by Mann-Whitney U test).
Table 4 Multivariate logistic regression analysis assessing the association of hospital mortality with APACHE III predicted hospital mortality, intensity of treatment, and BMI subgroup in 11,215 post-operative patients
Odds Ratio (95% CI) P-value
BMI
< 18.5 2.14(1.39–3.28) 0.0005
18.5–24.9 1.00
25.0–29.9 0.86 (0.66–1.13) 0.2752
30.0–39.9 0.68 (0.49–0.94) 0.0186
≥ 40.0 0.75 (0.45–1.26) 0.2771
Predicted mortality 1.066 (1.059–1.073) <0.0001
Intensity of treatment
Low-risk monitor 0.55 (0.42–0.71) <0.0001
High-risk monitor 0.66 (0.30–1.47) 0.3111
Active 1.00
Table 5 Observed and predicted length of ICU stay and ICU length of stay ratio for post-operative patients
Median (IQR) ICU Length of Stay
BMI Observed Predicted Ratio
< 18.5 1.54 (0.85–3.02) 3.80 (2.97–4.73) 0.45 (0.25–0.86)
18.5–24.9 1.03 (0.81–2.08) 3.57 (2.67–4.50) 0.40 (0.24–0.73)
25.0–29.9 1.04 (0.82–2.17) 3.61 (2.68–4.50) 0.41 (0.24–0.77)
30.0–39.9 1.03 (0.81–2.07) 3.55 (2.58–4.49) 0.41 (0.25–0.76)
≥ 40.0 1.57 (0.84–2.54) 3.29 (1.68–4.30) 0.54 (0.30–0.96)
LOS = Length of stay; ICU = Intensive care unit; IQR = Interquartile range
For non-operative patients, the crude hospital mortality rate was 16.4%, and the median (IQR) ICU LOS and ICU LOS ratio were 1.68 (0.89–3.69) and 0.45 (0.25–0.89) days, respectively. After adjusting for confounding variables, the patients with a BMI < 18.5 had a higher hospital mortality rate (Table 6). There were no statistically significant differences in the ICU LOS ratio among the five BMI subgroups as assessed by Kruskal-Wallis test (P = 0.3414) (Tables 7).
Table 6 Multiple logistic regression analysis assessing the association of hospital mortality with APACHE III predicted hospital mortality, intensity of treatment, admission source, and BMI subgroup in 8,450 non-operative patients
Odds Ratio (95% CI) P-value
BMI
< 18.5 1.51 (1.13–2.01) 0.0051
18.5–24.9 1.00
25.0–29.9 1.02 (0.87–1.20) 0.8022
30.0–39.9 0.98 (0.82–1.17) 0.7979
≥ 40.0 0.86 (0.63–1.20) 0.3967
Predicted mortality 1.045 (1.042–1.048) <0.0001
Intensity of treatment
Low-risk monitor 0.39 (0.31–0.51) <0.0001
High-risk monitor 0.87 (0.73–1.03) 0.1033
Active 1.00
Admission source
Other hospital 0.99 (0.75–1.32) 0.9596
ER/Direct 1.00
Same hospital 1.43 (1.24–1.65) <0.0001
*Four patients were excluded from this analysis because of missing data. CI = Confidence interval; RR = recovery room; OR = operating room; ER = emergency room; Direct = direct admission from the outpatient clinic
Table 7 Observed and predicted length of ICU stay and ICU length of stay ratio for non-operative patients
Median (IQR) ICU Length of Stay
BMI Observed Predicted Ratio
< 18.5 1.71 (0.87–3.80) 4.38 (2.82–6.15) 0.45 (0.24–0.87)
18.5–24.9 1.65 (0.87–3.62) 4.01 (2.70–5.82) 0.44 (0.25–0.90)
25.0–29.9 1.63 (0.90–3.58) 4.13 (2.71–5.83) 0.45 (0.25–0.87)
30.0–39.9 1.73 (0.91–3.71) 4.08 (2.83–5.87) 0.46 (0.25–0.91)
≥ 40.0 1.94 (0.92–4.34) 4.39 (3.05–6.25) 0.49 (0.28–0.93)
LOS = Length of stay; ICU = Intensive care unit; IQR = Interquartile range
In discriminating hospital survivors from non-survivors, the AUC (95% CI) of the model with BMI < 18.5 was 0.859 (0.854 to 0.864) and the AUC of the model without BMI < 18.5 was 0.858 (0.853 to 0.863) (P = 0.102).
Discussion
The results of our retrospective study suggest that a BMI <18.5 is independently associated with higher mortality in post- and non-operative patients admitted to the ICU. We found no difference in the ICU LOS among the BMI subgroups in non-operative patients. In post-operative patients, the LOS was longer in patients with a BMI <18.5 or BMI ≥ 40. We also noted that the addition of BMI does not improve significantly the predictive accuracy of the prognostic model.
In this study, we wanted to determine the impact of BMI on mortality in post- and non-operative patients separately since patients admitted to the ICUs from hospital wards have higher mortality than patients admitted from the operating room, independent of disease severity[17,18]. In our cohort, both post-operative and non-operative groups had a higher mortality rate when their BMI was < 18.5. Interestingly, a BMI between 30.0–39.9 was associated with lower mortality in post-operative patients. Although previous studies had included both post-operative and non-operative patients, they had not looked at the influence of BMI on mortality in these two groups separately[8,9]. Low BMI has been associated with higher mortality rate in hospitalized patient in both the ICU [8,9] and non-ICU settings[1,6,7]. Studies addressing the association of high BMI in patients admitted to the ICU and the hospital have given conflicting results. In a retrospective study of 184 blunt trauma victims, Choban et al found that mortality in patients with a BMI >31 and <27 was 42.1 and 5 %, respectively[19]. In a more recent study of 117 morbidly obese patients (BMI= 51.3 ± 25.9) compared to 132 other patients (BMI= 27.6 ± 3.1) selected randomly by a computer in the medical ICU, their mortality was 30 and 17 %, respectively[20]. In contrast, our study was similar to the studies by Tremblay et al.[8] and Garrouste-Orgeas et al.[9] in showing that a high BMI was not associated with high mortality in both post- and non-operative patients. We also found that a BMI between 30.0–39.9 (grade 2 overweight) was associated with decreased mortality in post-operative patients. Although obesity has long been considered a potential risk factor for poor outcome from a variety of surgical procedures, the evidence suggests that obesity does not result in an increase in mortality[21,22]. Obesity, however, has been associated with an increase in perioperative complications, particularly wound problems, which could explain our finding of a longer LOS in post-operative patients with BMI ≥ 40. Additionally, in a recent study of patients receiving mechanical ventilation for acute lung injury, a BMI > 30 was not associated with increased mortality [23].
Our study has several limitations. It has been speculated that a low BMI is simply the consequence of a serious illness before the hospitalization that it is ultimately fatal[1,6]. We have not adjusted for previous weight loss because this information was not available. However, studies that have adjusted for weight loss before the admission found that the independent effect on mortality of low BMI was unchanged[1,6]. Additionally, depending on the balance between fluid intake and output, the weight on admission to the ICU could be different to the patient's real weight, and because of the study design, we cannot ascertain the accuracy of the height and weight measurements. Although the APACHE III data were collected prospectively, our study has a retrospective design. Since our analysis was limited to the variables available in the APACHE III database, other confounding factors that might have influenced outcomes may not have been included. Moreover, because our study reflects the experience of a single tertiary institution with a unique system of health care delivery and without ethnic diversity, the results cannot be extrapolated to other medical centers.
Conclusions
This study shows that a BMI < 18.5 is independently associated with increased mortality in post- and non-operative patients admitted to the ICU; and in post-operative patients, the LOS was longer in patients with a BMI < 18.5 or BMI ≥ 40. We also found that the inclusion of BMI < 18.5 in a prognostic model does not improve the accuracy of mortality prediction.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JDF participated in conception, design, acquisition of the data, analysis of the data, and drafting of the manuscript.
OG participated in analysis of the data, and critical revision of the manuscript.
BA participated in conception, design, analysis of the data, statistical analysis, critical revision of the manuscript, and supervision.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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| 15462681 | PMC535928 | CC BY | 2021-01-04 16:31:01 | no | BMC Emerg Med. 2004 Oct 5; 4:3 | utf-8 | BMC Emerg Med | 2,004 | 10.1186/1471-227X-4-3 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-531556939010.1186/1471-2334-4-53Research ArticlePharmacokinetics of quinacrine in the treatment of prion disease Yung Lotus [email protected] Yong [email protected] Pierre [email protected] Giuseppe [email protected] Emil T [email protected] Michael [email protected] Stanley B [email protected] Chongsuk [email protected] B Joseph [email protected] Department of Clinical Pharmacy, School of Pharmacy, University of California San Francisco, San Francisco, California 94143-0622, USA2 Drug Studies Unit, Department of Biopharmaceutical Sciences, School of Pharmacy, University of California San Francisco, San Francisco, California 94143-0446, USA3 Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, California 94143-0446, USA4 Department of Neurology, School of Medicine, University of California San Francisco, San Francisco, California 94143-0114, USA5 Department of Biochemistry and Biophysics, School of Medicine, University of California San Francisco, San Francisco, California 94143-0448, USA6 Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, California 94143-0518, USA2004 29 11 2004 4 53 53 10 8 2004 29 11 2004 Copyright © 2004 Yung 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
Prion diseases are caused by the accumulation of an aberrantly folded isoform of the prion protein, designated PrPSc. In a cell-based assay, quinacrine inhibits the conversion of normal host prion protein (PrPC) to PrPSc at a half-maximal concentration of 300 nM. While these data suggest that quinacrine may be beneficial in the treatment of prion disease, its penetration into brain tissue has not been extensively studied. If quinacrine penetrates brain tissue in concentrations exceeding that demonstrated for in vitro inhibition of PrPSc, it may be useful in the treatment of prion disease.
Methods
Oral quinacrine at doses of 37.5 mg/kg/D and 75 mg/kg/D was administered to mice for 4 consecutive weeks. Plasma and tissue (brain, liver, spleen) samples were taken over 8 weeks: 4 weeks with treatment, and 4 weeks after treatment ended.
Results
Quinacrine was demonstrated to penetrate rapidly into brain tissue, achieving concentrations up to 1500 ng/g, which is several-fold greater than that demonstrated to inhibit formation of PrPSc in cell culture. Particularly extensive distribution was observed in spleen (maximum of 100 μg/g) and liver (maximum of 400 μg/g) tissue.
Conclusions
The documented extensive brain tissue penetration is encouraging suggesting quinacrine might be useful in the treatment of prion disease. However, further clarification of the distribution of both intracellular and extracellular unbound quinacrine is needed. The relative importance of free quinacrine in these compartments upon the conversion of normal host prion protein (PrPC) to PrPSc will be critical toward its potential benefit.
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Background
Prion diseases, while rare, invariably result in fatal neurodegeneration. At present, no therapy has been proven to be useful in the treatment of prion disease. However, acridine and phenothiazine derivatives have been evaluated [1,2] using an in vitro model, and quinacrine and chlorpromazine inhibit PrPSc formation in a scrapie-infected neuroblastoma (ScN2a) cell line [2]. Half-maximal inhibition of PrPSc formation at effective concentrations (EC50) for quinacrine was found to be 300 nM (120 μg/ml) [2]. While this concentration represents the concentration of quinacrine added to the cell culture, it does not reflect the effective intracellular quinacrine concentrations. In the past, quinacrine was used as an antiparasitic agent in the treatment of malaria and giardiasis, however, more effective, less toxic agents have since replaced this agent [3]. While quinacrine may be useful in the treatment of prion disease, its pharmacokinetics have not been extensively studied. Studies from the 1940s suggest that quinacrine is associated with extensive tissue distribution and a prolonged pharmacologic half-life [3,4]. However, the rate and extent of quinacrine penetration into brain tissue has not been characterized. If quinacrine administration in vivo results in brain tissue concentrations exceeding that shown to inhibit PrPSc in vitro, it may constitute an effective therapy for prion disease. While the precise site of antiprion action is unknown (intracellular versus extracellular) adequate quinacrine penetration into brain tissue might result in an effective agent for the treatment of prion diseases.
The objective of this study was to characterize the achievable plasma concentrations and associated tissue (brain, liver, spleen) penetration associated with quinacrine.
Methods
Animal Model
The protocol was approved by the institutional animal care and use committee. The Animal Facility of the Institute for Neurodegenerative Diseases provided animal samples from 2 different strains of mice, FVB and CD1. Twenty four animals of each strain (24 FVB and 24 CD1 strain animals) were fed 37.5 mg/kg/D and 24 animals of each strain were fed 75 mg/kg/D of oral quinacrine over a 4-week period (weeks 1–4), given ad lib as a chocolate flavored liquid diet. From weeks 5–8 animals were given regular feed without quinacrine. At weekly intervals, 3 animals of each strain were euthanized and tissues were collected for analysis.
Quinacrine level analysis
Preparation of standard solutions
Quinacrine dihydrochloride (purity: 98.6%) was purchased from Fluka, while sulfadimethoxine sodium salt (purity: 98%) which served as the internal standard (IS) was obtained from Sigma. Quinacrine stock solution was prepared as 1 mg/ml in methanol. Sulfadimethoxine stock solution was prepared as 1 mg/ml in 50% methanol. The working solutions were prepared by diluting the respective standard and control stock solutions with 50% methanol and 0.1% formic acid to 2 μg/ml and 100 ng/ml, respectively. All solutions were stored in a 4°C refrigerator in silinized brown glass containers.
LC/MS/MS system and conditions
The HPLC system employed a Shimadzu LC-10 AD pump and a Waters intelligent Sample Processor 717 Plus autosampler/injector. A BDS Hypersil C18 column, 50 × 4.60 mm, 5 μm particle size was directly coupled to a Micromass Quattro LC Ultima triple quadrupole tandem mass spectrometer using electrospray ionization in positive ion mode. The sample cone voltage and collision energy were 25 V and 20 eV respectively for both quinacrine and the internal standard and the source block and desolvation temperature were 100°C and 400°C, respectively. The mass scanning mode employed multiple reaction monitoring (MRM) with the singly charged quinacrine ion selected at m/z 400.5 giving a fragment ion at m/z/142.0, and the internal standard at m/z 311.0→156.0. The mobile phase consisted of CH3OH/H2O/TFA (45:55:0.05) with 1 mM ammonium formate. The flow rate was 0.8 ml/min with 1/4 split into the mass spectrometer. The injection volume was 5–10 μl with a run time of 3.5 min.
Sample preparation
All samples were stored at -70°C until analyzed. Each tissue sample was subjected to a specific method, as described below, for drug extraction and for the determination of concentration. All samples were analyzed by LC/MS/MS.
Accuracy and precision
Accuracy and precision was demonstrated throughout the working range with interday and intraday coefficient of variation and relative error <10%.
Plasma extraction
Each plasma sample was thawed at room temperature for 10–15 min; then 20 μl of plasma was aliquotted to a new test tube. To each tube 200 μl of 70% acetonitrile solution, containing 0.1% formic acid and 50 ng/mL of internal standard, was added. The test tubes were vortexed at high speed for 1 min and centrifuged at 10,000 rpm for 10 min. The supernatant was transferred into the autosampler for LC/MS/MS analysis. A set of standard curves with a duplicate set of quality control (QC) samples was generated for sample analysis.
Brain samples
Prior to the in vivo study, the stability of quinacrine in mouse brain tissue was determined. Using 3 different doses of quinacrine (4 mg/kg/d, 80 mg/kg/d, 160 mg/kg/d), mouse brain tissue was soaked in 100% methanol for 7 days. Samples were taken on days 0,1,2,3, and 7 to analyze quinacrine concentrations using LC/MS/MS. Complete equilibration was observed by Day 1 and no degradation in quinacrine was noted through day 7.
Brain samples were thawed (in plastic tubes) at room temperature for 15–20 min and weighed. To each sample 0.5 ml of 0.9% NaCl was added followed by incubation at room temperature for 1 h. For the standard curve and quality control samples, a brain tissue sample from an untreated mouse was spiked with different amounts of quinacrine and incubated at room temperature for 1 h. After the addition of 100 μl of 1 μg/ml internal standard solution in 50% methanol and 0.1% formic acid, 5 ml of 100% methanol was added into each sample and soaked at 4°C for two weeks. On the last day, 200 μl was aliquotted from each brain sample and placed into the autosampler for analysis via LC/MS/MS.
Liver samples
Liver samples were thawed at room temperature for 15–30 min and weighed. Considering the increased size of the liver samples and that the increased connective tissue in liver samples could potentially prevent the complete distribution of quinacrine into methanol, homogenization was used for these samples. To each sample 100% methanol was added (10 ml/g of tissue) and the tissue was homogenized in ice water for 1 min at speed 3 (Tissue Tearor, model 985-370, Biospec Products, Inc). The internal standard (100 μl of 10 μg/ml in 50% methanol, 0.1% formic acid) was added to 0.2 ml of each homogenized liver sample in a glass test tube. Samples were vortexed for 1 min, centrifuged at 3000 rpm for 10 min and 20 μl of each supernatant was transferred into a new test tube. Each sample was further diluted with 4 ml of 50% methanol, vortexed, and 200 μl was transferred to the autosampler for LC/MS/MS analysis. A set of standard curve with a duplicate set of QC samples was generated for sample analysis.
Spleen samples
Spleen sample preparation was similar to the preparation of brain samples using a methanol soak. Prior to the in vivo study, the stability of quinacrine in mouse spleen tissue was determined. Using 3 different doses of quinacrine (4 mg/kg/d, 80 mg/kg/d, 160 mg/kg/d), mouse spleen tissue was soaked in 100% methanol for 7 days. Samples were taken on days 0, 1, 2, 3, and 7 to analyze quinacrine concentrations using LC/MS/MS. Complete equilibration took place by Day 1 and no degradation in quinacrine was observed through day 7.
Samples from the in vivo analysis were soaked for 14 days at 4°C. On day 14, 50 μl was aliquotted from each spleen sample and diluted with 1 ml of 50% methanol. Each sample was vortexed for 1 min and 200 μl was transferred to the autosampler for LC/MS/MS analysis. A set of standard curves with a duplicate set of QC samples was generated for sample analysis.
Results
Quinacrine analysis
The mass spectrum of quinacrine (Q1) and its tandem spectrum (Q3), showing the fragment ion selected for MRM, are shown in Figure 1. Using MRM for m/z 400.5 ± 142.0 and the appropriate LC/MS/MS conditions described above, quinacrine could be selectively and sensitively detected in plasma (Fig. 2) and brain tissue (Fig. 3) after relatively simple sample preparation.
In vivo studies
In plasma, quinacrine concentrations with the 37.5 mg/kg/D dose ranged from 75 to 175 ng/ml for both FVB and CD1 strains mice and remained at this level during the 4-week dosing interval. Once discontinued, quinacrine was completely eliminated from plasma, with no drug detectable within one week. At 75 mg/kg/D quinacrine, plasma concentrations reached >2 × those observed with the lower dose, averaging 300 to 400 ng/ml.
Quinacrine levels in brain tissue for both mice strains were determined to be substantially greater than those achieved in plasma. Similar to observations with plasma, steady-state levels in the brain with the 37.5 mg/kg/D dose were achieved by the end of the first week, averaging 400 to 600 ng/g brain tissue. Figure 4 characterizes the brain tissue levels in the FBV mice. Quinacrine was undetectable in brain tissue within a week after discontinuing treatment. With the 75 mg/kg/D dose, quinacrine levels in brain tissue were observed to be greater than 2 × those observed with the 37.5 mg/kg/D dose, averaging 1500 ng/g. After high-dose (75 mg/Kg/D) treatment ceased, quinacrine levels in brain were detected for two weeks.
The analysis of liver samples showed particularly high quinacrine levels, at many times those observed in plasma and brain. At the end of the first week of treatment with 37.5 mg/kg/D, steady-state levels of 70 to 90 μg/g were achieved in liver tissue in both mice strains, which remained throughout the course of treatment. Figure 5 demonstrates the achievable liver tissue levels in FBV mice. In contrast, the 75 mg/kg/D dosing resulted in gradually increasing levels in liver tissue, rising from approximately 150 μg/g in the first week to a steady-state level of 300 to 400 μg/g by the end of the 4th week. Similarly, spleen tissue levels in both mice strains were very elevated, averaging 5 to 10 μg/g with the 37.5 mg/kg/D dose and 40 to 100 μg/g with the 75 mg/kg/D dose. Figure 6 characterizes the spleen tissue levels of the FBV mice.
Of note, in contrast with plasma and brain tissue, in which dose dependency was somewhat linear, increasing quinacrine doses in spleen and liver were associated with substantially greater tissue levels. In addition to the isolation of quinacrine, numerous metabolites were identified in all tested tissue samples (data not shown).
Discussion
The current study is the first comprehensive evaluation of quinacrine distribution in plasma and brain tissue. Shannon et al. evaluated the pharmacokinetics of quinacrine in the treatment of malaria [3]. Using a dog model, these researchers observed quinacrine to be concentrated in the liver and spleen, as well as muscle and lung. In the same study, humans receiving 100 mg quinacrine three times daily achieved maximal plasma concentrations of 100 ng/ml. Administration of a single intravenous dose of 2 mg/kg quinacrine in rabbits was associated with plasma levels of 10 ng/ml [4]. The quinacrine levels we observed in plasma are similar to those recorded from oral dosing for malaria [3]. We observed rapid and extensive distribution of quinacrine in brain, liver, and spleen tissue in association with much lower plasma concentrations. Although quinacrine was consistently observed one week after drug discontinuation in all tissue at the 75 mg/kg/D dose, none could be observed one week after cessation of the 37.5 mg/kg/D doses.
The concentrations we report in brain in terms of concentration/gm of tissue are several-fold greater than that shown for effective antiprion activity in ScN2a cells [2]. In that in vitro study, the EC50 for quinacrine was 300 nM, which approximates 120 ng/ml. We report here that quinacrine levels in brain tissue averaged 400 to 600 ng/g with the 37.5 mg/kg/D dose and 1500 ng/g with the 75 mg/kg/D dose, concentrations that exceed the in vitro EC50 by 3- to 10-fold.
Others have reported the antiprion function of quinacrine in vitro, including clearance of PrPSc (5) and/or inhibition of PrPSc formation [6]. Barret et al., who also noted the in vitro benefit from quinacrine, found the drug to be ineffective in the animal model under the conditions employed [1]. In contrast, some case reports in humans suggest quinacrine to be associated with clinical improvement, including the return of voluntary eye movement [7,8].
While the current study has confirmed brain tissue concentrations in excess of the reported EC50, it important to note critical limitations in their interpretation. Quinacrine has been determined to be highly protein-bound [3], measured at 83–90% in older studies. Additionally, the drug has been shown to be highly concentrated in white blood cells with intracellular levels ranging from 9,500–18,400 μg/L with accompanying low CSF levels (4.3–5.4 μg/L). Previous investigations confirm that it is free drug that is microbiologically active in the treatment of infection [9]. The brain tissue levels in the current investigation represent the sum total of intracellular, extracellular, protein-bound and unbound quinacrine. Highly protein-bound agents penetrate less well between plasma and tissue compartments, suggesting that quinacrine would more likely be plasma-bound. However, the results of our study strongly suggest deep brain tissue penetration of quinacrine, suggesting the possible contribution of membrane transporter proteins or other mechanisms facilitating passage of quinacrine into brain tissue. Considering the extensive intracellular white blood cell concentrations achieved with quinacrine, similar mechanisms may be responsible in actively pumping quinacrine into these cells. Consequently, it is critical to determine the actual free quinacrine concentrations in both intracellular and extracellular brain tissue and to document the relative contribution of these compartments in the pathogenesis and treatment of prion disease. Using microdialysis probes, Mindermann and colleagues [10] evaluated the penetration of rifampin into cerebral extracellular space, brain tumor, perifocal, and normal brain tissue. The findings confirmed consistent cerebral extracellular space concentrations, but remarkably different rifampin brain tissue levels, particularly concentrating in brain tumor tissue. Considering the pathogenesis of prion disease, heterogeneity of brain tissue concentrations may impact similarly the efficacy of quinacrine or other agents. Our results strongly suggest that similar microdialysis experiments take place with quinacrine to clarify the distribution characteristics of this agent.
In addition to steady-state levels of quinacrine, we observed a number of quinacrine metabolites in all tissue samples studied, which raises the possibility that these metabolites may have in vitro antiprion activity similar to or greater than that of the parent compound. These tests warrant further evaluation.
Conclusions
Based upon its oral bioavailability, favorable tissue distribution characteristics, and in vitro activity, quinacrine may be a useful agent in the treatment of prion disease. However, more detailed tissue distribution analyses linked with the critical sites of prion action are warranted.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
LY, YH, ETL developed the assay, performed sample analyses, contributed to the writing of the paper. PL, GL, MB, CR developed the protocol and performed the animal model experiments. SBP conceived of the study and participated in its coordination. BJG drafted the manuscript, participated in its coordination and mentored LY. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Figure 1 Q1 and Q3 mass spectra of quinacrine
Figure 2 LC/MS/MS chromatograms of quinacrine in mouse plasma
Figure 3 LC/MS/MS chromatograms of quinacrine in mouse brain
Figure 4 Quinacrine brain levels in mice (FVB strain) administered oral quinacrine at 37.5 or 75 mg/kg/D for 4 weeks
Figure 5 Quinacrine liver levels in mice (FVB strain) administered oral quinacrine at 37.5 or 75 mg/kg/D for 4 weeks
Figure 6 Quinacrine spleen levels in mice (FVB strain) administered oral quinacrine at 37.5 or 75 mg/kg/D for 4 weeks
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Korth C May BCH Cohen F Prusiner S Acridine and phenothiazine derivatives as pharmacotherapeutics for prion disease Proc Natl Acad Sci 2001 14 9836 41 10.1073/pnas.161274798
Shannon JA Earle DP Brodie BB Taggart JV Berliner RW The pharmacological basis for the rational use of atabrine in the treatment of malaria J Pharmacol Exp Ther 1944 81 307 330
Bjorkman S Elisson LO Gabrielsson J Pharmacokinetics of quinacrine after intrapleural instillation in rabbits and man J Pharm Pharmacol 1989 41 160 163 2568441
Sandberg MK Wallen P Wikstrom MA Kristensson K Scrapie-infected GT-1 cells show impaired function of voltage-gated N-type calcium channels (Ca(v) 2.2) which is ameliorated by quinacrine treatment Neurobiol Dis 2004 15 143 51 14751779 10.1016/j.nbd.2003.09.006
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Nakajima M Yamada T Kusuhara T Furukawa H Takahashi M Yamauchi A Kataoka Y Results of quinacrine administration to patients with Creutzfeldt-Jakob disease Dement Geriatr Cogn Disord 2004 17 158 163 14739538 10.1159/000076350
Lam YWF Duroux MH Gambertoglio JG Barriere SL Guglielmo BJ Effect of protein binding on serum bactericidal activities of ceftazidime and cefoperazone in healthy volunteers Antimicrob Agents Chemother 1988 32 298 302 3284457
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| 15569390 | PMC535929 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Nov 29; 4:53 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-53 | oa_comm |
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BMC Pregnancy ChildbirthBMC Pregnancy and Childbirth1471-2393BioMed Central London 1471-2393-4-221557419210.1186/1471-2393-4-22Research ArticleReexamining the effects of gestational age, fetal growth, and maternal smoking on neonatal mortality Ananth Cande V [email protected] Robert W [email protected] Division of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology, and Reproductive Sciences, UMDNJ-Robert Wood Johnson Medical School, New Brunswick, NJ 08901-1977, USA2 Departments of Pediatrics, and of Epidemiology and Biostatistics, McGill University, Montreal, Canada2004 1 12 2004 4 22 22 23 3 2004 1 12 2004 Copyright © 2004 Ananth and Platt; licensee BioMed Central Ltd.2004Ananth and Platt; 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
Low birth weight (<2,500 g) is a strong predictor of infant mortality. Yet low birth weight, in isolation, is uninformative since it is comprised of two intertwined components: preterm delivery and reduced fetal growth. Through nonparametric logistic regression models, we examine the effects of gestational age, fetal growth, and maternal smoking on neonatal mortality.
Methods
We derived data on over 10 million singleton live births delivered at ≥ 24 weeks from the 1998–2000 U.S. natality data files. Nonparametric multivariable logistic regression based on generalized additive models was used to examine neonatal mortality (deaths within the first 28 days) in relation to fetal growth (gestational age-specific standardized birth weight), gestational age, and number of cigarettes smoked per day. All analyses were further adjusted for the confounding effects due to maternal age and gravidity.
Results
The relationship between standardized birth weight and neonatal mortality is nonlinear; mortality is high at low z-score birth weights, drops precipitously with increasing z-score birth weight, and begins to flatten for heavier infants. Gestational age is also strongly associated with mortality, with patterns similar to those of z-score birth weight. Although the direct effect of smoking on neonatal mortality is weak, its effects (on mortality) appear to be largely mediated through reduced fetal growth and, to a lesser extent, through shortened gestation. In fact, the association between smoking and reduced fetal growth gets stronger as pregnancies approach term.
Conclusions
Our study provides important insights regarding the combined effects of fetal growth, gestational age, and smoking on neonatal mortality. The findings suggest that the effect of maternal smoking on neonatal mortality is largely mediated through reduced fetal growth.
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Background
Birth weight is arguably one of the strongest predictors of infant survival, yet its role as a causal predictor of mortality is poorly understood [1]. This is at least partly because low birth weight (<2,500 g) is a construct of two intricately intertwined components: preterm delivery and reduced fetal growth, or both. Our lack of understanding of the complex relationship among birth weight, gestational age and perinatal mortality stems from mixing etiologically distinct pathways to mortality, namely effects chiefly due to fetal maturity (i.e., gestational age) versus those related to fetal growth.
Disentangling the intricate pathways of gestational age and fetal growth to neonatal mortality gets even more complicated by the consideration of a third factor – maternal smoking during pregnancy. Smoking has been clearly associated with poor reproductive outcomes, including increased risk of preterm birth, stillbirth, and a range of other outcomes [2-6]. Recent studies suggest a more direct and stronger association between maternal smoking and "fetal growth" (birth weight-for-gestational age) than with preterm delivery [7], suggesting that the effect of smoking on mortality may be largely mediated through restricted fetal growth rather than preterm delivery.
To better understand the relationship among these indices of "fetal wellbeing", we examined neonatal mortality in relation to standardized birth weight (i.e., z-score birth weight), gestational age, and smoking during pregnancy. We applied nonparametric logistic regression based on generalized additive models to examine neonatal mortality in relation to 3 factors.
Methods
Cohort composition of United States live births
Data for this study were derived from the 1998–2000 United States vital statistics data files (live births linked to infant deaths), assembled by the National Center for Health Statistics of the Centers for Disease Control and Prevention [8]. The analysis was restricted to singleton live births, with neonatal mortality defined as deaths within the first 28 days. Gestational age assignment in these data are predominantly based on self-reported last menstrual period, with a small fraction (<5%) based on the clinical estimate [9]. Further, the National Center for Health Statistics imputed missing gestational ages in these data files prior to release of the data [10].
Information on smoking during pregnancy was available in two forms on the vital statistics data: one as an indicator variable (yes or no), and the other as a continuous variable denoting the number of cigarettes smoked per day during pregnancy. Both of these smoking measures were based on maternal self-report. Information on smoking patterns across different trimesters in pregnancy was not available on the vital records.
Fetal growth was defined as birth weight-for-gestational age, and was expressed as gestational age-specific birth weight z-score. This z-score construct is interpreted as units of standard deviations from the population-specific mean birth weight at each gestational age. The z-score or standardized birth weight follows a Gaussian distribution with mean 0 and variance 1.
In addition to the full analysis, we also examined in a sub-analysis the impact of implausible birth weight/gestational age combinations on overall results. These implausible birth weight/gestational ages were identified if infants' birth weights were outside the gestational age-specific birth weight cutoffs [11]. This was done to examine the impact of largely apparent gestational age errors (e.g., infant delivered at 26 weeks with a birth weight of 4,000 g) on neonatal mortality.
Data exclusions
There were 11,677,103 singleton live births from which we excluded infants with missing birth weight or gestational age (n = 237,433), and birth weight <500 g or gestational age <24 weeks (n = 28,732). Since smoking data was not reported on vital statistics in California, Indiana, New York state, and South Dakota [8], births from these states were also excluded (n = 1,326,841). After all exclusions, 6,117,808 singleton live births remained for analysis.
Statistical analysis
We examined the distributions of z-score birth weight, gestational age, and number of cigarettes smoked per day, and compared these distributions between the two groups of neonatal mortality. Neonatal mortality was then modeled using nonparametric logistic regression based on generalized additive models [12]. GAM is one modeling approach that makes no assumptions about the functional form of the exposure-disease relationship except for smoothness, i.e., continuity of the dose-response function and its low-order derivatives [13]. When combined with more traditional modeling approaches, GAMs are powerful graphical tools that can provide interesting insights about complex relationships. While polynomial models [14] could be used to the same end as GAM-based approaches, such models result in restricted shapes, especially at the tail of the distribution, and may not be as statistically efficient as nonparametric models. Therefore, these models were not considered.
All regression models were adjusted for the confounding effects due to maternal age and gravidity (i.e., number of pregnancies). We examined the associations between neonatal mortality and each of the 3 factors z-score birth weight, gestational age, and number of cigarettes smoked per day separately. We then fit a full model for mortality after forcing all 3 predictors (in addition to the confounders) as described in the Appendix [see additional file 1]. The independent effect of each of these 3 factors on neonatal mortality was assessed by comparing the residual deviances [12] between nested models (i.e., comparing the residual deviances from a full model to a model without the predictor). Under the large sample assumption, the deviance has an approximate chi-square distribution, with degrees-of-freedom for the test being the difference in the degrees of freedom between the nested models being compared. We also examined the distribution of partial residuals [12] from fitting the model to assess departures from adequate fit.
In addition, we tested for all possible two-factor interactions between the predictors. Although all interactions were statistically significant (owing to the large study size), none provided any additional insights that were different from a model that contained no interaction terms. Therefore, we did not consider assessing two-way interactions in the analysis.
All statistical analyses were performed in S-Plus (Insightful Corporation, Seattle WA) version 6.2 on the UNIX (Sun Microsystems, Inc: Palo Alto, CA). Nonparametric logistic regression models were fit using the gam( ) function based on the it loess scatterplot smoother [14], using the default span of 50%. Given the large size of the study, small changes in the span resulted in statistically significant improvement in the fit, while offering very little clinical insight. Thus, we resorted to the default span.
Results
The overall neonatal mortality rate was 2.4 per 1,000 live births. The distribution of z-score birth weight among infants that died during the neonatal period was shifted more towards lower standardized birth weights than among those that survived the neonatal period (Fig 1, left panel). Infants who died during the neonatal period were delivered earlier than those that survived the neonatal period (Fig 1, right panel). Surviving infants weighed, on average, 1,582 g more compared with those who died during the neonatal period (P < 0.0001; Table 1). Likewise, infants who died during the neonatal period were delivered, on average, 7 weeks earlier than those who survived the neonatal period (P < 0.0001). The proportion of mothers that smoked during their pregnancy was higher among infants that died during the neonatal period (19.1%) compared with those that survived the neonatal period (16.5%; P < 0.0001).
Figure 1 Distributions of z-score birth weight (left panel) and gestational age (right panel) among neonatal deaths (thick line) and neonatal survivors (thin line).
Table 1 Distributions of birth weight, gestational age, and maternal smoking in relation to neonatal survival status
Neonatal survivors Neonatal deaths
Total events 10,084,106 27,355
Maternal age (years)† 27.0 (6.2) 26.4 (6.7)
Primigravida 33.2% 34.1%¶
Birth weight (grams)† 3,347 (572) 1,765 (1,145)
Birth weight <2,500 grams 6.1% 69.6%
Birth weight <1,500 grams 1.1% 62.3%
z-score birth weight† 0.00 (1.00) -0.62 (1.07)
Gestational age (weeks)† 38.9 (2.3) 31.3 (6.6)
Delivered <37 weeks 10.3% 67.9%
Delivered <34 weeks 3.0% 63.6%
Delivered <32 weeks 1.7% 61.2%
Smoking during pregnancy
Smokers 16.5% 19.1%
Cigarettes smoked/day‡ 11 (1–40) 15 (1–40)
† Data expressed as mean (standard deviation).
‡ Data expressed as median (range) among all smokers.
¶ P-value <0.01. For all other comparisons, P < 0.0001.
We first separately examined the effect of each of the 3 covariates standardized birth weight, gestational age, and number of cigarettes smoked per day, on neonatal mortality. This was done by fitting nonparametric logistic regression models (GAM). The univariable GAM strongly suggests that the unadjusted association between standardized z-score birth weight and neonatal mortality is nonlinear (not shown). The association between gestational age and neonatal mortality was also nonlinear, whereas the association between number of cigarettes smoked per day mortality was virtually flat. The adjusted smooth curves for these 3 covariates, along with their corresponding 95% point-wise confidence bands are displayed in Figure 2. These curves were adjusted for the other two factors in addition to maternal age and gravida. It is interesting to note that the relationship between standardized birth weight and neonatal mortality (adjusted for gestational age and smoking and confounders) was virtually flat at increased birth weight z-scores (i.e., at z-scores ≥ 4.0).
Figure 2 Adjusted log-odds of neonatal mortality (thick curve) with 95 percent point-wise confidence bands (shaded area) for z-score birth weight (left panel), gestational age (middle panel), and number of cigarettes smoked per day (right panel). Each factor was adjusted for the other two factors as well as for maternal age and gravidity.
Since smoking was weakly associated with neonatal mortality, we examined if the effect of smoking on mortality was mediated through either standardized birth weight or gestational age (or both). We therefore modeled neonatal mortality in relation to these 2 covariates (and adjusted for confounders) within broad categories of smokers and nonsmokers (Fig 3). Compared with nonsmokers, neonatal mortality among women that smoked during their pregnancy was higher among infants that were between -5 and -1, and between 1 and 5 standard deviation units of the birth weight distribution among smokers. Infants with birth weight z-scores between -1 and 1 had mortality rates that were similar regardless of maternal smoking status. When neonatal mortality rates were examined by gestational age, the mortality curve was consistently higher at every gestational age among smokers than among nonsmokers (P < 0.001). In order to better understand whether smoking affects fetal growth, we examined the distributions of gestational age-specific standardized birth weight z-scores between the two groups of smokers (Fig 4). The results indicate that the adjusted mean z-score birth weight among nonsmokers is fairly constant across gestational age. However, among women that smoked during pregnancy, the adjusted mean z-score is higher that those of nonsmokers between 22 and 28 weeks, and begins to drop precipitously with increasing gestational age. This pattern indicates that smoking results in more growth restricted infants, and that the effect of smoking on reduced fetal growth appears to get stronger at gestational ages 32 weeks and beyond.
Figure 3 Adjusted log-odds of neonatal mortality based on z-score birth weight (left panel) and gestational age (right panel) among smokers (thick curve) and nonsmokers (thin curve). Each factor was adjusted for the other factor as well as for maternal age and gravidity.
Figure 4 Distribution of gestational age-specific mean z-score birth weight among smokers (thick curve) and nonsmokers (thin curve). The curves were adjusted for maternal age and gravidity.
The logistic regression models discussed thus far are based on the implicit assumption that the combined effects of standardized birth weight and gestational age are multiplicative on a logistic scale. We examined the sensitivity of this assumption by modelling neonatal mortality by allowing an interaction term between these two factors based on nonparametric smooth fit. The joint effect of standardized birth weight and gestational age on neonatal mortality reveals that both reduced fetal growth and early delivery result in increasing mortality risk, with the mortality plane progressively diminishing with increasing standardized birth weight and gestational age (Fig 5).
Figure 5 Adjusted smoothed surface of risk of neonatal mortality in relation to z-score birth weight and gestational age. The curve was adjusted for smoking, maternal age, and gravidity.
Discussion
For decades, several researchers have focused on trying to understand the complex biological relationship among pregnancy duration, infant size, and neonatal mortality. Not only are gestational age and birth weight highly correlated, but both are powerful predictors of neonatal mortality [14-16]. The chief findings from our study include (i) z-score birth weight and preterm delivery (independent of birth weight) exert strong influences on neonatal mortality; (ii) the effect of maternal smoking is mediated largely through reduced fetal growth and, to a smaller extent, through shortened gestation; and (iii) mortality among babies born to smoking mothers is virtually higher at every z-score birth weight (independent of gestational age) than those born to nonsmoking mothers.
The inverted "J"-shaped relationship between birth weight and mortality essentially holds for analyses relating to gestational age and mortality. While birth weight is considered a marker for fetal size, gestational age is thought of as an indicator of fetal maturity. Almost 3 decades ago, Susser and colleagues [15] proposed that gestational age is causally precedent to birth weight (implying that birth weight is in the causal pathway of the gestational age-mortality relationship). Wilcox and Skjaerven [16] examined close to 400,000 singleton births from Norway in an effort to separate the influences of birth weight and gestational age on neonatal mortality. They showed that, comparisons using the "relative birth weight" scale, there were two strong and separable factors related to mortality: gestational age independent of birth weight, and relative birth weight at any given gestational age.
On these similar lines, Herman and Hastie [17] examined neonatal mortality in relation to (absolute) birth weight and gestational age. They initially speculated that among preterm (<37 weeks) babies, maturity would serve as a strong predictor of mortality, while among term babies, the increased mortality was probably due to growth restriction. However, their analysis showed that mortality was associated only with birth weight and not with gestational age. Their approach to analysis may have suffered from collinearity (between birth weight and gestational age), perhaps leading to the attenuated gestational age-mortality relationship [17]. Coory [18] analyzed neonatal mortality in relation to birth weight and gestational age. He concluded that both birth weight and gestational age have independent effects on mortality, and that both are fundamental risk-adjusting variables. However, he was cautious in not interpreting the effects of gestational age, but focused his interpretations almost entirely on birth weight. Our construction of standardized birth weight z-score was developed conditional on gestational age. Thus, this birth weight z-score (independent of gestational age) enabled us to assess the effects of shortened gestation and fetal growth restriction on mortality.
It is widely acknowledged that smoking mothers give birth to infants that are lighter compared with those born to nonsmoking mothers. This reduction in birth weight is thought mainly to result in fetal growth restriction, as well as to shortened gestation [19,20]. Although the precise mechanism by which smoking during pregnancy affects the fetus is unclear, two possible pathways have been proposed. Smoking results in increased capillary fragility and vasoconstriction of arterial walls, leading to reduced blood flow to the uterus and eventually to the placenta [21]. The second is the "fetal hypoxia" hypothesis, whereby smoking leads to a villous shrinkage due to an alteration in the thickness of the villous membrane, thereby reducing oxygen transfer to the fetus [22]. Both mechanisms are likely to increase the risk of uteroplacental bleeding in pregnancy [23], which, in turn, increases the risk of not only neonatal deaths [20,24], but also preterm delivery and growth restriction [23]. Our study provides circumstantial evidence that after the general effects of (shortened) gestational age and (reduced) fetal growth are accounted for, smoking has little direct impact on neonatal mortality.
Our study has some limitations. First, errors in the estimation of gestational age [25,26] are likely to affect our results to some extent. Our study was based on gestational age largely determined from the date of last menstrual period as opposed to one based on early ultrasound. Sonographically estimated gestational age is likely to shift the overall gestational age distribution to lower gestational ages [26] sometimes by as much as a full menstrual cycle [25], possibly due to delayed ovulation or amenorrhea. Second, the impact of congenital malformations and chromosomal abnormalities on the risk of neonatal death could have been partly responsible for the findings noted here. Although data on malformations are contained on the vital statistics files, they are recorded poorly. Third, although we adjusted all the analysis for maternal age and gravidity, the study does not take into account other known or suspected risk factors for neonatal mortality. These risk factors may account for a part of the associations noted here, but is unlikely that these factors could explain the powerful effects of fetal growth restriction and preterm delivery on neonatal mortality. Finally, non-differential misclassification of smoking data on vital records is likely [27] and may have attenuated the smoking-mortality association to some extent.
Application of generalized additive regression models to examine neonatal mortality appears useful towards understanding the complex biological relationship amongst the predictors. However, we make no claim that GAMs serve as adjuncts to other modeling approaches; on the contrary, we believe that GAMs can provide the first step toward modeling complex exposure-disease relationships.
Conclusions
Our study provides important insights about the combined effects of gestational age, fetal growth, and smoking during pregnancy on neonatal mortality. Both standardized z-score birth weight and preterm delivery are strongly associated with neonatal mortality, and the effect of maternal smoking appears largely mediated largely through reduced fetal growth and, to a smaller extent, through shortened gestation.
Competing interests
The author(s) declare that there are no competing interests.
Authors' contributions
CVA and RWP conceived the idea for the study. CVA assembled the data and performed all statistical analyses. CVA drafted the manuscript with essential contributions from RWP. Both authors critically reviewed the manuscript, edited it for content, and revised it to the final form.
Both authors have read and approve the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Description of generalized additive models.
Click here for file
Acknowledgements
We thank Drs. KS Joseph, Michael Kramer, John Preisser, David Savitz, John Smulian, Anthony Vintzileos, and Michelle Williams, for their helpful discussions; for reviewing an earlier draft of the manuscript and providing critical comments and insights. We also thank the referees for their thoughtful comments that improved the manuscript considerably.
This paper was presented at the 15th annual meeting of the Society for Pediatric and Perinatal Epidemiologic Research, held in Palm Desert, CA, June 2002.
Dr Ananth is supported, in part, through a grant (R01-HD038902) awarded to him from the National Institutes of Health, USA. At the time this research was conducted, Dr Platt was a career scientist of the Canadian Institutes for Health Research, Canada.
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| 15574192 | PMC535930 | CC BY | 2021-01-04 16:32:03 | no | BMC Pregnancy Childbirth. 2004 Dec 1; 4:22 | utf-8 | BMC Pregnancy Childbirth | 2,004 | 10.1186/1471-2393-4-22 | oa_comm |
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BMC PsychiatryBMC Psychiatry1471-244XBioMed Central London 1471-244X-4-311548814610.1186/1471-244X-4-31Research ArticleSocial Phobia in an Italian region: do Italian studies show lower frequencies than community surveys conducted in other European countries? Carta Mauro Giovanni [email protected] Maria Carolina [email protected] Mariangela [email protected] Bernardo [email protected]'Osso Liliana [email protected] Mario Antonio [email protected] Hans-Ulrich [email protected] Division of Psychiatry, Department of Public Health, University of Cagliari, Italy2 Department of Psychiatry, Neurobiology, Pharmacology, Biotechnology, University of Pisa, Italy3 Division of Behavioural Sciences, Department of Neurological and Behavioural Sciences, University of Siena, Italy4 Institute of Clinical Psychology, Technical University of Dresden, Germany2004 15 10 2004 4 31 31 25 3 2004 15 10 2004 Copyright © 2004 Carta et al; licensee BioMed Central Ltd.2004Carta 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 lifetime prevalence of Social Phobia (SP) in European countries other than Italy has been estimated to range from 3.5% to 16.0%. The aim of this study was to assess the frequency of SP in Sardinia (Italy) in order to verify the evidence of a lower frequency of SP in Italy observed in previous studies (from 1.0% to 3.1%).
Methods
A randomised cross sample of 1040 subjects, living in Cagliari, in rural areas, and in a mining district in Sardinia were interviewed using a Simplified version of the Composite International Diagnostic Interview (CIDIS). Diagnoses were made according to the 10th International Classification of Diseases (ICD-10).
Results
Lifetime prevalence of SP was 2.2% (males: 1.5%, females: 2.8%) whereas 6-month prevalence resulted in 1.5% (males: 0.9%, females: 2.1%). Mean age at onset was 16.2 ± 9.3 years. A statistically significant association was found with Depressive Episode, Dysthymia and Generalized Anxiety Disorder.
Conclusions
The study is consistent with findings reported in several previous studies of a lower prevalence of SP in Italy. Furthermore, the results confirm the fact that SP, due to its early onset, might constitute an ideal target for early treatment aimed at preventing both the accumulation of social disabilities and impairments caused by anxiety and avoidance behaviour, as well as the onset of more serious, associated complications in later stages of the illness.
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Background
Several epidemiological studies have attempted to describe the prevalence, socio-demographic characteristics, comorbidity, and severity of clinical manifestations of Social Phobia (SP). Quality of life and functional status of affected individuals have also been investigated [1]. Lifetime prevalence of SP in European societies other than Italy ranges from 3.5% to 16.0% [2]. As discussed in several excellent review papers, these rate differences were partly attributed to probable genetic or cultural factors [3]. Furthermore, major methodological differences (type of diagnostic criteria used, assessment tools, age of the sample) affecting the estimates have been demonstrated [1].
This study, part of an extended epidemiological investigation "Health in Sardinia," aimed to assess the prevalence rates of SP in Sardinia (Italy) in order to confirm the evidence of low SP prevalence rates ranging from 1.0% to 3.1% observed in previous research projects in Italy [4-6]. The study also intended to evaluate the treatments and to verify the comorbid psychiatric disorders in the identified people with Social Phobia.
Methods
The sample, which had already been examined and described in greater detail in a previously published study [7], consisted of 1040 subjects recruited throughout the island of Sardinia, Italy. 393 subjects came from the city of Cagliari, 344 from rural areas, and 303 from an industrial mining district, thus representing fairly well all socio-economic strata present on the island. The age distribution of the sample is shown in table 1, with age range from 18 to 89 years. 79.2% out of a total of 1313 subjects approached, agreed to take part in the study, 12.5% refused to participate, and 8.3% could not be traced; the final sample did not differ respect to the population of origin with reference to the variables applied in stratification (Table 1). All subjects were interviewed "face to face" by trained physicians using a Simplified version of Composite International Diagnostic Interview (CIDI) [8], hence the acronym CIDI "Simplified" (CIDIS) [9,10]. The version used in this study had been translated into Italian and back-translated into the original French language under blind conditions respect to the first translation, by a bilingual researcher; approval of the final version was obtained from the original authors [11]. The CIDI structured interview in its various versions currently represents the most widely used diagnostic tool in psychiatric epidemiological studies conducted on the general population [12]. The CIDIS is a highly structured tool made up of 5 sections which investigate respectively: Somatoform Disorders and General Medical Conditions, Anxiety Disorders, Depressive Disorders, Substance-Related Disorders (Alcohol-Related Disorders and Substance-Related Disorders) and Eating Disorders. The computer elaboration of data obtained through application of the CIDIS enables calculation of both "lifetime prevalence" and, for the preceding six months, a series of psychiatric disorders (those more frequently observed in the general population) according to the ICD-10 diagnostic system [13]. This interview moreover enhances identification of both the type of therapist referred to and treatment already used by each patient for his or her specific problem and definition of degree of impact of the problem on the subject's daily routine. An ad hoc computer algorithm ascertained the presence of the disorders according to ICD-10 criteria [13], both in the past six months and in the lifetime. The items of the CIDIS concerning Social Phobia and the related algorithm is reported in Figure 1.
Table 1 Percentage of subdivision according to age, sex, and marital status of the sample.
N (1040)
MALES 461 (44.3%)
FEMALES 579 (55.7%)
AGE 18–24 146 (14.0%)
AGE 25–44 353 (33.9%)
AGE 45–64 310 (29.8%)
AGE >64 231 (22.2%)
UNMARRIED 370 (35.6%)
MARRIED 571 (54.9%)
WIDOWED/SEPARATED/DIVORCED 99 (9.5%)
Figure 1 The Composite International Diagnostic Interview Simplified (CIDIS) [9, 10] algorithm for the diagnosis of Social Phobia.
Results
Estimates of lifetime SP prevalence of 2.2% were found (males: 1.5%, females: 2.8%), with no statistically significant difference between the sexes (χ2 = 1.2, 1DF, P = 0.25). 6-month prevalence rates were lower (total: 1.5%, males: 0.9%; females: 1%; no significant difference between the sexes, χ2 = 1.6, 1DF, P = 0.13). Table 2 compares results obtained in this study with those of the major research projects carried out in Europe and the USA [4,5,14-23]. Table 3 illustrates the lifetime prevalence according to age and sex, and Table 4 shows the 6-month prevalence according to age and sex. An increased frequency of SP among the younger age groups was observed, although distribution in males resulted as being less homogeneous. In no case statistical significance was reached. Lifetime prevalence rates of SP respect to marital status of subjects studied were as follows: 3.0% among the unmarried; 1.2% among married subjects; and 3.5% among the divorced, separated and widowed (χ2 = 5.8, 1DF, P = 0.06). Mean age at onset was 16.2 ± 9.3 years.
Table 2 Lifetime prevalence of Social Phobia in the general population of Europe and USA.
Country Reference Diagnostic criteria N Male Female Total
Italy Faravelli et al., 1989 [4] DSM-III-R 1110 1.0
Switzerland Wacker et al., 1992 [16] DSM-III-R 470 16.0
ICD-10 9.6
ECA (USA) Schneier et al., 1992 [15] DIS 18571 2.0 3.1 2.4
Iceland Lindal and Stefansson, 1993 [32] DSM-III 862 2.5 4.5 3.5
Switzerland Degonda and Angst, 1993 [21] DSM-III 591 3.1 5.7 4.4
NCS (USA) Kessler et al., 1994 [17] CIDI 8098 11.1 15.5 13.3
France Lepine and Lellouch, 1995 [19] 2.1 5.4 4.1
Germany Wittchen et al., 1998 [23] DSM-IV 3021 2.2 4.8 3.5
Italy Carta and Rudas, 1998 [6] CIDI 783 1.7 4.6 3.1
Spain Arillo et al., 1998 [cited in 33] DIS 237 8.9
Netherlands Bijl et al., 1998 [20] DSM-III-R 7076 5.9 9.7 7.8
France Lépine and Pélissolo, 1999 [22] DSM-IV 7.3
Italy Faravelli et al., 2000 [5] FPI/CIDI 2355 1.9 4.0 3.1
Italy Carta et al., 2002 [7] CIDI 1040 1.5 2.8 2.2
Table 3 Lifetime prevalence N (%) of Social Phobia according to age and sex.
Age Male OR Female OR Total OR
<25 1 (1.3) 0.9 4 (5.0) 2.3 5 (3.4) 1.7
25–44 4 (2.5) 2.5 4 (2.1) 0.7 8 (2.2) 1.1
45–64 1 (0.7) 0.4 4 (2.4) 0.8 5 (1.6) 0.6
>65 1 (1.1) 0.7 4 (2.8) 1.1 5 (2.2) 0.9
Male according to age χ2 with Yathes correction = 1.6, 3 df, p = 0.89; female according to age χ2 with Yathes correction = 2.2, 3 df, p < 0.71; total sample according to age χ2 with Yathes correction = 1.8, 3 df, p = 0.82
Table 4 Six month prevalence N (%) of Social Phobia according to age and sex.
Age Male OR Female OR Total OR
<25 1 (1.5) 1.8 3 (3.7) 2.0 4 (2.7) 2.1
25–44 1 (0.6) 0.6 3 (0.7) 0.5 4 (1.1) 0.6
45–64 1 (0.7) 0.7 2 (1.1) 0.5 3 (0.9) 0.5
>65 1 (1.1) 1.4 4 (2.8) 1.4 5 (2.1) 1.6
Male according to age χ2 = 0.6, 3 df, p = 0.98; female according to age χ2 with Yathes correction = 3.4, 3 df, p = 0.44; total sample according to age χ2 with Yathes correction = 3.4, 3 df, p = 0.44
During the week prior to the study, 8 (50.0%) out of the 16 subjects who had been diagnosed with SP over the past six months had been taking low doses of anxiolytics (less than the equivalent of 2 mg of Lorazepam). 3 (18.7%) were on antidepressants, one of whom (6.2%) at non-therapeutic doses, and 1 subject (6.2%) was undergoing cognitive behavioural psychotherapy. The remaining 6 subjects (37.5%) were not on any treatment.
Six out of the 10 treated subjects (60%) were being supervised by their general practitioners (GP), 1 (10%) by a neurologist and 2 (20%) by psychiatrists. All subjects presented some degree of comorbidity with Depressive Episodes (DE), Panic Attack Disorder (PAD), and agoraphobia (AP). Only 1 subject (10%) was undergoing cognitive behavioural therapy with a psychologist/psychotherapist.
Table 5 illustrates the rate of comorbidity with major psychiatric disorders observed in the general population, as well as the degree (OR) of associated disorders observed with regard to frequency reported for the latter in populations not affected by SP. A statistically significant difference was revealed for association with DE, Dysthymia (DD), and Generalized Anxiety Disorder (GAD). In spite of their increased frequency among patients affected by SP, disorders such as PAD and Specific Phobia do not represent a statistically significant association. The mean age at onset of comorbid DE was 6.5 ± 6.6 years subsequent to onset of SP, whereas GAD was manifested at a mean of 4.3 ± 7.8 years later.
Table 5 Lifetime comorbidity of Social Phobia.
N (%) OR χ2
Depressive Episode (DE) 9 (39.1) 4.3 11.1*
Dysthymia (DD) 5 (21.7) 7.1 14.1*
Generalized Anxiety Disorder (GAD) 10 (43.4) 6.5 20.9*
Panic Attack Disorder (PAD) 2 (8.7) 3.3 1.1
Specific Phobia 1 (4.3) 8.6 1.6
*p < 0.001
Discussion
Several epidemiological studies carried out in Europe (Switzerland [16,21], France [19,22], Germany [23]) and in the USA [15,17], recently reviewed by Furmark [2], suggest that SP is one of the more frequently observed anxiety disorders in the general population in Western countries. However, frequency rates reported in the various studies differ from country to country and according to time and evaluation methods used. Indeed, the two American studies [15,17] carried out at an interval of approximately 15 years, illustrate distinctly contrasting results, and it is hard to establish what factors really determine variance in findings.
The present study is consistent with the tendency towards rather low lifetime prevalence rates of SP observed in other Italian research projects. If we take into account only those European researches that adopted ICD-10 diagnosis, our results seem to indicate a lower frequency than a study carried out in Formentera, Spain (lifetime prevalence of 2.8% against 8.9% in females [cited in [33]]) and Basel, Switzerland (lifetime prevalence in the total sample 2.2 against 9.6% [16]). However, lower frequencies emerged also in Italian surveys conducted using different methods [5,7]. The Italian studies were carried out over a considerable period of time and in two distinct areas: the Florence area [4,5] and Sardinia [[6], the present study]. It is therefore quite likely that the lower frequencies observed may be the result of an effectively reduced vulnerability of Italians to SP. The results of this study can indeed be assumed as being influenced by several cultural variables – the genetic diversity of the two populations examined, as well as the considerable genetic variance of the Sardinian population respect to other European populations [24,25].
Findings for six-month prevalence show lower frequencies than those evidenced in the recent studies: 1.5% against 4.0% in Iceland [26] and against 4.0% in Munich [14]. Our rates are lower than E.C.A. findings: 2.7% in Duke [27] and 2.2% in Baltimore, New Haven, and Saint Louis [28]. The 6-month prevalence rates obtained in Sardinia are similar only to data reported for Edmonton (1.2%) from a survey carried out more than 15 years ago [29] and published in 1994 [30]. However, the prevalence data emerging from this study further justify the considerable interest shown in this disorder from both a medical and a social point of view. This research confirms the fact that onset of the illness occurs primarily during childhood and adolescence [31], thus underlining the suitability of the condition as a candidate for early treatment aimed at preventing both the slow accumulation of social disabilities and impairment caused by anxiety and by avoidance behaviour, as well as the onset of more serious complications (e.g., DE or GAD), which may be manifested many years after onset of SP. Indeed, subjects affected by social phobia presented a high risk of comorbidity with both the latter disorders and DD [1] .
It should be underlined that 60% of subjects undergoing treatment (not all affected subjects) chose their general practitioner (GP). This view is corroborated by the fact that those patients treated by a psychiatrist invariably presented comorbidity with DE, PAD, and AP, thereby representing the more severely affected from a psychopathological point of view. Overall however, the low rate of patients with SP treated with first line-treatments is alarmingly low.
In the future, serious attempts should be made to improve the GPs' abilities to recognise SP in order to prevent the use of inappropriate treatments, such as insufficient doses of benzodiazepines, which may be linked to the physician's incorrect diagnosing of the disorder. Due to the fact that subjects affected by SP most frequently refer to their GP, the importance of preventing SP as opposed to other types of disorders, as well as the markedly incapacitating nature of SP reinforce this necessity for a better training of GPs.
It is however mandatory to briefly acknowledge some potential limitations of this study. First, the number of cases and the sample size is too small to allow firm conclusions to be drawn concerning the true rate and the degree to which they might actually differ from previous studies with higher estimates. Secondly, differences in the assessment strategy might have resulted in a diminished comparability.
Conclusions
The study is consistent with findings reported in several previous studies of a lower prevalence of Social Phobia in Italy and confirms the fact that onset of the illness occurs primarily during childhood and adolescence. Furthermore, the results confirm the fact that SP, due to its early onset, might constitute an ideal target for early treatment aimed at preventing both the accumulation of social disabilities and impairments caused by anxiety and avoidance behaviour, as well as the onset of more serious, associated complications in later stages of the illness, or many years after onset of SP.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MGC participated in the design of the study, performed the statistical analysis and drafted the manuscript. UHW participated in the statistical analysis and drafted the manuscript. MCH, MC, BC, LDO, MAR conceived of the study, and participated in its design and coordination. All 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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| 15488146 | PMC535931 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Oct 15; 4:31 | utf-8 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-31 | oa_comm |
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BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-451556084310.1186/1471-2474-5-45Study ProtocolCost-effectiveness of an intensive group training protocol compared to physiotherapy guideline care for sub-acute and chronic low back pain: design of a randomised controlled trial with an economic evaluation. [ISRCTN45641649] van der Roer Nicole [email protected] Tulder Maurits W [email protected] Johanna M [email protected] Mechelen Willem [email protected] Willemien K [email protected] Arjan C [email protected] Vet Henrica CW [email protected] Institute for Research in Extramural Medicine, VU University Medical Center, Amsterdam, The Netherlands2 Regional College for Physiotherapy Amsterdam, Amsterdam, The Netherlands3 Body@Work, Research Center Physical Activity, Work and Health, TNO-VUmc, Amsterdam, The Netherlands2004 23 11 2004 5 45 45 6 8 2004 23 11 2004 Copyright © 2004 van der Roer 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
Low back pain is a common disorder in western industrialised countries and the type of treatments for low back pain vary considerably.
Methods
In a randomised controlled trial the cost-effectiveness and cost-utility of an intensive group training protocol versus physiotherapy guideline care for sub-acute and chronic low back pain patients is evaluated. Patients with back pain for longer than 6 weeks who are referred to physiotherapy care by their general practitioner or medical specialist are included in the study. The intensive group training protocol combines exercise therapy with principles of behavioural therapy ("graded activity") and back school. This training protocol is compared to physiotherapy care according to the recently published Low Back Pain Guidelines of the Royal Dutch College for Physiotherapy. Primary outcome measures are general improvement, pain intensity, functional status, work absenteeism and quality of life. The direct and indirect costs will be assessed using cost diaries. Patients will complete questionnaires at baseline and 6, 13, 26 and 52 weeks after randomisation.
Discussion
No trials are yet available that have evaluated the effect of an intensive group training protocol including behavioural principles and back school in a primary physiotherapy care setting and no data on cost-effectiveness and cost-utility are available.
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Background
Low back pain is a very common complaint with major social and economical consequences. In a recent cross-sectional study the annual prevalence of low back pain in the general Dutch population was estimated at 44% [1]. The course of low back pain is usually relatively short: about 80–90% of people with low back pain spontaneously recover within four to six weeks. However, approximately 1–7% develop chronic low back pain. Although this is a relatively small group, the economic consequences are enormous [2]. The total costs of low back pain in the Netherlands in 1991 have been estimated at 1.7% of the Gross National Product [3]. About 93% of the total costs were due to absenteeism and disablement. Because of the enormous costs related to low back pain, effective interventions aimed at prevention and treatment of chronic complaints are necessary. The Cochrane Collaboration has published several systematic reviews on the effectiveness of different treatments for low back pain. Exercise therapy, back schools and behavioural therapy seem to be the most promising interventions for treatment of chronic low back pain [4]. Authors recommended future trials with sufficiently large sample sizes and sufficiently long follow-up periods. Cost-effectiveness and cost-utility analyses of treatments were also recommended, because the observed differences in effectiveness were only small.
Evidence-based physiotherapy for sub-acute and chronic low back pain patients consists of adequate information and an active approach, including behavioural principles. As physiotherapists have not yet put these principles into practice [5-7] two important barriers have to be dealt with. First, changing behaviour of health care providers is always very difficult, even when guidelines are actively implemented [8]. Second, physiotherapists usually do not have specific knowledge of behavioural principles and are usually not specifically trained to provide behavioural therapy. To solve these issues, physiotherapists in Amsterdam have developed a new intervention program. This program not only makes optimal use of the combination of the principles of exercise therapy, behavioural therapy and back schools, but has structured it into a protocol that facilitates physiotherapists to perform this intervention in clinical practice. This trial will evaluate the cost-effectiveness and cost-utility of the intensive group training protocol compared with physiotherapy guideline care.
Methods
Study design
The study is a randomised controlled trial (RCT). Alongside the trial a full economic evaluation will be conducted. The Medical Ethics Committee of VU University Medical Centre has approved the study design, protocols and informed consent procedures.
Setting
The trial will be conducted in a primary physiotherapy care setting in Amsterdam and its surroundings. Eighty-five physiotherapists will participate in the trial; 40 physiotherapists are trained to provide the intensive group training protocol and 45 physiotherapist are instructed to provide usual physiotherapy care according to the Low Back Pain Guidelines of the Royal Dutch College for Physiotherapy (KNGF).
Study population
Patients with non-specific low back pain referred to one of the participating physiotherapists by their general practitioner are eligible for participation in the trial. Patients are included if the current episode of low back pain lasts more than 6 weeks and if the complaints show no tendency to decrease, meaning that the patient has not increased his activities in the last three weeks. Furthermore, patients have to be between the age of 18 and 65 years old, live or work in Amsterdam and have a health insurance with one insurance company (Agis). This health insurance company covers about 80 to 90 percent of the Amsterdam population and is the only company that reimburses the intensive group training protocol. Patients are excluded from the study if 1) they have specific low back pain, attributable to e.g. infection, tumour, osteoporosis, rheumatoid arthritis, fracture, inflammatory process, radicular syndrome or cauda equina syndrome; 2) their general practitioner or medical specialist advised them not to perform physically straining activities; 3) they are pregnant; 4) they have pelvic pain/instability; 5) they are dealing with a lawsuit related to either their low back pain or related to their disability for work. Patients are recruited by participating physiotherapists. If patients are interested in participating in the trial, they receive written information about the trial and their name and phone number is given to a research assistant. The research assistant calls the patient two days later and explains the aim and implications of the study. If the patient agrees to participate, an appointment is made at a local research centre. At the local research centre a research physiotherapist checks again if the patients meets the eligibility criteria. Patients who meet the criteria and agree to participate in the trial must sign an informed consent form. Patients are asked to complete baseline questionnaires and the research physiotherapist will conduct baseline assessment of physiologic outcome measures.
In accordance with the CONSORT statement, information on number of recruited and eligible patients and reasons for exclusions or refusal to participate will be registered for all recruited patients by the participating physiotherapists and the research physiotherapist.
Treatment allocation
Patients are randomly assigned to either the intensive group training protocol or physiotherapy guideline care. Randomisation is stratified for duration of complaints to ensure a sufficient number of sub-acute and chronic patients in each treatment group. To avoid inconvenience for the patients, seven local research centres are set up in different parts of the city. For each research centre two randomisation lists are prepared and permuted blocks of 4 patients are made to ensure equal distribution of patients for each research centre. An independent statistician generated the randomisation lists, using series of random numbers. The principle investigator (NvdR), who is not involved in the selection of patients, prepared the opaque, sealed envelopes, guaranteeing concealed randomisation. At the local research centre the administrative assistant hands the next envelope to the patient who then opens the envelope. The administrative assistant then checks the envelope and informs the participating physiotherapist about the treatment allocation.
Blinding
Both the research physiotherapists and the principle investigator remain blinded for the allocation of treatment. Patients cannot be blinded for the interventions. As a consequence most outcome measures, consisting of self-report questionnaires are not blinded either. All physical outcome measures are blindly assessed by the research physiotherapist as we ask the patients not to reveal information about their treatment to the research physiotherapists. Participating physiotherapists can not be blinded for treatment allocation, but they are not involved in the assessment of outcome measurements.
Interventions
Patients who are assigned to physiotherapy guideline care are treated according to the recently published Low Back Pain Guidelines of the Royal Dutch College for Physiotherapy (KNGF) [9]. The guidelines recommend giving adequate information, advising to stay active and providing exercise therapy with a behavioural approach for patients with sub-acute and chronic low back pain. As the guidelines are relatively new, physiotherapists providing the guideline care receive two training sessions of 2,5 hours each to ensure that the guidelines are properly applied. Preparation time of 2 hours before each session is strongly recommended. Two experts provide background information and discuss the content of the guideline. Video clips and statements on expected barriers are used to start discussions in groups of 10–15 physiotherapists supervised by expert trainers. After 4 months a follow-up session of 2,5 hours is organised to discuss practical problems and to ensure that all physiotherapists are working according to the guideline. The physiotherapists are asked to complete a form for each participating patient they treat, to register treatment goals, content of the treatment, total number of sessions in the treatment period and, if applicable, arguments to deviate from the guideline.
The intensive group training protocol combines exercise therapy with principles of back school and behavioural therapy. Back school principles include group lessons with adequate information on causes of low back pain, factors influencing low back pain, advice on physical activity and dealing with a relapse. Operant conditioning and graded activity as components of behavioural therapy are included in the protocol. Baseline measurements, goal setting and time-contingency are the main elements of the intervention. The purpose of the protocol is to improve activities and participation in work or other social activities, instead of focussing on pain or anatomical impairments. The patient has an active role and is responsible for the results of the therapy. The physiotherapist has the role of coach and focuses on the achieved improvement instead of the remaining complaints [10]. Active behaviour will be reinforced by the physiotherapist.
The protocol has a total duration of 30 weeks and consists of three phases: the starting phase, the treatment phase and the generalisation phase. It concerns 10 individual sessions of 30 minutes per session and 20 group sessions of 1,5 hours per session. During the first phase of three weeks, six individual sessions are planned for patient history and physical examination, providing information on the treatment, determining baseline level of functional capacity and signing a treatment contract. During the treatment phase the group sessions have a frequency of twice a week for eight weeks. Every patient has his own gradually increasing exercise program, with an operant-conditioning behavioural approach based on the baseline level of functional capacity. The treatment phase gradually changes into the generalisation phase in which patients learn to apply everything they have learned in the treatment phase to their own daily situation. Therefore the frequency of the sessions decrease in the last four weeks; patients are encouraged to exercise more at home and to choose a physical activity they will continue after treatment has finished. Two individual sessions are planned for evaluation during the twelve week training period and two additional individual sessions are planned three weeks and three months after the group sessions have finished.
The exercise program consists of:
1. warming-up and cooling down
2. aerobic exercises on a rowing machine, stationary bike or treadmill
3. muscle strengthening exercises of the lower back, abdomen and buttocks
4. exercises that specifically apply to the patient's situation
5. home exercises
The exercises mentioned at point 4. are determined by a Patient Main Complaint Form [11]. During the first intake patients are given this form, consisting of thirty different activities (e.g. turning in bed, lifting, walking, etc.). The patient is asked to select and prioritise the activities he has had trouble with during the last week and would very much like to see improved in the following months. The physiotherapist discusses the form with the patient and designs specific exercises for these activities. Three baseline measurements are performed to determine the maximal performance (for example, the maximum number of repetitions) for each exercise separately. The starting point of the program is 70% of the mean of all three measurements, in order to avoid failure and ensure the experience of success. In agreement with the patient the training quota are determined by the physiotherapist using the starting point, goals and training period to provide a gradually increasing program. The exercise goals are determined by the patient and physiotherapist together to ensure that goals are realistic, concrete, trainable and measurable. The treatment contract is signed by the patient and physiotherapist. The contract states that training quota are always followed exactly and that the patient keeps the graphs of finished sessions.
For training, the physiotherapists will receive instruction on the background and content of the protocol, and will be trained to include behavioural principles in the physiotherapeutic management of low back pain at two meetings of six hours each. In groups of 7–8 physiotherapists discussions and role playing are supervised by one expert trainer with extensive experience in behavioural principles. Four months after the last meeting 2 follow up sessions of 4 hours are organised to discuss practical problems and practice difficult situations with the trainers. The principal investigator (NvdR) will regularly visit the group sessions at the physical therapy practices to monitor the conduct of the intensive group training protocol. For each participating patient the physiotherapist is asked to complete a registration form containing the treatment goals, content of the different sessions and evaluation of the protocol.
Contrast physiotherapy guideline care and intensive group training protocol
The intensive group training protocol is a standardised approach consisting of 30 treatment sessions. As the physiotherapy guideline care is not a protocol, the number of sessions will vary per patient. In daily practice the average number of treatment sessions is 9 and the average duration of treatment is 6 weeks [6]. The organisation of the intensive group training protocol is based on back school principles and will take place in groups of 5–8 patients. The physiotherapy guideline care is organised as usual physiotherapy care and patients are treated individually. The guidelines recommend exercise therapy with a behavioural approach. However, no further guidance is provided regarding the content of the exercise program (type, intensity, frequency and duration of exercises) or regarding integrating behavioural principles. In the intensive group training protocol the content of the exercise therapy, back school and operant condition are thoroughly described and the physiotherapists are trained to apply these skills in practice. So the contrast lies in the number of sessions, group versus individual therapy and the conduct of the behavioural therapy.
Co-interventions and compliance
During the intervention period, co-interventions are discouraged. However, co-interventions will be reported and evaluated. Compliance to the intensive group training protocol is assessed by registering the number of treatment sessions that patients attend. The content of treatment and number of treatment sessions received by the physiotherapy guideline care group will be registered.
Outcome assessment
In 1998 a proposal for standardised use of outcome measurement in low back pain studies was published [12]. An international group of investigators proposed a set of five domains that should be used in all low back pain studies: pain symptoms, back related function, general well being, disability and satisfaction with care. Additionally several other outcome measures that are commonly used in randomised trials in low back pain will be assessed.
Primary outcome measures
1. The functional status is assessed with the Roland Morris Disability Questionnaire [13]. The questionnaire consists of 24 questions related to activities of daily living. Each item is scored either 0 (disagree with statement) or 1 (agree with statement) and the total score ranges from 0 (no dysfunction) to 24 (maximum dysfunction).
2. General improvement is measured on a 6 point scale ranging from "much worse" to "completely recovered".
3. An 11-point numerical rating scale is used for determining pain intensity, ranging from 0 "no pain" to 10 "very severe pain" [14].
4. Work absenteeism is measured with the Short Form Health and Labour Questionnaire [15,16]. This questionnaire was developed for collecting quantitative data about the relation between illness, treatment and work-performance. Absence from work, reduced productivity at paid work, unpaid labour production and impediments to paid and unpaid labour are four dimensions that are addressed in the Health and Labour Questionnaire.
5. The EuroQol instrument is administered to assess the patient's general health status. The questionnaire describes the general health status in 5 dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression [17]. Because each of the five dimensions can be divided in 3 levels a total of 243 health states can be defined. Using the model by Dolan (1997) the total score will be expressed in utilities [18]. The official Dutch translation of the Euroqol will be administered.
Secondary outcome measures
6. The Tampa scale for kinesiophobia is developed as a measure of fear of movement/(re)injury by Miller et al. [19]. The questionnaire is relatively short and can easily be used in a primary care setting [20]. The scale consists of 17 items and each item is provided with a 4-point Likert scale ranging from "strongly disagree" to "strongly agree". The Dutch translation of the TSK by Vlaeyen et al. [21] will be used in the trial.
7. Cognitive and behavioural pain coping strategies are assessed using the Pain Coping Inventory [22]. This questionnaire consists of 6 factors: pain transformation, distraction, reducing demands, retreating, worrying and resting. All 34 items are scored on a four point scale where 1 equals "hardly ever/never" and 4 equals "very often". A recent validation study of the Pain Coping Inventory reported the coping scales to be reliable and sensitive enough to identify differences between coping strategies in pain patients [23].
8. Self-efficacy beliefs are measured using the Pain Self-Efficacy Questionnaire [24]. With the approval of Nicholas, the original 10-item questionnaire was translated into Dutch by the authors and subsequently translated back by a professional translator. Each item is scored on a 7-point scale ranging from 0 "not at all confident" to 6 "completely confident". By summarising the scores of all the items a total score is determined.
9. For measuring patient satisfaction four items (out of 17 items) of the Patient Satisfaction Scale of Cherkin et al. [25] are combined with nine items (out of 12 items) of the Patient Survey Instrument of Beattie et al. [26]. The Patient Satisfaction Scale was developed for measuring patient satisfaction with care they received from their physician and is a multidimensional disease specific measure, intended specifically for patients with low back pain. The Patient Survey Instrument is a multidimensional generic measure and was developed to determine the overall satisfaction with physical therapy. The items of the combined list are rated using a 5-point "agree-disagree" response format. The authors belief that the combination of both instruments is more applicable to the situation in the trial.
Physical measurements
The physical measurements will be performed at several local research centres. Therefore all physical tests must be easy to administer en practical. To minimize patient burden the tests should take as little time as possible en should not be too strenuous for the patient.
10. Anthropometric measurements will be done for interpretation of the physical outcome measures. Body weight, body height and skin fold measures are assessed. Skin fold thickness of biceps, triceps, subscapular and suprailiac will be assessed with a Harpenden skin fold calliper. The skin fold-thickness equation developed by Durnin and Womersley will be used to determine body fat mass [27].
11. Aerobic capacity will be assessed with the Chester Step Test [28,29]. This test was developed to determine the aerobic capacity in a relatively simple and practical way. The test is sub-maximal and ends when the heart rate of the participant reaches 75% of its predicted maximum. The test starts with a very slow step rate (15 steps per minute) and every two minutes the step rate increases with 5 steps per minute. Because the action of stepping is familiar to most people, the majority of the patients in the study will be able to perform the test.
12. The isometric endurance of the back muscles is evaluated with the test according to Ito [30]. The patient is positioned on the floor with a pillow under the abdomen and arms by the side. The patients raises the trunk to a horizontal position and the time the patient can maintain this position is measured.
13. The fingertop-to-floor distance is measured to determine the flexibility of the spine [31]. Standing with bare feet the participant will be asked to bend maximally forward with the feet together and the knees straight. The distance from the tips of the middle fingers to the floor is measured with a metal-ended tape measure.
Prognostic measures
At baseline, data of various prognostic measures will be collected to evaluate if randomisation successfully resulted in two prognostically comparable groups and to be able to adjust for baseline differences in the analysis, if necessary.
1. Data on individual factors such as age, gender, level of daily activity and preference for one of the treatment groups will be gathered by the administrative assistant.
2. Characteristics of low back pain: duration and severity of the current episode and number of previous episodes will be assessed by the research physiotherapists.
Cost data
The aim of the economic evaluation will be to determine and compare all back pain related costs of patients receiving the intensive group training protocol or physiotherapy guideline care. The costs will be related to the effects of the interventions. Cost effectiveness will be conducted from a societal perspective. Direct health care costs, including the costs for physiotherapy, additional visits to other health care providers, prescription medication, professional home-care and hospitalisation and direct non-healthcare costs such as out-of-pocket expenses, costs for paid and unpaid help and travel expenses will be included. Also data on indirect costs of loss of production due to back pain will be estimated for both paid and unpaid labour. Direct and indirect costs will be evaluated with cost diaries that patients keep during the whole time they participate in the trial [32]. The general health status is measured with the Dutch version of the EuroQol to compare the results of the cost-effectiveness analysis with other health care problems.
Patients will be asked to complete questionnaires at baseline and 6, 13, 26 and 52 weeks after randomisation. Physical measurements will be performed at baseline, 13 and 52 weeks after randomisation. Table 1 gives an overview of the data-collection.
Sample size
To be able to detect a clinically relevant difference in pain intensity (improvement of 2 points on the 11-point pain intensity numerical rating scale after 52 weeks [33]) with a power (1-β) of 90% and a significance level of 5% (two-sided), two groups of 48 patients are needed. A population of chronic low back pain patients typically has a mean score of 7 (SD 2) on an 11-point pain intensity numerical rating scale. To be able to find a clinically relevant difference in disability (improvement of 3 points on the RDQ after 52 weeks [34]) with a power (1-β) of 90% and a significance level of 5%, two groups of 60 patients are needed. Chronic low back pain patients typically have a mean score of 15 (SD 5) on the RDQ.
We expect a drop-out rate of 10% at most. Drop-out rates of similar RCT's on neck pain and tennis elbow conducted at our institute were less than 3%. Therefore, to get complete data sets of 120 patients with sub-acute and 120 patients with chronic low back pain, 280 patients will be recruited, 140 sub-acute and 140 chronic low back pain patients. 85 Participating physiotherapists will be asked to recruit 5 patients; the recruitment period will be of 12 months duration.
Statistical analysis
Intention-to-treat analyses will be conducted for all patients participating in both groups. A generalised linear mixed model will be applied to evaluate differences between groups over a period of 52 weeks. Subgroup analyses will be performed for duration of back pain: sub-acute (6–12 weeks) versus chronic (>12 weeks), for severity of complaints at baseline, for age, and for the psychosocial characteristics somatisation, fear avoidance, catastrophising and self efficacy.
Bootstrapping will be used for pair-wise comparison of the mean differences in direct health care, direct non-health care, total direct, indirect and total costs between the intervention groups. Confidence intervals will be obtained by bias corrected and accelerated (Bca) bootstrapping using 2000 replications [35]. Cost-effectiveness ratios will be calculated by dividing the difference between the mean costs of the two interventions by the difference in the mean effects of the two interventions. Ratios will include the primary clinical effect measures of the trial, i.e., general improvement, functional status, pain intensity and quality of life. Ratios will be graphically presented on a cost-effectiveness and cost-utility plane and acceptability curves will be calculated showing what the probability is that the intensive group training protocol is cost-effective at a specific ceiling ratio.
Discussion
The intensive group training protocol includes interventions, such as exercise therapy, behavioural treatment and back school, that have recently been proven to be effective in patients with low back pain. The graded activity intervention is considered the be a form of behavioural treatment and earlier studies have proven the effectiveness of this intervention for workers who are sick-listed due to low back pain [36,37]. The trial by Staal et al. (2003) was conducted at an occupational health service department of an airline company in the Netherlands. In this study graded activity was found to be more effective than usual care in reducing the number of sick leave days. Lindström et al. (1992) examined the graded activity intervention in sick-listed workers at the Volvo factories in Sweden and showed a significant reduction in the number of days of sick leave. All participants in both studies were workers on sick leave due to low back pain. In a primary care setting the graded activity intervention has not yet been studied. Although participants in our study will follow an individual, gradually increasing exercise program, the training and back school will take place in a group setting.
In the Netherlands, the national physiotherapy guidelines for low back pain consist of general recommendations regarding diagnostic and therapeutic management of low back pain while the intensive group training protocol prescribes the frequency, intensity and duration of the exercise therapy, the content of informative group lessons and graded activity in detail. The intensive group training protocol is expected to be more effective, because it is a detailed protocol and because it combines principles of exercise therapy back school and behavioural therapy, which have recently been proven to be effective for this patient population in systematic Cochrane reviews [38-40].
Although the general practitioner, and if applicable the occupational physician, will be informed about the treatment and progress of the patient, the intervention is mono-disciplinary. A multidisciplinary intervention in a primary care setting has major practical implications and would increase the costs of the intervention considerably. The intensive group training protocol itself probably generates higher costs than physiotherapy guideline care but we expect reduction in health care utilization and productivity losses in the long term, compensating for the increase in treatment cost. This trial will provide physiotherapists with more knowledge and experience in behavioural treatment for low back pain patients and may increase the efficiency of physiotherapeutic care for this complex and expensive patient group. If the intensive group training protocol appears to be more cost-effective than physiotherapy guideline care, a future update of the national physiotherapy guideline will include more specific recommendations in line with this protocol. In that case the protocol will be implemented throughout the Netherlands.
List of abbreviations
KNGF = Royal Dutch College for Physiotherapy
Bca bootstrapping = bias corrected and accelerated bootstrapping
Competing interests
The author(s) declare that they have no competing interests.
Author's contributions
NvdR is responsible for the data collection and drafted the manuscript. MWvT, JMB, WvM and HCWdV were involved in developing the original idea for funding and were co-applicants on the successful funding proposal. WKF and ACO both will contribute to data collection and processing. All authors participated in development of research protocols and in the design of the study. All authors read and corrected draft versions of the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study is granted by: The Netherlands Organisation for Health Research and Development (ZONMW).
Figures and Tables
Table 1 Overview of data collection
Outcome measures Follow-up
t = 0 t = 6 t = 13 t = 26 t = 52
Primary outcomes
Functional status (RDQ) X X X X X
General improvement X X X X
Pain intensity X X X X X
Work absenteeism (SF-HLQ) X X X X X
General health (Euroqol) X X X X X
Secondary outcomes
Fear avoidance (Tampa) X X X X X
Coping (PCI) X X X X X
Self-efficacy X X X X X
Patient satisfaction X X X X
Aerobic capacity X X X
Flexibility X X X
Strength X X X
Height X
Weight X X X
Skinfold measurements X X X
Other
General (age, gender, education, back pain episodes) X
Cost diaries X X X X X
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| 15560843 | PMC535932 | CC BY | 2021-01-04 16:03:42 | no | BMC Musculoskelet Disord. 2004 Nov 23; 5:45 | utf-8 | BMC Musculoskelet Disord | 2,004 | 10.1186/1471-2474-5-45 | oa_comm |
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BMC Ear Nose Throat DisordBMC Ear, Nose, and Throat Disorders1472-6815BioMed Central London 1472-6815-4-31557595710.1186/1472-6815-4-3Case ReportBilateral Ramsay Hunt syndrome in a diabetic patient Syal Rajan [email protected] Isha [email protected] Amit [email protected] Neuro-otology Unit, Department of Neuro-surgery, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Raibarely Road, Lucknow (UP) – 226 014 INDIA2004 2 12 2004 4 3 3 17 8 2004 2 12 2004 Copyright © 2004 Syal et al; licensee BioMed Central Ltd.2004Syal 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
Herpes zoster oticus accounts for about 10% cases of facial palsy, which is usually unilateral and complete and full recovery occurs in only about 20% of untreated patients. Bilateral herpes zoster oticus can sometime occur in immunocompromised patients, though incidence is very rare.
Case presentation
Diabetic male, 57 year old presented to us with bilateral facial palsy due to herpes zoster oticus. Patient was having bilateral mild to moderate sensorineural hearing loss. Patient was treated with appropriate metabolic control, anti-inflammatory drugs and intravenous acyclovir. Due to uncontrolled diabetes, glucocorticoids were not used in this patient. Significant improvement in hearing status and facial nerve functions were seen in this patient.
Conclusions
Herpes zoster causes severe infections in diabetic patients and can be a cause of bilateral facial palsy and bilateral Ramsay Hunt syndrome. Herpes zoster in diabetic patients should be treated with appropriate metabolic control, NSAIDS and intravenous acyclovir, which we feel should be started at the earliest. Glucocorticoids should be avoided in diabetic patients.
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Background
Varicella-zoster virus, member of Herpesviridae family has structural characteristics like a lipid envelope surrounding a nucleocapsid with icosahedral symmetry, a total diameter of 180–200 nm and centrally located double-stranded DNA about 125,000 bp in length. Varicella-zoster virus lies latent in sensory root ganglion for years in a patient who had chickenpox earlier. Some precipitating factor may reactivate it especially when immunity of patient wanes leading to Herpes zoster, a sporadic disease. Involvement of geniculate ganglion of sensory branch of facial nerve leads to Herpes zoster oticus also known as Ramsay Hunt syndrome. Involvement of facial nerve leads to otalgia, lower motor neuron homolateral facial paralysis and vesicular eruptions in auricle and external auditory canal. In severe cases of herpes zoster oticus, involvement of vestibulocochlear nerve leads to sensorineural hearing loss in 10% and vestibular symptoms in 40% patients. Herpes zoster oticus accounts for 10% cases of the facial palsy, paralysis is usually complete and full recovery occurs in only about 20% of untreated patients [1].
Herpes zoster rash is characterized by unilateral vesicular eruptions with in a single dermatome. In 16% of patients of zoster, vesicles develop beyond single dermatome [1]. Onset of disease is heralded by pain with in dermatome that may precede lesions by 48–72 hrs; total duration of disease is 7–10 days. In immunocompromised and elderly patients course of herpes zoster is more prolonged and severe. Rarely in such patients zoster may successively involve further dermatomes.
Considering, rarity of bilateral herpes zoster, a diabetic patient presenting with bilateral Ramsay Hunt syndrome is being reported here.
Case presentation
Diabetic male, 57 year old presented to us with history of pain in left ear for the last 8 days. 48–72 hrs after the onset of otalgia, patient developed facial weakness on left side along with vesicular eruptions on left conchae and in left external auditory meatus. After another 24–48 hrs patient had similar episode on right side. On the day of reporting to us patient was having bilateral facial weakness, impaired taste sensation, dryness of eyes along with decreased hearing on both sides but there was no history of vertigo or any ear discharge. There was history of stressful life events in past 6 months before the onset of rash.
On examination, there was bilateral lower motor neuron facial palsy which was complete. Bell's phenomenon was present on both sides Fig-1. There were adherent crusts and scabs in left conchae and external auditory meatus. While vesicular eruptions were present in right external auditory meatus. Tuning fork tests were showing bilateral sensorineural hearing loss.
Figure 1 Patient at time of presentation, photograph showing bilateral lower motor neuron type of facial palsy and presence of Bell's phenomenon.
The patient was admitted and investigated. His postprandial blood sugar was 339 mg%, HbAIc was 7%. Pure tone audiometery was showing mild to moderate bilateral sensorineural hearing loss, stapedial reflexes were absent on both sides on tympanometery. There was impaired taste sensation from anterior two third of tongue. ELISA and Western Blot tests for HIV infection were negative. Liver function tests, tumor markers, thyroid hormones; serum ACE levels were all with in normal limits. Lumbar puncture revealed normal pressure. Glucose, protein and white blood cell count were all with in normal limits in CSF. Plain X-ray views of mastoid, internal auditory meatus and chest were normal. Computerized tomography of brain stem, cerebellopontine angle, temporal bone and skull base were normal. A smear from floor of vesicle stained with Giemsa stain showed degenerating cells with multiple nuclei. This favoured the clinical diagnosis of herpes zoster oticus. This diagnosis was confirmed by detection of IgM antibodies to Varicella-Zoster virus by ELISA test.
Diabetes of this patient was controlled with insulin. Intravenous acyclovir was given in dose of 10 mg/kg every 8 hr for 7 days. Glucocorticoids were avoided in this patient due to diabetes, but NSAIDS were given. After 2 weeks of treatment and diabetes control, pure tone audiometery showed improvement in hearing by 10 db in all frequencies. 8 weeks later in the follow up, patient was able to close his eyes completely Fig-2. and facial nerve functions on both sides recovered; recovery was more on right side.
Figure 2 Patient after 8 wks of follow up, photograph showing complete closure of eyes.
Discussion
Severity and incidence of herpes zoster increases in elderly and in immunomodulated state like in AIDS, lymphoproliferative disorders, disseminated carcinomatosis, diabetes, during steroid therapy, during radio or chemotherapy [2]. Kubeyinje EP.(1995) found that varicella runs more aggressive course in diabetic patients as compared to otherwise healthy individuals [3]. Neu I et al (1977)in their study found basal metabolic disorders especially diabetes of particular significance inactivation and in primary and secondary manifestations of varicella zoster virus [4]. Muller C. et al (1989) concluded that metabolic derangement in diabetes leads to reversible disturbance in certain cellular immune functions which can be normalized by good metabolic control achieved by insulin treatment [5]. Postprandial blood sugar of our patient was 330 mg% at time of presentation and HbA1c was 7.0%, so blood sugar control was not appropriate in this patient. Patient was also under severe psychological stress since last 6 months and psychological stress is identified as a potential risk factor for zoster that might operate by suppressing cell-mediated immunity [6]. So uncontrolled diabetes and psychological stress were two risk factors present in this patient, making him prone for severe and recurrent infection of herpes zoster.
Hiroshige K et al (2002) conducted a study based on detection of varicella zoster virus DNA in tear fluid and saliva of patients with Ramsay Hunt syndrome and concluded that varicella zoster virus reactivation occurs in the unaffected side at the same level as in the affected side [7]. This explains the occurrence of bilateral Ramsay Hunt syndrome due to herpes zoster oticus in this patient. Shoji H et al (1980) also suggested that in Ramsay Hunt syndrome and its subgroups, bilateral involvement or wide spread of infection through nervous tissue can occur though its incidence is very rare[8].
In the management part, as metabolic control of diabetes improves leukocyte functions and overall immune status of patient [5] so insulin was started and dose was adjusted to achieve good metabolic control. As incidence of severe and disseminated infections of Herpes zoster is more in diabetes so intravenous acyclovir in dose of 10 mg/kg every 8 hr for 7 days was given. Recent reports suggest that treatment with i.v acyclovir decreases the incidence of permanent facial nerve palsy in Ramsay Hunt Syndrome. Results of relevant control trials have not yet been published [9]. Glucocorticoid therapy usually used in facial palsy due to herpes zoster oticus was avoided in this patient due to uncontrolled diabetes but NSAIDS and other analgesics were given in this patient.
On follow up of this patient, facial nerve recovered on both sides, recovery was more on right side. This was documented by electroneurography which showed greater amplitude of muscle compound action potentials on right side as compared to left. This might have been due to lesser time gap between the onset of acyclovir therapy and attack of zoster oticus on right side. Time gap between the onset of acyclovir therapy and attack of zoster oticus is most relevant prognostic factor in recovery of these patients and this hypothesis is supported by findings in studies of Mcgrath N. (1997) [10], Raschilas F (2002) [11] and Strupp M. et al (2004) [12]. There was also a significant improvement in hearing status of patient. This result was in accordance with other studies which keep age 64 years or younger, mild initial hearing loss, a cochlear pattern of hearing loss and absence of vertigo as factors favorable for recovery of auditory function [13].
Conclusions
Herpes zoster can cause severe infections in diabetic patient and can cause bilateral facial palsy and bilateral Ramsay Hunt syndrome.
-Herpes zoster in diabetic patients should be treated with appropriate metabolic control, NSAIDS and intravenous acyclovir, which we feel should be started at the earliest.
-Intravenous acyclovir therapy in cases of herpes zoster oticus is effective in control of disease and prevents the incidence of permanent facial palsy but treatment should be started early in the course of disease preferably with in 72 hrs from start of disease.
-Glucocorticoids should be avoided in Herpes zoster patients having uncontrolled diabetes.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All three authors
1) Have made substantial contributions in management of this case and in conception, design, analysis and interpretation of results of this case report
2) Have been involved in drafting the article or revising it critically for important intellectual content.
3) Have given final approval to the version to be published.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Written informed consent has been taken from patient and he has no objection in publication of this case report and his photographs.
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Anderson M Walton J Virus infections of the nervous system In brain's diseases of nervous system 1993 10 Oxford University Press, Oxford 317 50
Kubeyinje EP Severity of varicella infection in Saudis with diabetes mellitus: a possible role of acyclovir in treatment East Afr Med J 1995 72 739 741 8904068
Neu I Rodiek S Significance of diabetes mellitus in the activation of the varicella zoster virus (author's transl) MMW Munch Med Wochenschr 1977 119 543 6 194145
Muller C Zielinski CC Kalinowski W Wolf H Mannhalter JW Aschauer-Treiber G Klosch-Kasparek D Gaube S Eibl MM Schernthaner G Effects of cyclosporin A upon humoral and cellular immune parameters in insulin-dependent diabetes mellitus type I: a long-term follow-up study J Endocrinol 1989 121 177 83 2654321
Schmader K Studenski S MacMillan J Grufferman S Cohen HJ Are stressful life events risk factors for herpes zoster? JAM Geriatr Soc 1990 38 1188 1194
Hiroshige K Ikeda M Hondo R Detection of varicella zoster virus DNA in tear fluid and saliva of patients with Ramsay Hunt syndrome Otol Neurotol 2002 23 602 7 12170168 10.1097/00129492-200207000-00034
Shoji H Hirose K Uono M Koya M A case of facial diplegi following herpes zoster ophthalmicus Eur Neurol 1980 19 327 9 6967406
Durand M Joseph M Baker AS Fauci AS, Martin JB, Braunwald E, Kasper DL, Isselbacher KJ, Hauser SL, Wilson JD, Longo DL Infections of upper respiratory tract In Harrison's principles of internal medicine 1998 1 14 McGraw-Hill Health Professions Division, London 181
McGrath N Anderson NE Croxson MC Powell KF Herpes simplex encephalitis treated with acyclovir: diagnosis and long term outcome J Neurol Neurosurg Psychiatry 1997 63 321 6 9328248
Raschilas F Wolff M Delatour F Chaffaut C Broucker TD Chevret S Lebon P Canton P Rozenberg F Outcome of and prognostic factors for herpes simplex encephalitis in adult patients: results of a multicenter study Clin Infect Dis 2002 35 254 60 12115090 10.1086/341405
Strupp M Zingler VC Arbusow V Niklas D Maag KP Dieterich M Bense S Theil D Jahn K Brandt T Methylprednisolone, Valacyclovir, or the Combination for Vestibular Neuritis N Engl J Med 2004 351 354 61 15269315 10.1056/NEJMoa033280
Byl FM Adour KK Auditory symptoms associated with herpes zoster or idiopathic facial paralysis Laryngoscope 1977 87 372 9 557156
| 15575957 | PMC535933 | CC BY | 2021-01-04 16:31:44 | no | BMC Ear Nose Throat Disord. 2004 Dec 2; 4:3 | utf-8 | BMC Ear Nose Throat Disord | 2,004 | 10.1186/1472-6815-4-3 | oa_comm |
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BMC Womens HealthBMC Women's Health1472-6874BioMed Central London 1472-6874-4-101556084210.1186/1472-6874-4-10Research ArticleHysterectomy at a Canadian tertiary care facility: results of a one year retrospective review Toma Alina [email protected] Wilma M [email protected] R Hugh [email protected] Department of Obstetrics and Gynaecology, Queen's University, Victory 4, Stuart Street, Kingston, Ontario, Canada K7L 2V72 Clinical Research Centre, Kingston General Hospital and the Department of Community Health and Epidemiology, Queen's University, Kingston, Ontario, Canada K7L 2V72004 23 11 2004 4 10 10 15 3 2004 23 11 2004 Copyright © 2004 Toma et al; licensee BioMed Central Ltd.2004Toma 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 purpose of this study was to investigate the indications for and approach to hysterectomy at Kingston General Hospital (KGH), a teaching hospital affiliated with Queen's University at Kingston, Ontario. In particular, in light of current literature and government standards suggesting the superiority of vaginal versus abdominal approaches and a high number of concurrent oophorectomies, the aim was to examine the circumstances in which concurrent oophorectomies were performed and to compare abdominal and vaginal hysterectomy outcomes.
Methods
A retrospective chart audit of 372 consecutive hysterectomies performed in 2001 was completed. Data regarding patient characteristics, process of care and outcomes were collected. Data were analyzed using descriptive statistics, t-tests and linear and logistic regression.
Results
Average age was 48.5 years, mean body mass index (BMI) was 28.6, the mean length of stay (LOS) was 5.2 days using an abdominal approach and 3.0 days using a vaginal approach without laparoscopy. 14% of hysterectomies were performed vaginally, 5.9% were laparoscopically assisted vaginal hysterectomies and the rest were abdominal hysterectomies. The most common indication was dysfunctional or abnormal uterine bleeding (37%). The average age of those that had an oophorectomy (removal of both ovaries) was 50.8 years versus 44.3 years for those that did not (p < .05). Factors associated with LOS included surgical approach, age and the number of concurrent procedures.
Conclusions
A significant reduction in LOS was found using the vaginal approach. Both the patient and the health care system may benefit from the tendency towards an increased use of vaginal hysterectomies. The audit process demonstrated the usefulness of an on-going review mechanism to examine trends associated with common surgical procedures.
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Background
In Canada in 2001, 446 hysterectomies were performed per 100 000 women [1]. The rate however varies considerably as a consequence of factors such as acceptability of medical management in areas where there is limited availability of gynaecologists [2] and a lack of dissemination and implementation of guidelines to direct treatment decisions [3].
In response to the consistent demand for this procedure, recent reports have identified hysterectomy as a key health care indicator used to measure and compare hospital performance. In particular, the Ontario Hospital Association has identified the ratio of vaginal (VH) to abdominal hysterectomy (AH) as a measure of hospital performance [4], with a more favorable grade awarded to those hospitals with a higher proportion of VHs. In addition, length of stay (LOS) and complication rates associated with hysterectomy are also used to grade hospital performance [4].
Considerable attention has also been directed towards the high rate of concurrent oophorectomy (removal of both ovaries) with this procedure. This rate is of particular concern in premenopausal women because of the early menopause that ensues.
The purpose of this study was to compare abdominal and vaginal approaches to hysterectomy, investigate the rate of concurrent oophorectomy, and identify factors associated with length of surgery, LOS and approach, by auditing all hysterectomies performed over a one-year period at a university teaching hospital.
Methods
The study involved all patients who underwent a hysterectomy in 2001 at Kingston General Hospital (KGH), a teaching hospital affiliated with Queen's University at Kingston, Ontario. The Queen's University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board approved the study (OBGY-117-03). There were no exclusion criteria. Patients were identified by medical record tracking using ICD-9 codes and charts were reviewed to collect patient characteristics, length of stay, length of surgery, indication for surgery and approach. Readmissions, complications, infections and repeat laparotomies were also assessed.
Menopause was defined as one year since the last menstrual period. Up to three indications for surgery were obtained from the chart, including those identified in clinic letters, admission sheets and operative reports. All indications were collected regardless of whether or not the post-operative diagnosis coincided with the preoperative diagnosis.
VH included laparoscopically assisted vaginal hysterectomy (LAVH) and AH included VH converted to AH unless otherwise noted. Readmission was defined as a visit to the emergency room or an admission to the same hospital with a diagnosis that was related (readmission to another facility was unlikely as KGH is the only tertiary care facility in the region). Post-operative infections were defined as those that occurred within 30 days of surgery. A complication of excessive bleeding was defined as an intra-operative hemorrhage requiring transfusion or laparotomy, post-operative hematoma/seroma formation, or a significant post-operative vaginal bleed that required medical attention. All complications that occurred during the surgery or within 30 days of surgery were recorded, other than problems associated with removal of catheter, urinary retention, hypertension, hypotension, pain control, nausea and vomiting or headache. Any repeat laparotomy or unplanned laparotomy (other than for conversion of VH to AH) that occurred during the surgery or within 30 days of discharge was also noted.
Follow up information was tracked using hospital chart and clinic note information from the six-week post-operative visit. All data were analyzed using SPSS statistical software (Version 11.0.1, SPSS Incorporated, Chicago, Illinois, 2002). Between-group comparisons utilized two-sample t-tests and one-way analysis of variance (continuous data) and Chi-square analyses (categorical data). Predictors of LOS and length of surgery were identified using linear regression, while predictors of surgical approach were identified using logistic regression. Variables were offered into the models on the basis of the strength of the bivariate associations with the outcomes (p < 0.20).
Results
Three hundred and seventy two women underwent a hysterectomy in 2001. The characteristics of these patients can be found in Table 1. Sixty-nine percent were premenopausal at the time of the surgery.
Table 1 Patient Characteristics
N Minimum Maximum Mean Std. Deviation
Age (years) 372 27 87 48.5 11.5
Body Mass Index 357 16 79 28.6 7.3
Parity 365 0 9 2.1 1.5
Length of Stay (days) 372 1 62 4.7 4.4
Length of Surgery (minutes) 369 38 390 104.4 46.4
The majority of hysterectomies were AH (78%), 14% were VH, 5.9% were LAVH and 2.2% were VH converted to AH. Total hysterectomies accounted for 79.8% of hysterectomies, 16.1% were subtotal, and 4% were radical or modified radical hysterectomies. There were no significant differences between patients who had a subtotal and those that had a total hysterectomy for BMI, age, LOS, length of surgery, number of infections, or number of complications. The patients differed only in terms of parity, in that those who underwent a total hysterectomy had more children (2.12 versus 1.66, p = 0.026).
A concurrent procedure was performed in 26.6% of patients. This included biopsies (10.5%), reparative surgery (5.9%), procedures to establish urinary continence (3.5%), appendectomies (1.9%), and surgery to manage intra-operative events (2.2%). Table 2 outlines the indications for surgery overall and by type of hysterectomy. There were 526 indications listed for the 372 patients, as up to three reasons could be cited. For 245 of the women (65.9%), only one reason was identified, while 100 women (26.9%) had two reasons and an additional 27 (7.3%) had three reasons listed. Dysfunctional or abnormal uterine bleeding was the most common indication, at 26.4% of the sample. However, this indication accounted for 52.5% of the vaginal hysterectomies, while another 25.4% of the vaginal hysterectomies were for pelvic organ prolapse of stress incontinence. Significance testing of the indications by type of surgery was not carried out due to the large number of cells with a frequency of five or less.
Table 2 Indications for surgery by type of hysterectomy.
Indication Abdominal Vaginal Lap-Assisted Vaginal Vaginal Converted to Abdominal Total (Row Percent) Percent of Overall Total
Dysfunctional or Abnormal Uterine Bleeding 95 (68.3) 31 (22.3) 7 (5.1) 6 (4.3) 139 (100) 26.4%
Leiomyomas 80 (95.2) 3 (3.6) 0 1 (1.2) 84 (100) 16.0%
Adnexal or Pelvic Mass, Ovarian Neoplasm or Cyst 60 (100) 0 0 0 60 (100) 11.4%
Endometrial, Ovarian or Cervical Cancer 54 (93.1) 3 (5.2) 0 1 (1.7) 58 (100) 11.0%
Chronic Pelvic Pain, Severe Menstrual Related Mood Disorder or Dysmenorrhea 38 (67.9) 4 (7.1) 13 (23.2) 1 (1.8) 56 (100) 10.6%
Endometrial Hyperplasia, Cervical Dysplasia, or Family or Personal History of Cancer 37 (78.7) 3 (6.4) 6 (12.8) 1 (2.1) 47 (100) 8.9%
Pelvic Organ Prolapse or Genuine Stress Incontinence 22 (55.0) 15 (37.5) 2 (5.0) 1 (2.5) 40 (100) 7.6%
Endometriosis or Adenomyosis 22 (81.5) 0 4 (14.8) 1 (3.7) 27 (100) 5.1%
Chronic Salpingitis, Oophoritis, Hydrosalpinx, Pyosalpinx, Post Menopausal Bleed or Other 15 (100) 0 0 0 15 (100) 2.9%
Total 423 (80.4) 59 (11.2) 32 (6.1) 12 (2.3) 526 (100) 100%
Values are given as N (% of row total), with the exception of the final column, which contains the percentage of the overall total. Note that up to three indications could be listed, resulting in 526 reasons for 372 patients.
Fifty-eight (15.6%) of the patients had a diagnosis of cancer pre-operatively, which rose to 76 (20.4%) post-operatively. The population with cancer was older, had higher BMIs, longer surgeries, and longer lengths of stay than those without cancer (Table 3).
Table 3 Characteristics of patients with and without cancer.
Characteristic Cancer N Mean Std. Deviation p-value*
BMI No 285 28.0 6.1 .042
Yes 72 30.7 10.7
Age in years No 296 46.7 10.1 < .001
Yes 76 55.9 13.9
Length of Surgery in minutes No 293 98.9 41.1 < .001
Yes 76 125.7 58.4
Length of Stay in days No 296 4.3 3.4 .022
Yes 76 6.2 7.0
* p-values are based on the two-sample t-test
BMI was missing for 4 patients with cancer and 11 patients without cancer; length of surgery in minutes was missing for 3 patients without cancer.
Twenty-six patients visited the emergency room within 30 days of their discharge and an additional nineteen patients were readmitted to the hospital. Table 4 compares the characteristics of patients who were not readmitted to those who were seen in the ER or readmitted to the hospital.
Table 4 Characteristics of patients readmitted to the ER or hospital.
Characteristic Readmission N Mean Std. Deviation p-value*
BMI None 303 28.4 6.7 .007
ER Only 25 27.1 6.9
Readmitted 18 33.6 13.4
Age in years None 316 48.9 11.5 .110
ER Only 26 44.0 9.9
Readmitted 19 47.6 12.7
Length of Stay in days None 316 4.6 3.4 .007
ER Only 26 3.8 1.5
Readmitted 19 7.7 13.4
* p-values are based on one-way analysis of variance
Infections occurred in 15.3% of patients, including urinary tract infections (7.5%), incision site infections (5.6%) and pelvic infections (2.2%). Those who developed an infection had a higher mean BMI (p = 0.018), longer LOS (p = 0.018) and longer length of surgery (p = 0.036) than those who did not.
Four percent of patients had a repeat laparotomy or unplanned laparotomy (not including those for conversion of VH to AH). Other complications occurred in 24.5% of patients, the most common being excessive bleeding (11.3%) and post-operative ileus (5.4%). Other complications involving the bladder, bowel, pulmonary function, cardiac function or drug reactions occurred in less than 2% of patients respectively.
Table 5 contains the characteristics of the women by oophorectomy and hysterectomy type (excluding LAVH and VH converted to AH). Overall, 65% of women had both or last ovary removed, including 57% of the 257 premenopausal women and 84% of the 113 postmenopausal women (menopausal status was not documented in two patients). In women with dysfunctional uterine bleeding as the only indication, 35% had both or last ovary removed. In women with leiomyomas as the only indication, 71.4% had both or last ovary removed.
Table 5 Characteristics of Women By Oophorectomy and Hysterectomy Categories
Characteristic Mean (SD) Oophorectomy Hysterectomy
No Ovaries Removed n = 129 Both or Last Ovary Removed n = 243 Abdominal n = 275 Vaginal n = 52
Age in Years 44.3 (10.7) 50.8 (11.4)* 49.4 (11.5) 47.4 (11.9)
Body Mass Index 27.4 (5.3) 29.2 (8.2)* 29.2 (7.8) 25.8 (4.6)†
Length of Stay in Days 3.8 (1.7) 5.2 (5.2)* 5.2 (4.8) 3.0 (1.6)†
Length of Surgery in Minutes 109.3 (56.8) 101.8 (39.8) 106.3 (48.7) 84.7 (34.6)†
* Between-group differences significant at p < .05, 2-sample t-test
† Between-group differences significant at p < .01, 2-sample t-test
A comparison of the abdominal and vaginal approaches revealed no differences in terms of incidence of infection, readmission to the ER or hospital, incidence of excessive bleeding or complication rate.
LAVH and VH converted to AH were excluded from all regression analyses as they represented subgroups that were clinically different than routine AH and VH. Table 6 presents the results of the linear regression modeling for length of surgery and LOS. All variables with a significance level of p < .20 in the bivariate analyses were offered into the models. Predictors of length of surgery included higher BMI, younger age, higher parity, a higher number of concurrent procedures and an abdominal approach. These predictors account for 33.1% of the variation in length of surgery. Predictors of a longer LOS include an abdominal approach, older age and a higher number of concurrent procedures. Oophorectomy, which was significantly associated with LOS in the bivariate analyses, was not retained in the model since it was also associated with the abdominal approach, resulting in collinearity between the two variables. In order to normalize the distribution, the LOS regression model was developed without two outliers that had LOS of 45 and 62 days. The three predictors accounted for 19% of the variation in LOS. Post-hoc analyses (scatter plots of the residuals against the predicted values, influence diagnostics) were done to examine the model fitting and indicated that the fit was acceptable.
Table 6 Predictors of Length of Surgery and Length of Stay based on Linear Regression
Length of Surgery in minutes (r2 = .331) Coefficient p-value
Constant 77.11
BMI 1.16 < .001
Age in years -0.55 .008
Parity 4.06 .009
Number of concurrent procedures 45.24 < .001
Vaginal approach (compared to abdominal) -22.56 < .001
Length of Stay in Days (r2 = .189)
Constant 2.3
Vaginal approach (compared to abdominal) -1.7 < .001
Age in years 0.043 < .001
Number of concurrent procedures 1.2 < .001
Additional variables offered into the Length of Surgery model (but not selected) included menopausal status, number of indications, cancer as primary indication and oophorectomy.
Additional variables offered into the Length of Stay model included BMI, cancer as primary indication and oophorectomy.
Logistic regression for approach of hysterectomy indicated that a patient was 1.1 times more likely to have an AH for each one-point increase in BMI (p = 0.003), 47.6 times more likely to have an AH if she had a concurrent unilateral or bilateral oophorectomy (p < 0.001) and 1.7 times more likely to have a VH with each additional child (p < 0.001).
Discussion
The majority of the patients were overweight (29.6%, BMI 25–29.9) or obese (36.6%, BMI ≥ 30). These numbers define a population whose obesity level is 21.8 percentage points above the national average and although there is no known average BMI for all hysterectomy patients in Canada for comparison, the high obesity rate at this centre may have contributed to the reliance on the abdominal approach. A patient was in fact eleven times more likely to have an AH for every 10-point increase in BMI. Although recent studies exclude BMI as a factor in determining the route of hysterectomy, it has been noted that obesity of the buttocks may interfere with the exposure necessary for a VH [5].
The general trend in determining the route of hysterectomy has been to challenge the validity of the exclusionary criteria for VH, such as nulliparity, larger uterine size, previous cesarean delivery, and pelvic laparotomy. These are no longer considered to be strong contraindications to a vaginal approach [5-11]. Yet the abdominal approach is still the most utilized approach at this facility, accounting for 78% of the hysterectomies. The general impression from this and other studies is that surgeon expertise, patient weight and the need for adnexal surgery may play the strongest roles in determining the ultimate route for hysterectomy [6-12]. The need for concurrent oophorectomy may also have been a contributing factor. Oophorectomies, while able to be performed vaginally in the majority of circumstances, were more likely to have been performed abdominally in this population due to issues of accessibility (size of patient).
The overall ratio of abdominal to vaginal (alone or in conjunction with laparoscopy) surgeries is 5.6:1 but when only considering those surgeries performed for indications other than cancer (cancer found pre or post operatively), the ratio reduces to 3.9:1. This is consistent with the fact that most malignant indications for surgery require an abdominal approach in order to ensure access to structures and to allow for staging procedures. Our data did not demonstrate a significant difference between AH and VH in terms of outcome variables such as the rate of infection or complication, however, the two day reduction in LOS for VH may have significant cost reduction potential [8,13]. In our study, less than 20% of the hysterectomies performed in 2001 were VH or LAVH. This is below the average rate of 32% across Canada for 1999–2000 [14]. The average length of stay for hysterectomy was 4.7 days, which is only slightly above the average Canadian value of 4.4 from 1999–2000 [14]. In light of this comparison, an effort to increase the proportion of hysterectomies performed using a vaginal approach would be in keeping with the Society of Obstetricians and Gynecologists of Canada clinical practice guidelines which recommend offering VH to all women where that approach is deemed feasible by the surgeon [15]. Recent reports [16] have demonstrated a marked improvement in the ratio of VH to AH with the adoption of guidelines that clearly determine the correct surgical approach based on vaginal access, mobility with the Valsalva maneuver and uterine size. The application of guidelines [17] such as these warrants careful consideration in centers where a mandate exists to increase the rate of VH.
The merit of performing a concurrent oophorectomy during hysterectomy continues to be debated for women not at high risk of developing ovarian cancer. Estimates regarding the number of prophylactic oophorectomies needed to prevent one case of ovarian cancer range from 200 [18] to 300 [19]. Benefits such as prevention of ovarian cancer and perhaps breast cancer have to be weighed against an instantaneous surgical menopause that may increase a woman's risk of ischemic heart disease and osteoporosis [18]. In addition, although not all women decide to take HRT after oophorectomy, those that do, have to additionally consider the risks and benefits associated with that treatment. The main outcome from a recent study that investigated women's attitudes towards oophorectomy as an adjunct to hysterectomy concluded that while over half the women expressed a desire to decline oophorectomy, the majority were not well informed as to the long-term consequences of either decision [20]. Few clear guidelines exist to aid either the physician or the patient in the decision making process, making it all the more important to ensure that the patient is adequately informed about the long and short term risks and benefits of all treatment options.
The limitations of this study include uneven distribution of patients in each treatment group and lack of randomization due to the nature of the retrospective chart review process. Furthermore, because the audit process relied entirely on chart documentation, information may have been missed or incorrect as a result of improper or absent documentation. The broad range of information collected also prevented the researchers from employing more rigorous definitions and verification of outcomes. The retrospective nature of the study precluded an evaluation of the decision making process leading to oophorectomy as well as the influence of pre-operative indications, uterine size, parity, previous c-section and concurrent oophorectomy on surgical approach. This would need to be addressed prospectively, by surveying the surgeons at the time that the decision was made.
Conclusions
Both the patient and the health care system may benefit from the trend towards increased use of vaginal hysterectomies. However, the abdominal approach continues to dominate, likely related to patient size, surgeon preference and the need for adnexal surgery. The audit process proved to be an important method by which to assess trends associated with common surgical procedures. This study raises important questions about the relationship between patient characteristics, surgical approach and the indications for surgery, and a prospective approach, designed to address these questions more fully, is now indicated. Furthermore, in light of recent evidence [16] demonstrating the impact of a directed approach to affect the ratio of AH to VH, clear guidelines as provided by the Society of Pelvic Reconstructive Surgeons [17] should be considered to invariably increase the rate of VH. This study raises important questions about the relationship between patient characteristics, surgical approach and the indications for surgery, and a prospective approach, designed to address these questions more fully, is now indicated.
Competing Interests
The author(s) declare that they have no competing interests.
Authors' Contributions
AT performed data collection, participated in the study design and coordination and participated in drafting the manuscript. WH performed the statistical analysis and participated in drafting the manuscript. RHG conceived the study and participated in its design and coordination and participated in drafting 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
Supported by the Obs/Gyn Memorial Fund, Queen's University, 2002
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| 15560842 | PMC535934 | CC BY | 2021-01-04 16:30:33 | no | BMC Womens Health. 2004 Nov 23; 4:10 | utf-8 | BMC Womens Health | 2,004 | 10.1186/1472-6874-4-10 | oa_comm |
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BMC Womens HealthBMC Women's Health1472-6874BioMed Central London 1472-6874-4-71549810310.1186/1472-6874-4-7Research ArticlePregnancy weight gain and breast cancer risk Kinnunen Tarja I [email protected] Riitta [email protected] Mika [email protected] Elina [email protected] Leena [email protected] Tampere School of Public Health, 33014 University of Tampere, Finland2 UKK Institute, PL 30, 33501 Tampere, Finland3 National Research and Development Centre for Welfare and Health, PL 220, 00531 Helsinki, Finland4 Lombardi Cancer Center and Department of Oncology, Georgetown University, 3970 Reservoir Rd, NW, Washington, DC 20057, USA2004 21 10 2004 4 7 7 21 2 2004 21 10 2004 Copyright © 2004 Kinnunen et al; licensee BioMed Central Ltd.2004Kinnunen 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
Elevated pregnancy estrogen levels are associated with increased risk of developing breast cancer in mothers. We studied whether pregnancy weight gain that has been linked to high circulating estrogen levels, affects a mother's breast cancer risk.
Methods
Our cohort consisted of women who were pregnant between 1954–1963 in Helsinki, Finland, 2,089 of which were eligible for the study. Pregnancy data were collected from patient records of maternity centers. 123 subsequent breast cancer cases were identified through a record linkage to the Finnish Cancer Registry, and the mean age at diagnosis was 56 years (range 35 – 74). A sample of 979 women (123 cases, 856 controls) from the cohort was linked to the Hospital Inpatient Registry to obtain information on the women's stay in hospitals.
Results
Mothers in the upper tertile of pregnancy weight gain (>15 kg) had a 1.62-fold (95% CI 1.03–2.53) higher breast cancer risk than mothers who gained the recommended amount (the middle tertile, mean: 12.9 kg, range 11–15 kg), after adjusting for mother's age at menarche, age at first birth, age at index pregnancy, parity at the index birth, and body mass index (BMI) before the index pregnancy. In a separate nested case-control study (n = 65 cases and 431 controls), adjustment for BMI at the time of breast cancer diagnosis did not modify the findings.
Conclusions
Our study suggests that high pregnancy weight gain increases later breast cancer risk, independently from body weight at the time of diagnosis.
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Background
Sensitivity of the breast to hormones and environmental exposures varies throughout a woman's life span [1]. During pregnancy, the breast undergoes extensive changes in preparation for lactation. High estrogenicity during pregnancy causes marked cellular proliferation, in both in the normal and tumor cells. Normal breast cells differentiate to milk-secreting alveoli, while tumor cells, if present, continue to multiple and eventually form a detectable tumor. These two events probably explain the dual effect of pregnancy on breast cancer risk: pregnancy before age 20 reduces, whereas first pregnancy after age 30 increases, breast cancer risk [2]. In young women, pregnancy may eliminate future targets for neoplastic changes by differentiating target cells [3]; the breast tissue of older first time mothers is more likely to have acquired malignant cells that are stimulated by high pregnancy hormonal environment.
Women whose pregnancy estrogen levels are elevated are at an increased risk of breast cancer. For example, women who took the synthetic estrogen diethylstilbestrol (DES) during pregnancy are at an increased risk of developing breast cancer [4], as are women who suffered from severe pregnancy nausea [5] or who gave birth to heavy newborns [6]. Both nausea in pregnancy and high birth weight are linked to elevated pregnancy estrogen levels [7,8] Conversely, pregnant women having high alpha feto-albumin levels [9,10], or suffering from hypertension or pre-eclampsia [11,12], exhibit a reduced risk. Alpha feto-protein has direct antiestrogenic activity and binds estrogens, reducing their biological availability [13,14]. Hypertension during pregnancy is linked to reduced estrogen and increased testosterone levels [15]. A recent study in which estrogen levels were measured in stored blood samples of pregnant women later diagnosed with breast cancer, provides direct evidence in support of high estrogen and low progesterone levels in increasing maternal breast cancer risk [16]. However, some studies have failed to find an association between pregnancy estrogen levels, determined indirectly, and maternal breast cancer risk [11,17].
Adipose tissue aromatizes androgens to estrogens, and thus high body mass index (BMI) is linked to elevated estrogen levels in postmenopausal women [18]. Some studies suggest that high pregnancy weight gain may be associated with increased pregnancy estrogen levels [19], although this has not been confirmed in more recent studies [20,21]. The goal of this study was to determine whether high pregnancy weight gain affects breast cancer risk.
Methods
The cohort
The study population was a historic cohort of women pregnant between 1954 and 1963 in Helsinki, Finland (n = 4,090). The cohort was a sample gathered for a study on hormone exposure, including 2,022 exposed, 2,062 controls and 6 women with unknown hormone exposure status. Information on the cohort was collected from the maternity cards of municipal maternity centers, which are used by most pregnant Finnish women. The hormone-exposed women had been prescribed estrogen or progestin drugs during pregnancy to prevent early abortion or preterm delivery. For each exposed woman, a woman next in the maternity center file who gave birth during the same year and had not been prescribed hormones during pregnancy, was chosen as a control. The cohort has been previously prescribed in detail [22,23]. There were no differences in breast or other estrogen-dependent cancers between hormone-exposed and control mothers [22]. Visits to a private doctor were used as an indicator of socio-economic status, since no information on education or occupation at the time of the index pregnancy was available.
Cancer cases were identified through a record linkage to the national cancer registry until June 2001. Mortality and emigration data were obtained from the population registry until August 2001. The linkage between the cohort and the registries was based on a unique personal identification number.
Inclusions and exclusions
Inclusion criteria were the following: first and last visit at the maternity center between 4–45th gestation weeks, the time between the body weight measurements 3–300 days, and delivery between 22–45th gestation weeks. For each mother, the gestation week she gave birth was determined by using the date of estimated timing of delivery. Women who did not fulfill these criteria were excluded (Fig. 1). In addition, women with multiple births were excluded because their weight gain is not comparable to that of mothers of singletons. Mothers with pre-eclampsia or eclampsia were excluded because they accumulate weight as fluid retention during pregnancy, and have been reported to have a reduced breast cancer risk [11,12].
Figure 1 Study population and exclusions.
Pregnancy weight gain was first calculated as the difference between the first and last visit to maternity center. However, this window varied considerably among included mothers (range 3–295 days). The time-period of calculated weight gain was therefore adjusted by extrapolating a line for each mother to reflect her weight increase during pregnancy. The calculations are described in detail in Additional File 1. After the unstable period of early pregnancy, a mother's weight increases linearly [24]. Mothers usually begin to gain weight after the first trimester (e.g [25]). We extrapolated the line separately for 0–15th (Line A) and 15–40th gestation weeks (Line B) for each mother. Weight gain was extrapolated to continue until 40th gestation week for all mothers, although 22.2% of mothers delivered at 39th gestation week or before.
For mothers for whom both Line A (n = 2,143) and Line B (n = 2,184) were available, total pregnancy weight gain was calculated by adding the extrapolated weight gains from both periods. Thus, total pregnancy weight gain could be extrapolated only for 2,089 women. Cases and controls (66.5% vs 65.0%) did not differ concerning the number of available weight measurements. For the rest of the women, either the first weight measurement was later than 24th gestation week, the last weight measurement was before 30th gestation week, or information on pre-pregnancy weight, weight at the first or the last visit, or timing of the visits was not available. As indicated above, these subjects were excluded from the analyses.
We compared the characteristics of the mothers who were excluded (n = 2,001) to the characteristics of the mothers in the final study population for whom total pregnancy weight gain could be determined (n = 2,089). The two groups were similar in regard to breast cancer incidence, age at menarche, height and the frequency of visits to a private doctor. However, the excluded mothers were older (mean: 27.1 years vs. 26.5 years, p < 0.001), heavier (58.7 kg vs. 57.3 kg, p < 0.001; body mass index, BMI: 22.3 kg/m2 vs. 21.8 kg/m2, p < 0.001), older at first birth (25.2 years vs. 24.7 years, p = 0.016), had more children during index pregnancy (1.92 vs. 1.81 at index birth, p < 0.001), and were more often exposed to estrogen or progestin drugs (50.3% vs. 48.6%, p = 0.021). Their children were shorter (mean 49.3 cm vs. 50.3 cm, p < 0.001) and weighed less (mean 3,310 g vs. 3,472 g, p < 0.001), suggesting that excluded mothers' pregnancy weight gain might have been lower. It is probable that exclusion of these women had no major effect on the findings.
The case-control study
A nested case-control study was performed to determine whether later weight development confounded the association between pregnancy weight gain and breast cancer risk. A sample of women was chosen from the final cohort (n = 2,089) that included all breast cancer cases with data on pregnancy weight gain (n = 123). For each case, we chose seven randomly selected controls (n = 856) who were born in the same year as the case. These 979 women were linked to the Hospital Inpatient Registry to obtain information on the women's stays in hospitals. 117 cases were identified with a hospital visit in average 0.4 months after breast cancer diagnosis (median 0.0, range from -16.3 to 17.2), and of these cases information on body weight and height was available for 65 (53% of 123 cases). Among the controls, 699 had been a patient in a hospital at a similar age than their respective cases (maximum difference +/-5 years), and 431 of them had weight and height available in the hospital archives (50% of 856 controls, 6.6 controls/case).
The breast cancer cases with no information on later body weight did not differ from the cases used for the nested case-control analysis. The controls with no information on later body weight were approximately 1.5 years older at the time of the hospital visit than the controls included to the study (p = 0.027).
Statistical analysis
Statistical significance of possible differences in baseline characteristics of the study population, pregnancy weight gain and postpartum weight loss and weight retention by tertiles of pregnancy weight gain was tested by using analysis of variance for continuous variables and χ2-test for proportions. The incidence of breast cancer per 100,000 person years was counted by groups of 5 kg pregnancy weight gain, tertiles of pregnancy weight gain and tertiles of postpartum weight retention. Person years were calculated from the delivery to the diagnosis of breast cancer or other endpoint including death, emigration or end of the study.
The association between pregnancy weight gain and breast cancer risk was further examined using a Cox regression model. Total pregnancy weight gain was included as a categorical covariate (tertiles) in the model. Age at menarche, age at first birth, age at index pregnancy, BMI before pregnancy, and parity (at index birth) were all used as continuous covariates in the model. Postpartum weight retention 51 days after delivery (mean) was later added to the model.
The incidence of breast cancer was counted and the Cox regression model was carried out also separately for pre- and postmenopausal breast cancers. Information on the age at menopause was not available. Therefore all women were expected to have menopause at the age of 50 years.
In the case-control study, weight and BMI change between pre-pregnancy and at the time of later hospital visit were compared between the tertiles of pregnancy weight gain (analysis of variance). A Cox regression model that included later BMI was also used to analyze the data.
Results
In the cohort, 123 (5.9%) women developed breast cancer during the mean follow-up of 38.9 years. The mean age at diagnosis was 56.0 years (range 35–74). Background characteristics and index pregnancies are described by tertiles of pregnancy weight gain in Table 1. Low pregnancy weight gain (<11 kg) was associated to slightly higher ages during index pregnancy, during first pregnancy and at menarche, and to lower height, higher BMI before pregnancy, higher gestation weeks at delivery, lower weight of the placenta, smaller infant and lower proportion of users of estrogen drugs compared to women with higher pregnancy weight gain (11–15 kg or >15 kg).
Table 1 Background characteristics of index pregnancy, by tertiles of estimated pregnancy weight gain. Means (and SD) or percentiles are shown.
Pregnancy weight gain (kg)
< 11 (n = 696) 11–15 (n = 697) >15 (n = 696) p-value
Background
Mother's age (year)1 27.0 (5.3) 26.2 (4.9)2 26.2 (5.0)2 0.006
Mother's age at first birth (years) 25.2 (4.8) 24.4 (4.4)2 24.5 (4.4)2 0.003
Married (%)1 97 98 98 0.720
Visits to a private doctor (%)1 49 45 51 0.053
Mother's height (cm) 161.2 (5.5) 162.0 (5.2)2 162.9 (5.2)23 <0.001
Mother's body mass index before pregnancy (kg/m2) 22.3 (2.9) 21.6 (2.3)2 21.6 (2.5)2 <0.001
Mother's age at menarche (year) 14.2 (1.6) 13.9 (1.6)2 13.8 (1.6)2 <0.001
Regular menstrual cycles (%) 94 93 95 0.296
Parity (at index birth) 1.8 (1.1) 1.8 (1.0) 1.8 (1.1) 0.896
Index pregnancy
Gestation weeks at delivery (week) 40.6 (2.2) 40.4 (2.1) 40.2 (2.2)2 <0.001
Exposed to estrogens (%) 45 49 52 0.040
Weight of the placenta (g)4 603 (112) 634 (124)2 661(205)23 <0.001
Infant height (cm)4 50.0 (2.3) 50.3 (2.1) 50.6 (2.5)23 <0.001
Infant weight (g) 3,376 (514) 3,466 (504)2 3,577(544)23 <0.001
Low birth weight (%) 5 3 3 0.305
1 at the time of index pregnancy
2 a statistically significant difference compared to the lowest tertile (<11 kg)
3 a statistically significant difference compared to the middle tertile (11–15 kg)
4 total n = 2,055–2,089, except for placental weight (n = 1,217) and infant height (n = 2,006)
Weight development during and after pregnancy is presented by tertiles of pregnancy weight gain in Table 2. Higher weight gain during pregnancy was associated to higher weight loss after delivery, but also to higher weight retention and BMI at the postpartum check-up visit.
Table 2 Weight gain during and after pregnancy by tertiles of estimated pregnancy weight gain. Means (and 95% confidence intervals) are shown.
Pregnancy weight gain (kg)
<11 (n = 696) 11–15 (n = 697) >15 (n = 696) p-value
Mother's weight gain (kg)
Total weight gain, weeks 0–40 8.6 (8.5–8.8) 12.9 (12.9–13.0) 18.2 (18.0–18.4) <0.001
Weight after delivery1
Weight change from 40th week (kg) -7.4 (-7.7 – -7.2) -8.8 (-9.0 – -8.6) -10.6 (-10.9 – -10.3) <0.001
Weight compared to pre- pregnancy weight (kg) +1.3 (1.0–1.6) +4.1 (3.8–4.3) +7.6 (7.3–8.0) <0.001
BMI (kg/m2) 22.6 (22.4–22.9) 23.2 (22.9–23.4) 24.4 (21.1–24.6) <0.001
Weight at the hospital visit2 (n = 167) (n = 170) (n = 159)
Change from pre-pregnancy weight (kg) + 6.4 (5.1–7.8) +10.4 (9.0–11.9) +12.5 (10.8–14.3) <0.001
BMI (kg/m2) 25.0 (24.3–25.6) 25.6 (25.0–26.2) 26.3 (25.6–27.0) 0.021
Change from pre-pregnancy BMI (kg/m2) + 2.4 (1.9–3.0) + 4.0 (3.4–4.6) + 4.8 (4.1–5.4) <0.001
1 on postpartum day 51, range 40–78, total n = 1,314–1,713
2 29 years after pregnancy in average, range 9–47
Breast cancer incidence per 100,000 person years
The mean BMI before pregnancy was 21.8 kg/m2 and the mean total extrapolated weight gain during pregnancy was 13.3 kg (range -5.0–33.1 kg) in our cohort. Average pregnancy weight gain (and range) was 13.1 kg (-3.0–33.1) among primiparas, 13.5 kg (1.9–30.7) among women who gave birth to their second child and 13.2 kg (-5.0–32.4) among women who gave birth to at least their third child. The incidence of breast cancer by 5 kg categories of total pregnancy weight gain is shown in Table 3. Higher pregnancy weight gain was associated with a higher incidence of breast cancer. However, the number of women in some of the weight gain categories was small, and therefore the statistical analyses were carried out in tertiles of total pregnancy weight gain (Table 4). The incidence of breast cancer was significantly higher in mothers in the highest tertile of pregnancy weight gain (15–33 kg), when compared to the middle tertile (11–15 kg) (p = 0.04). Breast cancer incidence was lowest in the middle tertile, but no differences in the risk were seen in the mothers of the lowest tertile of weight gain (less than 11 kg), when compared with the other two categories.
Table 3 Breast cancer incidence (per 100,000 person years, py) by estimated total weight gain (weeks 0–40).
Weight gain (kg) Breast cancer cases (n) Number of women Py 171 Incidence 0
<0 0 4
0–4.99 1 42 1,677 60
5–9.99 23 423 16,614 138
10–14.99 53 954 37,266 1421
15–19.99 33 508 19,498 169
≥20 13 158 6,086 213
total 123 2,089 81,312 151
1 Breast cancer incidence was exceptionally high (200 per 100,000) in mothers who gained 10–10.99 kg during pregnancy.
Table 4 Incidence (per 100,000 person years) and unadjusted and adjusted rate ratios (RR)1 and confidence intervals (CI) on the Cox model for breast cancer by tertiles of estimated total weight gain (kg) in pregnancy (weeks 0–40), and by tertiles of postpartum weight retention.
Breast cancer cases Number of women Incidence p-value Unadjusted RR (95% CI) Adjusted RR (95% CI)1
Pregnancy weight gain (kg) 0.09
<11 39 696 143 1.18 (0.74–1.88) 1.11 (0.68–1.83)
11–15 33 697 121 1.00 (ref.) 1.00 (ref.)
> 15 51 696 190 1.59 (1.03–2.47) 1.62 (1.03–2.53)
Postpartum weight retention (kg)2 0.33
<3 26 558 121 1.00 (ref.) 1.00 (ref.)
3–5 35 539 170 1.29 (0.72–2.34) 1.36 (0.73–2.54)
>5 35 545 170 1.54 (0.87–2.74) 1.56 (0.85–2.86)
Pregnancy weight gain (kg), case-control study3
<11 19 167 - - 1.01 (0.54–1.91) 0.95 (0.49–1.84)
11–15 19 170 - - 1.00 (ref.) 1.00 (ref.)
> 15 27 159 - - 1.50 (0.83–2.69) 1.48 (0.81–2.69)
1 Adjusted for age at menarche, age at first birth, age at index pregnancy, parity (at index birth) and body mass index (BMI) before pregnancy. For the RR for postpartum weight retention, adjusted also for pregnancy weight gain (weeks 0–40).
2 Weight on postpartum day 51, range 40–78, compared to pre-pregnancy weight.
3 Adjusted for age at menarche, age at first birth, age at index pregnancy, parity (at index birth), BMI before pregnancy, and BMI during the hospital visit in average 29 years after pregnancy.
All analyses were initially carried out separately for pre- and postmenopausal breast cancers. The results for postmenopausal women were similar to the results for the whole cohort (results not shown). The incidence of premenopausal breast cancer was too low for statistical analysis and pre- and postmenopausal breast cancers were not separated further in the analyses. When these analyses were restricted to mothers who delivered after 39th gestation week, results were similar than in the whole cohort (results not shown).
The incidence of breast cancer was calculated separately for early (0–15th gestation weeks) and later pregnancy weight gain (15–40th gestation weeks). Early pregnancy weight gain was not associated with breast cancer risk. The impact of later pregnancy weight gain was similar to the impact of total weight gain, but more modest (results not shown).
Multivariate analysis
Both unadjusted and multivariate adjusted rate ratios and confidence intervals for the risk of breast cancer are presented in Table 4. In the Cox regression model, mothers in the highest tertile of pregnancy weight gain (>15 kg) had a 1.62-fold higher risk for breast cancer compared to mothers in the middle tertile (average weight gain 12.9 kg), when age at menarche, age at first birth, age at index pregnancy, BMI before pregnancy and parity at index birth were included in the model. To assess the sensitivity of these analyses, the lowest and highest weight gain groups were used as reference groups. When the lowest weight gain group was the reference group, no differences among the groups were seen. However, when the highest weight gain group was the reference group, women with average weight gain had significantly lower risk of breast cancer (multivariate adjusted RR 0.62, 95% CI 0.40–0.97).
When the middle tertile of weight gain was again used as the reference group and the analysis was restricted to mothers who delivered after 39th week of gestation, the results were essentially similar although statistically not significant (data not shown). The results did not either change when adjusted additionally for the year of index birth. The increased breast cancer risk in the highest tertile of pregnancy weight gain was found only for postmenopausal breast cancer (relative risk, RR = 1.80, 95% confidence interval, CI 1.05–3.07, p = 0.03). The RR for premenopausal cancer was 1.00 (95% CI 0.40– 2.48, p = 0.99). However, the number of premenopausal breast cancer cases with the information on all variables in the model was too low (n = 25) to yield sufficient power.
No statistically significant differences in breast cancer risk were observed between the tertiles of postpartum weight retention, determined approximately 51 days after delivery (Table 4).
Other results
Later age at menarche was marginally related to a decreased risk of breast cancer (adjusted RR = 0.99, 95% CI 0.97–1.00). Mother's age at the time of first pregnancy or at the index pregnancy, parity at index birth or BMI before pregnancy were not statistically significantly associated with the risk of breast cancer. The results were similar when height and weight were used as separate variables in the model, instead of BMI.
Lower pre-pregnancy BMI was associated with higher weight gain during pregnancy (p < 0.001) and higher postpartum weight retention (p = 0.003), but not with postpartum weight loss. The differences in the incidence of breast cancer were not statistically significant between the pre-pregnancy BMI-categories.
The case-control study
Women who gained at least 15 kg weight during pregnancy had a higher BMI at the time of later hospital visit (29 years after pregnancy in average) than women who gained <11 kg weight during pregnancy (p = 0.021) (Table 2.). Changes in body weight (p < 0.001) and BMI (p < 0.001) were also higher in women who gained 11–15 kg or >15 kg compared to women who gained <11 kg during pregnancy. These findings are in agreement with earlier reports showing a link between excessive pregnancy weight gain and becoming overweight/obese later on [26,27].
The time window between pregnancy and assessment of BMI during later hospital visit was similar among the tertiles of pregnancy weight gain (29.2 vs. 30.0 vs. 30.1 years, p = 0.397). In the Cox regression model, women's later BMI at the time of diagnoses was not associated with breast cancer risk (adjusted RR = 0.96, 95% CI 0.90–1.04). Further, results relating to pregnancy weight gain and breast cancer risk were not altered by adding data on later BMI to the model (Table 4). It is to be noted that since fewer women were included to this analysis, the effect of pregnancy weight gain did not reach statistical significance.
Discussion
The results obtained in our study indicate that higher than recommended pregnancy weight gain increased mothers' risk of developing breast cancer. Thus, women who gained more than 15 kg during pregnancy had a 62% increase in breast cancer risk, compared to those who gained between 11–15 kg. The Institute of Medicine (IOM) published their most recent recommendations for pregnancy weight gain in 1990 [28]. The recommended pregnancy weight gain in the USA is 11.5–16 kg for women with normal pre-pregnancy BMI; i.e., they are not obese or underweight. Pregnancy weight gain recommendations are lower (7–11.5 kg) for overweight women and higher (12.5–18 kg) for underweight women. As seen in Table 3, the incidence of breast cancer in our study was highest among women who gained more than 20 kg during pregnancy, suggesting that the increase in risk may apply primarily to women at the most extreme range of pregnancy weight gain.
An increase in breast cancer risk was seen mostly in women who were diagnosed with this disease after age 50 and thus were postmenopausal. However, the number of premenopausal breast cancers was low in the cohort, and we cannot exclude the possibility that pregnancy weight gain may also increase the risk of premenopausal breast cancer.
Data generated in epidemiological studies rarely provide causal relationships. We propose four different mechanisms that may link high pregnancy weight gain to a later increase in breast cancer risk. First, weight retention in women who gained excessive amounts of weight during pregnancy may have persisted into their postmenopausal years. Women prone to postpartum weight retention might also be prone to long-lasting weight gain after pregnancy [29], and high BMI during postmenopausal years increases breast cancer risk [26]. To examine this possibility, information on body weight at the time of breast cancer diagnosis was obtained. If the association between pregnancy weight gain and breast cancer risk was affected by later weight development, breast cancer cases should have had higher BMI at the time of diagnosis. As this was not the case, we propose that high pregnancy weight gain increases breast cancer risk independently from body weight at the time of diagnosis.
Another alternative is that women who gained an excessive amount of weight during pregnancy may have had higher pregnancy hormone and growth factor levels than women who gained within the recommended range, stimulating the growth of existing malignant cells in the breast, leading to development of a detectable tumor. Several studies have shown that markers of high pregnancy estrogen levels increase mother's breast cancer risk [4-6,9-12,16]. Estrogen levels may correlate with high pregnancy weight gain [19], but two recent studies have not confirmed this observation [20,21]. Other possible hormones that could be mediating the effect of pregnancy weight gain on breast cancer risk include leptin. Leptin levels correlate strongly with BMI [27], also during pregnancy [30], and leptin is suggested to increase breast cancer risk [31]. We did not have any biological samples available for hormone measurements.
It is known that high hormone levels increase the proliferation of normal breast cells that then is accompanied by increased genomic instability and accumulation of DNA adducts [16,32]. Thus, the third explanation is that high pregnancy weight gain increased the likelihood of DNA damage and mutations in genes that initiate breast cancer. Since the window between index pregnancy and diagnosis of breast cancer was approximately 30 years, there was enough time for the initiation to have taken place during pregnancy.
Finally, known and unknown causes of breast cancer may have confounded the results. For example, these causative factors might be more common in women who gain excessive amounts of weight during pregnancy or they caused women to gain excessive amounts of weight during pregnancy. A theoretical example is a gene mutation/polymorphism that could both make a woman more prone to gain weight during pregnancy and increases breast cancer risk.
Methodological limitations have to be considered when interpreting the results, and they include high rate of exclusion and an exposure to estrogenic drugs during pregnancy. Of the 4,090 women available for the study, 48.9 % were excluded for reasons listed in Figure 1 (109 of which were diagnosed with breast cancer). Total pregnancy weight gain could be extrapolated only for 2,089 women, of which 123 had developed breast cancer. Other information on background and index pregnancies indicated that the excluded women might have gained less weight during pregnancy than the final study population (see chapter Inclusions and exclusions). However, we found no evidence that breast cancer incidence was different between the women excluded and included to the study.
Some women in our cohort had been exposed to synthetic estrogens during pregnancy to avoid a threatening miscarriage, and this exposure might have affected the results. However, it was the initial reason for obtaining information from pregnant women, and we found no effect of the drug exposure on the incidence of breast cancer. We are not aware of any other cohort that could be used to assess the link between pregnancy weight gain and breast cancer risk, but if such a cohort becomes available, and it is not potentially compromised by high rate of exclusion of subjects or an exposure to drugs during pregnancy, the present results can be either confirmed or nullified. Follow-up of "old" cohorts similar to ours is rarely possible, making our study relatively unique.
Another area of potential source for errors is the variability in time period between the weight measurements during pregnancy (range 3–295 days), requiring us to extrapolate the pregnancy weight gain for each woman. This step also has been successfully used in other studies [33]. A further weakness of the study was that no information on weight gain in previous and subsequent pregnancies was available. Therefore, we cannot exclude the possibility that a woman who did not develop breast cancer and during the index pregnancy gained less than 15 kg, might have had subsequent pregnancies that were characterized by excessive weight gain.
Finally, in the case-control study that determined the impact of body weight at the time of diagnosis on breast cancer risk, information on this weight was obtained only for 53% of the cases and 50% of the controls. However, the direction of bias rising from exclusion may have diluted the effect, rather than caused it.
In conclusion, our findings suggest that excessive pregnancy weight gain increased later risk of developing breast cancer. This association needs to be further confirmed in prospective studies.
List of abbreviations
diethylstilbestrol – DES; body mass index – BMI
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
TK: PhD student who collected and put together all the material for the study, managed the data and did statistical analysis, and participated in writing the manuscript.
MG: Participated in statistical analysis of the data and writing the manuscript.
EH: Collected the original data base of pregnant women and was in charge of linking the data base to cancer registry, participated in statistical analysis planning and writing the manuscript.
RL: Generated the idea of testing the hypothesis in the Hemminki data base, and participated in all stages of the study and in writing the manuscript.
LH-C: Generated the hypothesis, obtained funding for the study, and was in charge of writing the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Calculation of line a and line b for each mother.
Click here for file
Acknowledgements
The work was supported by grants to LH-C from NCI (5 RO1 CA89950-02), the Susan G. Komen Breast Cancer Research Foundation (9847), Breast Cancer Research Foundation (BCRA-01) and Department of Defense (DAMD17-99-1-9196).
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| 15498103 | PMC535935 | CC BY | 2021-01-04 16:30:33 | no | BMC Womens Health. 2004 Oct 21; 4:7 | utf-8 | BMC Womens Health | 2,004 | 10.1186/1472-6874-4-7 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-271556938910.1186/1472-6920-4-27Research ArticleA randomized trial comparing digital and live lecture formats [ISRCTN40455708 Solomon David J [email protected] Gary S [email protected] Heather S [email protected] Kevin [email protected] Office of Medical Education Research and Development and the Department of Medicine, Michigan State University, East Lansing, MI, USA2 Department of Medicine, Michigan State University, East Lansing, MI, USA3 Department of Medicine, Michigan State University, East Lansing, MI, USA4 MSU-Kalamazoo Center for Medical Studies (MSU-KCMS), Michigan State University, Kalamazoo, MI, USA2004 29 11 2004 4 27 27 14 7 2004 29 11 2004 Copyright © 2004 Solomon et al; licensee BioMed Central Ltd.2004Solomon 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
Medical education is increasingly being conducted in community-based teaching sites at diverse locations, making it difficult to provide a consistent curriculum. We conducted a randomized trial to assess whether students who viewed digital lectures would perform as well on a measure of cognitive knowledge as students who viewed live lectures. Students' perceptions of the digital lecture format and their opinion as whether a digital lecture format could serve as an adequate replacement for live lectures was also assessed.
Methods
Students were randomized to either attend a lecture series at our main campus or view digital versions of the same lectures at community-based teaching sites. Both groups completed the same examination based on the lectures, and the group viewing the digital lectures completed a feedback form on the digital format.
Results
There were no differences in performance as measured by means or average rank. Despite technical problems, the students who viewed the digital lectures overwhelmingly felt the digital lectures could replace live lectures.
Conclusions
This study provides preliminary evidence digital lectures can be a viable alternative to live lectures as a means of delivering didactic presentations in a community-based setting.
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Background
Medical education is increasingly being conducted in community-based teaching sites outside of the traditional academic medical setting [1], At the same time, the economics of health care are requiring academic physicians to be more productive[2]. These trends in academic medicine are making it more difficult to provide students and residents with consistent, high quality instruction.
Our institution has a community integrated structure where medical students spend the clinical portion of their training in one of six community campuses spread throughout the State of Michigan. Although this structure has many advantages, it is difficult to provide a consistent educational experience for the students. To help address this challenge, we implemented an all-day lecture series held at one of the community campuses two weeks before the end of the internal medicine clerkship.
The students and faculty presenters from other campuses traveled from their home campus to the campus hosting the lecture series. End-of-clerkship feedback from the students has indicated the lecture series is both valuable and well received. Traveling to the host community, however, was inconvenient and time consuming for both students and faculty presenters. In addition, it is not practical for students at our rural medicine campus to attend due to the distance (approximately 400 miles) from the other communities.
There is evidence that delivering the audio from a lecture in combination with the presenter's slides can be an effective means of delivering lectures at remote sites, and may even be as effective as traditional lectures[3,4]. We saw this as a potential solution for providing a consistent didactic curriculum in our clerkship. With the availability of inexpensive, high quality digital camcorders and software for merging audiovisual material with presentation slides, we felt including video of the presenter as well as audio from a live lecture in combination with the presenter's slides might result in a more engaging and hence more effective presentation than audio alone. During the 2003–2004 academic year, we conducted a randomized trial comparing attending the lecture series with viewing a CD-ROM-based multimedia version of the same lecture series. If the digital lectures could help students master the material at the same or similar level of understanding as live lectures, they could potentially replace the live lectures and thereby save the time lost to travel for both the students and faculty presenters. Additionally, the digital lectures would provide the same instructional opportunities for students in our rural medicine program as students in our other campuses and provide all our students the opportunity to view the presentations at their convenience.
Methods
Students taking the third-year required internal medicine clerkship during the 2003–2004 academic year at our institution were offered the opportunity to participate in the study. Those agreeing to participate were randomized into to one of two arms of the study. The random assignment of students to the two arms of the study was done within each community to control for the potential of community differences. The control group traveled to the host community campus and attended the live lectures with their colleagues who chose not to participate in the study. The experimental group stayed at their home campus on the same day and completed a parallel set of CD-ROM-based multimedia modules made from digital recordings of the previous year's lectures. They completed these digital lectures in computer laboratories in either the community campus office or within one of the teaching hospitals.
The series included six lectures covering asthma, coronary artery disease (CAD), acute renal failure, liver disease, thyroid disease, and antibiotic pharmacology. The Clerkship Education Committee chose these topics based on their perceived importance and consistency with the Society for General Internal Medicine/Clerkship Directors of Internal Medicine (SGIM/CDIM) Curriculum Guide[5].
Between the 2002–2003 academic year when the lectures were taped and the 2003–2004 academic year when the study was conducted, the clerkship faculty decided to revise the lecture series to be more case-based though the topics were kept the same. Two of the lectures, CAD and renal failure, were not modified and kept as consistent as possible with the previous year in order to conduct the study. Though there might have been minor inconsistencies between the digital and live versions of these two lectures, the same faculty member presented each lecture during both the 2002–2003 academic year when the lectures were taped and the 2003–2004 academic year when presented live. The two lecturers also attempted to keep the live lectures as consistent as possible with the digital lectures and used the same slides that they had used the previous year. While the format of the other four lectures changed, the material covered and instructional objectives remained consistent.
At the end of the live lecture series, students were asked to complete a short examination that included four to five questions based on each of the six lectures. These questions were written by the presenters of the lectures and designed to assess student mastery of the lectures' key objectives. The students were informed that the purpose of the examination was to provide them with feedback on the mastery of the material and the presenters with feedback on the effectiveness of the lectures, and would not impact on their clerkship grade. After the students completed the exam, they were given a copy that included the correct answers and a short explanation for the correct answer. The exam forms contained no student identifiers, but students in the control group were asked to indicate on the examination form that they had agreed to participate in the study so they could be differentiated from the students who had chosen not to participate in the study.
Students in the experimental arm of the study completed the same examination in their home community after they had completed the digital lectures. They were also asked to complete a short feedback form asking whether they had any technical problems using the modules, to rate their agreement with what the researchers felt to be three potential advantages and three potential disadvantages of the modules, and whether they felt the modules could serve as a suitable replacement for live lectures.
The specific questions are listed below.
Advantages of the Modules
• Convenience of viewing the presentations when you choose.
• Avoiding having to travel to another community for an all day lecture series.
• Ability to keep copies of these presentations for use in the future.
Disadvantages of these modules
• Inability to ask questions of the presenter
• Lack of group interaction/discussion of a topic
• Just not like being in the room with the presenter
The CD-ROM modules were created using a technique developed by the first author. A manual outlining how to develop these modules is available from . They included digitized video and audio from the taped presentation inserted as a window in the PowerPoint® slides from the presentation. As students displayed each of the slides, they were able to observe the presenter in the multimedia window discussing the slide that was being viewed.
Nine of the items on the exam focused on the material in the CAD and acute renal failure lectures, where the lecturers presented the lectures in the same format as they had used in the previous year when the lectures were taped. The remaining 20 examination items covered material in the other four lectures. Group differences were tested for statistical significance by both an independent sample t-test for means and a Mann-Whitney test for ranks. A Levene's test for equality of variance between the two groups was also performed. These analyses were conducted separately for the subset of items covering the material in the CAD/acute renal failure lectures and the other four lectures. A power analysis was conducted to assess the magnitude of the difference between the groups that would likely be detectable given the number of students participating in the study. The coefficient alpha reliability of the exam was also estimated. The data were presented descriptively using means, standard deviations and mean ranks within the control and experimental groups. All analyses were conducted using the Statistical Package for the Social Sciences version 11. Approval for the project was obtained from the University Committee for Research Involving Human Subjects within our institution.
Results
A total of 96 students completed the internal medicine clerkship during the 2003–2004 academic year. As described below 56 of the students were eligible to participate in the study. A total of 29 students or 52% of the eligible students agreed to participate in the study. Complete data were available for 12 students who attended the live lectures and 17 students who completed the digital lectures.
During the first rotation, there were some technical problems in a demonstration of the digital lectures. The net result was that very few students chose to participate during that rotation. During the second and third rotations, approximately two-thirds of the students agreed to participate. There were also 20 students who were ineligible to participate. These included six students from the rural medicine program at Marquette who do not participate in the live lecture series due to the distance from the other communities. Additionally, some of the communities conducted a fourth rotation of the internal medicine clerkship due to space limitations in the three regular rotations and a live version of the lecture series was not given for the students in the fourth rotation. These 20 students all completed the CD-ROM modules but because they could not be randomized between the live and digital formats, they were not able to participate in the study.
Differences in the sample sizes for the two groups were due to some of the students in the live lecture group failing to mark that they were participating the study. During the second clerkship rotation, the proctor inadvertently failed to remind the students participating in the study to mark this information on their examinations when the exams were handed out.
A power analysis indicated that with the number of subjects in the study, it would be possible to detect differences of nine tenths of a standard deviation with a power of 80%, p < 0.05 for a one-tailed t-test.
Table 1 displays the mean, standard deviation, and average rank of the exam score for the control and experimental groups for the two sets of items. The differences between the groups for both sets of items were not statistically significant for means (t-test) or medians (Mann-Whitney) at the p < 0.05 level.
Table 1 Performance on the examination: CD-ROM versus live lecture format
Mean SD No. p-value
Items from CAD and renal failure (lecture format the same for each group) Live lecture 4.42 1.08 12
CD-ROM 4.88 2.00 17
t-test† 0.22 (one-tailed)
Mann Whitney U 0.56 (exact test)
Levene's Test for equality of variances 0.026
Items from other four lectures (lecture format differed for control & treatment groups) Live lecture 9.25 3.11 12
CD-ROM 9.00 2.72 17
t-test† 0.41 (one-tailed)
Mann Whitney U 0.91 (exact test)
Levene's Test for equality of variances 0.96
†The t-test was calculated based on unequal variances. Differences in variances were tested via a Levine test and found to be statistically different in the two groups.
‡The t-test was calculated based on equal variances. Differences in variances were tested via a Levine test and found not to be statistically different in the two groups.
The Levene's test for equality of variance between the control and experimental groups was statistically significant (p = 0.03) for the CAD and renal failure items. The variation among the scores of students who observed the CD-ROMs was almost twice as large as for students who observed the live lectures. There was no statistically significant difference among the groups for the variance of the items from the other four lectures.
The coefficient alpha reliability for the items covering CAD/renal failure and the items covering the other four lectures were 0.33 and 0.66 for the 9 and 20 item scales respectively and was 0.70 for the combined 29-item exam.
The 17 students who completed the digital lectures also completed a short feedback form on their experiences and impressions of the digital lecture format. These data are presented in Table 2.
Table 2 Feedback on the CD-ROM Based Lectures
Yes No
Did you have any technical difficulties viewing the modules? 16 (94.1%) 1 (5.9%)
Advantages of the Modules Very Important Important Slightly Important Not Important
Convenience of viewing the presentations when you choose. 12 (70.6%) 5 (29.4%) 0 (0.0%) 0 (0.0%)
Avoiding having to travel to another community for an all day lecture series. 15 (88.2%) 2 (11.8%) 0 (0.0%) 0 (0.0%)
Ability to keep copies of these presentations for use in the future. 9 (52.9%) 3 (17.6%) 5 (29.4%) 0 (0.0%)
Disadvantages of these modules
Inability to ask questions of the presenter 3 (17.6% 6 (35.3%) 6 (35.3%) 2 (11.8%)
Lack of group interaction/discussion of a topic 3 (17.6% 5 (29.4%) 1 (5.9%) 8 (47.1%)
Just not like being in the room with the presenter 0 (0.0%) 1 (5.9%) 5 (29.4%) 11 (64.7%)
Strongly Agree Agree Disagree Strongly Disagree
These modules can serve as an adequate replacement for the all day Crush the Boards lecture series. 10 (58.8%) 6 (35.3%) 0 (0.0%) 0 (0.0%)
Discussion
There were no statistically significant differences found between students who viewed the live and CD-ROM based lectures. The observed mean scores in the two groups were in fact almost identical. Unfortunately, the small sample size limits the power of the study and confidence in which we can assert that digital lectures can be as effective as live lectures in increasing students' knowledge. The study does suggest that it is unlikely that there are large differences in the performance of students who view CD-ROM based lectures as opposed to live lectures and adds to the growing body of literature concerning the effectiveness of technology for implementing distance learning in medical education.
There was a statistically significant difference in variances among the two groups for the items covering the CAD/renal failure lectures. The standard deviation in the scores was twice as large for the students who completed the digital modules. The differences in the dispersion are also evident in the range of values in each group. It is not clear why there was more variation in the scores among the students who completed the digital modules. It may be related to the technical problems encountered by many of the students in accessing the modules, though one would expect this would have resulting in extending the lower tail of the distribution but not the upper tail. It may have also in part reflected the impact of discussions that occurred during the live lectures that may have reduced the variability among the students in their responses to the examination.
Despite the fact that almost all of the students experienced some technical difficulties using the modules, they all agreed and most strongly agreed the modules could serve as an adequate replacement for live lectures. They were particularly appreciative of not having to travel to another community to attend didactic presentations and having the flexibility of viewing the modules at their convenience. Of the three potential disadvantages of the format that were listed on the feedback form, they felt their inability to ask a question of the presenter was the most important. In the future we are considering using a web-based bulletin board system as a means of allowing students to ask questions of the presenter.
The number of students with technical difficulties viewing the modules was surprising. We had tested the modules on a variety of different computers with very few problems. In a few cases, the CD-ROMs we distributed apparently had not been copied correctly. Additionally, we switched the video formats from MPEG to Windows media files. We assumed there would be less compatibility problems with the Windows media files given that this is a format developed by Microsoft. Unfortunately, we later found out the Windows media files require software that was not shipped with earlier versions of Windows. We expect this was a significant cause of the technical problems the students experienced.
We are now using a commercial software package which greatly simplifies the process of creating the digital lectures and allows them to be distributed over the Web as well as via CD-ROM requires no special software minimizing the compatibility issues. Students who completed the fourth rotation of the clerkship and did not participate in the study were provided with the new version of the modules. Only one of the 12 students indicated they had technical problems accessing the lectures off the CD-ROM disks on which the modules were distributed and that student was able to access the modules via the Web. Such combined Web and CD-ROM distance learning formats have been shown to be effective in a number of educational settings [6,7].
Limitations
There were several important limitations in the study. First is the very small sample size which limited the power of the study for detecting differences in the performance of the students completing the live and CD-ROM based lectures. It also increased the likelihood the two groups of students were not equivalent. Since there were no student identifiers on the exams, it was not possible to compare the characteristics of the two groups. The outcome measure was a locally developed test. While the items were written by the presenters and based on the major objectives in their lectures, there was no assessment of validity other than content validity. It is also not clear the extent the findings of this study can be generalized to other digital lecture formats.
Conclusions
Although the data collected in this study were limited, it provides some evidence that digital lectures are both well received by students and can provide a satisfactory substitute for live lectures from a performance standpoint.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
DJS designed the study, developed the feedback instrument on for the digital modules conducted the statistical analyses and wrote the first draft of the paper. GSF, HSF and KK developed and gave presentations, wrote questions for the knowledge examination and edited and helped revise the manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was funded in part by grant number 1 D16 HP 00119 from the Division of Medicine, Bureau of Health Professions, Health Services and Resources Administration.
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Markova T Roth LM E-conferencing for Delivery of Residency Didactics Acad Med 2002 77 748 749 12114169
Wofford MM Spickard AW Wofford JL The Computer-Based Lecture J Gen Intern Med 2001 16 464 7 11520384 10.1046/j.1525-1497.2001.016007464.x
Goroll AH Morrison G Bass EB Jablonover R Blackman D Platt R Whelan A Hekelman FP Reforming the core clerkship in internal medicine: the SGIM/CDIM Project. Society of General Internal Medicine/Clerkship Directors in Internal Medicine Ann Intern Med 2001 134 30 7 11187418
Mattheos N Nattestad A Attstrom R Local CD-ROM in interaction with HTML documents over the Internet Eur J Dent Educ 2000 4 124 127 11168475 10.1034/j.1600-0579.2000.040306.x
Curran VR Hoekman T Gulliver W Landells I Hatcher L Web-based continuing medical education (I): Field test of a hybrid computer-mediated instructional delivery system J Contin Educ Health Prof 2000 20 97 105 11232226
| 15569389 | PMC535936 | CC BY | 2021-01-04 16:30:53 | no | BMC Med Educ. 2004 Nov 29; 4:27 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-27 | oa_comm |
==== Front
BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-881557162510.1186/1471-2407-4-88DatabaseTumor taxonomy for the developmental lineage classification of neoplasms Berman Jules J [email protected] Cancer Diagnosis Program, National Cancer Institute, Bethesda, USA2004 30 11 2004 4 88 88 6 7 2004 30 11 2004 Copyright © 2004 Berman; 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 new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man.
Methods
The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document.
Results
The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length.
The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version.
Conclusions
This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article.
==== Body
Background
On March 19, 2004, the author published a new classification for neoplasms, now called "The developmental lineage classification of neoplasms"[1]. The classification was described as a schema providing a single class location for every tumor in man. The classification contains 39 class descriptors appearing as XML tags.
Good classifications encapsulate all information relating to a knowledge domain. Modern classifications allow us to understand complex entities by grouping them by their shared or inherited properties [2]. Modern classifications also allow us to retrieve and integrate many different kinds of data under a common conceptual framework [3-8]. Basic to every classification is a taxonomy, the complete listing of the instances of the knowledge domain [2]. Because computers make it easy to store, organize and retrieve any number of listed items, there is no reason to limit taxonomies to a small number of preferred terms. One of the the best examples of a large taxonomy is taxonomy.dat, which attempts to list every living organism on earth [9]. So thorough is taxonomy.dat that it not only lists all known variations of an organism's name, it also lists commonly used misspellings of an organism. An example of an entry in taxonomy.dat:
ID : 50
PARENT ID : 49
RANK: genus
GC ID : 11
SCIENTIFIC NAME : Chondromyces
SYNONYM : Polycephalum
SYNONYM : Myxobotrys
SYNONYM : Chondromyces Berkeley and Curtis 1874
SYNONYM : "Polycephalum" Kalchbrenner and Cooke 1880
SYNONYM : "Myxobotrys" Zukal 1896
MISSPELLING : Chrondromyces
A recent version of taxonomy.dat is dated June 20, 2004 and is 55,233,858 bytes in length. It has 246,800 entries The taxonomy.dat file is available for public download through anonymous ftp [9]. The creation and organization of biological information is one of the most active areas of biomedical research [10,11].
The taxonomy for the developmental lineage classification of neoplasms was loosely modeled on taxonomy.dat. An attempt was made to include the name of every tumor, including every known variant of tumor name, and to assign a unique numeric code to all synonyms for a given tumor. The purposes of this paper are: 1) to publicly release the neoplasm taxonomy database file; 2) to explain its role as the source of concept instances for the developmental lineage classification of neoplasms; 3) to describe the methods used to organize the taxonomy; and 4) to compare the taxonomy with the neoplasm nomenclature contained in the Unified Medical Language System Metathesaurus (UMLS), the largest medical nomenclature in existence, and with the Systematized Nomenclature of Medicine Clinical Terminology (SNOMED-CT) [see Additional file 1] [see Additional file 2][12,13].
Methods
The developmental lineage classification of neoplasms was described in a prior publication [1]. The classification was intended to be populated by a comprehensive taxonomy. The original publication contained a relatively short first-draft taxonomy, and the current taxonomy was built on the early draft [1]. To ensure compatibility between the classification and other biomedical databases that include neoplasm terms, terms and classes were formatted in XML [14,15].
Terms were grouped by concept and examined for completeness, with software written in the Perl programming language. Perl is a free, open source language, available for virtually every computer operating system, and widely used in the bioinformaitcs community [16,17]. Nomeclature terms often have alternate forms that can be discovered and accrued with the help of software [18]. Short Perl scripts were prepared to systematically add variant names for patterned term constructs. For instance, it was noticed that some terms appeared in the form of "adenocarcinoma of [organ]" and other terms appeared as "adenocarcinoma of the [organ]." A Perl script ensured that both forms were included for this and other examples.
As the taxonomy enlarged, inadvertent duplications of terms and codes were unavoidable. In addition, duplicate terms were occasionally placed into different classes within the hierarchy. Much of the value of a classification comes from the parsimonious deployment of taxons (i.e. no instance can appear in more than one class). A Perl script was prepared that was executed after each modification to the classification/taxonomy. The Perl script parsed through the updated XML classification/taxonomy file, validating that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML document. The script finds anomalous records, permitting facile repair of the taxonomy file. The validating Perl script, xmlvocab.pl is included as a supplemental file with this manuscript [see Additional file 3]
A perl script transformed the taxonomy XML file into a flat-file consisting of line-records for each term in the taxonomy (neoself.txt, 19+ Megabytes in length). The transforming Perl script is neoself.pl and is distributed with this article [see Additional file 4].
The taxonomy was compared with the neoplasm terms and concepts included in the UMLS [12]. UMLS is produced and curated by the U.S. National Library of Medicine. UMLS concepts and terms are drawn from over 100 different medical source vocabularies. It is the largest medical nomenclature in existence. The 2004 version of the UMLS Metathesaurus used in this study includes over 2,697,491 medical terms. This version of the UMLS is the first UMLS version to contain the Systematized Nomenclature of Medicine – Clinical terminology (SNOMED-CT) [13]. As in prior versions, the 2004 UMLS contains the National Cancer Institute Thesaurus, another rich source of neoplasm terminology [6]. The International Classification of Diseases – Oncology (ICD-O), is a nomenclature prepared by the World Health Organization [19]. Although the UMLS does not list the ICD-O as a contributing thesaurus, it can be noted that ICD-O terminology is incorporated into the SNOMED-CT nomenclature included in UMLS [20].
The UMLS is curated and distributed by the U.S. National Library of Medicine [12]. Although the UMLS is publicly available, there are numerous restrictions on its use, and those wishing to download the UMLS must enter into a license agreement with the National Library of Medicine before obtaining the Metathesaurus.
All terms from the UMLS Metathesaurus that have a neoplasm relationship were obtained through the use of a Perl script [see Additional file 5]. The 2004 UMLS files used for the extraction were:
MRCON (UMLS metathesaurus file, 2004 version, 198,586,537 bytes in length), containing the terms for each UMLS concept (CUI).
MRCXT (UMLS metathesaurus file, 2004 version, 8,347,732,946 bytes in length), containing the relationships for every UMLS code.
All MRCXT records with a SNOMED-CT derivation were extracted using a Perl script [see Additional file 6]. All MRCON records containing a neoplasm term and having a SNOMED-CT origin were extracted using another Perl script [see Additional file 7].
Results and discussion
Features of the taxonomy
The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts (neocl.xml). Each concept has, on average, 23 synonymous terms. The second-largest source of tumor names is contained in the licensed 2004 version of the Unified Medical Language System Metathesaurus (UMLS), which draws neoplasm terms from the National Cancer Institute Thesaurus and SNOMED-CT. SNOMED-CT incorporates the International Classification of Disease – Oncology) [6,12,13,19,20].
The UMLS contains 24,593 unique English neoplasm terms with a specific "neoplasms" relationship, about one fifth the number of terms contained in the taxonomy. However, when one counts the terms in UMLS that have ANY type of relationship to neoplasia, the number UMLS-derived neoplasia terms expands to 64,601, about half of the number of terms contained in the taxonomy. It is difficult, if not impossible, to determine a correct number of neoplasm names contained in UMLS. The reason is that in UMLS, a concept may have many different relationships. For instance, the UMLS concept for "abdominal pain" has 805 relationships. Among these are: colic, constipation, diarrhea, influenza-like symptoms, malaise, multiple organ failure AND GI neoplasm benign, GI neoplasm malignant. The last two items are relationships to neoplasms. These relationships are valid because abdominal pain can be associated with benign or malignant neoplasms. Although the Perl script ca_mrrec.pl [see Additional file 5] outputs over 64,000 terms with neoplasm relationships, many of these terms are not names of neoplasms. If all 64.601 terms were reviewed to determine which were valid tumor names, a subjective number would be obtained that would certainly differ with each reviewer. It is probably fair to say that the number of UMLS neoplasm terms is somewhere between 24,593 (terms with a specific "neoplasms" relationship) and 64,601 (terms with any kind of relationship to neoplasms).
Similarly, the number of SNOMED-CT terms with a neoplasm relationship is 35,920. This is the number of UMLS records with a SNOMED-CT derivation and with any type of neoplasm relationship [see Additional file 7]. This should be considered an upper limit estimate and is less than a third of the number of neoplasm terms included in the taxonomy.
In general, taxonomy concepts that were highly generic (such as adenocarcinoma of lung) and the concepts that were the most highly pre-coordinated (i.e. multi-word terms with modifiers) had the greatest numbers of synonymous representations.
For example, consider these 48 synonyms for adenocarcinoma of the lung:
adenoca arising from lung, adenoca arising in lung, adenoca of lung, adenocarcinoma arising from lung, adenocarcinoma arising from pulmonary, adenocarcinoma arising from the lung, adenocarcinoma arising from the lungs, adenocarcinoma arising in lung, adenocarcinoma arising in pulmonary, adenocarcinoma arising in the lung, adenocarcinoma arising in the lungs, adenocarcinoma of lung, adenocarcinoma of pulmonary, ca arising from lung, ca arising from lungs, ca arising in lung, ca arising in lungs, ca of lung, ca of lungs, cancer arising from lung, cancer arising from lungs, cancer arising in lung, cancer arising in lungs, cancer of lung, cancer of lungs, carcinoma arising from lung, carcinoma arising from lungs, carcinoma arising in lung, carcinoma arising in lungs, carcinoma of lung, carcinoma of lungs, lung adenoca, lung adenocarcinoma, lung ca, lung cancer, lung with adenoca, lung with adenocarcinoma, lung with ca, lung with cancer, lung with carcinoma, lungs with ca, lungs with cancer, lungs with carcinoma, pulmonary adenoca, pulmonary adenocarcinoma, pulmonary ca, pulmonary cancer, pulmonary carcinoma
Commonly occurring lesions have many different representations. The taxonomy contains closely-related but non-synonymous concepts as separate entries (e.g. there are 29 synonyms for bronchogenic carcinoma, and 15 synonyms for bronchioloalveolar adenocarcinoma)
Other items in the taxonomy that have multiple term-variants are the so-called pre-coordinated terms characterized by modifying phrases. These terms are the hardest to capture in a taxonomy, and seem to return the smallest value on the effort. For instance, the taxonomy contains 118 synonyms for "testis with mixed embryonal carcinoma and endodermal sinus neoplasm with seminoma." The large number of synonyms are the direct result of the many different ways that modifying phrases can be ordered and combined to create the same terms. A few examples of the 118 synonyms for this term are:
mixed embryonal cancer and endodermal sinus neoplasm with seminoma arising in testis
mixed embryonal cancer and endodermal sinus neoplasm with seminoma of testis
testis with mixed embryonal cancer and endodermal sinus tumor with seminoma
mixed embryonal cancer and endodermal sinus tumor with seminoma arising in testis
testis with mixed embryonal cancer and yolk sac neoplasm with seminoma
mixed embryonal cancer and yolk sac neoplasm with seminoma arising in testis
mixed embryonal cancer and yolk sac neoplasm with seminoma arising from testis
mixed embryonal cancer and yolk sac neoplasm with seminoma of testis
testis with mixed embryonal cancer and yolk sac tumor with seminoma
The taxonomy database is distributed as an XML or as a flat-file. A short excerpt from the XML file is shown:
<mesoderm>
<name nci-code = "C3731000">mesoblastic nephroma</name>
<name nci-code = "C3731100">cellular mesoblastic nephroma</name>
<mesoderm> indicates a class tag. Beneath it are two different terms and concepts. Each is given a unique concept number. The second term is similar to the first term, but not identical. Both code numbers share the first 4 digits.
The flat-file version of the taxonomy lists these two terms as line-records. Each line record contains the term name, the term code, and the ancestry of the term within the developmental lineage classification. Each ancestor is separated by its predecessor by an arrow character.
mesoblastic nephroma|C3731000|mesoderm>non_primitive>
embryonic>neoplasms>tumor_classification>
cellular mesoblastic nephroma|C3731100|mesoderm>non_primitive>
embryonic>neoplasms>tumor_classification>
Medical informaticians dream of the day when all medical data will be captured by computers in a highly structured format that ensures data uniformity. In this utopian vision, only canonical forms of medical terms will be used. Medical reports will have a uniform format, and will be computer parsable and human readable. Taxonomies will be small. Unfortunately, the current trend in medical reporting seems to favor unstructured narrative data entry. Personally, I can remember the early days of computers when data storage and memory constraints were at a premium. Years were entered as two-digit values (nobody worried about Y2K back in the 60s), and entry-words were selected from lists and typically represented by a single digit. Today, the storage and transmission of textual data are non-issues. Large vocabularies of millions of terms can reside in active memory. Physicians prefer narrative text over structured text [21], and most of the medical data entered by physicians appears in the form of free-text emails, memoranda, progress notes, hospital reports of every type, research publications, etc. Free expression results in a seemingly unlimited way of describing a single thought, and large taxonomies are sometimes useful tools for organizing and retrieving the many terms found in narrative free-text.
The neoplasm taxonomy was created to collect all ways of expressing the names of all human tumors. The purpose of the taxonomy is to make it possible for computer algorithms to index textual information about all tumors regardless of the terms used to describe particular tumors. At first inspection, this may seem like a hopelessly complex and ultimately futile endeavor. Can anyone seriously hope to make sense of narrative text? Won't the taxonomy become larger and more complex as additional clinical, genomic, and proteomic modifiers split tumors into incomprehensible subcategories?
Actually, the purpose of a taxonomy is to reduce the complexity of the knowledge domain. By focusing efforts on a relatively small area of medicine, it is feasible to create a product that does not exceed the limits of a single expert's mental capacity. The large number of terms contained in the taxonomy (122,632), is encompassed by just 5,376 concepts. We can parse through text, replacing the many different variants of a term with either a single concept code or with a preferred (so-called canonical) synonym. This means that the taxonomy gives us a method of reducing any document index of neoplastic terms down to a maximum of 5,376 canonical terms. In addition, the 5,376 concepts in the taxonomy are represented by 39 different ancestral classes. The developmental lineage classification of neoplasms is constructed using strict rules: no multi-class inheritance; each subclass endowed by the properties of its ancestor. This means that once we have identified a term, we can easily determine its place among just a few dozen classes, and its ancestry should tell us basic information about the biology of the tumor [1].
The taxonomy for the developmental lineage classification of neoplams is the largest neoplasm taxonomy. It should come as no surprise that large nomenclatures provide better coverage of textual terms than smaller nomenclatures. A 1997 study by Humphreys et al., showed that by combining controlled vocabularies, the UMLS provided substantially more exact matches to free-text terms than any individual vocabulary in the nomenclature [22]. Finding new ways of expanding terminologies is an active research area [18]. The current taxonomy is large and has benefited from the use of Perl scripts that validate the database for internal sense and consistency.
Conclusions
This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within a tumor hierarchy. The entire classification and taxonomy are available as open access documents (in XML and flat-file formats) with this article. The taxonomy will be merged into the U.S. National Cancer Institute's Thesaurus, a curated, publicly available nomenclature and ontology that includes neoplasms and cancer-related terminology.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
This work represents the opinions of the author and does not represent the policy of the NIH or of any other U.S. Federal Agency.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Supplementary Material
Additional File 1
Neoplasia classification structure (XML version) Neoclxml.gz is a compressed (gzipped) XML file. The downloaded file should be renamed neoclxml.gz so that the .gz suffix can be recognized by unzip utilities. Unzip the file (using a free, open source utility such as gunzip.exe [23], or a proprietary utility such as Winzip). Once unzipped, the file should be renamed neocl.xml, so that it will have an .xml suffix. If the file is too large for viewing on your web browser, it can be viewed on plain-text word processors.
Click here for file
Additional File 2
Neoplasia classification with taxonomy (flat-file plain-text version) Neoself.gz is a compressed (gzipped) ascii flat-file. If the filename is changed during download, it should be renamed neoself.gz so that the .gz suffix can be recognized by unzip utilities. Unzip the file (using an open source utility such as gunzip.exe [23], or a proprietary utility such as Winzip). Once unzipped, the file is 19+ Mbytes in length. The expanded file should be renamed neoself.txt.
Click here for file
Additional File 3
Taxonomy validating Perl script The validating Perl script is xmlvocab.pl. Perl scripts will execute on any computer with a Perl interpreter. It requires the external taxonomy file named "neocl.xml" residing in the same subdirectory as xmlvocab.pl.
Click here for file
Additional File 4
Perl script for transforming taxonomy XML file to a plain-text flat file The Perl script neoself.pl transforms the XML database file (neocl.xml) to a flat file (neoself.txt).
Click here for file
Additional File 5
Perl script for extracting neoplasm codes and terms from UMLS The Perl script ca_mrrec.pl produces a file (neomrcxt.txt) containing all UMLS codes and terms with a neoplasm relationship. This script requires the external files MRCXT and MRCON (available at no cost from the National Library of Medicine) to reside in the same directory as ca_mrrec.pl. This script may take more than one-half hour to execute.
Click here for file
Additional File 6
Perl script for extracting UMLS codes for SNOMED-derived terms The Perl script snomout.pl produces a file (snomout.txt) containing all UMLS terms with a SNOMED-CT vocabulary relationship It requires the external file MRCXT (available at no cost from the National Library of Medicine) to reside in the same directory as snomout.txt. It produces an output file, snomout.txt that has a size of 1,895,054,040 bytes. This script may take more than one-half hour to execute.
Click here for file
Additional File 7
Perl script for extracting neoplasm concepts and terms from the SNOMED-CT subset of UMLS The Perl script ca_snrec.pl produces a file (neosnom.txt) containing all UMLS terms derived from SNOMED-CT having a neoplasm relationship. It requires the external file MRCON (available at no cost from the National Library of Medicine) and snomout.txt (produced by snomout.pl) [see Additional file 6], both residing in the same directory as ca_snrec.pl. This script may take more than one-half hour to execute.
Click here for file
Acknowledgements
This work was conducted at the NIH as part of the author's customary work activities, and no specific financial support was received for this work.
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Berman JJ A tool for sharing annotated research data: the "Category 0" UMLS (Unified Medical Language System) vocabularies BMC Med Inform Decis Mak 2003 3 6 12809560 10.1186/1472-6947-3-6
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The gzip home page
| 15571625 | PMC535937 | CC BY | 2021-01-04 16:02:59 | no | BMC Cancer. 2004 Nov 30; 4:88 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-88 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1781555016710.1186/1471-2105-5-178Methodology ArticleGOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes Martin David MA [email protected] Matthew [email protected] Geoffrey J [email protected] Post-Genomics and Molecular Interactions Centre, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK2 The Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, UK2004 18 11 2004 5 178 178 5 7 2004 18 11 2004 Copyright © 2004 Martin 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 a novel gene product is typically predicted by transitive assignment of annotation from similar sequences. We describe a novel method, GOtcha, for predicting gene product function by annotation with Gene Ontology (GO) terms. GOtcha predicts GO term associations with term-specific probability (P-score) measures of confidence. Term-specific probabilities are a novel feature of GOtcha and allow the identification of conflicts or uncertainty in annotation.
Results
The GOtcha method was applied to the recently sequenced genome for Plasmodium falciparum and six other genomes. GOtcha was compared quantitatively for retrieval of assigned GO terms against direct transitive assignment from the highest scoring annotated BLAST search hit (TOPBLAST). GOtcha exploits information deep into the 'twilight zone' of similarity search matches, making use of much information that is otherwise discarded by more simplistic approaches.
At a P-score cutoff of 50%, GOtcha provided 60% better recovery of annotation terms and 20% higher selectivity than annotation with TOPBLAST at an E-value cutoff of 10-4.
Conclusions
The GOtcha method is a useful tool for genome annotators. It has identified both errors and omissions in the original Plasmodium falciparum annotation and is being adopted by many other genome sequencing projects.
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Background
It is now often possible to obtain the complete genome sequence of an organism in a few months, but without a directed approach, determining the function of potential gene products through biological experimentation is inefficient. Accordingly, methods for function prediction are required to direct experiments in function verification. In the context of this paper the term function is used to refer to all aspects of a gene product's behaviour. This includes the concepts described by the Gene Ontology classifications for Molecular Function, Biological Process and Cellular Component. It is explicitly stated in the text when a more specific interpretation of function is intended.
A powerful tool in the annotation of novel genomes is the prediction of function by similarity to a sequence of known function. Such 'transitive function assignment' can work very well where there is a clear match to a homologue with a well established function. However, accurate functional assignment is difficult in cases where the match is less well defined, either due to lower sequence similarity or the presence of many candidates with differing functions. Gerlt and Babbitt [1] reviewed a number of examples where sequence similarity alone cannot provide full function specificity. The examples they discussed included classes of proteins where the function is similar but sequences are diverse, and classes where sequences are similar but function is diverse, indicating potential pitfalls for automated analyses. These examples are however quite extreme; sequence similarity can be used to infer function for a large proportion of genes with good results. Function annotation of sequences by tools such as PEDANT [2] and GeneQuiz [3] was dependent on free text annotations in the sequence databases and was complicated by the difficulty of mining and interpreting natural language. For example, a function may be described in one way in one sequence annotation, only to have the same function described in a different way in another sequence annotation. Such inconsistencies make computational determination of function equivalence difficult if not impossible. The use of restricted vocabularies and keywords has gone some way towards addressing this problem since it allows direct comparison of sequences with identical annotation schemes, at least to a match/no match level. Ouzounis and Karp [4] proposed the Transitive Annotation Based Score (TABS) to assess qualitatively the differences between annotations provided by different schemes. This scale relies on a human curator to determine manually the relationship between potentially conflicting terms, so is not readily applicable to the automated analysis of annotations.
Keywords and restricted vocabularies do not solve the problem of conflicting assignments. Unless some computable form of relationship is present between terms, it is not possible to provide any automated form of conflict resolution between terms or to identify computationally where one term is a more specific descriptor than another.
An ontology represented as a graph can provide a solution to this problem. Ontologies are restricted vocabularies, or sets of terms where each term is explicitly related to parent terms and child terms (and hence to sibling terms). The Gene Ontology (GO) [5] is a description of biology represented as a directed acyclic graph (DAG) where each node represents a clearly defined biological concept. Gene Ontology is continually being developed but contained approximately 14,000 nodes as at March 2003.
The availability of the Gene Ontology has provided for the first time, a broadly accepted classification system for function assignment that can be analysed computationally. Previous work using other classification schemes, such as restricted vocabularies based on SwissProt keywords, suffered because of the lack of a distinct relationship between terms and/or due to typographical differences [6,7]. Since the establishment of GO, several authors have prepared tools that provide function assignment to Gene Ontology or a subset thereof. Jensen and co-workers [8] used neural networks to provide predictors for a small subset of 190 relatively non-specific GO terms. Schug et al. [9] used similarity to protein families defined as ProDom [10] or CDD [11] domains, by assigning the most specific common function represented in the set of proteins belonging to the family. This was a relatively conservative approach, taking similarity to clearly defined families annotated with relatively non-specific functions as a basis for transitive annotation.
Xie and coworkers [12] have combined sequence similarity data with protein domain matches, cellular location prediction and literature mining data to improve transitive assignments. Their tools provided mappings to individual GO terms using a complex collection of probabalistic models and single linkage clustering. The method appears to be extremely powerful, taking input from a wide variety of sources, but it is difficult to assess the overall accuracy.
Two tools based on BLAST searches have recently been presented in the literature. OntoBlast [13] provided a list of GO-terms prepared from gene-association links to similarity matches from BLAST searches. GO terms associated with the matching sequences are scored according to the E-value of the pairwise match. GOblet [14] also applies BLAST searches as the basis for assigning GO terms but does not give any estimates of validity beyond restricting matches to those below a user defined E-value threshold and counting the number of matching sequences.
In this paper we present a novel method, GOtcha, that can be applied to any database search technique that returns scored matches. We have initially implemented this with BLAST searches and extend the analysis from the similarity match scores for a search in order to provide an empirical estimate of the confidence in each predicted function. We have applied this method to Malaria (Plasmodium falciparum) [15] and six other well annotated genomes and compared the results obtained by the GOtcha method to the results of annotation with the top informative BLAST match. The two methods have been assessed quantitatively with seven-fold cross validation by comparing the predictions obtained by GOtcha with those provided by the curators of the respective genome sequence consortia.
The assessment of the global accuracy of a particular annotation method is extremely problematic in the absence of a computable annotation scheme. Gene Ontology provides such a computable scheme and we present here a quantitative measure for comparison of function annotations based on assignment to GO terms. This provides a metric for direct objective comparison of annotation methods that is independent of arbitrary cut off values. The new accuracy measure encompasses true positives, false positives and false negatives, so combining sensitivity and selectivity in one value.
Results
Two sets of annotation predictions were determined for each data set in the study. One was based on all available GO annotations and the other on a reduced set of GO annotations that excluded gene-associations with the evidence code IEA (Inferred by Electronic Annotation). IEA annotations are usually considered to be less reliable as they have not been assessed by a human curator. In contrast, ISS annotations (Inferred from Sequence Similarity) are annotations which, whilst being derived electronically, have been assessed by a human curator and can be considered sufficiently reliable. IEA annotations may however give a broader coverage than non-IEA annotations. On average, each dataset contained slightly more than 50% IEA annotations, though the vast majority of the sequences had some non-IEA annotation. The number of sequences for each dataset is listed in Table 1 along with a summary of the number of sequences annotated both with and without IEA annotations.
Function assignment using all gene-associations
Figure 1 illustrates the recovery of annotations by the two function assignment methods. In this and the following analyses the predicted term associations for all three ontologies are combined. The derivation of the P-score accuracy estimate (see Methods) normalises the data allowing combination of the three separate sets of results in one graph. The y-axis indicates the proportion of annotations provided by the genome project (given annotations) that were annotated to some degree by either GOtcha (Figure 1a) or TOPBLAST (Figure 1b). At a P-score of 50% GOtcha recovered 47% of the given annotations (35–59% s.d. 7.7%) whereas TOPBLAST with a cutoff of E = 10-4 recovered 28% of annotations (20–38% s.d. 5.1%). This E-value cutoff is at the top end of the E-values between 10-4 and 10-20 typically used as a threshold for confident function assignment [16-20]. The proportion of annotations recovered by TOPBLAST was on average 60% (s.d. 4.1) of the proportion of annotations recovered by GOtcha, clearly indicating the presence of much useful function information throughout the BLAST search results, even at relatively high E-values.
Figure 2 illustrates for each genome the total number of predicted GO term associations (GOtcha in Figure 2a, TOPBLAST in Figure 2b) and the number of sequences annotated (GOtcha in Figure 2c, TOPBLAST in Figure 2d) with respect to a scoring cut off for the annotation by each method. Figure 2e (GOtcha) and Figure 2f (TOPBLAST) illustrate the number of annotations per annotated sequence. Figures 2a,2c and 2e show the results for GOtcha with the x-axis representing the minimum P-score. A low P-score represents low confidence in the annotation. A high P-score represents high confidence in the annotation. Figures 2b,2d and 2f show the results for the top informative BLAST hit with the x-axis representing the maximum E-value. A low E-value represents high confidence in the annotation. A high E-value represents low confidence in the annotation. In Figure 2c the total number of sequences annotated by GOtcha with a P-score for the annotation above the value on the x-axis approaches the maximum relatively quickly when moving from high P-score to low P-score, typically coming very close to the total number of sequences annotated well before the P-score has dropped to 50%. This represents a broad coverage of sequence space, assigning annotation at a relatively nonspecific level to most sequences. In terms of the total number of annotations, these rise steadily as the P-score cut off drops. At very low P-scores (below 10%) the total number of annotations increases rapidly, indicating an increase in the spectrum of functions matched with only weak similarity. The number of annotations per sequence increases gradually as the P-score drops until a rapid rise at low P-scores (Figure 2e). The rapid increase in number of sequences annotated is a reflection of high confidence in GO term associations at a general level of specificity. At lower P-score values more specific terms can be associated with sequences but the total number of sequences annotated has already approached the maximum. In comparison, the average number of associated GO terms per sequence by the genome projects varies from 14.5 to 19.8 (mean 16.6 s.d. 1.9). Figure 2d shows the number of sequences annotated using the top BLAST hit with a score below the E-value indicated by the x-axis. In this case the number of sequences annotated increases more slowly with E-value (Figure 2d) but the number of annotations per sequence remains relatively constant, rising only modestly as E-value rises (Figure 2f). This arises from the key difference between GOtcha and TOPBLAST. In GOtcha a term-specific probability is calculated which allows some functions for a given sequence to be assigned more confidently than others. For a given sequence only the more general terms will appear in the prediction list above the P-value threshold. With TOPBLAST the whole set of annotations from the top matching hit is assigned with a common score, irrespective of the term's specificity. Thus either all or no terms for that sequence will appear below the E-value threshold.
The specificities of function prediction for both GOtcha and TOPBLAST are illustrated in Figure 3. Figure 3a shows the proportion of predictions by GOtcha that are correct with a P-score above the cutoff on the x-axis. At a P-score cutoff of 50%, the selectivity of GOtcha is 61.4% (54–68% s.d. 4.9). Figure 3b shows the proportion of predictions by TOPBLAST that are correct with an E-value below the cutoff on the x-axis. At an E-value of 10-4 TOPBLAST shows a selectivity of 53.4% (43–60% s.d. 5.7). Accordingly, GOtcha outperforms TOPBLAST with improved coverage and better selectivity for each genome examined. Both the GOtcha and the TOPBLAST analyses include gene associations that are children of obsolete (GO:0008369) and the three 'unknowns' (cellular_component_unknown, GO:0008372; molecular_function_unknown, GO:0005554; biological_process_unknown, GO:0000004). The obsolete terms comprise a very small proportion (1.5% mean 0 – 3.1% s.d. 1.1) of the total number of annotations (shown in Table 1) and would not be expected to have any significant effect on the results. The three 'unknowns' however are considered to be valid function descriptions. They indicate a clearly observed similarity to a sequence with a function that has not been determined more specifically.
Function annotation excluding IEA annotations
Function assignment was repeated using the same BLAST search results but excluding the IEA coded gene-associations. Figure 4 illustrates the recall rate for function assignments. Recovery was lower in all but one genome compared to when IEA terms were included. GOtcha retrieved 39% (30–54 s.d. 7.3) of annotations with a P-score above 50%. This is 83% (54–100 s.d. 14) of the proportion of annotations retrieved by GOtcha when IEA based term associations are included. TOPBLAST retrieved 18% (9–25 s.d. 5.3) of annotations with an E-value below 10-4. This is 31–81% of the proportion of annotations retrieved when IEA based term associations are included. TOPBLAST only recovers 47% (23–63 s.d. 11) of the number of annotations recovered by GOtcha.
The number of annotations per sequence was reduced by comparison to the data shown in Figure 2 though the trends were very similar (Data not shown). The difference between the analysis with and without IEA terms is consistent with the relative numbers of IEA and non-IEA annotations provided by the genome projects as there are only 9.7% (3.8–14.7 s.d. 4.0) GO term associations per sequence, 62% (23–100% s.d. 30) of the number of GO term associations per sequence when IEA terms are included.
Figure 5 illustratess the selectivity for the analyses with IEA terms excluded. Figure 5a shows the proportion of assosciations correctly predicted by GOtcha with a P-score above the cutoff on the x-axis. Figure 5b shows the proportion of assosciations correctly predicted by TOPBLAST with an E-value below the cutoff on the x-axis. GOtcha with a P-value cutoff of 50% shows a selectivity of 60% (35–79% s.d. 14). TOPBLAST with an E-value of 10-4 shows a selectivity of 49% (25–59% s.d. 11). In all cases except that of Arabidopsis GOtcha shows a clear improvement over TOPBLAST with a mean improvement in selectivity of 1.2 fold (0.85 – 1.4 s.d. 0.17).
One issue with excluding IEA annotations is that the coverage of functions in the genome is lowered. This inevitably will lead to a higher number of positives that have incorrectly been assigned as false as a result of the incomplete sequence annotations. Despite excluding terms for which there is no annotation to the ontology under examination, the results are skewed by assigning a proportion of true positives as false positives. This indicates that the method is performing more poorly than is in fact the case. We have examined the nature of the false positives in more detail below.
A metric for quantitative assessment of function annotation
Comparing function assignment methods is difficult. Typically the standard against which they are assessed is an incompletely annotated dataset. Both a lack of experminental data confirming potential functions and a lack of knowledge about potential functions can lead to the standard data being less perfectly annotated that would be desired. It is not realistically possible in an automated analysis to cope with unrecorded true positives that are registered in the analysis as false positives. It is therefore the case that any analysis of accuracy can only give an estimate of minimum accuracy.
Accuracy can also be difficult to compare between two methods that annotate to different subsets of GO. One method may only annotate to relatively general terms, allowing for a better claimed specificity than a method that attempts to annotate to a more specific level. GOtcha predicts at all levels of the GO hierarchy. It assigns a probability to every combination of GO term – sequence association and should be compared to other function assignment algorithms using a global metric, one which can account for over-specificity and under-specificity in a set of predictions as well as incorrect assignment.
Ouzounis and Karp [4] described the TABS system for qualitative assignment of function annotation to eight categories. The TABS categories are reproduced in Table 2.
When applied to annotation using a DAG such as GO the number of potential categories is reduced from the eight described in TABS to three. TABS was developed to compare annotations where the terms used are not implicitly related through a computable structure such as a DAG. As we are using a DAG where ancestor nodes are implicitly associated with the gene through direct association of a child node, the prediction for a particular sequence becomes a set of GO terms (the nodeset) comprising all nodes that match the prediction rather than just the most specific terms. The accuracy of a prediction can then be assessed by observing the presence of nodes in both the node sets for annotations and for the predictions rather than assigning qualitative values. The more distant a given prediction node set is from the annotation node set, the smaller a proportion of nodes (GO terms) they will have in common.
The effect of a quantitative approach on the TABS categories is as follows: TABS category 0 is unchanged. This is an exact match and is represented by the presence of the term in the node sets for both original annotation and current prediction. TABS category 1 is no longer relevant. A controlled vocabulary is being used so there is no scope for typographical errors of the type described by Ouzounis and Karp or by Tsoka [21] or Iliopoulos [22]. TABS category 2 is also irrelevant. GO has no undefined terms (though a small proportion of terms lack complete descriptions) and all annotation sources are attributed using evidence codes and references. TABS category 4 is an extreme case of category 3. Both these categories are represented by the existence of a function annotation in the original annotation node set but not in the predicted node set. Likewise TABS category 7 is an extreme case of TABS category 6. In many cases a false positive is represented as an underprediction in the true branch of the GO DAG and an overprediction in a false branch. TABS category 5 describes the mechanism of occurrence of an error rather than the error itself and is not relevant to this analysis. In this analysis we reduce the eight TABS categories describing the accuracy of a function prediction for an individual sequence to three categorise that describe each node in the nodeset comprising a function prediction for an individual sequence. These categories correspond to false positive, false negative and true positive nodes. A particular sequence annotation node set could potentially contain nodes from all three categories.
Quantitation of the analysis
Given two sets A and B corresponding to a given annotation set and a predicted set (each node in the set comprising a sequence – GO term association) we are interested in the true matches (intersection of A and B, n ∈ A ∩ B), false positives (term associations in B but not in A, n ∈ B, n ∉ A) and the false negatives (term associations in A but not in B, n ∈ A, n ∉ B). The aim of any prediction method is to maximise the number of matches (true positives) whilst minimising the errors (false positives and false negatives). The number of true negatives does not need to be considered as this number is very large and essentially constant over the analysis. We can use the following relation as an error quotient to assess prediction methods.
where REQ is the Relative Error Quotient, n is the total false negatives, p is the total false positives, w is a weighting factor and t is the total true positives. A low REQ represents a low proportion of errors. A higher REQ indicates a higher proportion of errors. Such a measure is dependent upon the population of the node set which in turn is dependent upon the cut off used for selecting predictions in the node set. Figure 6a shows the change in REQ with respect to P-score cutoff for the GOtcha analysis and Figure 6b the REQ with respect to E-value cutoff for the TOPBLAST assignments. A weighting factor of 1 was used in both cases, thus giving equal weight to both false positives and false negatives. In this figure the minima indicate optimum cutoffs for maximising the similarity between annotation and prediction nodesets. The GOtcha results (Figure 6a) indicate broad minima, suggesting that small differences in cut off selection may have only a slight effect on the accuracy of the results. The minima for BLAST are difficult to see as they are skewed to very high E-values as a result of a large proportion of false negatives. This indicates that the TOPBLAST search is rejecting important information present in matches with E-values approaching 1, much higher than those normally used for genome annotation. The REQ metric therefore appears to perform quite robustly. This metric assigns identical weight to each GO term association. More complex weighted measures of semantic similarity have been proposed by Lord and coworkers for searching databases based on annotation [23] but these are difficult to apply to the present problem in a manner that uses a non-arbitrary weighting.
In the absence of IEA annotations the spread of the REQ curves changes dramatically as shown in Figure 7. Figure 7a illustrates the REQ for GOtcha with the differences between the genomes far less marked than for Figure 6a. In contrast, the REQ for TOPBLAST is shown in Figure 7b and shows much higher and more diverse REQ than when IEA terms are included (Figure 6b).
Minimum REQ (i.e. maximum accuracy) has been determined for both GOtcha and top BLAST hit annotation sets, both with and without the use of automated annotations (IEA evidence code) for transitive function assignment (Table 3). When automated annotations (IEA codes) are included in the analysis, there is no significant difference between the minimum REQ obtained using GOtcha or that from TOPBLAST. The minimum REQ for TOPBLAST is obtained at very high E-values, 0.011–0.71 when IEA terms are included (Figure 6) and 0.12–0.71 when IEA terms are excluded (Figure 7). When IEA annotations are excluded from the analysis GOtcha performs significantly better than TOPBLAST (p ≤ 0.016 using the Wilcoxon signed rank test). GOtcha excluding IEA terms performs better (though this small number of genomes does not give a statistically significant result) than when IEA annotations are included (mean change: 15% reduction in REQ s.d. 11%, p = 0.2 using the Wilcoxon signed rank test). It may be that the annotation set used as the reference in comparing these results was incomplete. This would result in some true positives being incorrectly assigned as false positives with a corresponding increase in REQ. However, this would apply similarly to GOtcha and to the top BLAST hit analysis.
Assessment of incorrectly assigned false positives
Samples of the false positive function predictions by GOtcha with the highest P-scores from three P. falciparum chromosomes (representing the three genome centres in the Malaria Genome Sequencing Consortium) were assessed by hand to give an indication of the completeness of the curated annotations. Results for selected sequences in this set are shown in additional file 1. Twenty sequences were examined: ten from chromosome 12, and five taken from each of chromosomes 2 and 3. Chromosome 3 was the first to be sequenced and is the most carefully annotated of the chromosomes. In each case the sequences selected were those with the highest scoring false positive function assignments. Representative results from the analysis of GOtcha annotation with and without IEA terms are available as supplementary material. The proportion of correct annotations generally performed better than the P-score would suggest. Taking a P-score of > 50% as a cutoff, most GOtcha predictions agreed with the function assigned by the curator. The false positives fell into several categories:
Differences in curator judgement
In some examples, genes that were annotated as encoding hypothetical proteins could be re-annotated based on GOtcha predictions. GO terms had not been assigned during the manual curation phase of the P. falciparum genome project if no function had been identified during the first-pass automatic annotation. However, the addition of GO terms to sequences by GOtcha prompted the original annotation to be re-evaluated. For example, PFL1875w shows a hit to the Pfam K+ tetramerisation domain (Pfam:0224, E = 10-9) supports the GOtcha annotation although it is at a level that genome annotators may feel is marginal. In PFL1780w, stronger supporting evidence (a hit with E = 10-12 to Pfam:04140, isoprenylcysteine carboxyl methyl transferase domain) indicates again that GOtcha can suggest GO annotations that have been previously overlooked.
In several examples, GOtcha predicted either additional functions or more specific GO terms to describe previously annotated functions. PFC0495w encodes a putative aspartyl protease. When all evidence codes were included, a molecular function of pepsin A activity is predicted. This protein matches pepsin A domains defined by the InterPro entry IPR001461 ('Peptidase_A1 pepsin A'), thus the term from GOtcha is likely to be correct.
Human error
PFL2465 encodes a thymidylate kinase, which was correctly annotated by GOtcha as being involved in dTTP biosynthesis. GOtcha also indicates 'dTDP biosynthesis' as a suitable GO process term. Thymidylate kinase catalyses the synthesis of dTDP, a necessary step in dTTP biosynthesis. However, the human annotator missed the fact that dTDP biosynthesis is not a 'part of' dTTP biosynthesis within the ontology structure and in such cases, terms describing both processes must be employed.
Sometimes, GOtcha highlighted erroneous omissions in the GO annotation of the P. falciparum genome, many of which have arisen from retrospective corrections and amendments to gene models. For instance, GOtcha provides detailed annotation for a putative ATPase synthase F1 alpha subunit (PFB0795w) almost completely lacking useful GO terms. GOtcha also suggested GO terms relating to translation elongation for PFL1710c. A highly significant hit to Pfam:00009 (Elongation factor Tu GTP binding domain, E = 10-46) indicates that this GOtcha prediction may well be more accurate then the original genome annotation.
IEA vs non IEA
Annotations performed with IEA terms appeared to be more specific than those where IEA terms were excluded. In many cases, such as PFC0495w, the difference was quite pronounced. Here the protein was implicated in 'proteolysis and peptidolysis' when all annotations were included but filtering out IEA annotations resulted in the more general, and less useful, description of 'metabolism'.
Real false positives
Out of the 20 genes inspected, PFL1825w was the only example where GO terms were incorrectly suggested for the biological process, molecular function and cellular component aspects of GO. In other cases, mis-annotations often had low I scores (predictions made with P-scores > 50% but very low associated I-scores ≪ 0.1) or were due to terms taken from slightly too far down a branch in the ontology structure. For example 'ATP-binding and phosphorylation-dependent chloride channel' was predicted for PFB0795w, an ATP synthase.
The cellular component of gene products are hard to annotate – often BLAST is insufficient to recognise the targeting information encoded in signal and transit peptides and specific signal sequence detection methods such as PSORT II [24] must be used instead. GOtcha consequently made incorrect predictions of subcellular localisation in some cases. For instance PFL1710c is annotated as having mitochondrial and apicoplast localisation based on separate lines of evidence [15] but GOtcha predicted cytoplasmic localisation with a P-score of 52%.
It is hard to measure what proportion of the calculated false positives does in fact represent serious mis-annotation. Although the hand analysis may provide representative examples, it is too small to be of statistical significance. Genuine false positives (with high P- and I-scores) were fewer than would be expected from the P-score. Despite the small sample size, these results show that GOtcha performs well as a guide to the manual assignment of GO terms. Not only can it provide suggestions for more granular annotation but it can highlight terms that would otherwise be missed by a human annotator.
Discussion
Data interdependency and annotation accuracy
One of the major problems facing assessment of function assignment is the separation of annotation and test datasets. In this analysis we have tackled this issue by taking individual genome datasets as the test sets and using other genome datasets for the annotation source from which to transitively assign function. The scoring mechanism used for estimating accuracy values is independent of both test and annotation datasets, since it makes use of sequences that are found in neither. Whilst the sequences are independent, the annotations associated with these sequences may not be. Many of the computationally assigned annotations are derived from analyses involving the 'independent' datasets and can therefore not be regarded as entirely independent. IEA annotations are primarily obtained from sequence similarity searches. As a consequence it is not surprising that the results obtained for both GOtcha and TOPBLAST when IEA annotations are included are so similar. Interestingly, when IEA based annotations are excluded from the TOPBLAST analysis, the REQ goes up. This may well indicate a degree of inaccuracy in the IEA based annotations, or incomplete coverage by the human curated annotations. GOtcha, however, makes a significantly better use of the BLAST search result in the quality and coverage of the annotation.
False positive/false negative balance in the relative error quotient
The REQ analyses performed weighted under prediction errors (false negatives) equally to over prediction errors (false positives). In order to examine the effect of the weighting on REQ, the GOtcha predictions for the human genome were compared to the genome consortium annotations with weights ranging from 0.5 to 15 (Figure 8). As expected, an increased emphasis on false positives shifts the minimum REQ towards a higher P-score cutoff. Weighting can be adjusted depending on the aims of the study in question. The minimum REQ should give the best tradeoff between accuracy and coverage and can be used to estimate an optimum P-score cutoff for transitive assignment of function. Investigations that emphasise accuracy over coverage may increase the weight to reduce false positives. Investigations with less concern for accuracy but a greater emphasis on coverage will use a lower weight for minimal REQ determination to increase coverage.
The metric presented here is an objective measure of method performance but has some drawbacks. Using the REQ as described in this paper, each term in the nodeset is weighted equally. This may not be the most appropriate measure. The granularity of terms in Gene Ontology is not constant across the ontologies, nor is it readily quantifiable. This may lead to bias in the metric, where differences in the presence or absence of closely related terms is weighted equally to presence or absence of more distantly related terms even though they have the same graph path distance between them. There is also the issue of prevalence. Some terms occur in almost every nodeset, others are less prevalent. The most appropriate form for a quantitative metric will need to be examined in future work.
Transitive function assignment is limited by the sensitivity of the underlying search method and the scope of the dataset being searched. The GOtcha method of preparing a weighted composite view of the functions from a complete set of search results provides a significant improvement in the annotation of sequences when compared to a method that selects the most significant annotated hit. GOtcha also provides a confidence measure for the putative function assignments, allowing for the determination of an appropriate level of specificity for the annotation set. Hennig and co-workers examined the ability of BLAST analysis to transitively assign function from distant taxa, concluding that for the majority of cases, GO-based annotation would give a good result [14]. In this study we have performed seven-fold cross validation with seven distinct genomes across the taxonomic range. It is intended to improve the performance of this method by including further genomes and updating the annotations on those already used.
Conclusions
The GOtcha method has several significant advantages over the transitive assignment of function by TOPBLAST. Firstly each function assignment has a directly understandable accuracy estimate that can be interpreted without any knowledge of the prediction methodology. This accuracy estimate is function-specific, unlike general rules of thumb that are applied to interpretation of BLAST search results. Secondly, the GOtcha method provides much greater coverage than a top annotated match approach, annotating more sequences with reasonable confidence. In many cases it provides annotations for sequences that otherwise would have no annotations. Finally, it provides term specific annotation accuracy estimates. This is a significant advantage over TOPBLAST where every term in the set predicted for an individual sequence has the same value and a biologist interpreting the results is given little indication of which terms can reasonably be accepted. In contrast, GOtcha provides individual P-scores for each term. This allows a rapid visual examination of the prediction as a graph or a list, indicating appropriate points at which experimental verification may best be directed.
In order to assess the accuracy of annotations to tree-like ontologies we have developed an objective flexible scoring metric that provides a global analysis, including assessment of both false positives and false negatives. This metric also provides a means for comparison of methods that is not dependent on the selection of any particular parameter threshold or cutoff in the scoring method used.
The underlying mapping methodology applied in GOtcha can readily incorporate other search methods that provide a more sensitive similarity search. Combining search methods should also provide a better coverage of sequence space occupied by distant homologues [25], and such potential improvements are the subject of further work.
Methods
Data sources
All data were obtained in the same week (week 9, 2003) to provide a consistent time point at which to perform the analysis.
Sequence data
Malaria (Plasmodium falciparum) sequence data for the recently determined genomic sequence [15] were obtained from the malaria consortium. The whole genome annotated peptide set was dated 3 October 2002 and comprised 5334 peptides varying in length from 17 to 10589 amino acid residues.
Fruit fly (Drosophila melanogaster) data were obtained from Flybase [26] as release 3.1 of the annotated full genome transcript set. This set contained 18484 transcript sequences corresponding to 13656 genes. A non-redundant set was created for subsequent analysis by selecting the longest transcript to represent each gene. Transcript lengths varied from 15 to 69162 nucleotides (5 to 23054 amino acid residues).
Yeast (Sacchyromyces cerevisiae) data were obtained from the Sacchyromyces Genome Database [27]. The set of translated open reading frames for the whole genome was used and comprised 6356 peptides varying in length from 25 to 4911 amino acid residues.
Cholera Vibrio cholerae data were obtained from The Institute for Genomic Research [28]. The dataset contained 3836 sequences varying in length from 26 to 4588 amino acid residues.
Human (Homo sapiens) data were obtained from Swiss-Prot using the conceptual complete human proteome from the Swiss-Prot/EnsEMBL collaboration dated 6 March 2003 [29]. The dataset contained 39080 proteins with lengths varying from 3 to 34350 amino acid residues.
Worm (Caenorhabditis elegans) data were obtained from Wormbase release 97 [30]. The dataset contained 30753 peptides varying in length from 4 to 13100 amino acid residues.
Thale cress (Arabidopsis thaliana) data were obtained from The Arabidopsis Information Resource [31]. The complete genome peptide set dated 31 July 2002 was used. The dataset contained 27288 sequences varying in length from 20 to 4707 amino acid residues.
Each data set was formatted for BLAST searching with the formatdb program from the BLAST2 suite [32,33]. The URI for each genome dataset are listed in Table 4
Gene association and Gene Ontology data
Data for the Gene Ontology and gene associations for all proteome sets except Arabidopsis were downloaded from the Gene Ontology CVS repository in week 9, March 2003, parsed and loaded into a relational database. Arabidopsis data were obtained from The Arabidopsis Information Resource using gene association data dated 13 February 2003. A flat file database containing the Gene Ontology and gene association data was developed and indexed to allow rapid retrieval of individual entries by custom written Perl modules. The number of annotated sequences in each data set is shown in Table 1.
Software
The BLAST2 programs were obtained from NCBI. Analyses were performed on a cluster of 50 HP Netserver L1000 dual processor machines configured with two 1.4 GHz Pentium III processors, 70 Gb hard disk, 2 Gb RAM and running a customised Linux operating system. Job scheduling was performed with Grid Engine (Sun Microsystems). Results were stored in a relational database (PostgreSQL version 7.3) or as flat files where appropriate. BLAST result parsing was performed with the BioPerl toolkit (release 0.7) [34]. Sequence manipulation was performed with EMBOSS [35]. All processing scripts were written in Perl. A set of Perl modules were developed for accessing and manipulation of data entries.
Methods
GOtcha method overview
The GOtcha method is illustrated by a cartoon in Figure 9. We have implemented this method by searching against a cohort of seven well defined and annotated genomes. To predict the association of GO terms with a specific individual gene product a BLAST search is run against each genome data set using the appropriate program (blastx/tblastn when D. melanogaster was the query/subject set, blastp otherwise). Default parameters were used (Maximum expectancy score 10; maximum list sizes 250 and 500 hits). Each sequence database search produces a ranked set of sequences similar to the query sequence. The search result for each genome database search is parsed and a list of pairwise matches between the query sequence and the subject database sequences obtained.
For each similarity match between the query sequence and a database sequence, a set of GO terms corresponding to the gene-associations for the database sequence is retrieved from the appropriate gene-association dataset. The set of GO terms and all ancestral terms (the nodeset) are assigned a score R = max { -log10(E), 0 } where E is the expectancy score for that pairwise match. In this way the whole subtree to the root node is assigned the R-score. The GOtcha method allows mappings obtained from many sequence matches to be combined. For each node (which corresponds to an individual GO term, either directly associated or the ancestor of an associated GO term), R-scores for all pairwise matches which contain annotation to that node are summed and normalised to the total R-score for the root node of that ontology (Cellular Component, GO:0005575; Molecular Function, GO:0003674; or Biological Process, GO:0008150). This normalisation gives an internal relative score (the I-score), producing a weighted composite subgraph of the GO. This normalisation effectively removes bias in the E-value due to database size or search program used. A confidence measure is calculated as loge of the root node score (the C-score). Accordingly, this provides two measures for an individual predicted gene-association; A score relative to the other predicted gene-associations in the node set (the I-score) and a score for the function prediction as a whole (the C-score).
Each genome was searched individually and I-score and C-score for each GO term association were averaged across all genome searches that provide at least one annotated pairwise match. Averaging across genomes in this way provides some correction for individual genes with exceptionally high copy numbers in certain genomes. In this paper the term 'function prediction' relating to an individual sequence refers to a prediction of a set of GO term – sequence associations (also referred to as a node set). Averaging of the individual search results avoids the over-representation of large genomes in the final annotation set and allows the final result to be weighted towards a particular taxonomic grouping should that be desired. Each gene association represents a function assignment of a gene product with a GO term and is annotated with an evidence code providing an indication of the reliability of a particular annotation. The GOtcha method allows specific classes of annotation, such as those derived exclusively from computational analyses, to be excluded from the analysis if required.
Background accuracy estimates for individual GO terms – P-score table construction
Although higher C-score and I-score values correspond to greater confidence in the transitive assignment of function than lower C-score or I-score values, it is not immediately apparent how these values should be interpreted. Examination of preliminary results indicated that there was considerable variation between GO terms in the confidence that can be placed in a prediction with a given I-score and C-score (data not shown). Accordingly we have created an empirically based estimate of accuracy (the P-score, expressed as a percentage) that can be used to indicate confidence in the prediction of association between a GO term and a gene product.
A background set of 518226 annotated sequences from the SwissProt gene associations were included in the accuracy estimate after excluding taxa corresponding to the search databases and their subspecies. All background sequences were subject to a search against all 7 species specific datasets and a set of function predictions obtained as described above. A scoring table for each GO term was prepared by segregating all predictions for that GO term on I-score and C-score. I-scores were divided into ten rows by dividing the range (0 – 1) evenly. C-scores were divided into columns by unit ranges (0–1, 1–2, 2–3 and so on). This gave rise to approximately one hundred cells for each GO term table. Each prediction was assigned to a cell based upon its I-score and C-score. The overall accuracy of each cell was determined by comparison of the predicted associations in that cell to the annotations provided by the GO Annotation project (GOA) and calculated as the proportion of true positives to the sum of true and false positives. The table for a specific GO term was then used to deliver the P-score based on any given I-score and C-score pair for a predicted association between that GO term and the query sequence. A similar set of tables was constructed from background analyses from which terms with IEA associations were excluded. For GO terms where there are few datapoints with which to estimate accuracy reliably, accuracy estimation falls back to a scoring table that combines results over all GO terms from that ontology with the same number of ancestors.
Function assignment by top informative BLAST hit
The same BLAST searches used for function assignment with the GOtcha method were analysed. Function assignments for the nodeset corresponding to the top annotated BLAST match (TOPBLAST) for each genomic dataset were transferred to the query sequence with a score corresponding to the E-value for that hit.
List of abbreviations
DAG, Directed Acyclic Graph. URI, Uniform Resource Identifier. BLAST, Basic Local Alignment Search Tool. TOPBLAST, Top annotated BLAST match. Perl, Practical Extraction and Report Language. GO, Gene Ontology. TABS, Transitive Annotation Based Score. NCBI, National Centre for Biological Information. s.d., Standard deviation.
Authors' contributions
The GOtcha method was devised and implemented by DMAM who also prepared the manuscript. MB performed the manual assessment of false positives and provided feedback on the presentation of results. GJB provided essential guidance for the performance assessment and revision of the manuscript.
Supplementary Material
Additional File 1
The supplementary data contains representative examples from the manual assessment of false positives. It is portrayed in tabular format and indicates the benchmark annotation, the highest scoring predicted incorrect annotation by GOtcha and the lowest scoring predicted annotation by GOtcha.
Click here for file
Acknowledgements
DMAM is supported by JIF grant 060269 from the Wellcome Trust. The Authors would like to thank prof. Rein Aasland for the initial suggestion for GOtcha, Patrick Audley for expert computational assistance and Dr. Caleb Webber for useful discussions. This work was supported in part by the ELM project, part of the EU fifth framework program (Grant No. QLRI-CT-2000-00127).
Figures and Tables
Figure 1 Proportion of original GO annotations recovered versus cutoff for assignment of GO terms. (a) GOtcha (b) top informative BLAST hit (TOPBLAST). For GOtcha the P-score is defined in the text. For TOPBLAST the E-value is the expectancy score for the top annotated sequence match.Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 2 Annotations and sequences annotated. Number of GO term associations made by (a) GOtcha with a P-score over the cutoff and (b) TOPBLAST with an E-value below the cutoff. Number of sequences with an associated annotation predicted by (c) GOtcha with a P-score over the cutoff and (d) TOPBLAST with an E-value below the cutoff. P-score iscalculated to 1 percentage point resolution giving rise to the stepped nature of the graph. Mean number of annotations per annotated sequence predicted by (e) GOtcha and (f) TOPBLAST. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 3 Selectivity versus cutoff for assignment of GO terms using all evidence codes. (a) GOtcha with P-score cutoff (a) TOPBLAST with E-value cutoff. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 4 Coverage vs cutoff for assignment of GO terms excluding IEA evidence codes. (a) GOtcha (b) top informative BLAST hit. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 5 Selectivity versus cutoff for assignment of GO terms excluding IEA evidence codes. (a) GOtcha with P-score cutoff (a) TOPBLAST with E-value cutoff. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 6 Relative Error Quotient (REQ) vs cutoff for assignment of GO terms. REQ is defined in the text. (a). GOtcha analysis. (b). Top informative BLAST hit analysis. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 7 Relative Error Quotient (REQ) vs cutoff for assignment of GO terms. REQ is defined in the text. IEA terms were excluded from this analysis. (a). GOtcha analysis. (b). Top informative BLAST hit analysis. Key: ○ Arabidopsis thaliana; △ Drosophila melanogaster; □ Homo sapiens; ● Plasmodium falciparum; ■ Vibrio cholerae; ◇ Caenorhabditis elegans; ▽ Saccharomyces cerevisiae.
Figure 8 The effect of different weights on REQ. The REQ for GOtcha predictions of GO term associations for the human proteome was calculated with weighting factors of 0.5 (open circle), 1, 2,3,4, 5, 7, 10 and 15 (cross).
Figure 9 The GOtcha method. 1. A query sequence is subjected to a database search. The search results are processed to give a list of pairwise matches with associated R-scores. 2. The R-score for the pairwise match is added to the total score for each GO term associated with that match sequence. 3. The C-score is calculated as the natural logarithm of the total score at the root node. The I-score for each node is calculated as the ratio of the total node score to the root node.
Table 1 Sequences and annotations for each dataset.
Sequences annotated by ontology
Dataset Total associations Total sequences Cellular Component Molecular Function Biological Process
Arabidopsis thaliana 290952(94824) 20108(7969) [451] 14851(2115) 14467(7555) 10454(3481)
Drosophila melanogaster 129694(29311) 7536(7536) [0] 3613(3589) 6528(6520) 3730(3723)
Homo sapiens 409153(67357) 21251(9074) [659] 13723(6516) 19362(7328) 17080(7707)
Plasmodium falciparum 36952(32536) 2406(2209) [41] 2061(1227) 2094(2094) 2044(2044)
Saccharomyces cerevisiae 136938(36267) 6910(6849) [0] 6751(6751) 6831(6831) 6899(6838)
Vibrio cholerae 42616(42616) 2924(2924) [27] 189(189) 2721(2721) 2923(2923)
Caenorhabditis elegans 109360(18626) 6916(1870) [199] 3054(650) 5746(282) 5102(1557)
Values in parentheses do not include IEA associations. Values in [brackets] are sequences with annotations that are children of obsolete (GO:0008369).
Table 2 TABS scheme for qualitative assessment of annotation accuracy.
Category Description Comment
0 Total agreement Original annotation is correct, but annotations may be only semantically (but not computationally) identical
1 Typographical error Original annotation contains typographical errors that may be propagated in the database
2 Undefined source Original annotation contains undefined terms, non-homology based predictions, and so on
3 Under-prediction Original annotation predicts a nonspecific biochemical function although a more detailed prediction could have been made
4 False negative Original annotation does not provide predicted function although there is sufficient evidence to characterize the query protein
5 Domain error Original annotation overlooks different domain structure of query and reference proteins
6 Over-prediction Original annotation predicts a specific biochemical function without sufficient supporting evidence
7 False positive Original annotation predicts function without any supporting evidence
(from Ouzounis and Karp, Genome Biol. 2002;3(2):COMMENT2001)
Table 3 Minimum REQ values for seven datasets using two methods for annotation.
Dataset GOtcha Top Hit GOtcha (-IEA) Top Hit (-IEA)
REQ cutoff REQ cutoff REQ cutoff REQ cutoff
Arabidopsis thaliana 1.63 36 1.35 0.56 1.33 54 1.48 0.71
Caenorhabditis elegans 1.41 68 1.19 0.023 1.82 70 3.38 0.45
Drosophila melanogaster 2.20 35 1.74 0.71 1.15 31 1.99 0.71
Homo sapiens 1.40 52 1.34 0.35 0.95 39 3.12 0.71
Plasmodium falciparum 0.75 51 1.39 0.023 0.67 51 1.69 0.28
Saccharomyces cerevisiae 2.56 38 1.88 0.41 1.27 45 2.03 0.71
Vibrio cholerae 0.91 45 1.71 0.011 1.13 43 1.87 0.11
Mean (+/- SD) 1.55 (0.60) 1.51 (0.24) 1.19 (0.32) 2.22 (0.67)
Cut off values for GOtcha are P-score. Cut off values for BLAST are E-value.
Table 4 Genome Project URIs for datasets used in the study.
Dataset Genome Project Site URL
Malaria (Plasmodium falciparum)
Fruit fly (Drosophila melanogaster)
Yeast (Sacchyromyces cerevisiae)
Vibrio cholerae
Human (Homo sapiens)
Worm (Caenorhabditis elegans)
Thale cress (Arabidopsis thaliana) ftp://tairpub:[email protected]
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| 15550167 | PMC535938 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Nov 18; 5:178 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-178 | oa_comm |
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BMC Blood DisordBMC Blood Disorders1471-2326BioMed Central London 1471-2326-4-51557596110.1186/1471-2326-4-5Research ArticleCan mutations in ELA2, neutrophil elastase expression or differential cell toxicity explain sulphasalazine-induced agranulocytosis? Jacobson Annica [email protected] Håkan [email protected] Mia [email protected] Department of Medical Sciences, Uppsala University, Uppsala University Hospital S- 751 85 Uppsala, Sweden2004 2 12 2004 4 5 5 5 7 2004 2 12 2004 Copyright © 2004 Jacobson et al; licensee BioMed Central Ltd.2004Jacobson 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
Drug-induced agranulocytosis, a severe side effect marked by a deficit or absolute lack of granulocytic white blood cells, is a rare side-effect of the anti-inflammatory drug sulphasalazine. Mutations in the human neutrophil elastase gene (ELA2), causing increased intracellular concentration of this serine protease, inhibits neutrophil differentiation in severe congenital neutropenia (SCN). Since the clinical symptoms of agranulocytosis and SCN are similar, we hypothesized that it may origin from a common genetic variation in ELA2 or that sulphasalazine may affect human neutrophil elastase activity and protein expression.
Methods
We screened for genetic differences in ELA2 in DNA from 36 patients who had suffered from sulphasalazine-induced agranulocytosis, and compared them with 72 patients treated with sulphasalazine without blood reactions. We also performed in vitro studies of the blood cell lines HL60 and U937 after sulphasalazine exposure with respect to cell survival index, neutrophil elastase protein expression and activity.
Results
None of the mutations in ELA2, which previously have been reported to be associated with SCN, was found in this material. Protein expression of human neutrophil elastase in lymphoma U937 cells was not affected by treatment with concentrations equivalent to therapeutic doses. Cell survival of lymphoma U937 and promyelocytic leukemia HL-60 cells was not affected in this concentration range, but exhibited a decreased proliferative capacity with higher sulphasalazine concentrations. Interestingly the promyelocytic cells were more sensitive to sulphasalazine than the lymphoma cell line.
Conclusion
Neutrophil elastase expression and ELA2 mutations do, however, not seem to be involved in the etilogy of sulphasalazine-induced agranulocytosis. Why sulphasalazine is more toxic to promyelocytes than to lymphocytes remains to be explained.
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Background
Sulphasalazine (SA) has anti-inflammatory, immunosuppressive and antibiotic actions, and is a component in the therapy of Crohn's disease, ulcerative colitis and rheumatoid arthritis. Bacterial enzymes in the colon split sulphasalazine into sulphapyridine and 5-aminosalicylic acid before it is absorbed. Sulphapyridine acts as a sulphonamide antibiotic, whereas 5-aminosalicylic acid is believed to be the anti-inflammatory metabolite. Common side/toxic effects are vomiting, skin rash and headache. The incidence of the hematological adverse effects associated with sulphasalazine is generally low, but the reactions can be severe and sometimes fatal. The risk of sulphasalazine-induced agranulocytosis, i.e. profoundly depressed circulating neutrophils is highest within the first three months of sulphasalazine-treatment, with a fatality rate of 6.5 % [1]. Clinical symptoms of agranulocytosis include fever, malaise and susceptibility to infections. Patients with arthritic disorders have a greater risk of developing sulphasalazine-induced agranulocytosis than patients with inflammatory bowel diseases.
Severe congenital neutropenia (SCN) and cyclic neutropenia (CN) occur both as inherited and as sporadic diseases. SCN has a constant low neutrophil number if left untreated, whereas CN manifests with cyclic oscillations of neutrophil number with a 21-day cycle. Recently, diverse heterozygous mutations in ELA2, encoding human neutrophil elastase, have been identified in a majority of the cases with CN and two-thirds of the cases with SCN [2].
In this study, we hypothesized that sulphasalazine-induced agranulocytosis, with clinical symptoms similar to congenital neutropenia, may arise from genetic variation in the human neutrophil elastase gene. We genotyped 108 sulphasalazine-treated patients for ELA2, one third which of had experienced sulphasalazine-induced agranulocytosis. We, furthermore, tested for cytotoxic doses of sulphasalazine, and studied protein expression of human neutrophil elastase in sulphasalazine-treated blood cell lines.
Methods
Subjects
Patients were treated with sulphasalazine (Salazopyrin, Pharmacia, Sweden) for inflammatory joint diseases and inflammatory bowel disease. The cases with sulphasalazine-induced agranulocytosis were originally collected through the Swedish Medical Products Agency's register of adverse side effects [3]. The control group had been treated with sulphasalazine without adverse effects for at least 3 months. From the original patient material consisting of 39 cases and 75 controls, DNA was available for 36 cases and 72 controls. The patient journals were studied for information concerning neutrophil differentiation in bone marrow aspirates. The study was approved by the Ethics Committee of the Medical Faculty at Uppsala University, registration number 95–200.
Mutation analysis
Genomic DNA was extracted from whole blood using standard techniques. Fragments covering exons 2–5 of ELA2 were amplified by PCR using primer pairs listed in Table 1. The selection of exons 2–5 and some of the flanking intron sequences was based on previously reported mutations in cases with SCN and CN [2,4], as outlined in Figure 1. Products for exon 2–5 were amplified with 1.5 units of AmpliTaq Gold DNA polymerase (Applied Biosystems), activated by 15 min at 95°C followed by 4 cycles 94°C 30 sec, 65°C 30 sec, 72°C 1.5 min and 35 cycles of 94°C 30 sec, 67°C 30 sec, 72°C 1.5 min with a final extension of 10 min at 72°C. The exception was amplification of exon 5, where a 2°C lower annealing temperature was used. All primers contained a consensus M13 sequence to enable sequencing with the same primer, included in BigDye Primer sequencing kit from Applied Biosystems, Stockholm, Sweden. Applied Biosystems 310 analyzer and Sequence Analysis software was used for all sequencing. Thus, the 36 cases and 72 controls were analyzed for genetic mutations in ELA2.
Table 1 Primer sequences for PCR amplification of ELA2 exon 2–5
ELA2 target sequence Primers
Exon 2 F 5'-tgtaaaacgacggccagtgggaggggacaggctccttgg-3'
Exon 2 R 5'-caggaaacagctatgaccaccgggacgcggggtccgagc-3'
Exon 3 F 5'-tgtaaaacgacggccagtcaggcccgtcgccggatggg-3'
Exon 3 R 5'-caggaaacagctatgacctccgtcgcagcctccaccct-3'
Exon 4 F 5'-tgtaaaacgacggccagtgtgacgcgctgacgatctgt-3'
Exon 4 R 5'-caggaaacagctatgaccgcagtaccgggctgggagcg-3'
Exon 5 F 5'-tgtaaaacgacggccagtcagtccagcttccccacctt-3',
Exon 5 R 5'-caggaaacagctatgaccgacctactgaccattttcaac-3'
PCR primers sequences for ELA2 exon 2–5, for following sequencing reactions with BigDye primer, Applied Biosystems.
Figure 1 Outline of reported mutations in ELA2 exon-sequences in patients with severe congenital neutropenia and cyclic neutropenia Outline of mutations previously reported [2, 4] in ELA2 exons 1–5. The SNP S173 [6] is indicated as an extended arrow and represents base number 4890 in accession number Y00477 and is a base C→A substitution.
Cell culture
The lymphoma cell line U937 and the promyelocytic cell line HL-60 (American tissue culture collection) were cultured in Dulbeccos modified Eagles Medium, DMEM (Sigma) supplemented with 10 % fetal bovine serum (SVA, Uppsala, Sweden), L-glutamine and penicillin-streptomycin (Sigma).
Western blot
Equal numbers (8 × 106) of U937 cells were grown in 75 cm2 dishes in complete medium containing 0, 125 and 250 μM sulphasalazine for 24 h. For protein isolation, cells were washed in PBS and lysed in buffer containing 1% Triton X-100, 50 mM Tris-HCl pH 8.0 and protease inhibitor cocktail (Sigma) and were kept on ice for 30 min. Lysates were centrifuged for 10 min at 10 000 × g, and protein concentration was determined using BioRad protein assay. Criterion precast gels (BioRad, Sweden) were used to perform SDS-page with 20 μg protein loaded per well. After gel transfer to a nitrocellulose membrane, the membranes were blocked over night in 5 % dry milk in TBS-Tween. Primary antibody against human neutrophil elastase (Calbiochem, Sweden) was diluted 1:1000 in 5 % dry milk in TBS-T. After 2 h incubation, and four sets of washing, a secondary antibody was added (1:5000) and blots were developed using ECL (ECL Western blotting system, Amersham, Sweden). Western blot analysis of human neutrophil elastase expression was performed twice.
Elastase activity assay
Cells (HL-60 and U937) treated with 0, 125, 250 and 500 μM of for 24 h were lysed with 100 μl of buffer containing 100 mM Tris-HCl pH 7.4, 1 mM MgCl2, 0.1 % Triton X-100. After homogenization, 300 μl of 1.4 M NaCl in 0.1 % Triton X-100 was added and samples were centrifuged at 15 000 × g, for 15 min at 4°C. The supernatants were transferred to new tubes and assayed for elastase activity using Suc-Ala-Ala-Ala-pNA (Sigma) as a substrate. For each assay we took 25 μl sample, mixed with 100 μl buffer containing 100 mM Tris-HCl pH 8.5, 1 M NaCl, 500 mM MgCl2 and 0.1 % Triton X-100. To this, 50 μl of substrate was added, to a final concentration 1 μM. After 30 min of incubation in room temperature, absorbance was read at 405 nm and the concentration was calculated from a standard curve of elastase (Sigma).
Cell survival index
For the cell viability assay, we used a fluorometric microculture cytotoxicity assay (FMCA) previously described by Larsson et al [5]. Briefly, 20 000 cells/well were plated in 96-well plates (NUNC, DK) in complete medium with addition of increased concentrations of sulphasalazine (0, 125, 250, 500, 750 and 1000 μM) and incubated for 72 h in a humidified atmosphere used in regular cell culturing. All samples were plated in triplicates and three wells with cell culture medium served as blanks. As controls we had cells without additions and cells only with solvent, in this case 0.5 M NaOH, with equal molarities as in the wells with the highest sulphasalazine-concentration. At the end of the 72 h incubation period, plates were centrifuged (200 × g, 5 min) and medium was aspirated in a microtitre plate washer, washed with PBS and 100 μl of 10 μg/ml of fluorescein diacetate (Sigma, Sweden), was added. This dye exclusively binds intact cell membranes of viable cells. After 1 h incubation at 37°C, the fluorescence was read in the Fluoroscan 2 (Labsystems OY, Finland) at 480 nm excitation and 530 nm as emission. The results are presented as survival index, defined as fluorescence in test wells/ fluorescence in control wells (blank values subtracted) × 100. Thus, a low numerical value indicates high sensitivity to the cytotoxic effect of sulphasalazine. Effective concentration is defined as the concentration when 50 % of the cells are viable (EC50).
Statistics
Two-tailed Student's t-test was used to compare subject characteristics and results from cell culture between cases and controls. Frequencies of subject characteristics male versus females was tested with Chi2-test with one degree of freedom, using Minitab 14. A p-value less than 0.05 was denoted with (*), p < 0.01 with (**) and was considered as statistically significant.
Results
Subjects
The characteristics of the subjects are presented in Table 2. The agranulocytosis cases were significantly older than the control patients (p = 0.023). The white blood cell count (WBC) before sulphasalazine-treatment did not differ between cases and controls, nor did the dose of sulphasalazine. Bone marrow aspirates had been taken from 10 patients (cases). In all samples, the myelopoesis was seriously reduced and a maturation arrest at the promyelocyte-myelocyte stage of neutrophilic differentiation was seen.
Table 2 Characteristics of subjects
Cases (n = 36) Controls (n = 72) p-value
Age range (median) 11–77 (55) 13–90 (47) 0.023
WBC before a 9.3 ± 4.7 8.5 ± 2.5 0.26
Dose of sulphasalazine (gram/day) 2.2 ± 0.6 2.0 ± 0.4 0.13
Male : Female 17 : 19 33 : 39 0.891
a WBC data only available for 28 cases and 67 controls.
Cases are defined as the patients treated with sulphasalazine, who were diagnosed with agranulocytosis and controls were defined as patients treated with sulphasalazine without hematological side effects within the first three months of sulphasalazine treatment. The statistics of differences between cases and controls in age, white blood cell count (WBC) before sulphasalazine treatment and dose of sulphasalazine was calculated with 2-tailed Student's t-test. The frequency of males versus females was calculated with Chi2 test, one degree of freedom. Significance level was set at 0.05.
Mutation analysis
None of the previously reported mutations in ELA2 was found in this material, although we found a silent single nucleotide polymorphism, called S173 [6] that corresponds to a C4890A substitution in Genbank accession number Y00477 (marked with extended arrow in Figure 1). The incidence of the S173 polymorphism did not differ between controls and cases, 0.31 for both, and S173 has previously been detected in healthy subjects [6]. No correlation between the S173 polymorphism and white blood count before sulphasalazine-treatment was found (Table 3).
Table 3 White blood count (WBC), before sulphasalazine treatment, in subjects with or without the S173 polymorphism
WBC
Subjects with S173 (n = 34) 8.74 ± 2.51
Subjects without S173 (n = 74) 8.66 ± 3.51
White blood count (WBC) was estimated by the local physicians before starting with the sulphasalazine treatment. The average WBC in subjects with or without S173 is presented as the mean value ± SD. It was no statistical difference between the groups.
Elastase expression, elastase activity and cell survival after sulphasalazine exposure to HL-60 and U937
By western blot analysis, we analyzed neutrophil elastase protein expression in U937 cells. No difference in human neutrophil elastase expression was detected after treatment with 125 and 250 μM sulphasalazine (Figure 2), compared to controls. The elastase activity in HL-60 and U937 cells was not affected by increasing sulphasalazine concentration, ranging from 0 to 500 μM, and expressed as elastase activity/μg protein (data not shown). For the cell survival index, each FMCA experiment was performed three times separately with similar results (inter-assay variation less than 10 %). Concentrations below 250 μM sulphasalazine did not affect the survival index of U937 and HL-60 cells (Figure 3A,3B), but at 500 μM of sulphasalazine, the survival index of HL-60 cells decreased to a third (Figure 3A). The U937 was only marginally affected at 500 μM sulphasalazine-concentration, but cellular survival decreased with approximately 40 % at 750 μM of sulphasalazine (Figure 3B). The effective concentration (EC50) of sulphasalazine was approximately 370 μM for HL-60 cells and 820 μM for U937 cells.
Figure 2 Western blot analysis of human neutrophil elastase (hNE) expression after sulphasalazine exposure U937 cells were incubated for 24 h with 0, 125 and 250 μM sulphasalazine, followed by cell lysis and protein isolation. 20 μg of protein was applied in each lane, transferred to nitrocellulose membrane and incubated with human neutrophil elastase antibody.
Figure 3 Survival index of HL-60 and U937 cells, incubated with increasing concentrations of sulphasalazine Survival index of HL-60 (A) and U937 cells (B) treated with increasing concentrations of sulphasalazine (0–1000 μM) for 72 h and measured with FMCA. Survival index, defined as fluorescence in test wells/ fluorescence in control wells (blank values subtracted) × 100.
Discussion
Idiosyncratic drug-induced agranulocytosis can be due to several different mechanisms of action, including immunological, toxic and genetic [7,8]. Toxic drug-induced neutropenia is often dose-dependent, whereas immunological and genetic causes are less related to dose. In our study, bone marrow aspirates from patients with sulphasalazine-induced agranulocytosis revealed maturation arrest of neutrophils at the promyelocyte-myelocyte stage. These findings resemble promyelocytic maturation arrest seen in severe congenital neutopenia (SCN) and cyclic neutropenia (CN) [9]. In the majority of cases with SCN and CN, germline mutations in the human neutrophil elastase gene (ELA2) are implicated as the primary abnormality [2,4]. The focus of this study is therefore on the human neutrophil elastase gene as a possible cause of sulphasalazine-induced agranulocytosis. We found a coding synonymous polymorphism in ELA2, which, however, was equally represented among cases and controls.
Heterozygous mutations in ELA2 act in a dominant manner, interfering with sub-cellular trafficking of neutrophil elastase, and leading to an accumulation of neutrophil elastase in the cytosol [10]. For normal neutrophil cell maturation, the proliferative action of the granulocyte colony stimulating factor (G-CSF) is necessary [11]. When G-CSF is exposed to active elastase enzyme in vitro, G-CSF is rapidly cleaved and rendered inactive [11]. In theory, SCN and CN are caused by an accumulation of neutrophil elastase, leading to an inactivation of G-CSF and a negative feedback on granulopoiesis, which causes neutropenia. Other proteins, connected to expression and transportation of human neutrophil elastase, have also been linked to SCN disease. In canine cyclic hematopoieses, lack of the intracellular transport protein AP3β causes accumulation of canine neutrophil elastase in the cytosolic compartments [12], and mutations in ELA2 may disrupt the AP3β-recognition site [13]. Furthermore, mutations in the proto-oncogene GFI1, a transcriptional repressor of ELA2, causes over-expression of neutrophil elastase in mice, thus, making them neutropenic [14].
During maintenance therapy with sulphasalazine, trough serum sulpha concentration is on average approximately 100 μM at the Department of Clinical chemistry and pharmacology, Uppsala University hospital. To avoid toxic effects, trough serum concentration of sulpha should stay below 600 μM [15]. Our in vitro data suggest a decreased cell survival of sulphasalazine at concentrations around 500 μM. Interestingly promyelocytic leukemia HL-60 cells were more sensitive to sulphasalazine than lymphoma U937 cells, with EC50 values of 370 μM and 820 μM, respectively. Human neutrophil elastase expression in lymphoma U937 cells did not differ after sulphasalazine at 125 and 250 μM, indicating that human neutrophil elastase production is not affected by sulphasalazine at subtoxic levels.
Conclusions
In conclusion, neutrophil elastase does not appear to be involved in the etiology of sulphasalazine-induced agranulocytosis. No causative ELA2 mutations were found, and therapeutic concentrations of sulphasalazine did not increase the expression of human neutrophil elastase. High concentrations of sulphasalazine were toxic to white blood cells in vitro; however, there is no evidence that this toxicity is mediated through human neutrophil elastase. Promyelocytic cells were more sensitive to sulphasalazine than lymphoma cells, and the reason for this difference may also explain sulphasalazine-induced agranulocytosis.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
AJ carried out the molecular genetic studies, participated in the sequence alignment, drafted the manuscript and carried out the in vitro experiments. MW participated in the design of the study and performed the statistical analysis. HM conceived the study, and AJ, MW and HM participated in its design and coordination. 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 Professor Rolf Larsson, Uppsala University, for providing us with equipment for FMCA analysis. The cell line HL-60 was a kind gift from Dr. N-E Heldin, Uppsala University, Sweden. This study was funded by clinical research support (ALF) at Uppsala University.
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| 15575961 | PMC535939 | CC BY | 2021-01-04 16:31:41 | no | BMC Blood Disord. 2004 Dec 2; 4:5 | utf-8 | BMC Blood Disord | 2,004 | 10.1186/1471-2326-4-5 | oa_comm |
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BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-841556084910.1186/1471-2407-4-84Research ArticleSMAC is expressed de novo in a subset of cervical cancer tumors Espinosa Magali [email protected] David [email protected] Carlos M [email protected] la Garza Jaime G [email protected] Vilma A [email protected] Jorge [email protected] Subdirección de Investigación Básica. Instituto Nacional de Cancerología. Av. San Fernando # 22. Tlalpan 14080 México, D.F. MEXICO2004 23 11 2004 4 84 84 3 8 2004 23 11 2004 Copyright © 2004 Espinosa 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
Smac/Diablo is a recently identified protein that is released from mitochondria after apoptotic stimuli. It binds IAPs, allowing caspase activation and cell death. In view of its activity it might participate in carcinogenesis. In the present study, we analyzed Smac expression in a panel of cervical cancer patients.
Methods
We performed semi quantitative RT-PCR on 41 cervical tumor and 6 normal tissue samples. The study included 8 stage I cases; 16 stage II; 17 stage III; and a control group of 6 samples of normal cervical squamous epithelial tissue.
Results
Smac mRNA expression was below the detection limit in the normal cervical tissue samples. In contrast, 13 (31.7%) of the 41 cervical cancer biopsies showed detectable levels of this transcript. The samples expressing Smac were distributed equally among the stages (5 in stage I, 4 in stage II and 4 in stage III) with similar expression levels. We found no correlation between the presence of Smac mRNA and histology, menopause, WHO stage or disease status.
Conclusions
Smac is expressed de novo in a subset of cervical cancer patients, reflecting a possible heterogeneity in the pathways leading to cervical cancer. There was no correlation with any clinical variable.
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Background
Apoptosis is an evolutionarily conserved biological process that plays a fundamental role in development and tissue homeostasis in metazoans [1]. This type of cell death is executed by a family of proteases known as caspases [2]. There are two well-characterized apoptotic pathways that converge in caspase activation: the death receptor pathway and the mitochondrial pathway [3]. Inhibitors of Apoptosis Proteins (IAPs) are the most important regulators of caspases. These proteins inhibit caspase activation, thus preventing the induction of apoptosis [4]. In cells undergoing apoptosis, IAPs are inactivated by interaction with proteins containing the so-called IBM (IAP-binding motif) [4,5].
One IBM protein is the recently identified Smac/DIABLO [6,7]. Smac resides in the mitochondrial intermembrane space in healthy cells but is released into the cytosol during apoptosis, where it interacts with IAPs and disrupts their ability to bind caspases [8]. Smac is expressed ubiquitously, with high expression in adult testis, heart, liver, kidney, spleen, prostate and ovary and low expression in brain, lung, thymus, and peripheral blood leukocytes [9]. It is encoded in a nuclear gene and is post-translationally imported into the mitochondria via a targeting sequence in its amino terminus. Removal of this signal generates a mature polypeptide with the IBM at the amino terminal end [10]. Smac interacts with all mammalian IAPs examined so far: XIAP, cIAP-1, cIAP-2, survivin and ML-IAP [6,7,11,12]. The structure of the Smac-XIAP complex has been studied by X-ray crystallography [13] and high-resolution NMR [14]; it appears that the tetrapeptide AVPI is indispensable for the formation of this complex.
IAPs are highly expressed in human tumor cells [15-17], contributing to the intrinsic resistance of these cells to endogenous death receptor-induced apoptosis and consequently to chemotherapy [18]. For this reason, peptides mimicking the action of Smac have been generated and analyzed. Four publications to date have shown promising effects of these Smac peptides in vitro and in vivo; however, further studies are required prior to clinical testing [19-22].
Recently, Sekimura and colleagues found that Smac expression was significantly lower in primary lung cancers than in normal tissue [23]; patients with lower Smac mRNA levels had worse prognoses. These results indicate that Smac expression may play a role in the progression of primary lung cancer and may be useful for prognosis [23]. However, Smac expression has not been analyzed in other tumors. In view of the possible role of Smac in cervical carcinogenesis and its potential as a therapeutic target, we have investigated the expression of this apoptotic protein in cervical cancer patients.
Methods
Cell lines and tumor samples
Cervical cancer cell lines (HeLa, SiHa, CaSki and CaLo) were obtained from ATCC and cultured as monolayers in Dulbecco Modified Eagle's Medium (DMEM) containing 10% (V/V) fetal bovine serum (GIBCO, Bethesda, MD, USA) at 37°C in a humidified atmosphere of 5% (V/V) CO2.
Forty-one cervical cancer samples were obtained from the Instituto Nacional de Cancerologia of Mexico. Written consent was obtained from patients before the samples were collected. Tumors were staged according to the International Gynecology and Obstetric Federation (FIGO) system. The samples comprised 8 at stage IB, 16 at stage IIB and 17 at stage IIIB; and a control group comprising 6 samples of normal cervical squamous epithelial tissue (Table 1). The control samples were derived from hysterectomy specimens from patients with uterine myomatosis. Only samples with normal pathological reports were included.
Histology
Histopathological grading was done according to the WHO (World Health Organization) classification system (Table 1).
RNA isolation and RT-PCR
RNA extraction and RT-PCR analysis were performed as described previously [24]. Briefly, total RNA was extracted from cultured cells, tumors and non-neoplastic tissue samples with Trizol reagent (Invitrogen) following the manufacturer's protocol. RNA purity was confirmed by the 260/280 nm absorbance ratio and its integrity was established with agarose gels. Total RNA (2 μg) was reverse-transcribed in a final 20 μl reaction volume using 15 U ThermoScript reverse transcriptase, 2.5 × RT Buffer and random hexamers (ThermoScript RT-PCR, Invitrogen). The RT-PCR steps were 25°C for 10 min, 50°C for 50 min and 85°C for 5 min. Smac and GAPDH mRNA PCR reactions contained 0.25 μl Amplitaq gold polymerase (Applied Biosystems, ROCHE), 2.5 μl 10 × reaction buffer, 0.5 μl dNTP mix 10 mM, 1 μl sense primer 10 μM, 1 μl anti-sense primer 10 μM and 1 μl cDNA in 25 μl final volume. The Smac primers were: sense 5' GCGCGGATCCATGGCGGCTCTGAAGAGTTG 3' and anti-sense 5' AGCTCTCTAGACTCAGGCCCTCAATCCTCA 3'. The GAPDH primers were: sense 5' CCCCTTCATTGACCTCAACT 3' and antisense 5' TTGTCATGGATGACCTTGGC 3'.
The PCR cycle parameters for Smac were: 10 min enzyme activation at 95°C followed by 3 cycles of 30 s at 95°C and 2 min at 72°C, then 30 cycles of 30 s at 95°C and 30 s at 68°C, and finally 5 min at 72°C. The corresponding parameters for GAPDH were: 10 min enzyme activation at 95°C followed by 25 cycles of 30 s at 95°C, 30 s at 60°C and 30 s at 72°C. The products were electrophoresed on 1% agarose gels and stained with ethidium bromide. Smac mRNA data were expressed as ratios between the densitometric values (Scion Image software) of Smac gene expression. The PCR products were normalized to the amplified GAPDH, the internal reference gene. Gene expression measurements were repeated at least twice.
Statistical analysis
To detect a correlation between pathological tumor parameters and normalized Smac expression we used ANOVA (stage, current disease and menopause status) and chi square tests (stage, histology of tumors, menopause and current status). Kaplan-Meier curves for status were generated and log rank was used to test for differences. The mean follow-up was 14.7 months. The statistical package Intercooled Stata 7.0 was used for analyses and statistical significance was accepted when the p value was less than 0.05.
Results
To ascertain whether Smac is expressed in cervical cancer we performed semiquantitative RT-PCR analyses on a panel of cervical cancer lines, including HeLa, SiHa, CasKi and CaLo cells. As shown in Figure 1, the HeLa and CasKi lines contained Smac mRNA, but very low levels were observed in SiHa and CaLo cells.
Next, we measured Smac mRNA levels using the same approach in 41 cervical tumor and 6 normal cervical samples. To ensure accurate determinations and to verify equal RNA input, GAPDH mRNA was amplified simultaneously. Figure 2 shows a representative panel of results, which are given in Tables 1 and 2. Unexpectedly, Smac mRNA was below the detection limit in normal cervical samples. In contrast, as expected from the cell line data, 13 (31.7%) of the 41 cervical cancer biopsies contained detectable levels of this transcript. The samples expressing Smac were distributed equally among the stages (5 in stage I, 4 in stage II and 4 in stage III). We found no significant correlation between Smac mRNA level and histology, menopause, clinical stage or disease status (Table 2). When the Smac expression levels in the tumor samples were analyzed, there were no significant differences between clinical stages (Figure 3), menopause status (Figure 4) or disease status (Figure 5). Similarly, a survival analysis of the patients showed no statistical differences between patients expressing or not expressing Smac mRNA (Figure 6).
Discussion
Tumors proliferate beyond the constraints that limit growth in normal tissue. Therefore, the resistance of tumor cells to apoptosis is an essential feature of carcinogenesis. This has been confirmed by the finding that deregulated proliferation alone is not sufficient for tumor formation because there is concomitant induction of cell death [25]. Overexpression of growth-promoting oncogenes such as c-Myc sensitize cells to apoptosis [26]. Thus, tumor progression requires the expression of anti-apoptotic proteins or the inactivation of essential pro apoptotic proteins [27,28]. Indeed, it has been shown that survivin, a member of the Inhibitor of Apoptosis Protein (IAP) family, is upregulated in some tumors [29], correlating with prognosis [30,31].
Smac is a recently identified proapoptotic protein that interacts with and inhibits several IAPs, including survivin [6,11]. It has been shown that Smac mRNA levels in tumor tissues are significantly lower than in normal tissues [23]. Patients with lower Smac mRNA levels have worse prognoses. These results indicate that Smac expression may play a role in the progression of primary lung cancer, as expected by the known role of this protein in cell death induced by chemotherapeutic drugs. Unexpectedly, we found that during cancer progression, some cervical tumors express this protein de novo.
Unfortunately, we found no correlation between Smac expression and any clinical variable. This could be attributed to differences in tissue expression of IAPs, which are reported to have different binding affinities for Smac. On the other hand, alternative IAPs such as the recently identified Omi/Htra2 [32] might play an important tissue- or tumor-specific role. This is supported by the recent report of a null phenotype in Smac-deficient mice, in which a role for other IAP inhibitory proteins is suspected [33].
Cancer treatment by chemotherapy and γ-irradiation kills cells primarily by the induction of apoptosis. However, few tumors are wholly sensitive to these therapies, and the development of resistance to therapy is an important clinical problem. Failure to activate the apoptotic programme represents an important mode of drug resistance in tumor cells [34]. Modulation of the key elements in apoptotic signaling should directly influence therapy-induced tumor-cell death. Indeed, it has recently been suggested that peptides mimicking the Smac amino-terminus could be a novel therapeutic weapon [19]. Tumors with low or null Smac expression, such as the ones reported in this study, could be more susceptible to this approach.
Conclusions
During cervical cancer progression, a subset of tumors express the apoptotic protein Smac de novo. This finding contrasts with a previous report for lung cancer [23], underlining the notion that downregulation or even expression of Smac could be dispensable for tumor progression, at least in cervical cancer. This could be because other mitochondrial molecules such as Omi might substitute for its known proapoptotic function. There was no correlation between Smac expression and any clinical variable.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JMZ Conceived and coordinated the study.
VAML: Conceived and coordinated the study. Statistical Analysis
MEC: Performed RT-PCR assays
DCL: Provided the clinical samples and coordinated patient study
CMLG: Coordinated patient assessment, ethical guidelines.
JGGS: Provided clinical assessment
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This work was supported by grants CONACYT-2002-C01-42040/A-1 and SALUD-2002-C01-6579 from Consejo Nacional de Ciencia y Tecnolología, México.
Figures and Tables
Figure 1 Smac/Diablo mRNA expression in cervical cancer cell lines. Upper panel: RT-PCR analysis of HeLa, SiHa, CasKi and CaLo cervical cancer cell lines. To the left molecular weight marker (100 bp ladder, Invitrogen). Lower panel: RT-PCR of GAPDH, used as a mRNA load control.
Figure 2 Smac/Diablo mRNA expression in cervical cancer patients. Upper panel: RT-PCR analysis of Smac/Diablo mRNA. To the left molecular weight marker (100 bp ladder, Invitrogen). Clinical stage is showed at the top of the panel: C: control samples, 1, 2 and 3, clinical stages. Lower panel: RT-PCR of GAPDH, used as a load control.
Figure 3 Smac expression levels versus clinical stage of cervical cancer samples. Graph shows median, upper and lower quartiles. P value testing the significance of the difference by ANOVA.
Figure 4 Smac expression levels versus menopausal status of cervical cancer samples. Graph shows median, upper and lower quartiles. P value testing the significance of the difference by ANOVA.
Figure 5 Smac expression versus disease status of cervical cancer samples. Graph shows median, upper and lower quartiles. P value testing the significance of the difference by ANOVA.
Figure 6 Kaplan/Meier survival analysis of cases by Smac expression. Continue black line: Negative expression. Dotted line: Positive expression. Insert in the lower left corner of plot is the P value testing the significance of the difference in the survival curves by the Mantel/Cox log rank test
Table 1 Smac mRNA expression levels and clinicopathological factors in cervical cancer
Sample
Age
Stage
Histology of tumor
Menopause
Current status
Smac/GAPDH
Control 42 - Menopause Disease-free 0
Control 33 - Pre-menopause Disease-free 0
Control 28 - Pre-menopause Disease-free 0
Control 44 - Menopause Disease-free 0
Control 47 - Menopause Disease-free 0
Control 35 - Pre-menopause Disease-free 0
1 46 I Adenocarcinome Menopause Disease-free 0
2 52 I Squamous cell Post-menopause Disease-free 76.01
3 49 I Squamous cell Menopause Disease 203.36
4 44 I Squamous cell Pre-menopause Disease-free 0
5 65 I Squamous cell Post-menopause Disease-free 0
6 67 I Squamous cell Post-menopause Disease-free 86.53
7 47 I Squamous cell Menopause Disease-free 65.3
8 34 I Squamous cell Pre-menopause Disease-free 66.45
9 49 II Squamous cell Menopause Disease-free 78.44
10 63 II Squamous cell Post-menopause Disease-free 0
11 38 II Squamous cell Menopause Disease-free 0
12 48 II Squamous cell Menopause Disease-free 0
13 55 II Squamous cell Post-menopause Disease-free 0
14 35 II Squamous cell Pre-menopause Disease-free 166.25
15 66 II Squamous cell Post-menopause Disease 0
16 52 II Squamous cell Post-menopause Disease 0
17 80 II Squamous cell Post-menopause Disease 0
18 70 II Squamous cell Post-menopause Disease-free 0
19 65 II Squamous cell Post-menopause Disease 0
20 39 II Squamous cell Pre-menopause Disease 70.42
21 57 II Squamous cell Post-menopause Disease-free 55.45
22 37 II Squamous cell Pre-menopause Disease-free 0
23 59 II Squamous cell Post-menopause Disease 0
24 36 II Squamous cell Pre-menopause Disease 0
25 33 III Adenocarcinome Pre-menopause Dead 0
26 50 III Adenocarcinome Post-menopause Dead 0
27 60 III Squamous cell Post-menopause Disease-free 0
28 64 III Adenocarcinome Post-menopause Dead 0
29 80 III Squamous cell Post-menopause Dead 0
30 52 III Squamous cell Post-menopause Disease-free 173.96
31 56 III Squamous cell Post-menopause Disease-free 0
32 70 III Squamous cell Post-menopause Disease-free 81.5
33 72 III Adenosquamous Post-menopause Dead 0
34 33 III Squamous cell Pre-menopause Disease 0
35 82 III Adenocarcinome Post-menopause Disease 0
36 48 III Squamous cell Menopause Disease-free 0
37 32 III Squamous cell Pre-menopause Disease 98.59
38 48 III Squamous cell Menopause Disease 0
39 36 III Adenosquamous Pre-menopause Disease 0
40 52 III Squamous cell Post-menopause Disease-free 0
41 67 III Squamous cell Post-menopause Disease-free 88.95
Table 2 Smac positivity in cervical cancer tumor samples.
Variable No. Patients (n = 41) Smac Positive (n = 13) Smac Negative (n = 28)
P
Age 53.36 (32–82) 50 54.92 0.3
Stage
IB 8 5 3
IIB 16 4 12 0.11
IIIB 17 4 13
Histology of tumors
Squamous cell 34 13 21
Adenocarcinoma 5 0 5 0.14
Adenosquamous 2 0 2
Menopausal status
Pre-Menopause 12 5 7
Menopause 6 2 4 0.64
Post-Menopause 23 6 17
Current status
Disease free 21 9 12
Diseased 15 4 11 0.15
Dead 5 0 5
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| 15560849 | PMC535940 | CC BY | 2021-01-04 16:03:02 | no | BMC Cancer. 2004 Nov 23; 4:84 | utf-8 | BMC Cancer | 2,004 | 10.1186/1471-2407-4-84 | oa_comm |
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BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-551557163310.1186/1471-2334-4-55Technical AdvanceComparison of nested PCR and real time PCR of Herpesvirus infections of central nervous system in HIV patients Drago Lorenzo [email protected] Alessandra [email protected] Vecchi Elena [email protected] Giuseppe [email protected] Rosaria [email protected] Maria Rita [email protected] Laboratory of Clinical Microbiology, L. Sacco Teaching Hospital and Department of Clinical Sciences L. Sacco, University of Milan, Via GB Grassi74, 20157 Milan, Italy2004 30 11 2004 4 55 55 8 7 2004 30 11 2004 Copyright © 2004 Drago 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
Molecular detection of herpesviruses DNA is considered as the reference standard assay for diagnosis of central nervous system infections. In this study nested PCR and real time PCR techniques for detection of Herpes simplex virus type 1 (HSV-1), Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) in cerebrospinal fluid of HIV patients were compared.
Methods
Forty-six, 85 and 145 samples previously resulted positive for HSV-1, CMV and EBV by nested PCR and 150 randomly chosen negative samples among 1181 collected in the period 1996–2003 were retrospectively reassessed in duplicate by real time PCR and nested PCR.
Results
Samples giving positive results for CMV, HSV-1 and EBV with nested PCR were positive also with real time PCR. One of the negative samples resulted positive for HSV and one for EBV. Real time PCR showed comparable sensitivity and specificity vs nested PCR.
Conclusion
Real time PCR proved to be a suitable method for diagnosis of herpesvirus infections in CNS, showing comparable sensitivity and being less time consuming than nested PCR.
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Background
Opportunistic infections as well as tumors and vascular and metabolic disorders are common in HIV-infected patients [1-3]. Generally, opportunistic viral infections are caused by a broad spectrum of different species with similar clinical patterns, especially those affecting the central nervous system (CNS), where differential diagnosis requires simultaneous screening of a wide range of different viruses [4,5]. Moreover, immunodeficiency induced by HIV infection favours reactivation of herpesviruses which could cause important diseases by themselves [6].
Although the rate of CNS complications is relatively low, if compared with the high prevalence of herpesviruses in the population, these viruses represent the most important pathogens associated with viral encephalitis and meningitis [7,8], being cytomegalovirus (CMV) the most frequently identified virus in HIV-positive patients, followed by Epstein-Barr virus (EBV) and Herpes simplex virus type 1 (HSV-1) [9]. CMV, that is often responsible of asymptomatic infections in immunocompetent host, is able to cause serious manifestations such as retinitis, pneumonia and encephalitis in presence of an alteration of immunoresponse, while the recovery of Epstein-Barr virus (EBV) in cerebrospinal fluid (CSF) seems to be a prognostic sign for the development of cerebral tumors in patients with AIDS [10,11]. HSV-1 is the most commonly detected virus in diagnostic laboratory, being cause of a variety of clinical symptoms in different anatomical sites such as skin, lips, oral cavity and, especially in immunocompromised patients, CNS [12].
One-step or nested polymerase chain reaction (PCR) has rapidly replaced immunological assays based on virus specific Ig antibodies in CSF for laboratory diagnosis of Herpesvirus infections, even if serological methods are considered an additional tool for defining clinical diagnosis.
Although nested PCR is considered the method of choice in terms of specificity [9,13,14], some additional aspects should be considered.
In the last years, introduction of real time PCR has markedly increased the ease and the speed in the virology laboratory due to the relevant technology that permits rapid temperature cycling within a close system. Considering the importance of relationship between viral load in CSF and severity and outcome of disease, an additional advantage of Real Time PCR is the capability to perform simultaneous qualitative and quantitative analysis.
Here is reported our experience gained in the diagnosis of herpesvirus infections of the CNS in HIV patients by means of nested PCR and Real Time PCR, which has been recently applied in our laboratory.
Methods
CSF samples collection
A total of 1181 CSF samples collected in the period 1996 – 2003 from HIV patients attending at Luigi Sacco Teaching Hospital of Milan (Italy) affected by acute encephalitis or meningitis or encephalopathy or other neurological syndromes were considered.
Particularly, they consisted of 684, 954 and 933 CSF samples previously tested for HSV-1, CMV and EBV, respectively, by means of nested PCR. Of these, all the positive samples and 150 negative samples randomly chosen were retested by means of nested PCR and real time PCR. Each sample was re-extracted and run in duplicate.
CSF positive and negative samples were stored at -80°C until analysis.
Clinical data of patients with positive samples were available only in the 20% of the total. These patients generally recorded meningoencephalitis signs such as central motor or sensory alterations, consciousness loss, seizures defects.
Nucleic acid extraction
Spin-column based QIAamp Mini Kit (Quiagen, Hilden, Germany) protocol extraction for CSF was used as indicated by the manufacturer.
This procedure allowed for the rapid purification of DNA from 200 μL of CSF and comprised four successive steps carried out using QIAamp Spin Columns in a standard microcentrifuge. Purified DNA was concentrated at a final volume of 20 μL.
Nested PCR
Nested PCR was carried out in a 50 μl mixture containing 40 μl of first amplification mix (outer primers, buffer, dNTP)-(Amplimedical SpA-Bioline Division-Italy), 2U/μl Taq DNA polymerase (Roche Diagnostics-Germany) and 5 μl of purified DNA. Primer pairs selecting for glycoprotein D gene of HSV-1 [15], for the late protein gp58 of CMV [16], and for Bam HI-W region of EBV [17] are shown in Table 1.
After an initial 2 min denaturation at 94°C, 35 cycles of 94°C for 30 sec, 55°C for 30 sec and 72°C for 30 sec were carried out, followed by a 5 min extension at 72°C using a thermal cycler (Gene Amp PCR System 2400-Applied Biosystem – Monza – Italy). The reaction mixture for the second amplification round was the same as for the first one, except for the "inner" primers used instead of the "outer" primers. In the second amplification round 44 μl of amplification mix and 1 μl of the first amplification round PCR product were used. The thermal cycling was repeated as for the first amplification round but using 30 cycles after the initial 2 min denaturation. Each amplification run contained a negative control, consisting of water and a positive plasmidial control.
Analysis for the PCR products was performed by means of 4 % agarose gel electrophoresis followed by visualization with ethidium bromide (0.4 μg/mL) staining and UV illumination to confirm the expected products.
Real Time PCR
For diagnostic real time PCR Taq polymerase RT PCR Kit (Amplimedical SpA-Bioline Division-Turin Italy) was used. Target regions for HSV and CMV were the same as in nested PCR, while EBNA-1 gene was amplified for EBV [9] as shown in Table 2..
The RT PCR was performed in 25 μl mixture containing 20 μl of amplification mix (buffer, dNTPs, Taq gold polymerase, Rox passive fluorocrome, primers and MGB Eclipse probe) and 5 μl of purified DNA.
The amplification program included an initial decontamination with uracile N'-glycosilase at 50°C for 2 min, followed by denaturation at 95°C for 10 min and 45 two steps of 15 sec at 95°C and 1 min at 60°C.
The RT PCR products were detected by measuring fluorescence with passive reference dye in Sequence Detection System ABI Prism 7000 (Applied Biosystem). Control threshold (Ct) values were calculated by determining the point at which the fluorescence exceeded a background limit of 0.04. Each analytical session comprised also a negative control (distilled water).
Quantification was carried out by analysing four positive plasmidial standards at 102, 103, 104 and 105 copies/reaction. The standards were obtained by cloning the target amplification product in a plasmid, which was transformed and cultured in Escherichia coli. Plasmidic DNA was purified with a commercial kit (Qiagen) and its concentration determined spectrophotometrically. Then, plasmidic DNA was serially diluted in a stabilizing buffer to the final desired concentration. Amounts of copies/mL in each sample were determined by means of a quantification software (Amplimedical), by considering an extraction recovery of 80%.
Results
Nested PCR
Of 954 CSFs previously examined for CMV by means of nested PCR, 85 samples resulted positive. Among the 684 CSFs tested for HSV-1, 46 samples were found positive, while, 145 of 933 CSFs tested resulted positive for EBV.
Reassessment of these positives and of 150 negative samples confirmed results previously obtained with the same method, with the exception of one HSV-1 positive sample, which resulted negative when retested, as shown in Table 3.
Real Time PCR
Results from real time PCR are reported in Table 3. All the samples for which nested PCR gave positive results were confirmed by Real Time-PCR for CMV, HSV-1 and EBV. Of the 150 samples resulted negative by nested PCR, 148 were negative, while 1 sample resulted positive for HSV and 1 for EBV. The positive sample giving negative result when reassessed by nested PCR was negative also by real time PCR.
Sensitivity and specificity
By considering nested PCR as gold standard [9,13], sensitivity and specificity of real time PCR are described in Table 4. Comparable sensitivity (100%) and specificity (99–100 %) were found for real time PCR in respect to nested PCR.
Discussion
Introduction of PCR into routine diagnostic has rapidly gained a pivotal role for diagnosis of a wide range of diseases, supplanting, in many cases, other methods, such as the classical serodiagnosis. This is particularly true for diagnosis of herpes virus infections in immunocompromised patients, where diminished or suppressed virus-specific antibody responses do not reflect possible reactivated herpes mediated aethiologies [18].
Real time PCR has represented a further step forward, since it allows for quantitative detection of target DNA in a single sample over a large range, remaining possible qualitative detection. Even if contradictory results have been found [19-21], quantification of DNA could represent an important issue to evaluate the severity and outcome of herpesvirus encephalitis, and it may be also used to monitor the success of antiviral therapy. This strategy has already been used for monitoring of patients at risk for CMV infections, when viral load kinetic patterns are used to identify patients who are more likely to have recurrence of CMV disease after the initiation of therapy, as well as to identify patients needing treatment [22-24]. Moreover, in these cases, use of a highly sensitive assay could be of crucial value.
In the last years several real-time PCR methods have been developed for detection of herpesviruses in different biological specimens [25-27]. Real-time PCR has been well recognized to offer several advantages over nested PCR other than allowing quantification of viral load: it reduces the risk of amplicon contamination, being a close-system, is a safer laboratory protocol by not using ethidium bromide, and it allows a notable reduction of time required for response.
In the present work, we compared a real time PCR panel for detection of herpesvirus DNA in CSF with methods employing nested PCR. Our results indicate an overall agreement between the two methods, as reported by other authors [9,27,28]. Differences between the two assays were observed for HSV-1 and EBV analysis, where one negative sample was found positive by real time PCR.
Since extraction panels and primers for HSV and CMV were uniform for the both types of assays, inhibitors present in the DNA preparations could not explain the different results obtained for these samples. Moreover, being nested PCR generally considered as the gold standard for diagnosis of herpes virus in CSF, we tried to use similar primers, chemistry and amplification conditions in order to limit differences for a better comparison of the performance of the two methods. Thus, discrepancies could be likely attributed to the different detection of amplification products, although real time PCR has been reported to be as sensitive as nested PCR [29]. This suggestion is also supported by the fact that the two patients with CSF negative for HSV-1 and EBV with nested PCR showed clinical syndromes compatible with viral encephalitis and clinically improved with antiviral treatment (data not shown). These data seem to suggest that real time PCR could be more sensitive than nested PCR. Since the limit of detection is generally calculated by using plasmidial DNA, it may be possible that, although molecular sensitivity is reported to be similar for nested PCR and real-time PCR [27,28], some differences may occur for biological specimens. The sample classified positive for HSV-1, which resulted negative when retested with both methods, was one of the oldest in our collection, dating 1996, and it might have degraded over the 7 years storage. From this point of view, development of standardized quality controls might be very helpful [30].
Conclusions
Data obtained in this study confirms the validity of real-time PCR method for detection of herpesvirus DNA in CSF specimens of HIV patients, being sensitive, rapid and quantitative. Since specific and rapid diagnosis is the main target in the case of CNS infections, real time PCR could be considered the method of choice, due to its high specificity, sensitivity and rapidity, once proper quality controls will be available.
Competing interest
The company Amplimedical SpA-Bioline Division-Italy will pay the article processing fees for the manuscript.
Authors' contributions
LD conceived of the study and participated in its design and coordination, AL carried out real time PCR, EDV participated in data analysis and drafted the manuscript, GG performed statistical analysis, RB carried out nested PCR, MRG participated in design and coordination of the study. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Figures and Tables
Table 1 Nucleotide base sequence of primers used in nested PCR
Virus Region Primer (5'-3')
HSV-1 GpD Outer ATCACGGTAGCCCGGCGCTGTGACA
CATACCGGAACGCACCACACAA
Inner CCATATCGACCACACCGACGA
GGTAGTTGGTCGTTCGCGCTGAA
CMV MIEA Outer AAGCGGCCTCTGATAACCAAGCC
AGCACCATCCTCCTCTTCCTCTGG
Inner AGTGTGGATGACCTACGGGCCATCG
GGTGACACCAGAGAATCAGAGGAGC
EBV BamH I – W Outer GAGACCGAAGTGAAGGCCCT
GGTGCCTTCTTAGGAGCTGT
Inner GCCAGAGGTAAGTGGACTTTAAT
GAGGGGACCCTGAGACGGGT
Table 2 Nucleotide base sequence of primersused for real time PCR
Virus Region Primer (5'-3')
HSV-1 GpD Forward CATACCGGAACGCACCACACAA
Reverse CCATATCGACCACACCGACGA
CMV MIEA Forward AAGCGGCCTCTGATAACCAAGCC
Reverse AGCACCATCCTCCTCTTCCTCTGG
EBV EBNA-1 Forward ATCAGGGCCAAGACATAGAGATG
Reverse CCTTTGCAGCCAATGCAACT
Table 3 Results from nested PCR and real-time PCR
NESTED PCR REAL TIME PCR
POS NEG POS NEG
CMV 85 150 85 150
HSV-1 45 150+1a = 151 45+1b = 46 149+1a = 150
EBV 145 150 145 +1b = 146 149
a: sample resulted positive in previous nested PCR
b: samples resulted negative in nested PCR
Table 4 Sensitivity and specificity of nested PCR and real-time PCR
Real-time PCR
Sensitivity (%) Specificity (%)
CMV 85/85 = 100% 150/150 = 100%
HSV-1 45/45 = 100% 150/151 = 99%
EBV 145/145 = 100% 149/150 = 99%
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| 15571633 | PMC535941 | CC BY | 2021-01-04 16:03:30 | no | BMC Infect Dis. 2004 Nov 30; 4:55 | utf-8 | BMC Infect Dis | 2,004 | 10.1186/1471-2334-4-55 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047610.1371/journal.pbio.0030004Research ArticleBotanyEvolutionGenetics/Genomics/Gene TherapyPlant SciencePlantsA Gradual Process of Recombination Restriction in the Evolutionary History of the Sex Chromosomes in Dioecious Plants Plant Sex ChromosomesNicolas Michael
1
Marais Gabriel
2
Hykelova Vladka
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Janousek Bohuslav
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Laporte Valérie
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Vyskot Boris
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Mouchiroud Dominique
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Negrutiu Ioan
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Charlesworth Deborah [email protected]
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Monéger Françoise
1
1Laboratoire de Reproduction et Développement des Plantes, ENS LyonLyonFrance2Institute of Evolutionary Biology, School of Biological ScienceUniversity of Edinburgh, King's Buildings, West Mains Road, EdinburghUnited Kingdom3Laboratory of Plant Developmental Genetics, Institute of BiophysicsAcademy of Sciences of the Czech Republic, BrnoCzech Republic4Laboratoire de Biométrie et Biologie Evolutive, Bâtiment Gregor MendelVilleurbanne CedexFranceEllegren Hans Academic EditorUniversity of UppsalaSweden1 2005 21 12 2004 21 12 2004 3 1 e425 5 2004 12 10 2004 Copyright: © 2004 Nicolas 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.
Evolution of Sex Chromosomes: The Case of the White Campion
To help understand the evolution of suppressed recombination between sex chromosomes, and its consequences for evolution of the sequences of Y-linked genes, we have studied four X-Y gene pairs, including one gene not previously characterized, in plants in a group of closely related dioecious species of Silene which have an X-Y sex-determining system (S. latifolia, S. dioica, and S. diclinis). We used the X-linked copies to build a genetic map of the X chromosomes, with a marker in the pseudoautosomal region (PAR) to orient the map. The map covers a large part of the X chromosomes—at least 50 centimorgans. Except for a recent rearrangement in S. dioica, the gene order is the same in the X chromosomes of all three species. Silent site divergence between the DNA sequences of the X and Y copies of the different genes increases with the genes' distances from the PAR, suggesting progressive restriction of recombination between the X and Y chromosomes. This was confirmed by phylogenetic analyses of the four genes, which also revealed that the least-diverged X-Y pair could have ceased recombining independently in the dioecious species after their split. Analysis of amino acid replacements vs. synonymous changes showed that, with one possible exception, the Y-linked copies appear to be functional in all three species, but there are nevertheless some signs of degenerative processes affecting the genes that have been Y-linked for the longest times. Although the X-Y system evolved quite recently in Silene (less than 10 million years ago) compared to mammals (about 320 million years ago), our results suggest that similar processes have been at work in the evolution of sex chromosomes in plants and mammals, and shed some light on the molecular mechanisms suppressing recombination between X and Y chromosomes.
Similar processes have been at work in the evolution of sex chromosomes in plants and mammals. A recently evolved plant X-Y system is helping to shed light on these processes
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Introduction
Newly evolved sex chromosome systems, such as those in plants [1] and fish [2] allow study of the evolutionary processes causing degeneration of Y chromosomes. The genetic theory of sex chromosome evolution [3] predicts that initially one part of a chromosome pair containing the sex-determining genes evolves reduced recombination. Two questions are then particularly interesting. First, how is recombination suppressed throughout most of the initially homologous X and Y chromosomes, as in mammalian and Drosophila sex chromosomes and some plants [1], but not others [4]? Second, why does recombination suppression lead to genetic degeneration? Processes leading to degeneration in large nonrecombining genome regions have been well studied theoretically [5], and empirical data on the first stages of degeneration are starting to be obtained from the plant genus Silene [6,7] and from the neo-sex chromosomes of Drosophila miranda [8]. Recent neo-sex chromosome systems in Drosophila are excellent for studying the rate and causes of degeneration, but cannot shed light on question (i).
Studies of the evolutionary divergence of gene pairs on mammalian X and Y chromosomes suggest that recombination between the X and nonrecombining parts of the Y was successively suppressed. In many X-Y systems, including that in mammals, there is a “pseudoautosomal” region (PAR) where the X and Y recombine, and it has been found that DNA sequence divergence between homologous X- and Y-linked genes increases with distance from this region. This pattern has been termed “evolutionary strata” [9,10]. Part of the reason for different sequence divergence is that mammalian sex chromosomes are ancient neo-sex chromosomes [11]. In addition, the “strata” suggest a series of Y inversions disrupting X-Y recombination [9]. Strata have also been found in the chicken Z chromosome, which, like the Y, is present only in one sex (females in birds) and does not recombine with its homolog [12]. To further understand the evolution of suppressed recombination between X and Y chromosomes, we describe results from the plant genus Silene. This genus is a model for the study of plant sex chromosome evolution, since the sex chromosomes evolved recently [7,13].
One group of closely related dioecious Silene species (i.e., species with separate sexes) includes S. latifolia, S. dioica, and S. diclinis, which have an X-Y sex-determination system with a male-determining Y [1,14], while many Silene species are hermaphroditic or gynodioecious (i.e., some plants bear hermaphrodite flowers and others female flowers). Dioecy and sex chromosomes thus probably evolved within this genus [13]. All diploid Silene species have n = 12 chromosomes [15], so there is no evidence for neo-sex chromosome formation, although an autosomal region of unknown size has been duplicated on the Y [16].
Several sex-linked genes from S. latifolia have recently been identified and sequenced (Table 1), allowing progress in understanding the evolution of these sex chromosomes. Four genes have functional X- and Y-linked homologues. Very different X-Y divergence of two gene pairs suggested that different Y chromosome regions probably ceased recombining at different times in these species' evolutionary history [17]; testing this hypothesis requires knowing the genes' locations on the sex chromosomes. We here describe a new gene pair in S. latifolia, SlX3 and SlY3 (together termed locus 3; Table 1), and present the first genetic map for the X chromosomes in three dioecious species. Divergence between the X and Y chromosomal copies of the different genes indeed correlates with increased distance from the PAR, but the time scale is very different from that in mammals. Three genes (locus 3, the SlX4-SlY4 pair [termed locus 4], and DD44) ceased recombining long before the three dioecious species split, whereas the X and Y copies of SlX1-SlY1 (termed locus 1) continued to recombine until recently. We discuss the implications of these results for the mechanism of recombination arrest between the sex chromosomes.
Table 1 Description of the Four X-Y Gene Pairs and the PAR Marker Used in the Analyses
a For the four genes, the alignments include coding sequences of both X and Y copies in S. latifolia, S. dioica, and S. diclinis, and the orthologous sequence from a close outgroup (S. vulgaris or S. noctiflora). The values correspond to the number of sites with no gaps or ambiguous bases). Values in parentheses indicate the numbers of diverged sites
b
DD44 is also single-copy in S. latifolia [20], but at least two copies are found in other Silene species, including S. dioica (V. Laporte, unpublished data) and other species (J. Ironside, Univ. of Birmingham, UK, unpublished data).In our S. dioica material, there are three tightly linked X-linked copies (B. Janousek, unpublished data). Thus this duplication does not affect our mapping conclusions
Results
Characterization of Gene 3
Locus 3 was identified from S. latifolia cDNA. The SlX3 open reading frame of 575 amino acids encodes a protein sequence similar to calcium-dependent protein kinases (CDPKs) from tobacco, rice, and Arabidopsis thaliana (the best BLAST hits had 75%–80% amino acid identity, based on more than three-fourths of the length). CDPKs are associated with various kinds of stress responses [18]. Thus, locus 3 is probably a sex-linked housekeeping gene, like the previously characterized X-Y-linked genes in S. latifolia [17,19].
Phylogenetic Analysis of the Four Sex-Linked Genes
Figure 1 shows the estimated phylogenetic relationships based on single X and Y copies of the four loci from each species in which sex linkage has been confirmed. Except for locus 1 (discussed below), each gene falls into distinct X and Y clades, showing that these genes ceased recombining well before the split of the present dioecious species, consistent with large X-Y divergence in both S. latifolia and S. dioica [17,20]. Not surprisingly for such closely related species [13], the phylogenies of the three dioecious species are inconsistent for these genes. For example, one Y-linked gene supports each of the possible clades latifolia-dioica, latifolia-diclinis, and dioica-diclinis (Figure 1).
Figure 1 Phylogenetic Trees for DD44 and Loci 1, 3, and 4
All trees were estimated from coding sequence alignments (using all sites except gaps) under the BIONJ method with Kimura-two-parameters corrected distances, using Phylo_Win software [43]. Other methods (maximum parsimony and ML) give very similar results. Branch lengths correspond to total sequence divergence under the model assumed (see scale bars). Values indicated at the nodes are bootstrap values exceeding 50% (based on 500 replicates). S. vulgaris was used as an outgroup (except for locus 1, for which a closer outgroup, S. noctiflora, was used). Dic = S. diclinis, Dio = S. dioica, Lat = S. latifolia. The numbers of sites analyzed are in Table 1.
Gene 1 X-Y divergence is much less than that of the other genes studied [17]. We therefore tested whether divergence between the X and Y copies started before or after the speciation event. The grouping of this gene by species in Figure 1 suggests independent X1-Y1 divergence in the three dioecious lineages. For such closely related sequences, however, analysis using single X and Y sequences from each species confounds fixed differences between species with within-species polymorphisms, and can be misleading, given that S. latifolia is a highly variable species [21]. Ancestral polymorphisms persisting through the speciation event also obscure close phylogenetic relationships, particularly inferences using X-linked genes, which have large within-species polymorphism [7,22]. Finally, the well-documented introgression between S. latifolia and S. dioica [23] may contribute to the phylogenetic discrepancies.
We therefore analyzed the X1-Y1 gene pair separately, using multiple sequences from two species. If X1-Y1 divergence started sufficiently long before the species split, some sites should share the same fixed differences between X and Y sequences in both S. latifolia and S. dioica. The number of such sites depends on the amount of time after recombination ceased; for the genes other than gene 1, this number is large (see above), but for gene 1 no such sites were found. If, on the other hand, X1 and Y1 diverged after the species split, some sites should differ between the species, but not between X and Y of the same species. This is found for mammalian and bird sex chromosomes, and phylogenetic analysis suggests that some X and Y (or, in birds, Z and W) genes ceased recombining independently in different taxa [24,25]. However, because the dioecious Silene species are very closely related [13], there are few fixed differences, and, using global gap removal to be conservative, none between the X1 sequences. However, some Y variants are exclusive to each species; we found five nucleotide variants fixed only in the S. latifolia Y (plus nine indel variants), and ten fixed only in S. dioica Y (plus one indel). Since only 11 S. dioica Y sequences were analyzed, the number of fixed Y variants is probably overestimated, however (some may actually be polymorphic in this species). Furthermore, in a tree estimated excluding these sites with fixed differences in the Y-linked sequences (as is appropriate for such closely related species), the Y sequences are nested within those of the X of each species (Figure 2), implying suppression of X-Y recombination within these species. This suggests the possibility of independent cessation of recombination after speciation. However, we cannot exclude the possibility that recombination stopped shortly before the dioecious species split. Under this alternative, if the Y1 genes retained some polymorphism, variants in the Y1 genes would become fixed differences when Y chromosome diversity was lost within each species; according to this hypothesis, however, each species must, by chance, have retained Y1 variants closest to its own X sequences.
Figure 2 Phylogenetic Tree for Gene 1, including Within-Species Diversity
The tree was estimated using PHYML software [52] from a DNA alignment including coding sequences and introns of 12 X and 11 Y S. dioica alleles, and 26 X and 22 Y from S. latifolia [22]. There were 973 sites, excluding gap regions, among which 154 variable sites were used. The estimation used the BIONJ algorithm with global gap removal. The percentage of invariant sites, the transition-transversion ratio, and the α parameter of a γ distribution of substitution rates, were estimated by the program, and we assumed four categories of evolutionary rates, to take into account the different evolutionary dynamics of coding and intron sites. The HKY substitution model was used. Bootstrap values exceeding 50% (based on 100 replicates) are indicated at the nodes, but some bootstrap values exceeding 50% for terminal nodes are omitted because of lack of space.
Correlation between X-Y Divergence and Position on the X Chromosome
The gene order is the same in S. latifolia and in the S. diclinis × S. latifolia hybrid (Figure 3A). Locus 1 is closest to the PAR. If the S. diclinis and S. latifolia maps differed by an inversion or other rearrangement, the map using hybrid parents should contain a non-recombining region; this was not observed. Thus, the gene order determined in the S. diclinis x S. latifolia hybrid must also apply in S. diclinis. In S. dioica, however, the map order of locus 1 and DD44 is reversed relative to the other species (Figure 3A).
Figure 3 X Genetic Maps in the Three Dioecious Species versus Plot of Synonymous Divergence
(A) Gene orders and the map positions of the genes. The PAR is not drawn to scale, as there is only one marker, and the map shows only the portion of the X chromosome containing our marker genes. Vertical lines connect the homologous genes in the three species, and the chromosomal rearrangement in S. dioica, are shown by crossed lines for locus 1 and DD44.
(B) Plot of synonymous divergence between X and Y pairs (dS
X-Y), estimated using PAML, against the map position using the gene order in S. latifolia and S. diclinis (see text). Synonymous divergences are statistically significantly distinguished for the following three groups of genes: locus 1, the DD44 gene, and loci 3 and 4 (see text). The figure also indicates minimum and maximum X-Y divergence time estimates for the genes, assuming a molecular clock and 1.8 × 10–8 synonymous substitutions per synonymous site per year (the mean of the values 1.4 × 10–8 and 2.2 × 10–8 discussed in the text).
Synonymous divergence (dS) between the X and Y sequences of S. latifolia and S. diclinis (dS
X-Y) correlates with the gene's distance from the PAR in the X chromosome genetic map (Figure 3B). X-Y synonymous divergence in S. latifolia does not differ significantly between genes 3 and 4, but these genes' synonymous divergence values differ significantly from that for genes 1 or DD44 (with p < 0.01). X-Y synonymous divergence also differs significantly between genes 1 and DD44 (p = 0.01). These results suggest progressive suppression of the recombination between X- and Y-linked alleles of different genes. In S. dioica, the same correlation exists, using the S. latifolia or S. diclinis gene order; thus, the rearrangement probably arose recently in S. dioica, consistent with its absence in the other dioecious species. A recent rearrangement, such as an inversion, after the DD44-X and -Y sequences had diverged for some time, would not affect this gene's X-Y divergence relative to that of gene 1. In mouse species, where rearrangements have occurred, evolutionary strata corresponding to those on other mammalian X chromosomes are still plainly discernible [26].
Comparing Sequence Divergence of X and Y Copies
Analysis of the coding sequences shows that all four Y-linked genes appear to encode functional sequences; in each case, the nonsynonymous divergence (dN) was less than dS for divergence between X and Y sequences (dN/dS values in Table 2); although dN is high for the DD44 gene pair, it is considerably below dS. These results are consistent with cDNA representation of all sequences except the Y-linked copy of gene 3; despite repeated attempts, this copy never amplified from leaf cDNA, whereas the X chromosome copy amplified consistently (see Materials and Methods).
Table 2 Comparison of Evolutionary Rates in the X and Y Clades
LR tests between models with X = Y and X ≠ Y were performed using the ML-based software HyPhy. RYX is the X/Y ratio of dS or dN values. For the dN/dS analyses, the values in the table are the dN/dS values for X and Y lineages
Nonsignificant differences are indicated as “X = Y.”
a Significance of the LR test is p < 5 × 10−2
b Significance of the LR test is p < 5 × 10−3
The Y copies of all genes have higher dS, dN, and dN/dS values than the X-linked copies, except for DD44 (Table 2). However, the differences are significant only for dN. The differences in the numbers of synonymous differences are also nonsignificant, taking into account diversity within species. Synonymous site evolution is significantly faster in DD44-X than in DD44-Y, in contrast to the other genes, where the Y tends to evolve faster than X copies (although the differences are nonsignificant; Table 2). Exon 1 of DD44 is particularly divergent [20], but our results for this gene are similar if we exclude this exon (unpublished data). The results for gene 1 presented in Table 2 cannot be interpreted reliably because of polymorphisms within the species (see above), which would cause overestimation of numbers of substitutions. Overall, therefore, dN is clearly higher in the Y copies of genes 3 and 4, but its mutation rate is not higher, since X-Y differences in dS are nonsignificant; combining the probabilities from the likelihood ratio (LR) tests for these two genes, the dN/dS difference between Y and X is highly significant (χ2 = 11.7, with 4 degrees of freedom). Our observation of similar dS values contrasts with previous analyses [27], probably because we used only synonymous sites, rather than synonymous plus noncoding sites. The S. diclinis Y3 gene also seems to evolve faster than the other Y3 genes (see Figure 1); for this gene, the difference is seen for both synonymous sites (6-fold increase) and nonsynonymous ones (3.6-fold increase), but it is significant only for synonymous sites.
Discussion
Progressive Differentiation of the X and Y Chromosomes
The correlation of dS
X-Y of these dioecious plants with distances from the PAR in the X chromosome genetic map suggests that suppression of recombination between X and Y genes progressed, starting from an “ancient” sex chromosomal region (presumably containing the primary sex determining loci) and moving toward the current PAR. This pattern resembles the “evolutionary strata” for mammalian X-Y gene pairs based on K
s values, a measure of divergence per site similar to dS [9,10]. However, the time scale is much shorter for the plant sex chromosomes. The largest dS
X-Y values among our four gene pairs is 26% for locus 3 in S. diclinis. This overlaps the values for the mammalian stratum 4 and 3 genes (mean K
s values 8% and 30%, respectively); these strata are inferred to have ceased recombining between the X and Y 30–50 million years ago (MYA) for stratum 4, and 80–130 MYA for stratum 3, whereas strata 1 and 2 diverged 130–320 MYA [9,11].
The S. latifolia, S. dioica, and S. diclinis X-Y sequence divergence data show that X-Y differentiation was already advanced in the common ancestor of these species, except for locus 1. The maximum synonymous X-Y divergence observed for our genes is approximately 25%, including SlAp3, which probably transposed from an autosome onto the Y soon after the sex chromosomes evolved [16]; all these genes appear to be functional. This divergence is also similar to that for MROS3-X/Y, whose Y-linked copy is degenerated [6]. Unless genes with higher divergence are discovered in the male-determining region of Y chromosomes of dioecious Silene species, the Silene sex chromosomes must have evolved much more recently than mammalian sex chromosomes.
There are few reliable absolute molecular clock calibrations in plants [28], and none for Silene. For the nuclear genes Chs and Adh in the family Brassicaceae, estimated rates are, respectively, 1.4 × 10–8 to 2.2 × 10–8 substitutions per synonymous site per year [29], and a similar value was estimated for Ipomoea [30]. Using synonymous site divergence values suggests an age estimate of 5–10 MYA for the sex chromosomes of the S. latifolia group of species. However, substitution rates for some plant Adh genes are almost ten times slower, particularly for plants with long generation times [31]. Thus, a greater age cannot be excluded. It is nevertheless clear that the X and Y copies of genes3, 4, and DD44 differentiated before the S. dioica–S. latifolia–S. diclinis speciation, whereas gene 1 may have ceased recombining after this, perhaps independently in S. latifolia and S. dioica; no analysis can be done in S. diclinis without diversity data for this species, but suppression of X-Y recombination within this species after its split from the other dioecious species is also possible (Figure 1). If this event occurred shortly before the dioecious species split, our results show that it must have happened in such a way that the Y-linked copy of gene 1 retained some diversity, in other words, by some mechanism other than an inversion (see below). Suppression of X-Y recombination (diminution of the PAR) has occurred quickly, and probably independently, in different mammalian and bird lineages [24,25,32].
The mechanism suppressing X-Y recombination is unknown. Recombination could be reduced either by inversions (or other major recombination rate changes), and/or by modifiers reducing local crossover rates. The “strata” of different divergence in mammalian sex chromosomes may have resulted from a series of Y inversions disrupting X-Y recombination [9]. Inversions exist between human X and Y chromosomes [10], but have not yet been explicitly related to the strata, so they may not be the sole cause of the divergence differences. Moreover, new pairs of X-Y linked genes recently analyzed do not suggest clear-cut boundaries between strata; divergence values for strata 3 and 4 genes are not discontinuous [10]. Finally, the amelogenin gene, at the strata 3–4 boundary, is not disrupted by an inversion [25]. Thus, gradual modification of recombination rates may have played a part in reducing recombination in some regions of the X-Y pair, in both Silene and mammals. Testing this for the dioecious Silene species requires a Y-chromosome map. The present map, based on deletion mutants in Silene [20,33], requires further markers and deletions for detailed comparison with the genetic map of the X chromosome. S. latifolia Y deletion mutants with altered meiotic X-Y pairing (unpublished data) suggest that the S. latifolia Y may carry genes suppressing recombination, and should help test whether mechanisms other than inversions contributed to reduction of the PAR.
The mechanism of recombination reduction between X and Y chromosomes is important for understanding the diversity in loci that recently ceased recombining, such as gene 1 in Silene. Recombination suppression may be selectively favored to preserve advantageous Y-linked combinations of alleles at different loci, such as genes that are advantageous in males but not in females [34], although it seems unlikely that all three dioecious species studied here could recently have acquired advantageous Y-linked genes. Involvement of selectively favored inversions causing the formerly pseudoautosomal gene 1 to become Y-linked might be detectable from sequence data, since a selective sweep would be expected. This would contribute to low diversity for all the Y-linked genes, consistent with the long branches in Y lineages (Figures 1 and 2). However, although Y-linked diversity is low, there is no evidence of such events in the frequency spectra of the genes [7,22].
Degeneration of the Y Chromosome
Our analyses suggest that both reduction of recombination and Y degeneration may be in progress for Silene sex chromosomes. Degeneration is likely, since genotypes with a Y but no X chromosome are inviable [1,35], but so far, only one degenerated plant Y-linked gene has been found, MROS3-Y in S. latifolia [6]. The extent of genetic degeneration and gene loss in the Silene Y is uncertain, because most currently known sex-linked genes in these plants were ascertained from a cDNA-based search for Y-linked genes. Bacterial artificial chromosome clone sequencing may provide unbiased comparisons of homologous X- and Y-linked regions, and this has been started in papaya [4]. Some degeneration of Y-linked genes in Silene can also be inferred when dN values in the Y are elevated compared with X lineages. This is seen for the two “old” Silene Y-linked genes, locus 3 and locus 4 (Table 2). Differences in dS are systematically lower than in dN (the ratio of dS values for X and Y lineages is close to 1, but dN is roughly 3-fold larger overall for Y lineages). Thus the higher dN in the Y-linked alleles is not due to a higher mutation rate (higher dS) in the Y than the X. Moreover, the Y-linked copy of locus 3 fails to amplify in RT-PCR experiments, and may be degenerated.
These observations, plus those for gene 1 (see Results), suggest that Y copies of genes loci 1, 3, and 4 evolve faster than X copies, due either to a higher rate of fixation of advantageous mutations in the Y copies or to accumulation of slightly deleterious amino acid variants in the Y copies (Y degeneration). To discriminate between these hypotheses, McDonald-Kreitman tests can be done to compare fixed differences (divergence) and polymorphisms and test for an excess of selectively advantageous nonsynonymous substitutions [36]. At present, this is possible only for genes 1 and 4; there were no nonsynonymous polymorphisms for DD44, and no diversity data have yet been obtained for gene 3. The result of this test was nonsignificant; there is thus no evidence that Y1 and Y4 evolution is driven by selection. There is, however, very low polymorphism in the Y copies, so the test has low power [27].
Genetic degeneration is supported by low levels of polymorphism of Y- compared with X-linked genes, taking into account the lower Y effective population size [7,22]. This difference is predicted in a degenerating Y chromosome, because various hitchhiking processes leading to degeneration, including selective sweeps, background selection, and weak Hill-Robertson effects [5] reduce diversity, even at loci that are not themselves degenerating.
Why is degeneration so slight for our Silene Y-linked genes? Our analyses suggest that degeneration of the genes studied here is partial, at most, consistent with a recent origin of the Silene sex chromosomes. However, there has probably been enough time for degeneration, since this occurred rapidly for genes on the neo-Y chromosomes of D. miranda [8], which are much younger than the Silene Y. Silene sex chromosomes are more advanced in sex chromosome evolution than in some other plants. The papaya sex-determining region is just a small nonrecombining part of one chromosome, yet there is evidence for considerable differentiation, including addition of repeat sequences and some evidence for gene loss [4]. More likely, the Y-linked genes we have studied (which are all housekeeping genes) are under selective constraints. The lower effective population size of Y-linked genes, and thus the expected reduced efficacy of selection ([5]) may thus be too slight to allow the Y copies of these genes to lose function, but merely allows higher amino acid substitution. Our findings parallel those for most loci on the D. miranda neo-Y chromosome [37], the bird W chromosome [38], and in other situations in which effective population size in reduced, such as protein-coding genes of the endosymbiont Buchnera [39]. In all these cases, genes evolving without recombination retain homology with their ancestral copies, but undergo faster amino acid replacement (including several frameshift and deletion mutations in the D. miranda neo-Y [37]), suggesting that the common factor is weakened ability of natural selection to preserve adaptation.
Materials and Methods
Plants used and nucleic acid extraction.
S. latifolia plants were from Edinburgh (D. Charlesworth personal collection) and from Fontainebleau forest (France). S. dioica plants were collected in Corrèze (France). S. dioica plants from the Sherringham population (Sherringham, England), used for isolation of the ScOpa09 marker, were kindly provided by D.L. Mulcahy (Department of Biology, University of Massachussetts). S. noctiflora and S. vulgaris were obtained from the seed collection of the Lyon Botanical Garden (Lyon, France). Seeds of S. diclinis were obtained from the seed collection of the Institute of Biophysics in Brno (Czech Republic). Interspecific hybrid Silene diclinis × latifolia plants were generated by pollination of a S. diclinis female with pollen of an MAV line male (S. latifolia) kindly provided by S. Matsunaga (Department of Biotechnology, Osaka University). The S. latifolia U9 line, which was used for pollination of the interspecific hybrid, was kindly provided by S. Grant (Department of Biology, University of North Carolina).
Genomic DNA was extracted from leaves as described [19]. For RT-PCR from total leaf RNA, first-strand cDNA was reverse transcribed using RevertAid M-MuLV RT (Fermentas, Vinius, Lithuania) and the oligo-dT primer T11VN (5′-
TTTTTTTTTTTVN-3′).
Isolation of SlX3/SlY3
Locus 3 was identified in S. latifolia by the approach that yielded loci 1 (SlX1/SlY1 [19]) and 4 (SlX4/SlY4 [17]). From an initial partial cDNA sequence of a clone that hybridized to a probe containing Y-linked sequences, both 3′ and 5′ RACE-PCR were performed [19], and the final coding sequence was obtained by sequencing the RT-PCR product obtained using primers 11S10 (5′-
ATCACCATCATCATTTCCACC-3′) and 11AS11 (5′-
CAGTGAAATCTTTCATTTACCACG-3′). Segregation analysis (see below) showed that this sequence corresponds to SlX3. The SlY3 sequence was obtained from genomic DNA by PCR genomic walking [40], using the specific primers 11AS15 (5′-
TCAGTGTCTCCTTGAGTTTCTTGCAC-3′) and 11AS15C (5′-
TGCACAAGATGGACTGGCTACAATACG-3′) for the first and second PCR, respectively, and Ex Taq polymerase (Takara Bio, Otsu, Shiga, Japan) for both PCR reactions. Similarly to gene 4 [17], Southern blot analysis showed that gene 3 is present as a single copy in the S. latifolia genome.
Amplification and sequencing of orthologous sequences.
The orthologs of each of the four gene pairs in Table 1 were amplified in S. dioica, S. diclinis, and S. noctiflora or S. vulgaris using primers designed from S. latifolia sequences (Table 3, which also provides GenBank accession numbers). All sequences were amplified from cDNA, except for Y3, which was amplified from genomic DNA. The PCR conditions, using Taq polymerase (Amersham Pharmacia, Piscataway, New Jersey, United States), were as follows. One incubation at 94 °C for 5 min; 35 cycles of: denaturation at 94 °C for 30 s, annealing at a temperature that depended on the primers for 30 s, and elongation at 72 °C for a time depending on the length of the amplicon (Table 3); and a final extension at 72 °C for 5 min.
Table 3 Primers and Annealing Temperatures Used for RT-PCR Amplification of the Genes Listed in Table 1
All genes, except gene 1, are single-copy (see Table 1). In S. latifolia, three or four copies of gene 1 are detected in Southern blots [19], and the A. thaliana genome has five copies, AtMSI4 (GenBank accession number AF028711) being the probable ortholog of SlY/X1. In S. latifolia, some of the copies have been shown not to be sex-linked (F. Monèger, personal communication). Southern blots were not done in S. dioica or S. diclinis, but RT-PCR reactions in all the dioecious species always amplified a single sequence in the females plus another very similar sequence in males, clearly representing the expected X and Y copies. Thus, for the analyses presented later, this gene can be treated as a single-copy gene, as was also the case in previous mapping work [20].
For locus 3, the 3′ two-thirds of the coding sequences of S. dioica, S. diclinis, and S. vulgaris were amplified, either by RT-PCR (X copies) or from genomic DNA (Y copies). PCR products were cloned into pGEM-T Easy vector (Promega, Madison, Wisconsin, United States), and multiple clones were sequenced for each gene. Sequencing reactions were carried out with ABI Big Dye Terminator V1.1 DNA sequencing kit, on an Applied Biosystems 3100 sequencer (Applied Biosystems, Foster City, California, United States).
Sex linkage and genetic mapping.
Sex linkage of three gene pairs studied here was previously demonstrated in either S. dioica or S. latifolia. We have now confirmed sex linkage of all four loci by segregation analysis in all three dioecious species (Figure 4), and, for genes 1, 4, and DD44, by population studies using allele-specific PCR reactions to show that the putative Y-linked alleles are consistently present only in males, while the X-linked ones amplify in both sexes [17,22] and unpublished data).
Figure 4 Segregation Analysis of Locus 3 in S. dioica
To test for sex linkage, the female and male parents of the family and six progeny of each sex were genotyped. Genomic DNA preparations from these plants were used in PCR amplifications. The PCR products were separated by electrophoresis and visualized using ethidium bromide.
(A) With primers specific for the X3 allele, the restriction enzyme RsaI reveals an allelic polymorphism. The maternal plant is heterozygous and has both cut and uncut alleles while the male parent has only the uncut allele. Female progeny always have the uncut allele (like the father), and male progeny have one of the maternal alleles, but never both, corresponding to the expected segregation of the X chromosome.
(B) Primers specific for the Y gametolog. A product amplifes only with male DNA, corresponding to the expected segregation of the Y chromosome.
Only X-linked genes can be mapped genetically, because the Y chromosome recombines with the X only in the PAR. For each locus, gene-specific primers were used to amplify X alleles from genomic DNA of potential seed parents. The PCR product was directly sequenced and the chromatograms inspected for polymorphisms scorable by restriction enzyme digestion (Table 4). Progeny of heterozygous mapping family females were sexed and genotyped for the maternal alleles. In S. diclinis, no suitable polymorphisms were found, so the loci were ordered in a S. diclinis × S. latifolia hybrid plant pollinated by a S. latifolia male.
Table 4 Primers Used for Mapping
Included here are primers used for mapping the four X-Y gene pairs and the PAR marker; nature of the polymorphisms used as markers (i.e., DFLP indicates length polymorphism of amplified fragments; restriction enzymes used for CAPS analysis are included in parentheses); and accession numbers of either the partial cDNA sequences obtained by RT-PCR (listed as X sequence, Y sequence) or the genomic sequence of the pseudoautosomal sequence. Details of PCR primer annealing temperatures for the different species are available from the authors on request. ND, no data
a The annealing temperature for ScOPA09 was 55 °C, and elongation time was 1 min 30 s
To orient the X genetic map, we used a pseudoautosomal marker. For this, we cloned and sequenced a RAPD fragment incompletely linked to the X chromosome of the pollen donor of the S. dioica family in which this marker was originally developed [41]. The sequence encodes a protein with similarity to an Oryza sativa putative non-long terminal repeat reverse transcriptase (E value = 2.5 × 10–10; accession number Q9FW98 in the UniProt/TrEMBL database, but contains stop codons. Primers (ScOPA09F1: 5′-
GCAATTCACCATCCTCTGCT-3′ and ScOPA09R1: (5′-
ATGGTCTTTGGGCCCTTATC-3′) were designed from this sequence. In plants grown from seeds of this family, presence or absence of the expected amplified fragment accords exactly with results using the original pseudoautosomal RAPD marker primer OPA09. With our primers, S. dioica plants from the Corrèze population were genotyped by digesting PCR products with the restriction enzyme TaqI; the recombination frequency between the marker locus and sex was approximately 2.5%, confirming the pseudoautosomal location. In S. latifolia, genotyping was done using the same primers and an AluI site polymorphism. Genotype data were analyzed by both three-point and multipoint mapping, using JoinMap version 1.4 [42]. Thus the gene orders are well established; Table S1 gives estimated genetic distances between markers and their standard errors.
Phylogenetic analysis.
The primer sequences were removed before sequence analyses. For each gene, the nucleotide sequences were aligned using the corresponding amino acid sequences as a guide, using ClustalW with the Seaview interface (http://pbil.univ-lyon1.fr/) [43]. Alignment lengths are given in Table 1.
Phylogenetic trees were estimated including all sites except those with gaps by neighbor joining (NJ), maximum parsimony, and maximum likelihood (ML), using Phylo_Win (http://pbil.univ-lyon1.fr/) [43]. For NJ trees, we used Kimura two-parameter corrected distances; results with other distances corrected for multiple hits were similar, as the sequences are not highly diverged and have similar GC content (unpublished data). Branches were tested by bootstrapping (500 replicates). Trees were edited with NJplot (http://pbil.univ-lyon1.fr/) [44] and TreeView (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html) [45].
Divergence analysis.
Both dS and dN site divergences were estimated using PAML 3.13 (http://abacus.gene.ucl.ac.uk/software/paml.html) [46] and JaDis (http://pbil.univ-lyon1.fr/) [47]. Estimates of dS and dN are similar under various substitution models (namely, Goldman and Yang 1994 [46], Yang and Neilson 2000 [48], and Nei and Gojobori 1986 [49], implemented in PAML; and Li 1993 [50] using JaDis). We report values from the ML approach based on the Goldman and Yang 1994 codon-based model [46].
Values for dS or dN of X and Y sequences were compared using HyPhy 0.99 (Kosakovsky Pond, personal communication; http://www.hyphy.org, using the alignment and NJ tree for each gene, including the outgroup species (Figure 4) to polarize the synonymous and nonsynonymous substitutions between X and Y genes into X-specific and Y-specific lineages, using ML. To build the likelihood function, we used the MG94xHKY85 codon-based substitution model with different transversion and transition rates. We compared dS values under two models for each gene. Model 1 (“relative synonymous rates”) expresses dS values for Y lineages as multiples of values for X lineages: Rsyn = dS
Y/dS
X. In model 2 (“equal synonymous rates”), Rsyn was constrained to be equal to 1 (dS
Y = dS
X). We compared the ML values by a LR test with model 2 as the null hypothesis. We used the same approach to compare dN values (with dN/dS replacing Rsyn). To compare dN/dS using LR tests, we again defined two models. Model 1 assumed two global variables (dN/dS)X and (dN/dS)Y so that the nonsynonymous rates of branches of the X lineage were expressed in terms of the synonymous rate (dN/dS)X, and similarly using (dN/dS)Y for Y branches (“shared dN/dS”), while model 2 (“shared and equal dN/dS”) assumed (dN/dS)X = (dN/dS)Y.
To test whether S. diclinis Y3 evolves faster than other Y3 sequences, we assumed a common Rsyn for S. dioica and S. latifolia, as in model 1 above, but added a further parameter, the dS
Y/dS
X ratio for S. diclinis (model 1*). We compared models 1 and 1* using a LR test as above; we tested dN and dN/dS differences similarly.
McDonald-Kreitman tests were done using DNAsp software, version 3.95 [51]. The divergence and polymorphism data used are from previous work and were available only for genes 1 and 4 [17]; there were no nonsynonymous polymorphisms for DD44, and no diversity data are yet available for gene 3.
To test for differences in divergence between the X and Y sequences of different genes, we compared numbers of fixed X-Y differences in S. latifolia by contingency tests, using DNAsp. To infer fixed differences rigorously, we used diversity data within S. latifolia for genes 1, 4, and DD44. For gene 3 no diversity data are yet available; however, because this gene pair has high X-Y divergence, raw divergence values should suffice, so for this gene we estimated numbers of differences from single X and Y sequences.
Analysis of gene 1.
To test whether X and Y sequences of gene 1 continued recombining and started to diverge after the dioecious species split, a C program was written to find fixed differences in a set of multiple S. latifolia and S. dioica Y and X sequences, plus one sequence from each of two outgroup species, S. vulgaris and S. conica; this enables us to identify whether the changes were in the X or Y lineages, using parsimony. With global gap-removal, the program unambiguously distinguishes fixed differences, including insertions and deletions, from polymorphisms within species. The outgroup sequences are shorter than the other sequences, so some fixed differences in the S. dioica Y could not be analyzed.
As this dataset includes the first approximately 2,000 sites of gene 1, including coding and intron sequences [22], a more sophisticated model for sequence evolution is required for phylogenetic analysis than for the coding sequences analyzed above. We estimated the percentage of invariant sites, and the transition to transversion ratio, and fitted a GAMMA distribution, estimating its ALPHA parameter with four categories of sites evolving at different rates, using the HKY (Hasegawa, Kishino, Yano [52]) model as the global substitution model. The tree was estimated using NJ (BIONJ) with global gap removal, using a fast ML-based program, PHYML (http://www.lirmm.fr/guindon/phyml.html) [53], excluding fixed differences from the multiple alignment (to avoid conflicting phylogenetic signals between fixed and polymorphic differences). The statistical support for the tree was estimated by bootstrapping (100 replicates), using SEQBOOT, followed by CONSENSE to make a consensus tree with the resulting 100 PHYML trees.
Supporting Information
Table S1 Recombination Fractions (Rf) between the Loci, and Standard Errors of Rf Values
(36 KB DOC).
Click here for additional data file.
Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/) accession number for AtMSI4 is AF028711; the UniProt/TrEMBL (http://www.ebi.ac.uk/trembl/) accession number the ScOpa09 marker is Q9FW98.
We thank DL Mulcahy for kindly providing seeds of S. dioica used for ScOPA09 marker characterization. We also thank the Jardin Botanique de la ville de Lyon, P Vergne, S Matsunaga, and S Grant for providing Silene seeds. We thank C Trehin, P Chambrier, and S Garcia for help in sequencing locus 3; S Guindon for help with PHYML; SL Kosakovsky Pond for help with HyPhy; and T Johnson and S Glémin for discussions on gene 1. This project was funded by Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Ecole Normale Superieure, Université Lyon I, Institut Federatif de Rechereche 128. BV, VH, and BJ were funded by the Grant Agency of the Czech Republic (grant numbers 204/02/0417 and 522/02/1485) and the Institutional Research Plan (AV0Z5004920). VH was also funded by the Ministère des Affaires Etrangères. BJ and GM were funded by European Union (Marie Curie postdoctoral fellowships HPMF-CT-2002–02101 and HPMF-CT-2002–02010, respectively), and VL was funded by a postdoctoral fellowship from the Biotechnology and Biological Sciences Research Council of the United Kingdom.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. GM, BJ, VL, BV, DC, and FM conceived and designed the experiments. MN, VH, BJ, VL, and FM performed the experiments. GM, VH, BJ, and DC analyzed the data. GM, DC, VL, DM, and FM contributed reagents/materials/analysis tools. IN discussed the paper, and GM, DC, and FM wrote the paper.
Citation: Nicolas M, Marais G, Hykelova V, Janousek B, Laporte V, et al. (2004) A gradual process of recombination restriction in the evolutionary history of the sex chromosomes in dioecious plants. PLoS Biol 3(1): e4.
Abbreviations
CDPKcalcium-dependent protein kinase
cMcentimorgans
dNnonsynonymous divergence per site
dSsynonymous divergence per site
dSX
dS for the X lineage
dSX-Y
dS between the X and Y sequences
dSY
dS for the Y lineage
LRlikelihood ratio
MLmaximum likelihood
MYAmillion years ago
NJneighbor joining
PARpseudoautosomal region
RAPDrapid amplification of polymorphic DNA
Rsyn
dS
Y/dS
X
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| 15630476 | PMC536007 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 21; 3(1):e4 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030004 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030028SynopsisBotanyEvolutionGenetics/Genomics/Gene TherapyPlant SciencePlantsEvolution of Sex Chromosomes: The Case of the White Campion Synopsis1 2005 21 12 2004 21 12 2004 3 1 e28Copyright: © 2005 Public Library of Science.2005This 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 Gradual Process of Recombination Restriction in the Evolutionary History of the Sex Chromosomes in Dioecious Plants
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There are many different sex-determining systems in plants and animals with separate sexes (dioecious species). In some species, environmental factors activate sex-determining genes that trigger expression of genes leading to male or female development. Other species have evolved specialized sex chromosomes. In the well-known X-Y system of mammals, individuals inheriting a Y chromosome become males, and XX individuals become females.
Sex chromosomes have arisen independently in many taxonomic groups. It is an interesting question whether the same mechanisms were involved each time. Similarities in sex chromosome evolution have been reported between birds and mammals (although in birds, females are the heterozygous sex). In a new study, Michael Nicolas and colleagues uncover striking parallels in the details of sex chromosome evolution between mammals and a far more distant group: plants.
Sex chromosomes are an oddity in flowering plants. They are limited to dioecious species, a subset of plants that carry male and female organs (stamens and carpels, respectively) on separate individuals (most flowering plants are hermaphrodites). The genus Silene, which includes the White Campion, includes both dioecious and hermaphrodite species. The authors focus on three dioecious species, Silene dioica, S. latifolia, and S. diclinis, which share an X-Y sex-determination system where Y specifies maleness.
The theory of sex chromosome evolution holds that sex chromosomes were once homologs (a pair of equivalent autosomes—the non-sex chromosomes) that evolved different morphology and gene content because they lost their ability to recombine. Suppression of recombination is thought to start around the sex-determining region, but may eventually affect much of the sex chromosomes. Recombination is a key genetic process in which two chromosomes pair and exchange their sequences. In the absence of recombination, the two chromosomes of a pair evolve separately.
Flowers of Silene species, clockwise from top left: male flowers of S. latifolia and S. dioica, hermaphrodite flower of S. vulgaris, male flower of S. diclinis
In the case of mammals, whose sex chromosomes evolved about 320 million years ago, loss of recombination led to widely diverged X and Y chromosomes that pair only over a very small region, the pseudoautosomal region (PAR; because in this region the X and Y still behave like autosomes). The X and Y chromosomes of dioecious Silene species are morphologically distinct, like those of mammals, and they also have a PAR and a nonrecombining region. Nicolas and colleagues' results shed some light on how recombination suppression evolved on the Silene sex chromosomes.
The authors studied four genes outside the PAR on the Silene X chromosomes that are also present on their Y chromosomes. They mapped the genes relative to the PAR and compared the nucleotide sequences of the X and Y version of each gene in each species. As expected of sequences that no longer recombine, the X and Y versions of each gene have diverged. Strikingly, the extent of nucleotide divergence increases with the gene's distance from the PAR.
Evolutionary biologists use sequence divergence as a clock: the longer two originally identical sequences have been isolated from one another, the more independent mutations they accumulate. The picture that emerges from the Silene data is one of a progressive suppression of recombination, gradually diminishing the PAR. A similar scenario has been proposed in mammals and birds. However, the authors estimate that the Silene sex chromosomes started diverging only 10 million years ago. The Silene chromosomes might therefore offer a better chance to observe recombination suppression in its early stages, and perhaps to get at its mechanisms.
The authors also report evidence for some degeneration of the Silene Y chromosome genes. Y degeneration is well documented in mammals, in which most X-linked genes have no Y-linked counterparts. Understanding X-Y divergence in Silene species may thus shed light on the evolution of sex chromosomes in vertebrates as well.
| 0 | PMC536008 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 21; 3(1):e28 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030028 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-411550069410.1186/1471-2148-4-41Research ArticleThe structurally constrained protein evolution model accounts for sequence patterns of the LβH superfamily Parisi Gustavo [email protected] Julián [email protected] Centro de Estudios e Investigaciones, Universidad Nacional de Quilmes, Roque Saenz Peña 180, B1876BXD Bernal, Argentina2004 22 10 2004 4 41 41 23 12 2003 22 10 2004 Copyright © 2004 Parisi and Echave; licensee BioMed Central Ltd.2004Parisi and Echave; 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
Structure conservation constrains evolutionary sequence divergence, resulting in observable sequence patterns. Most current models of protein evolution do not take structure into account explicitly, being unsuitable for investigating the effects of structure conservation on sequence divergence. To this end, we recently developed the Structurally Constrained Protein Evolution (SCPE) model. The model starts with the coding sequence of a protein with known three-dimensional structure. At each evolutionary time-step of an SCPE simulation, a trial sequence is generated by introducing a random point mutation in the current coding DNA sequence. Then, a "score" for the trial sequence is calculated and the mutation is accepted only if its score is under a given cutoff, λ. The SCPE score measures the distance between the trial sequence and a given reference sequence, given the structure. In our first brief report we used a "global score", in which the same reference sequence, the ancestral one, was used at each evolutionary step. Here, we introduce a new scoring function, the "local score", in which the sequence accepted at the previous evolutionary time-step is used as the reference. We assess the model on the UDP-N-acetylglucosamine acyltransferase (LPXA) family, as in our previous report, and we extend this study to all other members of the left-handed parallel beta helix fold (LβH) superfamily whose structure has been determined.
Results
We studied site-dependent entropies, amino acid probability distributions, and substitution matrices predicted by SCPE and compared with experimental data for several members of the LβH superfamily. We also evaluated structure conservation during simulations. Overall, SCPE outperforms JTT in the description of sequence patterns observed in structurally constrained sites. Maximum Likelihood calculations show that the local-score and global-score SCPE substitution matrices obtained for LPXA outperform the JTT model for the LPXA family and for the structurally constrained sites of class i of other members within the LβH superfamily.
Conclusion
We extended the SCPE model by introducing a new scoring function, the local score. We performed a thorough assessment of the SCPE model on the LPXA family and extended it to all other members of known structure of the LβH superfamily.
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Background
Protein structure is more conserved than protein sequence during molecular evolution [1-3]. Remote homologous proteins constitute an extreme example of sequence divergence where proteins with similar function and no apparent sequence similarity present almost the same fold [4]. However, protein sequences are far from being random. Rather, they are selected through evolution in such a way that functional constraints modulate sequence variability. Usually, only a few residues are directly related to the protein function. However, these residues must maintain adequate spatial relationships for the protein to remain functional, so that the whole 3D structure is conserved. In turn, structure conservation constrains sequence variability in such a way that residue substitution does not disturb the overall 3D structure of the protein. This results in emergent non-random sequence patterns.
The restrictions imposed by the environment of a given protein site onto its pattern of amino acid substitutions have been largely discussed [2,5-9]. Briefly, highly constrained positions are more conserved. Furthermore, each site has a biased composition related to its structural environment. Models of protein evolution that take this into account outperform other, simpler, models [10-13]. Recently, a number of models of protein evolution have been developed that take explicit account of protein structure, stability, and/or foldability [14-22]. Even though such models have not been used yet for phylogenetic inference purposes, they are useful to gain insight into the detailed mechanism of protein evolution. Noteworthy, some of these models have been able to reproduce quantitatively observed amino acid substitution patterns [12,14,23].
To study how protein structure conservation modulates sequence divergence, we recently developed the Structurally Constrained Protein Evolution (SCPE) model [14]. The starting point of an SCPE simulation is the coding-sequence of a protein of known three-dimensional structure, which we shall call the "ancestral sequence". At each evolutionary time-step, a new "trial sequence" is generated by random mutation, at DNA level, of the "current sequence" (accepted at the previous time-step). Then, the trial DNA is translated using the universal genetic code and a "score" that estimates the protein structure perturbation introduced by the mutation is evaluated. The trial sequence is accepted, becoming the new current sequence, only if its score is below a certain "cut-off", λ, that measures the amount of structural perturbation allowed by natural selection. In this way, for λ = 0 only synonymous mutations are accepted, whereas for λ ~ ∞ all mutations are accepted. The procedure is repeated until a desired number of mutations are reached. In the present work the DNA is mutated using the Jukes-Cantor model, so that each nucleotide substitution occurs with the same probability.
The model depends on one parameter, the cut-off λ, that must be fit by comparison to actual sequence data. Different properties could be used to fit the cut-off. As we will show below, the model is quite robust with respect to the property used. Therefore, we have used the simplest way, which is to fit λ such that the acceptance rate, ω, inferred for actual sequences is reproduced. The acceptance rate is the probability that an amino acid mutation is accepted. Thus, it can be estimated by the ratio between the number of amino acid substitutions (accepted mutations) and the total number of trial amino acid mutations. The acceptance rate has been extensively used to characterize the strength of the selective pressure under which proteins evolve [24-27]. If all mutations were neutral they would be accepted and ω would be 1. In general the ω values are usually below 0.5 due to the deleterious effects of most amino acid mutations [28]. In proteins under very strong selective pressure ω can take values very close to zero.
One of the main factors determining the quality of the SCPE model is the scoring function. Given the structure of the ancestral protein, which we assume constant throughout the simulation, the score of a given trial sequence is defined as the RMSD between the mean-field energy profile of the trial sequence and that of a reference sequence. In our previous work, the same reference sequence, the ancestral one, was used for each time-step. Therefore, the score of each trial sequence was a measure of the dissimilarity between the trial sequence and the ancestral sequence, given the structure. Such a score depends only on the trial sequence and the ancestral sequence, but not on the particular sequence mutated to obtain the trial. Hence, it does not depend on the precise evolutionary path between the ancestral and the trial. Therefore, this will be called from now on "global score".
Even though the global score has been proved to be very good at reproducing the sequence patterns of a test case, it also shows some problems. Mainly, at the beginning of a simulation most mutations fall below the optimum cut-off. This results in too high values of the acceptance rate. Only after about 5% of the sites have been substituted, the cut-off is purifying enough to reproduce the acceptance rate inferred for the actual family. From a more qualitative point of view, since at the beginning of global-score simulations almost all mutations are accepted, erroneous amino acids, which are not found in the natural sequences of the family studied, can be introduced with relatively high probability during the first few steps of a global-score simulation. We shall see below that these are unwanted artefacts of the global-score SCPE simulations.
To tackle the problems described in the previous paragraph, in this paper we introduce a "local score", in which the reference sequence for a given trial is that accepted in the previous evolutionary time step, the current sequence, rather than the ancestral one. Thus, the local score measures the mutational perturbation introduced in a given time-step, rather than the global difference between the trial and ancestral sequences.
The new approach is compared with the previous one on the same test system studied before: UDP-N-acetylglucosamine acyltransferase (LPXA) from Escherichia coli. A portion of this protein presents a left-handed parallel beta helix (LβH), a fold generally associated with transferase activity and broadly distributed in different taxons [29-31] (see Figure 1a). All the LβH proteins contain a hexapeptide-repeat motif which is closely related with the topology of the fold (Figure 1b). This superfamily is characterized by the high conservation of the fold that contrasts with an elevated sequence and functional divergence.
Figure 1 (a) Structure of the UDP-N-acetylglucosamine acyltransferase (LPXA). This protein forms a Left-handed parallel β Helix (LβH). (b) Detail of one coil of the helix. Each coil is formed by three hexapeptides (shown in different colours). Note that hexapeptide positions i and i+4 point towards the inside of the prism whereas the other positions point outwards.
We shall show below that when the local score is used, the acceptance rate averaged over independent runs does not depend on the amount of divergence from the ancestral sequence. Furthermore, no erroneous amino acids are accepted during the simulations. Thus, these artefacts of the global-score simulations are absent when the new scheme is used. To further compare both schemes, other properties were analysed. Specifically, we evaluated and compared structure conservation, entropy profiles, amino acid distributions, and substitution matrices. We show that SCPE simulations that use the LPXA from E. coli as ancestral sequence can be used to estimate site-dependent amino acid substitution matrices [32,33] which outperform the usually used JTT model [34]. Moreover, we consider the applicability of the SCPE substitution matrices obtained from LPXA simulations to other protein families which adopt the LβH fold.
Results and discussion
Acceptance rates
In Figure 2 we show the number of nonsynonymous substitutions versus the number of nonsynonymous mutations averaged over several independent simulations. Note that nonsynonymous substitutions (mutations) at DNA level are amino acid substitutions (mutations) at protein level. The slope of each plot is the acceptance rate ω. Figure 2 shows that for the global-score case, ω decreases from ω = 1 when the simulation begins to a constant asymptotic value ω < 1 for longer times. In an actual case, such behaviour could be due to a sequence that for some reason is particularly robust with respect to mutations. In the present case, however, this is an unintended artefact of our model. It happens because the global score of the mutations introduced in the first steps of a simulation lie below the global cut-off, no matter how nonconservative the mutation is. Thus, at the beginning of a global-score simulation almost all amino acid mutations are accepted, leading to an acceptance rate ω = 1. Furthermore, clearly wrong amino acids, that will irreversibly upset the structure, can be introduced. In contrast, the local-score simulations display a constant average ω, which we think is more consistent with a neutral model, such as SCPE, with constant selection pressure λ. We should mention that despite the constancy of the average ω, the acceptance rate ω of a single simulated run changes from sequence to sequence. This is expected, since any substitution at a given site changes the scores of the sites that are in contact with it in the 3D structure. This could account for features such as overdispersion of the molecular clock and rate-shifts in substitution rates (heterotachy).
Figure 2 Number of nonsynonymous (amino acid) substitutions observed as a function of the number of nonsynonymous (amino acid) mutation trials for local-score and global-score SCPE simulations. Results are averaged over 300 independent runs. Note that global-score simulations present a definite change in slope (acceptance rate ω) between the first steps of the simulations and longer times. In contrast, local-score simulations present a constant slope (acceptance rate ω).
Determination of optimal λ
As discussed in Methods, we have chosen to determine the optimum value of the SCPE model parameter λ so that the acceptance rate of the simulations matches that inferred from actual sequences. For the SCPE simulations, the value of ω is easily estimated by just counting the number of amino acid substitutions accepted throughout the simulations and dividing by the number of trial amino acid mutations. For the reference alignment, however, one has to estimate ω using some inference method. These methods tend to overestimate the actual ω. This can be seen in Figure 3, where we show two different ω inferences for a set of SCPE simulations as a function of the cut-off λ, together with the value calculated by counting the proportion of accepted mutations (see Methods). The inferences were made with the module yn00 of PAML [35] as explained in Methods. It is worthwhile to note that the inferred ω departs from the calculated ω as λ increases. This behaviour is expected since, for a given number of mutations, for larger λ there are more accepted nonsynonymous substitutions, which results in loss of sequence signal.
Figure 3 Inferred and calculated acceptance rates of data sets simulated with SCPE. (a) local-score simulations. (b) global-score simulations. yn00 and yn00+w+f are two different methods to infer acceptance rates included in PAML (see Methods). The average acceptance rate inferred in the LPXA reference alignment (obtained with yn00+w+f) is 0.2246 ± 0.11. Using this value in (a) and (b) the optimal local and global λ obtained are 1.10 and 7.00, respectively. In (c) we plot the ω inferred using yn00+w+f versus the calculated value for SCPE runs from (a) and (b). The value inferred for the observed LPXA family is shown as a dotted line. Using this value the optimal ω for local score is 0.15(0.12–0.27) and for global score is 0.19(0.12–0.26).
Using the method yn00+w+f, which best estimates the calculated ω, we obtained the ω of the reference alignment of 25 sequences homologous to the UDP-N-acetylglucosamine acyltransferase from Escherichia coli (LPXA reference alignment). The average ω for this alignment is 0.22. Using this value in Figure 3a and 3b the optimal values of λ obtained are 1.10 and 7.00 for local and global score, respectively.
We note here that the optimal λ values for local and global score are very different. Thus, for the sake of comparison, we take advantage of the one-to-one relationship between λ and ω, shown in Figures 3a and 3b, and use the calculated acceptance rate ω instead of λ as model parameter. In Figure 3c we plot the inferred ω versus the calculated ω for local-score and global-score simulations. Using this plot and the inferred ω value for the LPXA reference alignment, ω = 0.22 ± 0.11 (0.11 is the standard deviation of ω), we calculate an optimal ω of 0.15 (0.12–0.27) for local score and 0.19 (0.12–0.26) for global score.
Assessment of structure conservation
It is important to assess if the SCPE models are able to preserve protein structure. To this end we used THREADER 3 to analyze the percentage of sequences that recognize the correct structure using different models. Results are shown in Table 1. Clearly, JTT is unable to conserve structure even for relatively low amounts of divergence: at Ka = 0.28 only 20% of sequences obtained from JTT simulations recognize the correct structure. In contrast, a significant proportion of sequences simulated with SCPE recognize the correct structure even after long simulations of 1.7 substitutions per site: 62% for local-score SCPE and 39% for global-score SCPE.
Table 1 Evaluation of structure conservation. The table shows the percentage of output sequences that recognize correctly the LβH fold for local-score SCPE, global-score SCPE, and JTT for two different amounts of amino acid substitutions per site (Ka).
Amount of Divergence
Model Ka = 0.28 Ka = 1.7
Local-score SCPE λ = 1.10, ω = 0.15 87% 62%
Local-score SCPE λ = 8.00, ω = 0.92 19% 4%
Global-score SCPE λ = 7.00, ω = 0.19 68% 39%
Global-score SCPE λ = 90.00, ω = 0.95 8% 0%
JTT 20% 0%
When both SCPE schemes are compared, Table 1 shows that local-score simulations perform better than global-score ones. This result is counterintuitive, because one might expect, a priori, that in the long term the global-score would be better at conserving structure than the local-score, since in the later case the reference sequence is reset at each step so that it would be easier to lose memory of the ancestral protein. One of the reasons of the global-score SCPE being worse at conserving structure could be the erroneous amino acid substitutions introduced at the beginning of the simulations (see above). To gain more insight into this issue, further work involving much longer simulations would be needed. However, for long enough evolutionary time it is not longer reasonable to assume that structure remains constant. In this limit, any model based on assuming structural conservation will break down.
Entropy profiles
To evaluate the capacity of the SCPE model to reproduce the sequence patterns found in the LPXA family, the variability of each site was analysed. The different protein positions were accumulated into 6 structural classes. For each class, we calculated the entropies corresponding to the equilibrium distributions of SCPE models. These entropies represent the average structural constraints of each structural class and do not depend on simulation time. SCPE entropy profiles are compared with those obtained from the reference alignment of the LPXA family. One could argue that these are not only determined by structure, but also contain historical information. However, since we are accumulating over several sites of the same class, which would have independent evolutionary histories, we expect such information to be somewhat averaged out. For the sake of comparison we also calculated the entropy profiles of JTT simulations of 0.28 amino acid substitutions per site (see Methods). The resulting entropy profiles are shown in Figure 4. It can be seen from this figure that both, the local-score and the global-score schemes reproduce very well the variability pattern of the LPXA family. Also, in Figure 4 we show that simulations performed using the JTT model produce less accurate results, especially for the most conserved (low entropy) structural classes i and i+4.
Figure 4 Entropy profiles. Each structural class corresponds to a particular position in the hexapeptide motif found in the LβH proteins. Structural classes i and i+4 are the most conserved while the other classes present a more variable composition. SCPE profiles correspond to equilibrium amino acid distributions (see Methods). The SCPE parameters were fit to the minimum of the entropy error (see Figure 5). The profile obtained from JTT simulations of 0.28 substitutions per site is shown for comparison.
To further study the effect of varying the model parameter, we calculated an "error" which quantifies the difference between simulated and observed entropy profiles (see Methods). In Figure 5 this error as a function of ω is shown. Comparing Figures 5 and 3, we see that the ω for which the entropy error is minimum is consistent with the value at the optimum cut-off for both the local and the global score schemes.
Figure 5 Error in entropy profiles between observed and equilibrium SCPE amino acid probability distributions versus calculated acceptance rate. Results for local-score and global-score SCPE simulations are shown, together with those obtained from JTT simulations of 0.28 amino acid substitutions per site.
Probability distributions
Although entropy is commonly used to evaluate sequence conservation in an alignment [36-38] and to compare simulated data with natural sequences [39,40], it is not enough for a thorough assessment of the sequence pattern. An entropy value of 0 at a given site, for example, means that there is only one amino acid, but it could be any one out of twenty. Thus, to perform a more complete evaluation of the SCPE model, we looked into the amino acid probability distributions. To this end, we calculated a similarity score between the asymptotic SCPE distributions and those obtained from observed sequences. We used the similarity score used by Yona and Levitt to perform sequence profile-profile comparisons [41]. In Figure 6 we show the similarity score between observed and SCPE equilibrium amino acid distributions as a function of the calculated acceptance rate ω. We also show results for a simulation performed using the JTT model [34] of evolution. Overall, it can be seen that the local-score SCPE performs somewhat better than the global-score SCPE, and that both SCPE models clearly outperform JTT for a significant range of parameter ω around the optimum value.
Figure 6 Similarity score between observed and equilibrium SCPE amino acid probability distributions versus calculated acceptance rate. Results for local-score and global-score SCPE simulations are shown, together with those obtained from JTT simulations of 0.28 amino acid substitutions per site. For ω = 0, the distribution is that obtained from the ancestral sequence, LPXA of Ecoli by grouping sites of the same class, since in this case no substitution is accepted and therefore it is impossible to obtain the SCPE substitution matrices. For SCPE we used equilibrium distributions, which do not depend on time. JTT results become worse for longer times (Ka>0.28).
A more detailed analysis shows that the maximum of the local-score plot corresponds to a ω = 0.12, that is in good agreement with the optimum cut-off determined from the acceptance rates, as explained previously. In contrast, for the global-score case the cut-off at the maximum of the similarity score plot is significantly below the optimum ω value previously obtained. This difference would be due to the wrong behaviour of the global-score scheme for small amounts of divergence (see Figure 2), which will affect the SCPE substitution pattern and, therefore, the amino acid probability distributions. The same behaviour, though less marked, is found in the plots of Figure 5.
Finally, it is interesting to note that the similarity score for ω = 0 is much better than JTT. Since ω = 0 corresponds to a simulation where no nonsynonymous substitutions are accepted, this is the score obtained using just the initial sequence. Memory of this sequence might favour the good agreement observed for SCPE. However, it is noteworthy that the actual agreement increases for ω > 0, showing that the good fit is not due exclusively to a memory effect. The substitution matrix assessment described in the next section should be less sensitive to memory effects.
Substitution matrices
Even though it has long been recognized that substitution patterns are site-specific and depend on protein family, it is in general very difficult to estimate site-specific and family-specific substitution matrices due to a lack-of-data problem. As we reported previously, a possible strategy to overcome this obstacle is to obtain site-specific substitution matrices from SCPE simulations [12]. To further evaluate how the SCPE model is able to reproduce the substitution pattern of the LPXA family, a maximum likelihood analysis was used. SCPE runs were used to obtain a substitution matrix Qc for each structural class. Then, these matrices were used to calculate the maximum likelihood of the LPXA reference alignment using a given topology (see Methods).
In Figure 7a we show the likelihood vs. ω plots obtained using local-score and global-score SCPE substitution matrices. The global-score likelihood peaks near ω = 0.18 in good agreement with the previous determinations, showing that it reproduces quite well the amino acid substitution patterns found in real sequences. The best ω of the local-score SCPE likelihood (see Figure 7a) corresponds to ω = 0.4, larger than that determined previously (Figures 3, 5, and 6). To understand this behaviour, we analysed the log likelihood components for each structural class, which are shown in Figure 7b. It is seen from this figure, that the local-score maximum likelihood peaks near ω = 0.4 mainly because of the contributions of structural classes i+1, i+2, i+3, and i+5, which, being the least structurally constrained sites, are not expected to be very well reproduced by SCPE. In contrast, for those sites that point towards the inside of the LβH helix, which are the ones the model should best describe (conserved classes i and i+4) the maximum likelihood peaks near ω = 0.2, in better agreement with Figures 3, 5, and 6. In the global-score case, from Figure 7b, the maximum likelihood plots for different classes behave more evenly: for all classes, the maximum likelihood peaks near ω = 0.15.
Figure 7 Maximum likelihood as a function of calculated acceptance rate. (a) Likelihood obtained using local-score and global-score SCPE substitution matrices as a function of ω. We also show the likelihood obtained using JTT. (b) Likelihood for the six structural classes using local-score and global-score SCPE matrices.
Figure 7a shows that for LPXA, local-score simulations lead to better substitution matrices than global-score ones. Inspection of Figure 7b reveals that this is mainly due to the local-score SCPE giving better results for sites i+4 and, to a lesser degree, i+2. Figure 7a also reveals that both, local and global, SCPE models outperform JTT (dotted line of Figure 7a) for almost the whole ω range studied. This is due to the fact that site-specific amino acid substitution patterns, especially for constrained structural classes i and i+4, are not well described by general models such as JTT.
Other LβH families
As a further example of the applicability of the SCPE model, we considered other families of the LβH superfamily (see Table 2). We used the local-score and global-score schemes with the optimum cutoffs estimated using acceptance rates, as explained previously, to obtain site-dependent probability distributions and substitution matrices for the six different structural classes.
Table 2 LβH superfamily members studied.
Gene name or synonym Function PDB ID Number of sequences aligned
LPXA UDP-N-acetylglucosamine acyltransferase 1lxa 25
SATA Streptogramin A Acetyltransferase 1kk6 48
LACA Galactoside O-Acetyltransferase 1kru 43
CAT Xenobiotic Acetyltransferase 1xat 39
DAPD Tetrahydrodipicolinate-N-Succinlytransferase 1tdt 50
CAM Carbonic Anhydrase 1qre 26
GLMU N-Acetylglucosamine-1-Phosphate Uridyltransferase 1g97 50
In Figure 8 we show the probability distributions of the LβH families considered (Figure 8a) and the equilibrium distributions obtained using the local-score and global-score SCPE models (Figure 8b). It can be seen that both SCPE schemes perform quite well in reproducing the sequence pattern of our test system.
Figure 8 Amino acid frequency distributions for the hexapeptide sites. (a) 7 LβH families of Table 2. (b) Local-score (grey) and global-score (black) SCPE equilibrium distributions (see Methods).
To test the substitution matrices, we performed maximum likelihood calculations on each family of Table 2. Since the models compared have the same number of parameters, they can be compared using Maximum Likelihood (ML) values obtained using a reasonable phylogenetic tree topology [42,43] (see Methods). In Table 3 we show the ML values per site for local-score SCPE, global-score SCPE, and JTT, applied to different sets of sites. Better models have larger ML values.
Table 3 Comparison of models on 7 families of the LβH superfamily. Logarithm of the Maximum Likelihood per site obtained with different models for the families studied. Better models lead to larger ML values. The three numbers reported for each case correspond to structural classes i and i+4, considered separately, and to the average over the six structural classes.
Family Local score Global score JTT model
LPXA -19.9 -20.0 -23.7
-21.7 -22.7 -24.9
-28.0 -28.1 -29.3
SATA -16.2 -16.1 -18.7
-20.1 -19.2 -20.2
-25.2 -24.7 -25.9
LACA -35.7 -34.2 -36.3
-36.4 -35.0 -35.9
-38.0 -37.0 -37.4
CAT -13.8 -13.7 -16.4
-15.8 -15.9 -17.4
-19.3 -19.0 -19.8
DAPD -17.0 -17.1 -19.6
-22.0 -23.8 -23.2
-18.7 -20.0 -19.4
CAM -13.9 -14.1 -15.9
-20.3 -18.5 -18.6
-23.1 -21.2 -20.4
GLMU -38.1 -38.0 -39.9
-39.0 -38.1 -38.1
-49.3 -47.8 -47.9
For LPXA, SCPE (both local and global) are clearly better than JTT for all sites considered. CAT and SATA behave similarly, though the advantage of SCPE over JTT is less marked. For other families, SCPE (local and global) is better than JTT for class i sites. For other structural classes there is no definite advantage of SCPE over JTT.
When comparing local-score SCPE with global-score SCPE one finds no definite advantage of either one over the other. For sites i, where the more meaningful results are expected, local and global give very similar results for all families except for LACA where global is better than local.
Conclusion
We presented in full detail the Structurally Constrained Protein Evolution Model (SCPE), developed recently. We improved on our previous model by introducing a new scoring function. Our previous work was based on a "global" score, which measures how a trial sequence differs from the ancestral sequence in its ability to fit a reference structure assumed constant. In contrast, the "local" score measures the perturbation introduced by a given mutation with respect to the previously accepted sequence, rather than the ancestral one.
Both schemes, global and local, were compared in their ability to match the substitution patterns of the protein family LPXA. We performed a thorough assessment comparing structure conservation, entropy profiles, amino acid distributions, and substitution matrices. LβH proteins were found to be particularly suited for such a detailed characterization of the sequence pattern, because of the fact that most of their sites belong to one of only six different structural classes. Furthermore, these properties were studied as a function of the single parameter of the model: a cutoff that measures selection pressure against structural divergence. Finally, we applied the model to all other members of the LβH superfamily whose structure is known, extending previous studies performed only on the LPXA family.
In general, we found that the local-score SCPE behaves either similarly or better than the global-score scheme, depending on the property considered. Furthermore, for LPXA, and for sites of the structurally constrained class i of all other families studied, both SCPE models clearly outperform the widely used JTT model, showing the power of the SCPE model to account for substitution patterns conditioned by structural constraints.
Currently, we are using the SCPE model to investigate several issues important in protein evolution, such as overdispersion of the molecular clock, correlation between the evolution of different sites, and heterotachy. Also, we are testing the applicability of the SCPE model to other protein families, in order to assess its generality. Nevertheless, we should mention that since most protein families do not display the regularity of LβH proteins, it is more difficult to perform a detailed quantification of sequence patterns, which makes such tests at the same time more difficult and less demanding than the LβH superfamily.
Methods
Test system
The LPXA family belongs to a large and diverse group of proteins [31], the LβH (Left-handed parallel β Helix) superfamily. All the sequences of this superfamily contain an imperfect tandem-repetition of a hexapeptide motif [29]. This motif is typically described by [LIVMA]-X3- [ASCVTN]-X. The first position of the hexapeptide is called i, and the following i+1, i+2, up to i+5. The sequence forms a left-handed parallel β helix, forming an equilateral triangular prism [44] (Figure 1a). Each coil of the helix is formed by three hexapeptides. Equivalent positions of different hexapeptides fall into similar structural environments. Residues at positions i and i+4, for example, point towards the inside of the β helix (Figure 1b). Thus, each site of the hexapeptide pattern corresponds to a different structural class. In this study we did not analyse sites that are at loop regions. Also, the first and last coils of the β helix of LPXA were not considered, since the structural environments of sites in these coils are not exactly the same as those of the other coils. Although all the LβH members have a homo-trimeric active form, we only use the monomer form in this study. We also analyse other LβH families, which are summarized, with a brief description, in Table 2.
SCPE score
The first step in the calculation of the SCPE score is the calculation of a profile of mean energies per position. In the present case we used the Cβ-Cβ potential of the program PROSA II [45]. The original coordinates of the ancestral sequence were modified in order to provide with Cβ coordinates to those residues without them. Thus, all the GLY residues were substituted for ALA residues and an adequate rotamer was chosen using the program SCWRL [46]. Later, the substituted ALA residues were converted back to the original GLY, keeping the Cβ coordinate of ALA to use when a GLY mutates to a residue with Cβ. Once the energy per position is obtained the score is calculated using:
where N is the length of the protein sequence, Emut(p) is the mean-field energy of position p in the trial (mutated) sequence and Eref(p) is the corresponding value of the reference sequence. The "global score" is calculated using the ancestral sequence as reference. The "local score" is calculated using the sequence accepted in the previous step in the simulation (i.e. the sequence that is mutated to obtain the trial).
SCPE simulations
The ancestral sequence was the UDP-N-acetylglucosamine acyltransferase (LPXA) from Escherichia coli. The coordinates were obtained from the PDB database [47] (ID code 1lxa). The cutoff range covered was 0–2.00 with a step of 0.1 for local score and 0–20 with a step of 1.00 for global score. For each cutoff value we performed 300 independent simulations, each one of 2500 mutational steps.
Sequence analysis
Using the LPXA from Escherichia coli as the reference protein, we recovered 25 homologous sequences using sequence similarity searches. This set constitutes the reference LPXA family. For each of the other members of the LβH superfamily for which at least one member has known structure, we used this member's sequence to characterize putative homologous proteins. See Table 2 for details. All the similarity searches were performed using the program BLASTP [48] at the NCBI server and the sequence alignments were obtained using Clustal X [49].
Estimation of acceptance rates
To assess the optimal selective pressure in our SCPE simulations, we inferred the mean ω value in the homologous LPXA family. Also, we inferred the ω in our SCPE simulations for different cut-offs. All the ω inferences were made using the program yn00 from PAML [35]. We used options "w", which applies a weighting scheme between codons, and "f", which takes into account the codon frequencies of the data.
In the SCPE simulations, we also estimated ω directly by counting: ω is the ratio between the number of amino acid substitutions (accepted mutations) and the total number of amino acid mutation trials. We use "calculated", as opposed to "inferred" to designate the acceptance rates obtained in this way.
Estimation of the amount of divergence
Some of the comparisons performed depend on the amount of divergence. For these cases, we estimated the average divergence of the LPXA family using the program PAML[50]. Maximum likelihood distances were estimated using the JTT model with the frequencies estimated from the data and a gamma distribution with 8 categories to estimate the relative rates (JTT+F+Γ). The average time calculated was Ka = 0.28 amino acid substitutions per site.
Assessment of structure conservation
We evaluated whether sequences produced by evolutionary simulations using SCPE recognize the correct structure using THREADER 3 [51]. We considered the following schemes: local-score SCPE with λ = 1.10 (ω = 0.15); local-score SCPE with λ = 8.00 (ω = 0.92); global-score SCPE with λ = 7.00 (ω = 0.19); global-score SCPE with λ = 90.00 (ω = 0.95). To compare, we also ran simulations using JTT. For each model, we performed 50 independent runs of lengths Ka = 0.28 and Ka = 1.7 amino acid substitutions per site. For each sequence, structure recognition using THREADER 3 was performed. The ability of models to conserve structure was measured by the percentage of sequences which recognized correctly (Z-score > 2.7) the LβH fold.
Substitution matrices
Site-specific replacement matrices are obtained straightforwardly by "counting" substitutions in SCPE simulations. For the test system considered, sites can be classified into c = 1,2,...6 site classes. Then, for each class we set up a matrix of counts: for i ≠ j, is half the number of mutational steps that result in either i → j or j → i amino-acid replacements at site class c, and is the number of mutational steps for which amino acid i remains constant (i → i replacement). Then, for each class, the matrix of substitution rates, Qc, is obtained using:
Given the rate matrices, Qc, the probability matrices are obtained using
Pc = exp(tQc)
The vector of amino acid equilibrium frequencies of class c is, then, obtained with
Since there are some substitutions that do not occur during the simulations (very low probabilities), we have found it convenient to re-calculate each Qc using a pseudocounts procedure similar to that developed by Tatusov [52] as follows
where and are, respectively, the substitution matrix elements and equilibrium frequencies of a reference model. Here we used JTT [34] and α = 0.01. Accordingly, equilibrium frequencies were also corrected using
Entropies and amino acid distributions
To study the sequence variability profile, we calculated the entropy for each structural class using:
where is the probability of finding residue i at structural class c.
For SCPE, we used the equilibrium probabilities obtained from the substitution matrices, as described in the previous section. For the reference alignment, we grouped all columns of the same structural class together, counted the number of times each amino acid occurred in each class, and obtained the corresponding amino acid frequencies.
The difference between the entropy profiles obtained from the SCPE models, , and the profile of the observed reference family, , was quantified by the following "error" function:
To assess the similarity between the equilibrium SCPE amino acid distributions and those obtained from the reference alignment, we used the similarity score based on information theory proposed by Yona and Levitt [41]. The score is calculated by adding together the similarity scores of the six structural classes.
JTT distributions and entropies
The equilibrium SCPE distributions and their corresponding entropies were compared with JTT distributions and entropies. In contrast to SCPE, the equilibrium JTT distribution does not depend on structural class. Therefore, instead of the equilibrium distributions, we chose to use the distributions and entropies from the alignment of sequences obtained from simulations with the JTT model. To this end, we performed 100 independent simulations using the JTT substitution matrix. The simulation length was set to the average number of substitutions obtained for the LPXA family (Ka = 0.28). We aligned the 100 output sequences, grouped all columns of the same structural class together, counted the number of times each amino acid occurred in each class, and obtained the corresponding amino acid frequencies.
Maximum likelihood calculations
In order to assess the SCPE substitution patterns, we performed Maximum Likelihood (ML) calculations using the site-dependent SCPE substitution matrices, Qc. The maximum likelihood of a model, Q, given the data, s, for topology, T, is obtained by maximizing the probability L = Pr(s|T, Q).
For the SCPE model, the reference alignment was partitioned into 6 sub-alignments corresponding to the 6 structural classes. Using these sub-alignments and the corresponding SCPE Qc matrices, we calculated the maximum likelihood using PAML. In all cases a gamma distribution was used to take into consideration the rate heterogeneity among sites of the same class. Similarly, we performed ML calculations using the JTT substitution matrix with gamma distribution of rates (JTT+Γ), for each of the six structural classes. The ML values obtained for each class were added together to obtain the total ML, as was done with the SCPE models.
It has been shown that as long as the tree topology is reasonable, model comparison is robust with respect to variations in topology [43]. In the present case, topologies were obtained using the program FITCH [53] of PHYLIP 3.57c [54] with ML distances obtained using JTT with PAML.
All the models compared here have the same number of parameters. Therefore, models were compared by comparing ML values. One should note, however, that when models with different number of parameters are compared, one should use a statistic that takes explicit account the number of parameters of each model [42,43].
Authors contributions
GP and JE developed the mathematical model. GP implemented the model, run the simulations, performed the analysis and wrote the first draft. JE edited and wrote the revised versions. All authors read and approved the final manuscript.
Acknowledgements
We thank Jeff Thorne and an anonymous reviewer for their useful comments. This work was supported by the Universidad Nacional de Quilmes, the Fundación Antorchas, and the Agencia Nacional de Promoción Científica, Tecnológica y de Innovación.
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| 15500694 | PMC538250 | CC BY | 2021-01-04 16:29:01 | no | BMC Evol Biol. 2004 Oct 22; 4:41 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-41 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-231554649610.1186/1476-7120-2-23ResearchThe use of microbubbles to target drug delivery Tsutsui Jeane M [email protected] Feng [email protected] Richard Thomas [email protected] Department of Internal Medicine, Section of Cardiology, University of Nebraska Medical Center, Omaha, Nebraska, USA2004 16 11 2004 2 23 23 17 8 2004 16 11 2004 Copyright © 2004 Tsutsui et al; licensee BioMed Central Ltd.2004Tsutsui 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.
Ultrasound-mediated microbubbles destruction has been proposed as an innovative method for noninvasive delivering of drugs and genes to different tissues. Microbubbles are used to carry a drug or gene until a specific area of interest is reached, and then ultrasound is used to burst the microbubbles, causing site-specific delivery of the bioactive materials. Furthermore, the ability of albumin-coated microbubbles to adhere to vascular regions with glycocalix damage or endothelial dysfunction is another possible mechanism to deliver drugs even in the absence of ultrasound. This review focuses on the characteristics of microbubbles that give them therapeutic properties and some important aspects of ultrasound parameters that are known to influence microbubble-mediated drug delivery. In addition, current studies involving this novel therapeutical application of microbubbles will be discussed.
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Introduction
The recent advances in gene therapy and molecular biology have improved the interest in methods of noninvasive delivery of therapeutic agents. Besides the well known application of microbubbles as contrast agents for diagnostic ultrasound, microbubbles have also been demonstrated an effective technique for targeted delivery of drugs and genes [1-6]. Drugs can be incorporated into the microbubbles in a number of different ways, including binding of the drug to the microbubble shell and attachment of site-specific ligands. As perfluorocarbon-filled microbubbles are sufficiently stable for circulating in the vasculature as blood pool agents, they act as carriers of these agents until the site of interest is reached. Ultrasound applied over the skin surface can then be used to burst the microbubbles at this site, causing localized release of the drug [7-10]. This technique then permits using lower concentrations of drugs systemically, and concentration of the drug only where it is needed. This improved therapeutic index may be extremely advantageous in cases of drugs with hazardous systemic side effects, like cytotoxic agents. Albumin-encapsulated microbubbles have also demonstrated to adhere to the vessel walls in the setting of endothelial dysfunction [11]. This also may be a method of targeting delivery with microbubbles but without the application of ultrasound.
Mechanisms for Target Drug Delivery Using Microbubbles
Two possible strategies for delivering drugs and genes with microbubbles are emerging. The first consists on the ultrasound-mediated microbubble destruction, which is based on the cavitation of microbubbles induced by ultrasound application, and the second is the direct delivery of substances bound to microbubbles in the absence of ultrasound. Different drugs and genes can be incorporated into the ultrasound contrast agents. It has already been demonstrated that perfluorocarbon-filled albumin microbubbles avidly bind proteins and synthetic oligonucleotides [12]. In a similar way, microbubbles can directly take up genetic material, such as plasmids and adenovirus [12,13], and phospholipid-coated microbubbles have a high affinity for chemotherapeutic drugs [14]. Furthermore, specific ligands for endothelial cell adhesion molecules, such as P-selectin and leukocyte intercellular adhesion molecule 1 (ICAM-1), can be attached to both lipid- and albumin-encapsulated microbubbles, which increases their deposition to activated endothelium [15,16].
The mechanisms by which ultrasound facilitates the delivery of drugs and genes result from a complex interplay among the therapeutic agent, the microbubble characteristics, the target tissue, and the nature of ultrasound energy. The presence of microbubbles in the insonified field reduces the peak negative pressure needed to enhance drug delivery with ultrasound. This occurs because the microbubbles act as nuclei for cavitation, decreasing the threshold of ultrasound energy necessary to cause this phenomenon. The results of optical and acoustical studies have suggested the following mechanisms for microbubble destruction by ultrasound: 1- gradual diffusion of gas at low acoustic power, 2- formation of a shell defect with diffusion of gas, 3- immediate expulsion of the microbubble shell at high acoustic power, and 4- dispersion of the microbubble into several smaller bubbles. Cavitation of the bubbles is characterized by rapid destruction of contrast agents due to a hydrodynamic instability excited during large amplitude oscillations, and is directly dependent on the transmission pressure [17,18]. It has been reported that the application of ultrasound to contrast agents creates extravasation points in skeletal muscle capillaries [2,19], and this phenomenon is dependent on the applied ultrasound power. High intensity ultrasound (referred to as a high mechanical index) can rupture capillary vessels, resulting in deposit of protein and genetic material into the tissues. Skyba et al [1] demonstrated in an exteriorized spinotrapezius preparation that ultrasonic destruction of gas-filled microbubbles caused rupture of microvessels with diameter ≤ 7 μm (capillaries), with local extravasation of red blood cells. Price et al [2] have shown that polymer microspheres could be driven as much as 200 μm into the parenchyma with this method. The authors calculated that only a small number of capillary ruptures were required to deliver large quantities of the colloidal particles to the muscle. Using the same model of polymer microspheres bound to microbubbles and ultrasound, it has also been demonstrated that the ultrasound pulse interval and microvascular pressure influence the creation of extravasation points and the transport of microspheres to the tissue. Both were greatest when the pulse interval was around 5 seconds, which allows maximal microbubble replenishment within the microcirculation after destruction by the ultrasound pulse [19].
The formation of pores in the membranes of cells as a result of ultrasound-induced microbubble cavitation has been proposed as a mechanism for facilitating the drug deposition. Taniyama et al [7] demonstrated the presence of small holes in the surface of endothelial and vascular smooth muscle cells immediately after transfection of a plasmid DNA by ultrasound-mediated microbubble destruction, using electron microscopic scanning. It was then postulated that these transient holes in the cell surface caused by microbubbles and ultrasound resulted in a rapid translocation of plasmid DNA from outside to cytoplasm. Mukherjee et al [10] demonstrated by electron microscopy of a rat heart performed during application of ultrasound, that disruption or pore formation of the membrane of the endothelial cells occurred with acoustic power of 0.8 to 1.0 W/cm2. However, it was a lower intensity of ultrasound (0.6 W/cm2) that caused more drug delivery with microbubbles. More recently, voltage clamp techniques were used to obtain real-time measurements of membrane sonoporation in the presence of albumin-coated microbubbles (Optison). Ultrasound increased the transmembrane current as a direct result of membrane resistance due to pore formation [20].
Another important therapeutic property of microbubbles is their increased adherence to damaged vascular endothelium. Albumin-coated microbubbles do not adhere to normally functioning endothelium, but their adherence does occur to activated endothelial cells or to extra-cellular matrix of the disrupted vascular wall, and this interaction could be a marker of endothelial integrity [11]. Because of this characteristic, the delivery of drugs or genes bound to albumin-coated microbubbles could be selectively concentrated at the site of vascular injury in the presence [21] or absence of ultrasound application [22].
Microbubbles Use for Gene Therapy
The clinical use of viral vectors for gene therapy is limited because viral proteins elicit an immune response within the target tissue [23], and have been shown to cause an intense inflammatory activation of endothelial cells [24]. On the other hand, the nonviral delivery of vehicles, such as plasmids and antisense oligonucleotides, has been associated with a lower transfection efficiency and transient expression of the gene product [25]. The first published report of targeted DNA delivery was performed in 1996, using surface ultrasound and intravenously delivered microbubbles carrying antisense oligonucleotides [3]. In 1997, Bao et al [26] described the use of ultrasound and albumin-coated microbubbles to enhance the transfection of luciferase reporter plasmid in cultured hamster cells. Since then, many studies have confirmed the efficacy of ultrasound-mediated microbubble destruction for drug and gene delivery, both in vitro and in vivo [3,7-9]. Shohet et al [9] demonstrated for the first time with an adenovirus vector that the ultrasound-mediated disruption of gas-filled microbubbles could be used to direct transgene expression to the heart in vivo. They showed that intravenously injected recombinant adenovirus vectors encoding a beta-galactosidase reporter gene were successfully delivered to normal rat myocardium using microbubbles and transthoracic 1.3 MHz diagnostic ultrasound, at a mechanical index of 1.5, delivered at a burst of 3 frames of ultrasound every 4 to 6 cardiac cycles. Of note, transfection was not observed if the adenovirus was administered in the same dose without microbubbles, or if the adenovirus was administered with microbubbles but in the absence of ultrasound. Importantly, using the same model the authors confirmed that plasmid transgene expression can be directed to the heart, with an even higher specificity than viral vectors, and that this expression can be regulated by repeated treatments [27].
Taniyama et al [7] have also shown effective transfection of a plasmid DNA to endothelial and vascular smooth muscle cells with albumin-coated microbubbles (Optison) and ultrasound. In vivo studies demonstrated that transfection of wild-type p53 plasmid DNA into balloon-injured blood vessels was effective and resulted in significant inhibition of the ratio of neointimal-to-medial area, as compared with transfection of control vector. In contrast, transfection of p53 plasmid DNA by means of ultrasound without microbubbles failed to inhibit neointimal formation in the rat carotid [7]. In a recent study, Teupe et al [28] have documented efficient transfer of plasmids encoding either beta-galactosidase or endothelial nitric oxide synthase to the endothelial cells of conductance arteries with preservation of the functional integrity of the transfected endothelial cell layer after ultrasound treatment.
Other Potential Therapeutic Applications of Microbubble Target Drug Delivery
Restenosis after vascular balloon injury or stent deployment has been shown to result from neointimal hyperplasia due to smooth muscle cell migration and proliferation. The c-myc protooncogene is responsible for the regulation of gene expression involved in the process of intimal hyperplasia that leads to restenosis. Synthetic antisense oligonucleotides, such as those to the c-myc protooncogene, can bind to the messenger ribonucleic acid and inhibit the synthesis of the protooncogenes. Therefore, antisense to c-myc protooncogene can prevent its translation into proteins that may be mediators of the pathologic process of restenosis. These synthetic agents, when administered directly into the vessel, have successfully inhibited restenosis after coronary or carotid injury [29]. In 1996 Porter et al [3] demonstrated that perfluorocarbon-exposed sonicated dextrose albumin (PESDA) microbubbles, unlike room air-containing sonicated dextrose albumin microbubbles, have bioactive albumin on their surface that can avidly bind synthetic antisense oligonucleotides, and then release them in the presence of ultrasound. In the initial study that examined the effectiveness of PESDA and ultrasound in enhancing the delivery of the antisense to c-myc, 21 pigs had carotid balloon injury performed with an oversized balloon catheter and were randomized to receive intravenous antisense to c-myc bound to PESDA, intravenous antisense alone, or no treatment. The pigs that received antisense bound to PESDA also had transcutaneous 20 kHz ultrasound applied over the carotid wall following injections. The ultrasound targeted group showed a significantly lower percent area stenosis (8 ± 2%) than the two control groups (19 ± 8% and 28 ± 3%; p < 0.01) [21].
Since PESDA microbubbles adhere to sites of endothelial injury even in the absence of ultrasound, the efficacy of this therapy in inhibiting coronary restenosis has been evaluated in animals. Porter et al [22] measured the uptake of antisense to c-myc into coronary arteries using high phase liquid chromatography in pigs. Intravenous PESDA containing anti c-myc was given in the presence or absence of transthoracic 1 MHz ultrasound (0.6 W/cm2). In this study, the authors demonstrated that anti c-myc can be selectively concentrated within a stretch-injured coronary artery segment when given intravenously bound to PESDA. The decrease in neointimal formation following intravenous injection of anti c-myc with PESDA without ultrasound was similar to that observed with higher doses of the same antisense given directly into the coronary artery using an infiltrator delivery system [30]. The basis for this hypothesis stems from previous observations that albumin-coated microbubbles adhere to activated endothelial cells [11,21,31]. Albumin-coated microbubbles have been observed binding to activated leukocytes and monocytes which slowly roll along injured venular endothelial cells [11]. Since leukocyte and monocyte accumulation has also been observed early following arterial balloon injury [32], it is possible that PESDA microbubbles were concentrated at the injured coronary artery surface by adherence to these activated cells. Other potential mechanisms could be related to complement activation, since both albumin- and lipid-encapsulated microbubbles take up complement proteins [33], and thus may bind to upregulated complement receptors at the injured surface. It was recently demonstrated that albumin-coated microbubbles adhere to sites of arterial endothelial dysfunction induced by balloon-injury of carotid arteries [34]. Figure 2 illustrates an example of microbubble binding to the endothelium of an injured carotid artery, which was confirmed by scanning electron microscopy. Lu et al [35] have also shown that albumin-coated microbubbles significantly improved transgene expression in skeletal muscle of mice, even in the absence of ultrasound. However, in this study, the delivery was an intramuscular injection of microbubbles and plasmid into otherwise normal tissue, and not in the setting of endothelial injury [35].
Figure 2 Ultrasound images with low mechanical index pulse sequence scheme showing the presence of microbubbles binding to the arterial endothelium in a balloon-injured carotid artery (Panel A, right) and the absence of microbubbles in the control noninjured carotid artery (Panel B, right). Scanning electron microscopy (Bar = 10 μm; magnification 1420 ×) revealed sites of injury with endothelial denudation and attachment of microbubbles (black arrows) to the denuded endothelium only in the injured vessel (A) and normal appearing endothelium in the control vessel (B). (Reprinted with permission from Tsutsui JM, Xie F, Radio SJ, Phillips P, Chomas J, Lof J et al. Non-invasive detection of carotid artery endothelial dysfunction due to hypertriglyceridemia and balloon injury with high frequency real time low mechanical index imaging of retained microbubbles. J Am Coll Cardiol 2004;44:1036-46).
Another innovative application of microbubbles and ultrasound is in the delivery of proteins that induce growth of endothelial cells, such as vascular endothelial growth factor (VEGF). Mukherjee et al [10] demonstrated a marked increase in endothelial VEGF uptake using ultrasound alone (eight-fold increase) and using ultrasound and PESDA (ten- to thirteen-fold increase, as compared to control) in the myocardium of rats. In a canine model of chronic myocardial ischemia, intravenous infusion of VEGF combined with ultrasound and an albumin-based contrast agent significantly reduced the infarct area/risk area ratio, and increased myocardial blood flow in the ischemic territory, suggesting a new potential therapeutic approach for angiogenesis [36].
Optimization of Ultrasound Parameters for Cardiac Drug and Gene Delivery
The effect of several ultrasound parameters, including transducer frequency and acoustic power, are known to influence microbubble destruction and, thus, the transfection of genes and drugs. Although the optimal ultrasound parameters for maximizing this process are not known, we will briefly discuss some important aspects. Unger et al [6] have shown that the type of ultrasound used to destroy phospholipid-coated microbubbles may regulate how much drug is released in vitro. When analyzing the number of acoustically active particles that persist after exposure to different types of ultrasound in a flow chamber, they demonstrated that a 2.5 MHz transducer resulted in some destruction, but the addition of a lower-frequency transducer (100 kHz) significantly increased the destruction. When the 100 kHz energy was given in a pulsed-wave mode as opposed to a continuous wave, the destruction of microbubbles was even faster. In a similar way, Porter et al [21] have demonstrated that a continuous wave diagnostic ultrasound frequency of 2 MHz was not able to enhance the carotid uptake of antisense to c-myc protooncogene (0.19 ± 0.04 μg/mg), but low-frequency 20 kHz ultrasound significantly increased vascular uptake (0.28 ± 0.04 μg/mg; p = 0.008 vs other groups) when compared to antisense bound to PESDA alone (0.21 ± 0.06 μg/mg). The results of this study suggested that a lower frequency could be better suited to target antisense deposition into major vessels. Because there were minimal differences in peak negative pressure generated by 2 MHz and 20 kHz in this study (46 kPa and 13 kPa, respectively), the enhanced uptake was attributed to a lower threshold for cavitation with 20 kHz ultrasound frequency.
In another study, the efficacy of ultrasound-mediated delivery of VEGF bound to PESDA into the myocardium of rats was evaluated with an ultrasound frequency of 1.0 MHz at various acoustical outputs (0.2, 0.4, 0.6, 0.8 and 1.0 W/cm2). The authors found a significant increase in VEGF uptake with the combination of ultrasound and PESDA at all power outputs when compared with controls, but there was a dose-dependent increase in the amount of VEGF uptake with increasing power until 0.6 W/cm2 and a subsequent plateau. Table 1 illustrates some parameters used in previous studies for drug and gene delivery. It seems that at higher frequencies, higher peak negative pressures are necessary to induce cavitation-mediated drug and gene delivery using microbubbles and ultrasound. In a recent study of Chen et al [8] it was shown that, when using ultrasound at diagnostic frequencies, optimal ultrasound parameters for gene expression by ultrasound-targeted microbubble destruction to the myocardial microcirculation included a low-transmission frequency (1.3 MHz), high mechanical index, and electrocardiogram triggering to allow complete filling of the myocardial capillary bed by microbubbles. The authors found that maximal acoustic pressure resulted in higher myocardial gene expression, providing indirect evidence that high peak negative pressures increase the amount of gene delivery from microbubbles. Furthermore, the optimal ultrasound parameters for targeted delivery may be dependent on the desired site for delivery. While a triggered mechanism of once every four to five seconds may work for delivering drugs by ultrasound-mediated destruction of microbubbles in the myocardial microcirculation, a more frequent pulsed delivery may be required for vascular delivery.
Table 1 Ultrasound parameters and microbubbles used for delivering genes and drugs.
Author Transfection Transducer frequency Delivery mode Delivery site Output Peak negative pressure Microbubble Efficacy
Porter TR, et al1 Antisense c-myc protooncogene 1 MHz PW Coronary arteries 0.6 W/cm2 PESDA +
Zhou Z, et al2 VEGF 1 MHz CW Myocardium 1.2 W/cm2 Sonazoid +
Taniyama Y, et al3 Luciferase Carotid artery 2.5 W/cm2 Optison +
Teupe C, et al4 β-galactosidase/ eNOS 2.2–4.4 MHz CW Coronary arteries Gas-filled albumin microbubble +
Porter TR, et al5 Antisense c-myc protooncogene 2 MHz CW Carotid artery 13 kPa PESDA -
20 kHz CW Carotid artery 46 kPa PESDA +
Mukherjee D, et al6 VEGF 1.0 MHz CW Myocardium 0.2 W/cm2 0.164 MPa PESDA 9.37 ± 1.98*
1.0 MHz CW Myocardium 0.4 W/cm2 0.194 MPa PESDA 18.58 ± 2.46*
1.0 MHz CW Myocardium 0.6 W/cm2 0.328 MPa PESDA 23.12 ± 3.95*
1.0 MHz CW Myocardium 0.8 W/cm2 0.394 MPa PESDA 25.46 ± 2.78*
1.0 MHz CW Myocardium 1.0 W/cm2 0.419 MPa PESDA 26.48 ± 3.98*
Shohet RV, et al7 β-galactosidase 1.3 MHz ECG-triggered Myocardium Perfluorocarbon-filled microbubbles +
Bao S, et al8 Luciferase 2.25 MHz Cultured cells 0.2–0.4 MPa Albunex +
* Efficacy is demonstrated as mean ± SD endothelial vascular growth factor uptake by enzyme-linked immunosorbent assay. CW = continuous wave; ECG = electrocardiogram; eNOS = endothelial nitric oxide synthase; PESDA = perfluorocarbon-exposed sonicated dextrose and albumin; PW = pulsed wave; VEGF = vascular endothelial growth factor.
However, a high peak negative pressure may have detrimental bioeffects. Several investigators have reported on the occurrence of tissue hemorrhage and endothelial cell damage after ultrasound exposure of cultured cells and organs containing air, such as the lungs or the intestine [37-39]. Ay et al [38] examined the functional and morphological effects of ultrasound and contrast in an isolated rabbit heart preparation, using increasing levels of acoustic energy. Simultaneous exposure to contrast and high-energy ultrasound resulted in a reversible and transient decrease in left ventricular contractile performance, increase in the coronary perfusion pressure, increase in the myocardial lactate release, and presence intramural hemorrhage in the plane of ultrasound transmission. Additionally, light microscopy revealed the presence of capillary ruptures, erythrocyte extravasation and endothelial cell damage. These effects were directly related to the mechanical index. These studies indicate that although high-energy ultrasound seems to be necessary to induce tissue permeability facilitating local drug delivery, it may also have significant bioeffects in the myocardium. Therefore, the optimal ultrasound parameters to enhance drug delivery with microbubbles remain to be determined.
Competing interests
Dr. Jeane M. Tsutsui – declares no competing interests.
Dr. Feng Xie – declares no competing interests.
Dr. Thomas R. Porter – declares ImaRx Therapeutics, Inc.: Grant support and Consultant; Bristol Myers Squibb Medical Imaging: Grant support; AVI BioPharma, Inc.: Grant support
Figure 1 Intravascular ultrasound examples of the proximal reference site and balloon injury site 30 days after the angioplasty. Note that there was both greater intimal thickening (arrows) in the vessel treated with intravenous antisense alone, and a reduction in lumen size when compared to the proximal reference segment. The balloon injury site of the vessel treated with intravenous antisense plus PESDA and 20 kHz transcutaneous ultrasound did not exhibit any reduction in lumen area or visually evident plaque. (Reprinted with permission from Porter TR, Hiser WL, Kricsfeld D, Deligonul U, Xie F, Iversen P et al: Inhibition of carotid artery neointimal formation with intravenous microbubbles. Ultrasound Med Biol 2001, 27:259-265).
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Cardiovasc DiabetolCardiovascular Diabetology1475-2840BioMed Central London 1475-2840-3-101557419910.1186/1475-2840-3-10EditorialWhich is the best lipid-modifying strategy in metabolic syndrome and diabetes: fibrates, statins or both? Tenenbaum Alexander [email protected] Enrique Z [email protected] Cardiac Rehabilitation Institute, the Chaim Sheba Medical Center, 52621 Tel-Hashomer, Ramat-Gan, Israel2 Sackler Faculty of Medicine, Tel-Aviv University, 69978 Ramat-Aviv, Tel-Aviv, Israel2004 1 12 2004 3 10 10 1 12 2004 1 12 2004 Copyright © 2004 Tenenbaum and Fisman; licensee BioMed Central Ltd.2004Tenenbaum and Fisman; 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.
Although less clinical intervention studies have been performed with fibrates than with statins, there are evidences indicating that fibrates may reduce risk of cardiovascular events. The potential clinical benefit of the fenofibrate will be specified by the ongoing Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, which rationale, methods and aims have been just published.
Controlled clinical trials show similar or even greater cardiovascular benefits from statins-based therapy in patient subgroups with diabetes compared with overall study populations. Therefore, statins are the drug of first choice for aggressive lipid lowering actions and reducing risk of coronary artery disease in these patients. However, current therapeutic use of statins as monotherapy is still leaving many patients with mixed atherogenic dyslipidemia at high risk for coronary events. A combination statin/fibrate therapy may be often necessary to control all lipid abnormalities in patients with metabolic syndrome and diabetes adequately, since fibrates provide additional important benefits, particularly on triglyceride and HDL-cholesterol levels. Thus, this combined therapy concentrates on all the components of the mixed dyslipidemia that often occurs in persons with diabetes or metabolic syndrome, and may be expected to reduce cardiovascular morbidity and mortality.
Safety concerns about some fibrates such as gemfibrozil may lead to exaggerate precautions regarding fibrate administration and therefore diminish the use of the seagents. However, other fibrates, such as bezafibrate and fenofibrate appear to be safer and better tolerated. We believe that a proper co-administration of statins and fibrates, selected on basis of their safety, could be more effective in achieving a comprehensive lipid control as compared with monotherapy.
Diabetes mellitusDyslipidemiaFibratesMetabolic syndromeStatins
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Due to their beneficial effects on glucose and lipid metabolism, peroxisome proliferator activated receptors (PPAR's) alpha agonists (fibrates) are good potential candidates for reducing the risk of myocardial infarction (MI) in subjects with metabolic syndrome [1-3]. Although less clinical intervention studies have been performed with fibrates than with statins, there are evidences indicating that fibrates may reduce risk of cardiovascular disease and particularly non-fatal MI [4-10]. Interestingly, reduction of cardiovascular disease with one of the fibric acid derivates – gemfibrozil – was more pronounced in patients displaying baseline characteristics very similar to metabolic syndrome definitions [4,5].
There have been no direct head-to-head comparisons of a statin with a fibrate in any clinical endpoint trial. However, compared with statins, fibrates appear to more selectively target the therapeutic goals in obese individuals with features of insulin resistance and metabolic syndrome (i.e. with near-goal low-density lipoprotein (LDL)-cholesterol and inappropriate high-density lipoprotein (HDL)-cholesterol and triglyceride levels).
The primary-prevention trial Helsinki Heart Study showed that treatment with gemfibrozil led to a significant reduction in major cardiovascular events [4]. Regarding secondary prevention, in the VAHIT study (Veterans Affairs High-density lipoprotein cholesterol Intervention Trial) – which included 30% of diabetic patients – gemfibrozil reduced the occurrence of major cardiovascular events by 22 % [5]. Similarly, reduction of cardiovascular disease with gemfribrozil was more pronounced in patients displaying above three of the features of metabolic syndrome [11,12].
In two previous small studies bezafibrate decreased the rate of progression of coronary atherosclerosis and decreased coronary event rate [6,7]. In another large trial in 1568 men with lower extremity arterial disease with a relatively short follow-up period, bezafibrate reduced the severity of intermittent claudication for up to three years. [8]. Ingeneral, the incidence of coronary heart disease in patients on bezafibrate has tended to be lower , but this tendency did not reach statistical significance. However, bezafibrate had significantly reduced the incidence of non-fatal coronary events, particularly in those aged <65 years at entry, in whom all coronary events may also be reduced [8]. In the Bezafibrate Infarction Prevention (BIP) study an overall trend of a 9.4% reduction of the incidence of primary end point (fatal or non-fatal myocardial infarction or sudden death) was observed. The reduction in the primary end point in 459 patients with high baseline triglycerides (≥200 mg/dL) was significant [9]. These results are consistent with studies in experimental models showing that pre-treatment of rats with the PPAR-alpha agonist clofibrate causes a significant reduction in induced myocardial infarct size of 43% [13].
Recently, reduced incidence of type 2 diabetes in patients with impaired fasting glucose level on bezafibrate has been demonstrated [14]. The potential clinical benefit of the other widespread fibric acid derivative, fenofibrate, on the reduction of cardiovascular disease is still unknown and will be specified by the ongoing Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study, which rationale, methods and aims have been just published [15]. It will be the largest (approximately 10000 patients) ever conducted fibrate-based controlled clinical trial in diabetic patients. The results are expected for 2005. An added strength of this trial is its ability to examine important clinical outcomes across diverse ethnic and gender subgroups. The results of this study will clarify whether the beneficial lipid-modifying effects of micronised fenofibrate lead to a reduction of cardiovascular morbidity and mortality.
Despite increasing use of statins, a significant number of coronary events still occur and many of such events take place in patients presenting with the metabolic syndrome. Whereas statins remain the drug of choice for patients who need to achieve the LDL-cholesterol goal, fibrate therapy may represent the alternative intervention for subjects with atherogenic dyslipidemia typical for metabolic syndrome and an LDL-cholesterol already close to goal values. In addition, the concomitant use of fibrates seems to be attractive in patients whose LDL-cholesterol is controlled by statin therapy but whose HDL-cholesterol and/or triglyceride levels are still inappropriate [16-19]. This strategy will be tested in the ongoing Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial [20].
The factor that dominates in overweight-related metabolic syndrome is the permanent elevation of plasma free fatty acids (FFA) and the predominant utilization of lipids by the muscle inducing a diminution of glucose uptake and insulin resistance. Currently, an insulin-resistant state – as the key phase of metabolic syndrome – constitutes the major risk factor for development of macrovascular complications [21-23]. On the basis of the current concept of the evolution of adipogenesis via PPAR modulation toward insulin resistance and atherothrombotic macrovascular complications (including MI), the decreasing of plasma FFA and improving of insulin sensitization by PPAR agonists seems to be a logical and valuable goal for therapy.
It is important to note that on a whole-body level, lipid and glucose metabolisms interact intimately. Briefly, PPAR alpha is activated by fibric acids (e.g. bezafibrate) and form heterodimers with the 9-cis retinoic acid receptor (RXR). These heterodimers bind to peroxisome proliferator response elements, which are located in numerous gene promoters and increase the level of the expression of mRNAs encoded by PPAR alpha target genes. Bezafibrate reduces triglyceride plasma levels through increases in the expression of genes involved in fatty acid-beta oxidation and by decrease in apolipoprotein C-III gene expression. Fibric acids increase HDL-cholesterol partly by increasing apolipoprotein A-I and apolipoprotein A-II gene expression. Their triglyceride-lowering and HDL-cholesterol raising effects lead to decreased systemic availability of fatty acid, diminished fatty acid uptake by muscle with improvement of insulin sensitization and reduced plasma glucose level [24-28].
Evidence also suggests that there is a 'fibrate effect' that mediates the reduction in CHD risk beyond the favorable impact of these agents on HDL-cholesterol levels. This last notion is consistent with the pleiotropic effects of fibrates which are known to be related to their mechanisms of action [29]. Being PPAR alpha ligands, fibrates have a significant impact on the synthesis of several apolipoproteins (apo) and enzymes of lipoprotein metabolism as well as on the expression of several genes involved in fibrinolysis and inflammation. Such changes contribute to improve the catabolism of triglyceride-rich lipoproteins, leading to a substantial increase in HDL-cholesterol levels accompanied by a shift in the size and density of LDL particles (from small, dense LDL particles to larger, more buoyant cholesteryl ester-rich LDL).
Controlled clinical trials show similar or even greater cardiovascular benefits from statins-based therapy in patient subgroups with diabetes, impaired fasting glucose, and metabolic syndrome, compared with overall study populations. Therefore, statins are the drug of first choice for aggressive lipid lowering actions and reducing risk of coronary artery disease in these patients. However, current therapeutic use of statins as monotherapy is still leaving many patients with mixed atherogenic dyslipidemia at high risk for coronary events.
A combination statin/fibrate therapy may be often necessary to control all lipid abnormalities in patients with metabolic syndrome and diabetes adequately, since fibrates provide additional important benefits, particularly on triglyceride and HDL-C levels. Thus, this combined therapy concentrates on all the components of the mixed dyslipidemia that often occurs in persons with diabetes or metabolic syndrome, and may be expected to reduce cardiovascular morbidity and mortality.
Safety concerns about some fibrates such as gemfibrozil may lead to exaggerate precautions regarding fibrate administration and therefore diminish the use of the seagents. However, other fibrates such as bezafibrate and fenofibrate appear to be safer and better tolerated [30-36]. We believe that a proper co-administration of statins and fibrates, selected on basis of their safety, could be more effective in achieving a comprehensive lipid control as compared with monotherapy.
Competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
This work was supported in part by the Cardiovascular Diabetology Research Foundation (RA 58-040-684-1), Holon, Israel, and the Research Authority of Tel-Aviv University (Citernick grant 01250239).
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| 15574199 | PMC538252 | CC BY | 2021-01-04 16:38:07 | no | Cardiovasc Diabetol. 2004 Dec 1; 3:10 | utf-8 | Cardiovasc Diabetol | 2,004 | 10.1186/1475-2840-3-10 | oa_comm |
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Int J Health GeogrInternational Journal of Health Geographics1476-072XBioMed Central London 1476-072X-3-271554833210.1186/1476-072X-3-27MethodologyHistorical measures of social context in life course studies: retrospective linkage of addresses to decennial censuses Rose Kathryn M [email protected] Joy L [email protected] Sarah [email protected] Ricardo A [email protected] Eric A [email protected] Roux Ana V [email protected] DongKeun [email protected] Gerardo [email protected] Department of Epidemiology, School of Public Health, The University of North Carolina at Chapel Hill, USA2 Department of Medicine, The University of North Carolina at Chapel Hill, USA3 The University of Michigan at Ann Arbor School of Public Heath, Ann Arbor, MI, USA4 Department of City and Regional Planning, Cornell University, Ithaca NY, USA2004 17 11 2004 3 27 27 8 9 2004 17 11 2004 Copyright © 2004 Rose et al; licensee BioMed Central Ltd.2004Rose 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
There is evidence of a contribution of early life socioeconomic exposures to the risk of chronic diseases in adulthood. However, extant studies investigating the impact of the neighborhood social environment on health tend to characterize only the current social environment. This in part may be due to complexities involved in obtaining and geocoding historical addresses. The Life Course Socioeconomic Status, Social Context, and Cardiovascular Disease Study collected information on childhood (1930–1950) and early adulthood (1960–1980) place of residence from 12,681 black and white middle-aged and older men and women from four U.S. communities to link participants with census-based socioeconomic indicators over the life course.
Results
Most (99%) participants were linked to 1930–50 county level socioeconomic census data (the smallest level of aggregation universally available during this time period) corresponding to childhood place of residence. Linkage did not vary by race, gender, birth cohort, or level of educational attainment. A commercial geocoding vendor processed participants' self-reported street addresses for ages 30, 40, and 50. For 1970 and 1980 censuses, spatial coordinates were overlaid onto shape files containing census tract boundaries; for 1960 no shape files existed and comparability files were used. Several methods were tested for accuracy and to increase linkage. Successful linkage to historical census tracts varied by census (66% for 1960, 76% for 1970, 85% for 1980). This compares to linkage rates of 94% for current addresses provided by participants over the course of the ARIC examinations.
Conclusion
There are complexities and limitations in characterizing the past social context. However, our results suggest that it is feasible to characterize the earlier social environment with known levels of measurement error and that such an approach should be considered in future studies.
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Background
Consideration of the impact of neighborhood social environment on health is now common in social epidemiologic studies [1-7]. While studies of the influence of individual measures of socioeconomic status (SES) on health often include queries for various points during the life course [8-10], estimates of the impact of the neighborhood environment have tended to characterize only the current social context. Current addresses are typically sent to a commercial geocoding vendor and proprietary software is used in conjunction with the Topologically Integrated Geographic Encoding and Referencing (TIGER/Line®) files to link the addresses with spatial coordinates within statistical tabulation areas [block group, tract, zip code tabulation area, county]. Notwithstanding concerns about the accuracy in the assignment of statistical tabulation areas by commercial geocoders [11-14], efforts to geocode current addresses are generally successful with reported match rates of 90% or higher at the tract and block group level [1,15].
Obtaining and geocoding historical addresses is more complex and has rarely been undertaken despite the potential advantages derived from its inclusion in life course studies. The completeness and accuracy of historical addresses may not be as high as that of current addresses, unless added care is taken during data collection. Further, widespread use of geocoding in research applications is relatively new and commercial geocoding databases are typically optimized to current street atlases and most recent census tract boundaries. Accurate past addresses, even when assigned correct spatial coordinates, would not be linked with correct historical social contextual data if census tracts had not been defined or summary census data was not available for the area when an individual resided at the address or if the census boundaries had changed over time.
The Life Course SES, Social Context, and Cardiovascular Disease (LC-SES) Study retrospectively collected place of residence during childhood and earlier adulthood on a cohort of middle-aged and older persons. We report on the methods used and our success rate in placing participants into historical census areas and linking them with measures of the social context over time based on self-reported place of residence during childhood and at ages 30, 40, and 50 years.
Results
The procedures used to obtain the results described in this section are explained in detail in the methods section of this paper as well as in the LC-SES Study manual of procedures, available on the study website [16].
Linkage of childhood residence to county level census data from 1930–1950
Of 12,681 participants, we excluded 304 who reported living outside of the United States during most of their childhood. Of the remaining 12,377 participants 86% provided apparently correct information on county and state and 10% provided a county which was misspelled. Spelling errors were corrected using a listing of counties in the U.S. available on a publicly accessible website [17]. The remaining 4% of participants did not provide any information on city or county, transposed city and county information, or provided information on a city but not county. Obvious transposition errors were corrected and in cases where the participant provided a city but not a county we searched the publicly available website [17] to identify the matching county. In instances where a city of the same name was listed in multiple counties (n = 27), we did not assign a county. In all, 12,187 (98.5%) of participants reporting a childhood residence in the U.S. were successfully linked with county level U.S. census data. Linkage did not vary by race, gender, adult educational attainment or birth cohort (data not shown).
Birth cohort and geographical distribution of participants
Participants ranged in age from 45–64 years at the baseline ARIC examination (1987–89). Given their 20 year age span, the years at which they were aged 30 (and 40 and 50) years ranged over several decades, requiring that those from different birth cohorts be linked to data from different census years (Table 1).
Table 1 Number of participants assigned to 1960–1980 censuses, overall and by age decade, the LC-SES study, 2001–2002
Census Year Age 30
N Age 40
N Age 50
N Total
N
1960 7085 1115 - 8200
1970 5596 5965 1110 12671
1980 - 1891 1386 3277
Total 12681 8971 2496 24148
At baseline, ARIC participants were recruited based on their stable residence in the four study communities. However, at age 30 participants were residing in all 50 states, at age 40 in 47 states, and at age 50 in 31 states. Nonetheless, as shown in Table 2, for all three ages, most participants were already residing in the study areas (as defined by county and state). By age 50, 91% were residing in the study area and only 5% lived out of state.
Table 2 Correspondence of county and state of residence at ages 30, 40, and 50 to that at time of ARIC visit 1 exam, the LC-SES study, 2001–2002
Age 30 Age 40 Age 50
County and state of residence at ages 30–50 vs. that at ARIC baseline examination N = 12,681
% N = 8,971
% N = 2,496
%
Residence in same county and state 72 85 91
Residence in different county but same state 11 6 4
Residence in different county and state 17 9 5
Linkage of addresses at ages 30, 40, and 50 to 1960–1980 census tract data
Table 3 provides a summary of results for linking with 1990 geocoding maps (stage 1 of the process). We submitted 22,140 (92%) of all historical addresses [address refers to a street name and number (if available) and city and state] provided by participants to the geocoding vendor. Those not submitted included P.O. box addresses, as well as those for whom no street information was provided. Of the addresses submitted to the vendor, 75% were assigned spatial coordinates that placed the addresses within a 1990 census tract. About half of the 5,550 addresses that were not assigned coordinates within a census tract were street names without numbers; the rest were cross-streets and apparently complete addresses.
Table 3 Historical addresses queried, geocoding success rates and characteristics of addresses not successfully geocoded, the LC-SES study, 2001–2002. 1The address information was assigned to the centroid of a zip code area in which all addresses fell within a single block group or census tract or more than 80% of addresses fell within the same census tract.
N %age
Addresses for ages 30, 40, and 50 queried 24,148 100
Partial or complete street address provided 22,140 92
No address, P.O box, or no street name 2,008 8
Commercial Geocoding Results 22,140 100
Address match (geocoded to 1990 census tract) 16,445 74
Usable centroid match1 145 <1
Not matched or geocoded to census tract 5,550 25
Characteristics of addresses not matched or geocoded to census tract 5,550 100
Street with number 1,690 30
Cross-street 923 17
Street name without number 2,937 53
Table 4 summarizes our linkage of addresses using the two step process of first linking to the 1990 geocoding maps to get spatial coordinates, and then using the spatial coordinates to obtain the comparable 1960, 1970, and 1980 census tracts. The proportion of addresses judged to be adequate for commercial geocoding (participant recalled at least a partial street address and a city and state) was modestly lower for 1960 than for later years. The proportion that were successfully geocoded to a 1990 tract, and the proportion that were assigned a tract for the historical census, increased steadily from 1960 to 1980. Most addresses with a 1990 tract assignment were placed into the appropriate historical tract for the 1970 and 1980 censuses. Although 61% of 1960 addresses were successfully geocoded to a 1990 tract, only 26% could be assigned a 1960 tract, largely because much of the U.S. was not assigned census tracts in 1960. Use of tract data from the next available census (1970) increased the yield by 16%.
Table 4 Percentages of addresses geocoded to 1990 census and then assigned to a 1960, 1970, and 1980 census tract, the LC-SES study, 2001–2002. 1Jackson, MS & Washington Co., MD print files of U.S. Bureau of the Census housing data for 1960 [34]. 2Includes some addresses sent to vendor but not assigned a latitude and longitude.
% of All Addresses
Street addresses for 1960 (N = 8200)
Sent to geocoding vendor 89
Vendor assigned latitude and longitude 61
1960 tract assigned using overlay/comparability file 26
1960 Assigned area by overlay (non tract area)1 16
1970 tract Assigned (non tract area-1960) 16
1960 tract Assigned manually2 8
Total addresses assigned tract 66
Street addresses for 1970 (N = 12671)
Sent to geocoding vendor 93
Vendor assigned latitude and longitude 71
1970 tract assigned using overlay 69
1970 tract assigned manually2 7
Total addresses assigned tract 76
Street addresses for 1980 (N = 3277)
Sent to geocoding vendor 94
Vendor assigned latitude and longitude 81
1970 tract assigned using overlay 80
1970 tract assigned manually2 5
Total addresses assigned tract 85
Manually assigning tracts to addresses modestly increased the proportions that were successfully assigned a census tract for the censuses corresponding to places of residence at ages 30–50 (increase of 8% for 1960, 7% for 1970, and 5% for 1980). Success rates associated with efforts to manually assign historic tracts to addresses varied considerably across study areas and also according to the reasons for the failure of the automated geocoding procedure. Of the addresses which we attempted to manually assign a census tract, we were successful for 54% of Forsyth, NC addresses, 45% of Jackson, MS addresses, 30% of Minneapolis, MN address and 29% of Washington County, MD addresses (data not shown). Rates were particularly low in MD because many roads were located in areas not classified into tracts in the 1960 census and because the conversion to a grid address system – during which time some streets renumbered and renamed – did not occur until the early 1990s. In contrast, the low success rate in MN occurred primarily because the tracts were physically small and streets tended to cross multiple tracts.
Figure 1 shows the proportion of participants not linked to a historical census tract by adult age and census year. Overall, the rate of successful assignment to a historical census tract was lower at younger ages (67% for age 30, 81% for age 40, 86% for age 50) and within each age decade, for earlier censuses.
Figure 1 Percentage of addresses not assigned a census tract by age and census year, the LC-SES study, 2001–2002.
Variation in linkage to census tracts by sociodemographic characteristics
Table 5 presents childhood and midlife socio-demographic characteristics of participants by success of census tract assignment at ages 30, 40, and 50. There was no difference in the proportion of participants assigned a census tract by mean age at baseline. African-Americans comprised modestly higher proportions of those groups not assigned to census tracts. For ages 30 and 40, a modest but greater proportion of men were in the group not assigned a census tract. There were differences in both educational attainment and family income between those assigned and not assigned census tracts. Those in the lowest strata of income and education tended to be more heavily represented in the group not assigned census tracts; this pattern was also observed for those in the highest educational group at age 30.
Table 5 Comparison of sociodemographic characteristics of those with and without tract assignment1, the LC-SES study, 2001–2002. 1Chi Square test used to statistically compare differences in proportions and t-test used to statistically compare differences in means of those assigned and not assigned census tracts. 2Ns for each characteristic vary slightly due to missing data. * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001
Age 30 Age 40 Age 50
Tract assigned Tract assigned Tract assigned
Characteristics at baseline examination Yes No Yes No Yes No
Total N (%)2 8,448 (67) 4,233 (33) 7,262 (81) 1,709 (19) 2,143 (86) 353 (14)
Mean age 53 54 56.3 57 62 62
% Male 41 46**** 43 47* 46 46
% African American 25 26 23 25 21 25
Educational attainment (%)
< 12 years 21 21**** 23 26*** 28 35***
12 years or equivalent 44 36 42 37 40 40
> 12 years 35 43 35 37 32 35
Family income (1987–89) (%)
< $16,000 19 21** 20 25**** 28 36*
$16,00–49,999 55 51 54 53 56 51
$50,000 26 28 25 22 16 13
% Born outside of study state 20 38**** 23 36**** 23 35****
Father's education (%)
0–9 years 53 48**** 55 52 57 61
9–12 years 35 34 32 34 29 23
> 12 years 12 17 13 14 14 15
Father's occupation (%)
Professional & management 11 14 11 12 10 15
Technical & sales 11 11 11 11 12 8
Mechanical & crafts 21 18 20 19 21 15
Farming 30 31 32 33 34 39
Laborers, operators & drivers 21 19 20 19 17 14
Service 7 7 7 6 7 8
% Parents owning home 92 86**** 93 82**** 93 80****
There were variations, albeit inconsistent, in early life sociodemographic characteristics and assignment to census tract of residence at ages 30, 40, and 50. Those living outside of their current (at baseline) state of residence during childhood were markedly less likely to be linked with a census tract at ages 30–50, while those whose parents were homeowners were more likely to be assigned tracts. Those who had fathers with twelve or more years of education or who were in managerial and professional or in farming occupations were modestly but more heavily represented in the groups not assigned tracts. In contrast, those with fathers who were in blue collar occupations (mechanical and crafts; laborers, operators, and drivers) were consistently less likely to be in the group not assigned tracts. These differences by fathers' occupations, while consistent, were generally modest.
Discussion
Assessment of social circumstances in childhood and early adulthood in life course studies is typically limited to individual level measures of parental / own occupation or education [8-10]. The contribution of the contemporaneous social context to a variety of health outcomes [1,4-6] suggests that evaluation of the impact of earlier socio-environmental exposures on health is also of interest. Its inclusion in life course paradigms is not novel [18,19] but its implementation in population-based studies in the U.S. is, in part because of uncharted approaches to the measurement of historical context on a scale suitable to epidemiologic studies. We report on our methods, completion, and error rates in retrospectively collecting former places of residence in a middle-aged and older cohort and linking this information with census data. We successfully linked 99% of participants with 1930–1950 county level census data corresponding to their childhood place of residence. Successful linkage of addresses from ages 30–50 with corresponding census tract level data from the 1960–80 censuses was lower. Approximately two-thirds of participants were assigned a census tract for 1960, 76% for 1970 and 85% for 1980. For purposes of comparison, ARIC participant addresses at the time of each of the ARIC examinations (1987–1999) achieved geocoding match rates (by the same vendor) of 94%.
Match rates of participants' addresses to the 1960–1980 U.S. census tracts were lower largely for three reasons: limited ability to recall complete historical addresses, obsolete or unusable addresses (e.g., change in street numbering, renaming of rural routes), and the previously incomplete coverage of census tracts in the U.S. Linkage rates were considerably higher in 1970 when census tracts were in place at all of our study sites. Now the coverage of census tracts is complete for the U.S. and grid address systems are common even in rural areas. Thus, studies of more recent birth cohorts though still faced with limitations of recall would be expected to have higher linkage rates.
The yield from attempts to commercially geocode incomplete street addresses (e.g., street name but not number) were quite low, even when street fell completely within the boundaries of one census tract. TIGER/Line® files represent streets as a series of segments. When streets consisted of more than one segment, even when all were located within a single tract, it appears that commercial geocoding software was not able to assign a census tract. We were able to assign census tracts to a sizable portion of these addresses by using detailed street maps overlaid with historical census boundaries. However, this process involves multiple steps and is labor intensive and thus can be practically implemented only in areas where a sizable number of addresses are located.
Recall of county, city, and state of residence during childhood was virtually complete, while recall of street address of former places of residence was more limited. The potential limitations of retrospectively recalled data are known [20,21], suggesting the need to assess the accuracy of addresses corresponding to former places of residence information provided by interviewees. Review of a subset of decedents indicated that recall of county and state of birth showed greater than 90% concordance with that recorded on their birth certificate (KM Rose, unpublished data). While it is technically possible to use historical city directories to verify addresses, privacy concerns prevent us from linking addresses to participant names. In future studies, advanced notification to the interviewee should be considered as it would offer them the opportunity to consult records and / or a spouse, potentially reducing the degree to which some individuals may not be able to recall a complete street address.
Linkage to county-level place of childhood residence did not vary by participant sociodemographic characteristics. In contrast, successful linkage of the later but more detailed address information to 1960–1980 census tract data varied by sociodemographic characteristics (gender, family income, father's and own education). More striking was the substantial difference seen between those born in vs. outside the study state. Those born outside of the study states were between 1.5 and 1.9 times more likely to not be assigned a tract than those born in one of the study states. To some extent, this occurred because a higher proportion of the participants born in other states originated from areas lacking census tracts at the time of the pertinent historical census.
The optimal geographical unit of analysis for contextual measures is discussed in the literature [22-24]. Studies tend to use either census tracts or block groups, and reports suggest that the two produce similar results [1,22]. There is concern that data aggregated at the county level is not optimal to characterize the social environment. However, ecological studies as well as those including an assessment of individual-level SES [25-29] have reported inverse associations between county-level socioeconomic characteristics and health outcomes. Since childhood county of residence is recalled quite well per our results and it corresponds to the smallest level of geographical aggregation at which census data is available prior to 1960, its use as a measure of the social environment in life course studies deserves consideration.
Our purpose in assigning current and former adulthood places of residence to census tracts was to link participants with census-based neighborhood profiles to provide area-based measures of the social context (s) across epochs spanning early to later adulthood. Although the approach presented here had not been previously attempted and has logistical complexities, its feasibility, success and error rates are now documented. The opportunity to acquire area-based measures of SES in a historical context does not obviate the methodologic challenges associated with life course research, however. Among the latter it is worth mentioning that many census variables differ across censuses (see table entitled "SES var by census in the Census Tract SES section of the LC-SES Study website [30]). For example, the percentage living below the poverty level was not calculated until the 1970 census and prior to 1940, years of education were not collected. Also, the meanings and distributions of census variables are subject to secular change (i.e., over time the average educational level of the U.S. population has increased, mean/median incomes and housing values change across time). Thus, careful consideration of birth cohort effects and of the social and economic contexts at each point of data collection is required.
Conclusions
The importance of the social and economic environments in influencing health is increasingly recognized, yet most research to date is limited to the current social context [1-6]. We believe that this deficit is largely driven by the greater complexity and limitations inherent in retrospectively characterizing the past social context. The experience of the LC-SES study suggests that it is feasible to do this effectively. Studies incorporating such an approach offer the potential of improved understanding of socioenvironmental influences over the life course on health, and should be considered.
Methods
Study participants
The Atherosclerosis Risk in Communities (ARIC) study is an investigation of the etiology and natural history of atherosclerosis and its sequelae. At baseline (1987–89), 15,792 African American and white middle-aged men and women from four U.S. communities (Forsyth County, NC; Jackson, MS; the northwest suburbs of Minneapolis, MN; and Washington County, MD) were included. An account of the design and procedures is published [31]. Since baseline, the ARIC study telephones participants annually to establish vital status and assess indices of cardiovascular disease, including hospitalizations. Institutional Review Boards (IRB) at each ARIC centre approved the study, and the investigators obtained informed, written consent from all participants.
An ancillary study to ARIC, the LC-SES Study was initiated in Spring 2001 to examine the association between SES across life and adult CVD-related conditions, and to determine the extent to which the current and historical context [neighborhood estimated at the county (early childhood) and census tract (early adulthood) level] modify the association of individual-level SES exposures and CVD. Trained interviewers administered a telephone questionnaire including 44 questions about parental and early adulthood occupational and educational exposures, current sociodemographic characteristics and childhood and earlier adulthood places of residence. Participants responding to the questionnaire (N = 12681), represent 81% of the ARIC baseline cohort and approximately 94% of cohort survivors. Additional details about the LC-SES Study can be found in the manual of procedures [16] and other documents available on the study website [32].
Ascertainment of childhood and early adulthood residences
Participants were asked "Where did you mostly live when you were a child? If possible, give me the city/town, county, and state of residence." Participants were also asked to provide their address (street number and name, city, county, state, and zip code) at various points during adulthood. Everyone was asked to provide addresses for age 30 (n = 12,681), and those who had first participated in the ARIC study after age 49 (n = 8,971) or 59 (n = 2,496) were also asked to provide addresses for ages 40/50 and 50, respectively. Those unable to provide an exact address were asked to provide the street name and the closest cross-street.
Editing & linking childhood county of residence with 1930–50 censuses
The year at which participants were aged ten years, which represented the approximate midpoint of childhood, was determined in order to link with the county-level socioeconomic data from the closest census year (1930, 1940, 1950). When a city, but not a county was provided, we used a publicly available website to attempt to identify the correct county [17]. County was chosen, as it was the smallest level of aggregation universally available in published census data before 1960. These data were obtained electronically through the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan.
Preparing addresses at ages 30, 40, 50 for geocoding
Prior to geocoding, all state data was standardized to conform to the two-digit U.S. Postal Services state coding system. Within each state the accuracy of the spellings of cities were verified. Street addresses were reviewed and computer programs written to correct obvious misspellings and to standardize formats. We did not submit zip codes because those accompanying the historical address could have changed over time. Because our goal was to classify the social environment where participants lived, we excluded post office box addresses as they do not necessarily correspond to actual residences. These along with other incomplete and unusable addresses (e.g., institutions, military APO, c/o, etc.) were not sent for geocoding. After editing, addresses and an encrypted study ID number were sent to a commercial vendor under contractual terms of confidentiality negotiated by university counsel and approved by the IRB.
Geocoding
The vender assigned to each address: spatial coordinates, Federal Information Processing Standards (FIPS) codes for statistical tabulation areas corresponding to 1990 census boundaries, and a match code describing the degree of accuracy of the geocoding. The accuracy rating assigned by the vendor ranged from" house range address matches" (best) to the "centroid of county" (worst). As we were interested in accurately classifying each participant's place of residence at the level of the census tract (the smallest geographical unit at which data for all censuses since 1960 was available), we accepted only house range address matches (e.g., accuracy at level of exact address, intersection, or street segment) or matches to centroids of zip code areas where everyone lived within a census block group, census tract or where more than 80% of addresses in area were located in the same tract. Rural routes were sent to the vendor but these addresses were not successfully geocoded.
Comparison of geocoding methods
Two methods were considered to link the spatial coordinates obtained from the vendor with the appropriate historical census tract. The overlay method uses the spatial coordinates assigned to exact address matches in conjunction with historical boundary maps to place addresses into historical tracts. The comparability file method uses current US Bureau of the Census tract assignments that are traced back in time stepwise to 1980 tracts, then from 1980 to 1970 tracts using files that describe tract changes from decade to decade. As a test, we compared the 1970 tract assignments by the two methods for 13,044 addresses that were successfully geocoded to the 1990 census by the geocoding vendor. While all addresses were assigned tracts using the overlay method, 36% could not be assigned a 1970 tract using the comparability files due to census tract merges (a tract contains parts of more then one tract from the previous decade). Of the remaining addresses (n = 8348), 97% were assigned the same tract by both methods. Because there are known errors in the assignment of spatial coordinates by commercial geocoding vendors [11,12,14], we could not rule out minor errors in the placement of tract boundaries included in polygon files. We also found an error in a comparability file during this test. We chose the overlay method to link with 1970 and 1980 censuses because it allowed us to locate addresses that could not be assigned tracts using the comparability files with no obvious lack of accuracy.
Linking addresses at ages 30, 40 and 50 with 1960–80 census tracts
We determined the census year (1960, 1970, 1980) that corresponded most closely to when the participant resided at each address. Arcview GIS Version 3.3 software was used to overlay the spatial coordinates assigned to addresses by the vendor onto Geolytics, Inc. shape files of census tract and block numbering area boundaries of the appropriate census year [Census CD 1970, Census CD 1980]. The spatial coordinates falling within the historical tracts were assigned the corresponding tract number. Figure 2 provides an example of the overlay of the spatial coordinates of Forsyth County, NC addresses that were matched with the 1970 census boundaries.
Figure 2 1970 census tracts in Forsyth County, NC and geocoded 1970 participant addresses, the LC-SES study, 2001–2002.
Electronic shape files were not available for the 1960 census. Thus, 1960 addresses were placed into 1970 tracts using the overlay method and then mapped to the appropriate 1960 tracts using files providing data on the correspondence between 1970 and 1960 tracts. These were obtained from print volumes of comparability files published by the US Bureau of the Census [33] and keyed into a database. If the 1970 tract was a merged tract it was not possible to uniquely identify the 1960 tract. In these circumstances we attempted to manually place the address in a tract as described below.
Assigning tracts when commercial geocoding efforts failed
When a 1960 address fell into a 1970 tract made up of merged 1960 tracts or when addresses were not geocoded by the commercial vendor [street name but not number, cross streets, obsolete addresses (road renamed or renumbered)], we attempted to manually place addresses into historical census tracts. Because this process is labor intensive, we undertook this effort only for addresses which were located within the four ARIC study communities (as a large number of addresses were not clustered in other areas). First, we obtained detailed street maps for the four study areas and overlaid them with census tract boundaries and numbers from the three historical censuses. Then, using web-based Mapquest® tools and the street map legends, we attempted to locate each address. If a street was contained within the boundary of a census tract, we assigned it the corresponding tract number. If a street crossed a census tract boundary or was the boundary for two or more tracts, we did not assign a census tract.
A large number of historical addresses in Washington County, MD were obsolete, because in the early 1990s the state changed to a grid address system to improve emergency response systems. Thus, we obtained historical street maps from the Hagerstown, MD Public Library and tried to locate the original street names in an attempt to manually assign a census tract using the procedure described above.
Linking with 1960–1980 socioeconomic census data
For addresses placed within a 1960–1980 census tract, we linked with tract level socioeconomic data. For 1970 and 1980 we used data from Geolytics, Inc. (Census CD 1970, Census CD 1980). The ODUM Institute at the University of North Carolina, USA provided electronic 1960 census tract data. Jackson MS and Washington County MD had not been assigned census tracts in 1960. For Jackson, MS and the portion of Washington County, MD falling within the Hagerstown city limits, we obtained 1960 census housing data at the level of city blocks from print volumes [34], and aggregated them into tract data using the1970 tract boundaries. However, for other areas without census tracts in 1960 this information was either not available (e.g., areas near Hagerstown but outside of the city limit) or it was not feasible to collect it from print volumes because of a small number of participants in the areas. For these addresses, data from the next closest census, 1970, were substituted.
Authors' contributions
KMR conceived of and led the writing of the manuscript. JLW analyzed the data on early adulthood and developed the methods for assigning census tracts to historic addresses. GH, the principal investigator of the LC-SES Study, contributed to the conceptualization and writing of this manuscript. EAW assessed the accuracy of the commercial geocoding and developed standardized procedures for manual geocoding. SK developed standardized procedures for editing the recalled address data. RP analyzed data pertaining to childhood place of residence. DY was instrumental in reviewing methods for placing participants into their historical tracts. AVDR provided expert input on methods of geocoding. All authors helped to frame the ideas, interpret findings, and review drafts of the manuscript.
Acknowledgements
The National Heart, Lung, and Blood Institute supported this research under the following contracts and grants: R01-HL064142, N01-55015, N01-55016, N01-55017, N01-55018, N01-55019, N01-55020, N01-55021, N01-55022, and R01-HL064142.
The authors thank the staff and participants in the ARIC study for their important contributions. They also thank Brigitt Heier for her assistance in preparing this manuscript.
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-401556938710.1186/1477-7819-2-40ResearchAtrial fibrillation and survival in colorectal cancer Walsh Stewart R [email protected] Kelly M [email protected] Nicholas J [email protected] Timothy A [email protected] Neil J [email protected] Department of Colorectal Surgery, West Suffolk Hospital NHS Trust, Hardwick Lane, Bury St Edmunds, Suffolk IP32 7TG, United Kingdom2004 29 11 2004 2 40 40 31 8 2004 29 11 2004 Copyright © 2004 Walsh et al; licensee BioMed Central Ltd.2004Walsh 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
Survival in colorectal cancer may correlate with the degree of systemic inflammatory response to the tumour. Atrial fibrillation may be regarded as an inflammatory complication. We aimed to determine if atrial fibrillation is a prognostic factor in colorectal cancer.
Patients and methods
A prospective colorectal cancer patient database was cross-referenced with the hospital clinical-coding database to identify patients who had underwent colorectal cancer surgery and were in atrial fibrillation pre- or postoperatively.
Results
A total of 175 patients underwent surgery for colorectal cancer over a two-year period. Of these, 13 patients had atrial fibrillation pre- or postoperatively. Atrial fibrillation correlated with worse two-year survival (p = 0.04; log-rank test). However, in a Cox regression analysis, atrial fibrillation was not significantly associated with survival.
Conclusion
The presence or development of atrial fibrillation in patients undergoing surgery for colorectal cancer is associated with worse overall survival, however it was not found to be an independent factor in multivariate analysis.
==== Body
Background
In general, colorectal cancer patients are three times more likely to be in atrial fibrillation preoperatively than matched controls undergoing non-colorectal cancer surgery [1,2]. It may also occur postoperatively. Recent data suggest that atrial fibrillation may be an inflammatory complication, resulting from the initiation of an inflammatory response to surgery or infection [3-6]. Colorectal cancer patients may have elevated C-reactive protein (CRP) levels [7] indicating a systemic inflammatory response. Elevated CRP levels may be associated with a worse prognosis in colorectal cancer patients [8]. Postoperative dysrhythmias may [9] or may not [10] be associated with worse survival following surgery for lung cancer. We hypothesised that atrial fibrillation (AF) is an adverse prognostic indicator in patients undergoing surgery for colorectal cancer.
Patients and methods
Patients who underwent a resection for colorectal cancer between 1st January 2000 and 31st December 2001 in a 600-bed district general hospital in the United Kingdom National Health Service were identified. The hospital serves a population of approximately 230,000. About 90 elective and emergency laparotomies are performed each year for colorectal cancer. Patients were identified from the prospectively maintained colorectal cancer database maintained by the colorectal surgical department. Patients with radiological, endoscopic or clinical examinations suspicious of colorectal cancer are referred to the weekly colorectal multi-disciplinary team (MDT) meeting. In the case of suspicious radiology, the referral to the MDT is made automatically by the radiology department. This avoids the possibility of the responsible clinical firm failing to refer a patient for consideration. Similarly, the pathology department automatically refers any patient in whom histology shows colorectal malignancy. In addition, patients who undergo surgery where a suspicion of colorectal cancer is raised are referred for consideration. The colorectal meeting is attended by the colorectal surgeons, radiologists, pathologists, palliative care physicians and nursing staff. Patients determined to have colorectal cancer by the MDT are entered into the database. The colorectal department periodically compares the database to clinical coding data for patients with colorectal cancer in order to ensure complete data capture. All patients are followed-up regularly by a team of colorectal nurse specialists in a dedicated clinic.
Age, sex, mode of presentation (emergency or elective), Dukes stage, postoperative anastomotic leakage and adjuvant therapy were recorded for all patients. The colorectal cancer database was cross-referenced with the hospital clinical-coding database to identify those patients who were in atrial fibrillation at any time before or after their surgery. Patients with colorectal cancer who did not undergo surgery or who only underwent palliative stoma formation were excluded. All patients were followed up for at least two years postoperatively. Overall survival and recurrence-free survival were recorded. Recurrence-free survival was defined as the time interval between operation and first diagnosis of local or distant recurrence. Patients with no recurrence were censored at the time of death from any cause other than cancer or at the time they were last seen by the colorectal team. Characteristics between those with and without AF were compared using the Student t-test and Fisher Exact test for continuous and categorical data respectively. Potential prognostic factors were compared by the log-rank test. Significant prognostic factors identified from the univariate analysis were entered into a multivariate Cox regression model of survival to test for independence. The 5% level was considered significant in the multivariate analysis. Statistical analysis was performed using Statsdirect® version 2 (Statsdirect Ltd., UK).
Results
One hundred and seventy-five patients (M:F = 111:64) who underwent bowel resection for colorectal cancer were identified from the database. Their median age was 74 years (interquartile range 66 to 80 years). Tumour site, Dukes stage and mode of operation (emergency or elective) are summarised in Table 1. Anastomotic leaks occurred in three patients while another three patients received preoperative radiotherapy. Median follow-up was 2.38 years. There were 60 deaths (42 cancer-specific deaths) during the follow-up period. The remaining 18 patients died from conditions such as pneumonia, pulmonary embolus or myocardial infarction. Cause of death was recorded for all patients in the database. Twenty-eight patients (16%) developed recurrence during postoperative surveillance. The remaining 14 patients who died were noted to have incurable disease at the time of surgery.
Table 1 Baseline characteristics of study group
No. of patients (%)
Age
≥72 years 98 (56%)
< 72 years 77 (44%)
Gender
Male 111 (64%)
Female 64 (36%)
Site
Colon 119 (68%)
Rectum 56 (32%)
Dukes' Stage
A 26 (15%)
B 75 (43%)
C 57 (33%)
D 17 (9%)
Presentation
Elective 147 (84%)
Emergency 28 (16%)
Cross-referencing with the clinical coding database identified thirteen patients with a history of atrial fibrillation. Five patients were in AF preoperatively. The remaining eight patients developed postoperative AF. A comparison of baseline characteristics between patients with and without AF is shown in table 2. The AF was paroxysmal in three patients, persistent but eventually resolved in five and permanent in the remaining five patients. There were seven deaths among the patients with atrial fibrillation: two from recurrent colorectal cancer, four from other causes (pneumonia in two patients, respiratory failure secondary to pulmonary fibrosis in one and left ventricular failure in one) with recurrent cancer present and one non-cancer related death (peri-operative myocardial infarction).
Table 2 Comparison of characteristics between patients with AF and those without.
Characteristic Sinus rhythm Atrial fibrillation p
Male gender 102 9 0.77
Mean age 72 73 0.64
Rectal cancer 52 4 0.99
Elective surgery 139 8 0.05
Pre-operative radiotherapy 2 1 0.22
Anastomotic leak 3 0 0.79
Dukes Stage
A 24 2 0.73*
B 69 6
C 53 4
D 16 1
*Fisher-Freeman-Halton Exact Test
Survival analysis
There was no correlation between overall survival and the following variables in the univariate survival analysis: gender, postoperative anastomotic leak, site of tumour (rectal versus colonic) or preoperative radiotherapy. Mode of surgery (emergency or elective), age (<72 years or ≥ 72 years) and Dukes' stage had a significant effect on survival. When patients with atrial fibrillation were compared to those without (Figure 1), atrial fibrillation correlated with worse overall survival. Dukes' stage, mode of surgery, age and atrial fibrillation were entered into a Cox regression model overall survival (Table 2). Mode of surgery, age and Dukes' stage retained significance but atrial fibrillation did not (Model chi-square 49.6; 7 degrees of freedom; p < 0.0001). There was no significant correlation between atrial fibrillation and recurrence-free survival (p = 0.74).
Figure 1 Kaplan-Meier survival curves for patients with a history of atrial fibrillation (1) versus those without (0). P = 0.04 (Log-rank test).
Discussion
Tumours stimulate an inflammatory response when they invade or metastasise [11]. This inflammation may cause a rise in serum levels of inflammatory markers such as CRP. Serum CRP levels correlate with survival in colorectal cancer patients [8]. Atrial fibrillation may be precipitated or maintained by an inflammatory mechanism [3]. Thus, we hypothesised that the presence of AF, an inflammatory complication, would be associated with poorer survival in colorectal cancer as it is a manifestation of more advanced disease. Our data demonstrate a significant correlation between atrial fibrillation and survival following surgery for colorectal cancer in univariate analysis. Previous investigators have found that atrial arrhythmias were significantly more common in those patients with a history of cancer. In addition, CRP levels were higher in those patients with a history of cancer. However, there was no independent link between cancer and arrhythmias. This is consistent with inflammation being the causal link between cancer and arrhythmias [12].
In our cohort, atrial fibrillation was not an independent predictor of survival. Dukes' stage was the strongest predictor of survival. Previous data from colorectal cancer patients suggest the degree of inflammatory response may reflect the degree of disease progression [13]. The incidence of liver metastases, peritoneal tumour deposits, lymph node metastases and intravascular invasion are higher in patients with elevated CRP levels [14]. Thus, atrial fibrillation may be a manifestation of systemic inflammation due to more advanced disease and would not be independent of Dukes' stage. Cox regression analysis of our study cohort (Table 3) appears to show poor survival in Dukes B patients (Hazard ratio 9.13) compared to Dukes C patients (Hazard ratio 0.15). There was a trend for Dukes B patients to be older (mean age 73.79 years) than Dukes C patients (70.99 years; p = 0.1, student t-test, β = 0.67) and this may account for the reduced survival. There were no differences in the Dukes' stage distribution between patients with and without AF (Table 2). However, the small numbers in our series (only thirteen patients with AF) render it underpowered to detect a correlation between the presence of AF and colorectal cancer stage.
Table 3 Overall univariate and multivariate survival analysis
Variable Univariate p Multivariate p Hazard Ratio 95% Confidence interval of hazard ratio
Age (< 72 years versus ≥ 72 years) 0.03 0.003 2.38 1.339 to 4.224
Pre-operative radiotherapy 0.26 - - -
Anastomotic leak 0.23 - - -
Gender (Male versus female) 0.33 - - -
Site of tumour (Rectal versus colonic) 0.87 - - -
Emergency surgery <0.0001 0.004 2.35 1.312 to 4.205
Atrial fibrillation 0.04 0.06 2.21 0.979 to 4.985
Dukes' stage (A, B, C, D) <0.0001 <0.0001 Dukes A 0.09 0.032 to 0.267
Dukes B 9.13 4.343 to 19.206
Dukes C 0.15 0.073 to 0.312
Dukes D 1 Reference
There are several other limitations to our study. The quality of the clinical coding has not been assessed. Thus, the sensitivity and specificity of our clinical coding department in coding AF is unknown. Only 13 patients (7.4%) developed AF. Previous work suggests that 13% of elective colorectal patients develop postoperative AF [15]. Thus, it is possible that some patients with unrecorded AF have been incorporated into the control arm of our series. The study cohort has been followed for a relatively short time, only two years. However, most trials of follow-up following surgery for colorectal cancer report a mean time to relapse of approximately 24 months [16]. We were unable to demonstrate a significant relationship between atrial fibrillation and recurrence-free survival. This may be due to an insufficient sample size. However, atrial fibrillation does correlate with overall survival. The presence of atrial fibrillation may be a clinical marker of poor overall survival in patients undergoing surgery for colorectal cancer.
Competing interest
The author(s) declare that they have no competing interests.
Authors' contribution
SRW conceived the study, performed the statistical analysis and drafted the paper.
KMG and NJW performed the literature search and collected the data.
TAJ and NJK co-wrote the paper with SRW.
All authors approved the manuscript.
Funding support
None declared
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| 15569387 | PMC538254 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Nov 29; 2:40 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-40 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-321557419410.1186/1476-4598-3-32Short CommunicationMitochondrial inhibition of uracil-DNA glycosylase is not mutagenic Kachhap Sushant [email protected] Keshav K [email protected] Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Bunting-Blaustein Cancer Research Building, 1650 Orleans St., Baltimore, MD 21231 USA2 Department of Cancer Genetics, Cell and Virus Building, Room 247, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263 USA2004 1 12 2004 3 32 32 1 7 2004 1 12 2004 Copyright © 2004 Kachhap and Singh; licensee BioMed Central Ltd.2004Kachhap and Singh; 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
Uracil DNA glycosylase (UDG) plays a major role in repair of uracil formed due to deamination of cytosine. UDG in human cells is present in both the nucleus and mitochondrial compartments. Although, UDG's role in the nucleus is well established its role in mitochondria is less clear.
Results
In order to identify UDG's role in the mitochondria we expressed UGI (uracil glycosylase inhibitor) a natural inhibitor of UDG in the mitochondria. Our studies suggest that inhibition of UDG by UGI in the mitochondria does not lead to either spontaneous or induced mutations in mtDNA. Our studies also suggest that UGI expression has no affect on cellular growth or cytochrome c-oxidase activity.
Conclusions
These results suggest that human cell mitochondria contain alternatives glycosylase (s) that may function as back up DNA repair protein (s) that repair uracil in the mitochondria.
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Introduction
Mitochondrion plays an important role in various cellular functions ranging from synthesis of lipids to maintenance of ion homeostasis [1,2]. However, the singular function that defines this organelle is the production of energy by the electron transport chain. Mitochondrion is also a significant source of reactive oxygen species (ROS), known to be a potent DNA damaging agent [3]. The integrity of the mitochondrial genome is essential for effective cellular processes. The mitochondrion has various active and passive safe guard strategies to deal with the damaging effects of ROS on the mitochondrial DNA (mtDNA), one of them being the repair of the lesions caused by the ROS production [3]. Mitochondrial repair is not well studied. It is interesting to note that mtDNA experience more DNA damage than nuclear DNA [5]. Unlike the nuclear DNA that does not replicate in terminally differentiated cells mtDNA is continuously replicated in cells that have undergone differentiation. Hence lesions in the mtDNA can prove to be more deleterious [6]. Earlier it was believed that the mitochondria lack DNA repair mechanisms as thymidine dimers were not repaired in the mtDNA [4]. However, recent evidence indicates that DNA repair mechanism do function in the mitochondria [7,8,25,26]. Various enzymes that are involved in nuclear DNA repair have isoforms that are targeted to the mitochondria [9,10]. Whether these enzymes function in an identical fashion in the repair of both the nuclear and the mtDNA is not clear.
The uracil DNA glycosylase (UDG) removes misincorporated uracil or deaminated cytosine from DNA. Human UDG gene encodes two alternative spliced isoforms, UNG1 and UNG2 [11-13]. Of these the UNG1 is translocated to the mitochondria [14,15]. UNG2 localizes to the nucleus [15]. Although UNG2's role in repairing nuclear DNA is well established, the role for mitochondrial UNG1 is not well studied. In this paper we inactivated mitochondrial UNG1 by expressing a natural uracil DNA glycosylase inhibitor (UGI) from PBS2 phage that binds to the active site of UDG in equimolar ratio and inhibits the UDG enzyme [16]. UGI has been successfully used as a tool to examine the role of nuclear UNG2 in base excision repair of misincorporated uracil or deaminated cytosine in the nuclear DNA [17,18]. In order to elucidate the role of UDG in in vivo mtDNA repair we targeted UGI to the mitochondria to inhibit UDG activity. Our studies suggest that mitochondrial inhibition of UDG is not mutagenic. This study indicates that alternative DNA glycosylase(s) may be operative in the mitochondria that might repair uracil in the mitochondrial genome.
Materials and Methods
Constructs
The reading frame of uracil DNA glycosylase (UDG) that codes for functional UDG was amplified by PCR using forward primers (5'CCAGTGCCGCGCGCCAAGATCCATTCGTTGTTTGGAGAGAGCTGGAAGAAG) specific to human uracil DNA glycosylase that had a BssH II site at the 5' end and the reverse primers 5'TTGA TCTCGAGTCACAGCTCCTTCCAGTCAATGGG that had the Xho I site engineered at the 5' end. The template used for the amplification was pTUNGΔ84 [13]. The PCR fragment was cloned into pCMV/myc/mito (Invitrogen) treated with BssH II and Xho I. The vector has a mitochondial targeting signal of the subunit VIII of human cytochrome c oxidase that facilitates targeting of the cloned protein to the mitochondria. The construct was named as pCMV UNG.
The complete reading frame of uracil DNA glycosylase inhibitor gene was amplified using pTZUgi (a gift from Dr. Umesh Varshney) as a template with forward primers (5'CCAGTGCCGCGCGCCAAGATCC ATTCGTTGATGACAAA TTTATCTG ACATC) specific to uracil DNA glycosylase inhibitor from phage PBS2 that had a BssH II site at the 5' end and the reverse primer(5'CGCCCGTTTGATCTCGAGTTATAAC ATTTTAATCCATTAC) which had the Xho I site engineered at the 5' end. The PCR fragment was cloned into pCMV/myc/mito (Invitrogen). The construct was named as pCMV UGI.
Transfections
Stable transfectants of the above constructs were made in immortalized normal breast epithelial MCF 12A cells using lipofectin as a transfecting agent. Briefly, MCF12A cells were plated to 70 % confluency in a 35 mm dish and transfected with 2 ug of pCMV UNG and pCMV Ugi. The cells were selected using G418 as a selection medium. The clones were selected after plating the cells in a 96 well plate to single cell dilution and the clones were screened for integration using PCR. A pCMV/myc/mito/GFP that has a GFP protein fused to the mitochondrial signal was used as a control to assay the efficiency of transfection and the expression of the protein using the vector. An empty vector was stably transfected and used as a control in all the experiments.
PCR Screening of clones for stable integration of the constructs
Each construct was assayed for stable integration after transfection using PCR. The primers were the same that were used for amplifying the gene for cloning namely UDG specific primers, forward primer: 5'CCAGTGC CGCGCGCCAAGATCCATTC GTTGTTTGGAGAGAGCTGGAAGAAG reverse primer 5'TTGATCTCGAGTCAC AGCTCCTTCCAGTCAATGGG, for screening UDG stable integrants and UGI specific primers, forward primer 5'CCAGTGCCGCGCGCCAAGATCCATTCGTTGATGACA AATTTATCTGACATC and reverse primer 5'CGCCCGTTTGATCTCGAGTTATAAC ATTTTAATCCATTAC for screening Ugi stable integrants. Briefly, the each clone was transferred from the 96 well plate to a 24 well plate and DNA was extracted when the wells were confluent using standard methods. 100 ng of the DNA was used to PCR amplify the DNA that was transfected. Clones that showed an intact gene were selected for further analysis.
Isolation of mitochondria
Stable clones and parental MCF12A cells were grown in T75 flask to seventy percent confluency. The cells were washed with 1X PBS and treated with 1.5 ml of 0.04% Digitonin solution (0.4 mg Digitonin /ml,2.5 mM EDTA,250 mM mannitol, 17 mM MOPS., pH 7.4). The cells were thoroughly resuspended and homogenized using ten strokes of a dounce homogenizer on ice. One ml of 2.5 X sucrose mannitol buffer (525 mM Mannitol, 175 mM Sucrose, 12.5 mM tris-HCl., pH 7.49) was added and homogenized further using 20 strokes of the homogenizer. Ten micro liter of the homogenate was visualized under the microscope to assess complete breakdown of the cells. The mitochondria were isolated by differential centrifugation [19]. The homogenate was centrifuged at 2500 rpm at 4°C to pellet the nuclei and the supernatant was further centrifuged at 2500 rpm till no pellet was visually observed. The supernatant was finally centrifuged at 14000 rpm at 4°C to pellet the mitochondria.
Western Blotting
Stable transfectants were assayed for production of the UDG protein in the mitochondria by western blotting. Twenty micrograms of the mitochondrial protein was electrophoresd on a 12% SDS polyacrylamide gel and transferred on a nitrocellulose membrane. The membrane was blocked overnight in a blocking solution containing 5% non-fat milk and probed with the primary antibody (1:1000 dilution) against UDG (a gift from Dr. Hans Krokan, Norway). The membrane was washed twice with TBST and probed with a secondary antibody linked to horseradish peroxidase. The bands were visualized using ECL (Amersham Pharmacia) kit. The membrane was then probed for the house keeper protein beta actin to assess for equal loading.
RT-PCR
RNA from Ugi stably transfected MCF 12A cells was extracted using TRIZOL reagent following the manufacturers instruction. One and a half micrograms of total RNA was used for reverse transcription using Superscript II Rnase H-reverse transcriptase (Invitrogen). Two microlitres of the reverse transcribed products was used in the subsequent PCR reactions. Twenty-five microlitres of the PCR reactions contained 20 mM Tris-HCL, pH 8.4, 50 mM KCl, 1.5 mM MgCl2, 200 μM dNTP and 10 picomoles of each primer (forward primer: 5'CCAGTGCCGCGCGCCAAGATCCATTCGTTGATGACAAATTTATCTGACATC and reverse primer 5'CGCCCG TTTGATCTCGAGT TATAACATTTTAATCCATTAC and one unit of Taq DNA polymerase (Invitrogen). The PCR profile consisted of an initial denaturation at 94°C for 5 minutes and 32 cycles of denaturation at 94°C for 45 sec, annealing at 58°C for 1 min and extension for 2 min at 72°C with a final extension at 72°C for 10 min. The PCR products were electrophoresed on a 1% agarose gel stained with ethidium bromide (0.5 μg/ml) and visualized under UV.
Flow Cytometric Analysis
Proliferation assay was done using a flourescent lipophilic molecule, 5-(and-6)-carboxyfluorescein diacetate succinimidyl ester (CFSE) that gets incorporated into live cells and gets diluted into daughter cells with every cell division. The dilution in the intensity of the dye as estimated by flow cytometry with respect to a "0" hour time point gives an indication of the proliferation of the cells. Cells were plated at a density of 1 × 105 in a 60 mm dish and stained for 15 min using the fluorescent dye CFSE (Molecular Probes). Cells were fixed in 70% alcohol just after staining to have a 0 hour time point and after a period of 72 hours. Proliferation was then estimated using flow cytometry using a FACSvantage™, Becton Dickinson [20,21].
SIN1-1 and SNAP treatment and of mitochondrial damage
MCF12A parental cells were used for dose optimization of the SIN1 and SNAP. An optimal dose was used for further experiments. The parental and the transfected cells were plated on a 60 mm dish to 70% confluency. Each of the cell lines were treated with 4 mM 3-morpholinosydnonimine (SIN-1) and 2 mM S-nitroso-N-acetylpenicillamine (SNAP), NO donors for a period of 1 hour after which the medium was changed and cells were harvested after 0, 2, 4, 6 hour period intervals. DNA was extracted from these cell lines and Cox I was PCR amplified and sequenced using an automated sequencer (ABI PRISM) for mutation analysis.
Uracil DNA Repair Assay
Uracil DNA repair assay was conducted as described by Radany et al., [17]. Oligonucleotides used for the assay were and T-34-mer 5'AGCTTGGCTGCAGGTXGACGGATCCC CGGGAATT-3' containing a uracil or thymine residue at position 16 (X=U or T, respectively) and (A-34-mer and G-34-mer) 5'-AATTCCCGGGGATCCGTCXACCTGC AGCCAAGCT-3' containing an adenine or guanine residue at position 19 (X = A or G, respectively). Twenty picomoles of oligonucleotide substrates were labeled with 32P using T4 polynucleotide kinase. The labeled products were precipitated and then resuspended in a lower volume of distilled water. These were directly used as single stranded substrates in the enzyme assay. To prepare double stranded substrates twenty picomoles of the labeled products were annealed to 10 pmoles of the unlabelled complementary or mismatch oligos by heating at 70°C and slowly cooling it down to room temperature for an hour. UDG assay was performed using 50 μg of mitochondrial extract in 1X UNG buffer (20 mM Tris-HCl pH8.0,1 mM EDTA,1 mM DTT) and 4 pmoles of labeled oligos. The reaction was carried out at 30°C for 45 min. The assay using commercially available Ugi (NEB) was performed using similar conditions. Ten units of Ugi per reaction was used. Apyrimidinic sites (AP-sites) generated by uracil removal from DNA substrates were hydrolyzed by the addition 0.1 N NaOH and incubating for 10 min at room temperature and terminated using a formamide buffer (80% formamide in 1XTBE) to generate single stranded products. Half of the reaction was electrophoresed using a 15% acrylamide gel containing 8.3 M urea and 1X TBE buffer. The gel was autoradiographed after electrophoresis to visualize the bands.
Results
Generation of stable transfectants expressing UGI and UDG in the mitochondria
Previous studies have shown that uracil DNA glycolyase can be inhibited by PBS2 phage protein UGI in a stoichiometric fashion [17,18]. This protein has been used to inactivate nuclear UDG by targeting it specifically to the nucleus by attaching a nuclear localization signal [17]. We have used the pCMV/myc/mito (Invitrogen) vector to target UGI protein in the mitochondria to inhibit UDG activity. Expression of UDG (UNGΔ84), that retains the wild type function of the enzyme, was also targeted to the mitochondria and was used as control. The pCMV/myc/mito vector contains a mitochondrial localization signal (MLS) of subunit VIII of human cytochrome c oxidase that specifically targets a protein of choice to the mitochondria. Clones containing stable integration were isolated and were confirmed by PCR upon transfection with UGI gene and the UDG after G418 selection. To confirm that the UGI gene was expressed in transfected cell lines we did RT-PCR analysis (Figure 1). Our results show that UGI was expressed (Figure 2). Western blot analysis on extracts isolated from mitochondria using antibody against UDG protein demonstrates that cells containing UNG stable integration express higher level of UDG protein in the mitochondria (Figure 3, lane 3). It is important to note that the UDG band was absent in cells expressing UGI because UDG epitope was not available for binding with antibody.
Figure 1 PCR screening for stable integrants of pTZUGI in MCF12A cells. PCR using pTZUGI primers were used to screen for stable integrants. Lane 1 is a positive control (pTZUgi plasmid DNA), lane 2, 3, 4, 5, 7, 8, 9 and 10 show the presence of stable integrants.
Figure 2 RT PCR to verify expression of Ugi gene transfected in MCF12A cells using primers specic to the UGI gene: RT PCR products electrophoresed on a 1% agarose gel. Lane 1 shows RT PCR product from MCF12A cell line, lane 2 shows RT PCR product from MCF12A cells transfected with pCMV UNG, lane 3 shows RT PCR product from MCF12A transfected with empty pCMV/myc/mito control vector, lane 4 shows RT PCR product from MCF12A transfected with pCMV UGI vector.
Figure 3 Western blot analysis of mitochondrial UDG expression in transfected cell lines: Upper panel shows western blotting of mitochondrial extracts with UDG antibody the lower panel shows the same blot probed with Cox II antibody to assess for equal loading of the samples. Lane 1 is mitochondrial extract from parental MCF12A cells, lane 2 is mitochondrial extract from MCF12A cells transfected with empty pCMV/myc/mito vector, lane 3 is mitochondrial extract from MCF12A cells transfected with pCMV UNG vector, lane 4 is mitochondrial extract from MCF12A cells transfected with pCMV UGI vector. A band of lower molecular weight was seen in some extracts.
Expression of UDG and UGI in the mitochondria does not affect cell growth
It is possible that inhibition of UDG in the mitochondria may affect cell growth. To determine if UGI expression in the MCF12A cells resulted in a difference in cellular growth, cell cycle analysis was conducted using flow cytometry. The cell cycle distribution of parental MCF12A cells, wild type UNG and UGI transfected cell line and the cell line containing the control vector is shown in figure 4. Interestingly, a very similar growth pattern was observed between all the cell lines examined. We conclude that expression of UGI in the mitochondria does not affect cell growth.
Figure 4 FACS analysis of growth rate using fluorescent dye CFDA-SE: The first graph (green) in each panel shows fluorescent cells at 0 hour time point and the second (black) shows a decrease in fluorescence at 72 hr after the cells proliferate. There is no difference in the growth rate between the parental cell line and the transfected one.
Lack of mutations in COXI, COXII, and COXIII gene encoded by mtDNA
Our previous studies suggest that inactivation of UDG in yeast Saccharomyces cerevisae leads to mutations in mtDNA [25]. We therefore asked whether UGI transfected cells showed spontaneous increase in level of mutation in mtDNA. We isolated mtDNA from cell expressing wild type UNG, UGI and the control MCF12 A cells containing vector. We amplified mtDNA encoding COXI, COXII and COXIII by PCR. PCR fragments were sequenced. Sequencing revealed no differences in mtDNA sequence between the cell lines expressing UGI, wild type UNG1 and the cell line containing the vector (data not shown). We also treated the transfected cell lines with two agents SIN1 and SNAP. Both SIN1 and SNAP are known to deaminate mtDNA [22]. The transfected cells were treated for one hour with the agent and were harvested at different time intervals to accumulate mutations. The DNA from these cell lines was isolated and analyzed by sequencing for mutations in the COXI, COX II and the COX III genes encoded by the mtDNA. Our analysis showed no increase in mutation in mtDNA in the treated cell lines (data not shown). We conclude that UGI expression in the mitochondria does not lead to mutations in mtDNA.
Uracil repair is unaffected by inhibition of UDG in the mitochondria
It has been previously reported that the UGI protein when targeted to the nucleus lowers the activity of the nuclear UDG enzyme [16]. To analyze the effect of UGI expression on the mitochondrial UDG activity in the transfected cell line, we carried out UDG activity measurements in mitochondrial extracts with and without commercially available UGI as a control. The commercially available UGI was found to inhibit mitochondrial UDG. However, constitutively expression of UGI in the mitochondria in cell line transfected with UGI was not observed (Figure 5). These results suggest two possibilities i) that an alternative uracil glycosyalase activity is present in the mitochondria and/or ii) mitochondrially expressed UGI is incapable of inhibiting UDG present in the mitochondria. Since commercially available UGI does inhibit mitochondrial UDG activity, it is likely that alternative uracil glycosylase(s) are present in the mitochondria. We conclude that uracil repair is unaffected by inhibition of UDG in the mitochondria.
Figure 5 UDG activity in mitochondrial extracts of parental MCF12A cells and transfected cell lines: Lanes 1, 3, 5 and 7 show UDG activity in mitochondrial extracts from MCF12A parental cell line, cells transfected with pCMV UNG, cells transfected with pCMV UGI, cells transfected with pCMV/myc/mito control vector and commercially available UDG enzyme, that acted as a positive control, respectively. Lanes 2, 4, 6, 8 and 10 shows an inhibition of UDG activity when commercially available Ugi was added in mitochondrial extracts from MCF12A parental cell line, cells transfected with pCMV UNG, cells transfected with pCMV UGI, cells transfected with pCMV/myc/mito control vector and commercially available UDG enzyme, that acted as a positive control, respectively.
Discussion
Cells are exposed to DNA damaging agents generated both as a process of normal physiology as well through extrinsic mutagens. Cells repair damage done to the DNA by a variety of repair mechanisms each specific for the type of DNA damage [23]. Many proteins involved in the repair mechanism are conserved in prokaryotes and eukaryotes. One of the repair mechanisms is the base excision repair pathway that repairs lesions of DNA that involve base modification as well as damage by reactive oxygen species. The enzymes involved in the base excision repair pathway for the repair of the nuclear DNA are well studied [23,24]. Base excision repair involves a DNA glycosylase that cleaves the damaged base by hydrolysis of the glycosidic bond, producing an abasic site. The abasic site generated is then removed by AP endonuclease and the gap is filled by DNA polymerase and then ligated by DNA ligase [23,24]. The first enzyme involved in the base excision repair pathway differs depending upon the lesion introduced in the DNA. Thus uracil DNA glycosylase is specific for misincorporated uracil or deaminated cytosine and would only act on these lesions [11-13]. Oxoguanine DNA glycosylase is specific for 8-oxoguanine and other oxidative species, and 3-methyl adenine glycosylase is specific for alkylated residues [10]. The mitochondrial DNA is subjected to a greater risk of DNA damage due to reactive oxygen species generated as a result of normal physiology of this organelle. The proximity of the mitochondrial DNA to the electron transport chain makes it more vulnerable to the DNA damaging effects of the reactive oxygen species. Therefore, many of the base excision enzymes including UDG have isoforms that are targeted to the mitochondria [9,10].
UDG's role in the nucleus is well established [17]. It is also established that UGI, a PBS2 phage encoded protein when expressed inhibits UDG activity in the nucleus [17,18]. In this paper we investigated whether UDG is the major protein that plays an important role in repairing uracil residues in the mitochondria. In order to address this question, we cloned UGI gene in frame with the mitochondrial localization signal present in the pCMV/myc/mito vector. We isolated stably transfected MCF12A cell lines and measured uracil-DNA repair activity in the mitochondria. We found no difference in DNA repair activity of uracil in mitochondrial extracts. These results were further substantiated by lack of spontaneous mutations in mtDNA in the COXI, COXII and COXIII genes. Similar results were obtained after treating the cells with SIN1 and SNAP that deaminate DNA [22]. Cells expressing UGI also showed no difference in the growth rate suggesting a lack of mitochondrial defect due to UGI inhibition of mitochondrial UDG.
Our results of UGI expression in the mitochondria are different when compared with UGI expression in the nucleus. A previous study has shown that expression of UGI results in inhibition of uracil DNA repair in the nucleus and subsequently mutation in the nuclear DNA [17]. Our results are intriguing and hints to the presence of alternative DNA repair proteins that may repair uracil in mtDNA. Indeed, cells contain several classes of enzymes that can remove uracil residues from DNA and maintain genomic integrity [27]. These include the thymine-DNA glycosylase (TDG), mismatch specific uracil-DNA glycosylase (MUG) and the single-stranded monofunctional uracil-DNA glycosyalse (SMUG1) [27,28]. It is not clear whether any of these proteins are present in the mitochondria and may function as a back up enzyme when UDG is inactivated by UGI.
It is also possible that an extremely low level of mutant mtDNA may be present in the cells expressing UGI in the mitochondria and PCR technique used to identify mutant copies among a heterogeneous population of mtDNA was unable to detect mutant mtDNA molecules. It is also conceivable that targeted UGI is present in a subset of mitochondria and at any given time there is always enough active UDG in vivo and in the extract from untargeted mitochondria to carry out the uracil repair activity in vitro. However, these possibilities are ruled out because UGI expression did not result in lower level cytochrome C oxidase activity (data not shown).
Acknowledgement
We thank Drs Hans Krokan for hUDG antibody and Umesh Varshney for pTZUgi plasmid DNA. This research was supported by grant from the National Institutes of Health RO1-097714 and Elsa Pardee Foundation to KKS.
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| 15574194 | PMC538255 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 1; 3:32 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-32 | oa_comm |
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Comp HepatolComparative Hepatology1476-5926BioMed Central London 1476-5926-3-101556657310.1186/1476-5926-3-10ResearchExpression of leukemia inhibitory factor (LIF) and its receptor gp190 in human liver and in cultured human liver myofibroblasts. Cloning of new isoforms of LIF mRNA Hisaka Toru [email protected]ère Alexis [email protected] Jean-Luc [email protected] Sophie [email protected] Véronique [email protected] Nathalie [email protected] Jean-Frédéric [email protected] Jean-François [email protected] Jean [email protected] INSERM, E362, Bordeaux, F-33076 France; Université Victor Segalen Bordeaux 2, Bordeaux, F-33076 France2 CNRS, UMR 5164, Bordeaux, F-33076 France; Université Victor Segalen Bordeaux 2, Bordeaux, F-33076 France3 IFR 66, 33076 Bordeaux France4 Kurume University School of Medicine, Department of Pathology, Fukuoka, Japan2004 26 11 2004 3 10 10 2 7 2004 26 11 2004 Copyright © 2004 Hisaka et al; licensee BioMed Central Ltd.2004Hisaka 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 cytokine leukemia inhibitory factor (LIF) mediates its biological effects through binding to its high affinity receptor made of the low-affinity LIF receptor subunit gp190 (LIF-R) and the gp130 subunit. LIF exerts several important effects in the liver, however, data on liver expression of LIF are scarce. The aim of this study was to examine the expression of LIF and LIF-R in human liver.
Results
LIF expression, analyzed by immunohistochemistry, was barely detectable in normal liver but was strong within cirrhotic fibrous septa and was found in spindle-shaped cells compatible with myofibroblasts. Accordingly, cultured human liver myofibroblasts expressed high levels of LIF as shown by ELISA and Northern blot. Biological assay demonstrated that myofibroblast-derived LIF was fully active. RT-PCR showed expression of the LIF-D and M isoforms, and also of low levels of new variants of LIF-D and LIF-M resulting from deletion of exon 2 through alternative splicing. LIF receptor expression was detected mainly as a continuous sinusoidal staining that was enhanced in cirrhotic liver, suggestive of endothelial cell and/or hepatocyte labeling. Immunohistochemistry, flow cytometry and STAT-3 phosphorylation assays did not provide evidence for LIF receptor expression by myofibroblasts themselves. LIF secretion by cultured myofibroblasts was down regulated by the addition of interleukin-4.
Conclusions
We show for the first time the expression of LIF in human liver myofibroblasts, as well as of two new isoforms of LIF mRNA. Expression of LIF by myofibroblasts and of its receptor by adjacent cells suggests a potential LIF paracrine loop in human liver that may play a role in the regulation of intra-hepatic inflammation.
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Background
Leukemia inhibitory factor (LIF) belongs to the interleukin (IL)-6 family of cytokines, together with IL-11, ciliary neurotrophic factor, cardiotrophin-1, oncostatin M and neurotrophin-1/B cell stimulating factor-3. LIF is widely expressed in tissues and in many isolated cells. LIF expression is commonly up-regulated during inflammation. Nevertheless, its role seems to be complex as both pro- and anti-inflammatory properties have been described for that cytokine. Although LIF, like IL-6, is able to drive a significant acute-phase reaction in non-human primates [1], this has been questioned in humans [2]. LIF exerts its biological activities through its binding to a hetero-oligomeric receptor complex between the low-affinity LIF receptor subunit gp190 and the signal-transducing subunit gp130. The gp130 subunit is common to all members of the IL-6 family.
Several isoforms of LIF consecutive to alternative splicing have been described. The second and third exons are common to all isoforms, whereas there are 3 alternate first exons – D, M, and T. The fate of the mature LIF molecule is highly dependent on exon 1 usage; thus, the human LIF-D transcript encodes a secreted protein that is biologically active and can signalize via the LIF receptor. The human LIF-M transcript does not contain any in-frame AUG, but it is known to be translated into both secreted and intracellular proteins [3]. The secreted LIF-M protein can also be found sequestered in the extracellular matrix where it is biologically active [4]. Finally, the first exon from the human LIF-T, which does not contain any in-frame AUG, is responsible for the synthesis of an intracellular protein with a leucine zipper motif that might function as a transcription factor [5].
As outlined above, LIF is potentially involved in liver physiology and pathophysiology; however, data on liver expression of LIF are scarce. LIF expression was not detected in normal rat liver but it was highly induced following partial hepatectomy, mainly in non- parenchymal cells [6], suggesting its involvement in liver regeneration. To our knowledge, the expression of LIF has not been described in human liver.
Therefore, the aim of this study was to examine the expression of LIF and of its specific receptor gp190 in human liver. Results obtained with immunostaining of liver sections led us to examine LIF expression by cultured liver myofibroblasts, cells that play a major role in liver fibrogenesis.
Results
LIF expression
Human liver tissues were examined for LIF expression by immunohistochemistry. In normal liver, a faint but consistent LIF expression was detected in the stroma of portal tracts (Fig. 1A). No signal was observed along sinusoids. In fibrotic liver tissues, an intense expression of LIF was seen along fibrous septa which is consistent with the presence of myofibroblasts (Fig. 1B). Staining adjacent sections with LIF antibody and with an antibody to alpha-smooth muscle actin (that labels myofibroblasts) suggested a large degree of colocalization (Figs. 1C,1D). Part of the LIF staining also appeared to be extracellular. There was no difference in the type of staining whatever the etiology of liver fibrosis. No labeling was found when the LIF antibody was replaced by a species-matched control antibody.
Figure 1 Immunohistochemical analysis of LIF expression in normal and cirrhotic human liver. (a): LIF expression is seen in normal liver in the stroma of portal tracts (arrows); (b): LIF is strongly expressed in fibrotic septa in cirrhotic liver (arrows); (c) and (d): consecutive sections of a cirrhotic liver analyzed for LIF (c) or alpha-smooth muscle actin (d) expression. No labeling was seen when the antibodies were replaced by a species-matched control antibody.
Analysis of total RNA from cultured human liver myofibroblasts by Northern blot revealed a single 4.5 kb transcript (Fig. 2A). RT-PCR experiments, described in more detail later, demonstrated the expression of both D and M isoforms of LIF (Fig. 2B). When cell supernatants were tested with an ELISA assay specific for human LIF, levels ranged between 800 and 8 000 ng/ml in different isolates. In order to make sure that this corresponded to biologically active LIF, the supernatants were tested for their ability to promote the growth of the LIF-dependent cell line BaF3, stably transfected with the human gp190 and gp130 isoforms. As shown in Fig. 3, myofibroblasts supernatants efficiently stimulated the growth of these cells in a dose-dependent fashion, confirming that biologically active LIF was effectively produced. Furthermore, the effect on BaF3 transfectants growth was abolished in the presence of the blocking gp190 LIF receptor antibody 12D3. The results of the ELISA combined with the 100 fold inhibition of biological activity, seen after anti-gp190 addition, further confirmed that most of the BaF3 growth-promoting activity produced by cultured myofibroblasts is likely to be LIF.
Figure 2 Detection of LIF transcripts in cultured human liver myofibroblasts. (A): Northern blot. Total RNA from cultured human liver myofibroblasts was hybridized with a cDNA probe to human LIF. A single 4.5 kb band was observed; (B) and (C): RT-PCR. Total RNA was subjected to reverse transcription then to PCR with the hLIF-D3/hLIF-N4 (B) or with the hLIF-M3/hLIF-N4 primers (C).
Figure 3 Biological activity of myofibroblast-derived LIF. BaF3 cells stably transfected with the gp130 and the gp190 subunits were exposed to dilutions of recombinant human LIF (starting concentration: 4 ng/ml) (open circles), culture medium (filled squares), myofibroblast conditioned medium alone (filled circles) or together with the blocking anti-gp190 antibody 12D3 at 20 μg/ml (filled triangles). Cell growth was monitored with a colorimetric assay. The figure shows the mean ± SD of 3 experiments performed in duplicate (SD are not always visible due to their small size).
As shown in Figure 4, when cells were incubated with graduated amount of recombinant human IL-4, the constitutive LIF secretion was dose-dependently reduced, demonstrating that this production may be regulated in vivo.
Figure 4 Regulation of LIF secretion by interleukin-4. Confluent cultures of human liver myofibroblasts were cultured in the presence of the indicated concentrations of IL-4, for 48 h in serum-free medium. LIF was measured by ELISA in the supernatant and the results were normalized according to the DNA content of the monolayer (mean ± SD of 3 experiments). The effect of IL-4 was highly significant, as assessed by ANOVA (p = 0.001).
Cloning of new isoforms of LIF mRNA
In order to test whether myofibroblasts transcribed all the alternatively spliced D, M or T first exons, a first set of RT-PCR experiments was carried out using the forward primers chosen in the alternative D, M or T first exons (hLIF-D3, hLIF-M3 and hLIF-T5), and a common reverse primer chosen in exon 3 (hLIF-N4) (Table 1 and Fig. 5). As shown in Fig. 2B, PCR with D- or M-specific primers was positive. Moreover, it always yielded a second, shorter, PCR product in addition to the expected amplified product (Fig. 2B). Similar results were obtained with other primer sets specific for either LIF-D (hLIF-D) or LIF-M (h-LIFM5) combined with hLIF-3N (data not shown), which strengthened the previous observation. No amplification products were obtained with the T primer. Then, we designed a reverse primer within exon 2 (hLIF-2N) that was used in conjunction with the forward hLIF-D and hLIF-M2 primers. In that case, we detected only a product of the expected size for both D and M PCRs (not shown).
Table 1 Primers used for PCR
Primer Sequence (*): 5'> 3' Orientation Ref.
hLIF-D ATAATGAAGGTCTTGGCGGCAG Forward
HLIF-D3 AAACTGCAGGCATCTGAGGTTTCCTCCAA Forward
hLIF-M2 CTGGAAGCGTGTGGTCTG Forward
HLIF-M3 AAACTGCAGCTGGAAGCGTGTGGTCTG Forward
hLIF-M5 TAGAATTCTGGAAGCGTGTGGTG Forward [3]
hLIF-T5 ATGAATTCTGTCACCTTTCACTTTCCT Forward [3]
hLIF-2N AATAAAGAGGGCATTGGCAC Reverse
hLIF-3N TTCTGGTCCCGGGTGATGTT Reverse [3]
HLIF-N4 GCTCTAGAGAAGGCCTGGGCCAACAC Reverse
(*) Bases in italics refer to restriction sites.
Figure 5 Sequence of LIF-D, M and T isoforms. Exons D, M and T are the 3 alternate first exons. Primers used for PCR are underlined. Primers hLIF-M2, M3 and M5 cover the same sequence but differ because of the presence or the absence of restriction sites.
The sizes of the additional products obtained with the hLIF-N4 primer were shorter by about 200 bp, which is the exact size of exon 2, raising therefore the possibility that the shorter PCR products were derived from a hitherto not described mRNA species where exon 2 was deleted through alternative splicing. In order to investigate this possibility, the short D and M fragments were cloned into a plasmid and sequenced. Sequencing indeed revealed that the first exon (either D or M) was directly spliced to the third one resulting in new, short transcripts that we have designated s-LIF-D and s-LIF-M.
The existence of these alternate transcripts could be observed in several hepatocellular carcinoma cell lines (HepG2, HuH7, Hep3B) and in the HEK293 cell line, derived from embryonic human kidney (Fig. 6A). They were also expressed in normal human liver samples (Fig. 6B) as well as in cirrhotic ones (Fig. 6C).
Figure 6 RT-PCR analysis of LIF-M expression in various cell lines and in human liver. (A): LIF-M expression was analyzed with the hLIF-M2 and hLIF-3N primers: Line 1, human liver myofibroblasts; Line 2, HepG2; Line 3, Hep3B; Line 4, HuH7; Line 5, HEK293. Product sizes are shown in bp; (B): normal human liver samples. LIF-D expression was analyzed with the hLIF-D3 and hLIF-N4 primers in 4 different samples. The same samples also expressed LIF-M (not shown). Product sizes are shown in bp; (C): diseased human liver samples. In that case, LIF-M expression was analyzed with the hLIF-M3 and hLIF-4N primers in 4 cases of cirrhotic liver. The same samples also expressed LIF-D (not shown). Product sizes are shown in bp; (D): semi-quantitation of LIF-D and s-LIF-D expression in a human liver myofibroblasts sample. LIF-D expression was analyzed with the hLIF-D3 and hLIF-N4 primers. The left part shows the migration pattern of the PCR-amplified products with the number of cycles above and the size of the products indicated by arrows, in bp. The graph on the right shows the signal quantification. Similar results were obtained with LIF-M.
The relative abundance of the variant transcripts relative to the classical transcripts was studied using a semi-quantitative RT-PCR method, where PCR was carried out for varying cycles numbers. As can be seen in Fig. 6D, expression of the s-LIF-D transcript lagged several cycles behind that of the long transcript. Similar results were obtained with the s-LIF-M transcript (not shown).
LIF receptor expression
The expression of the gp190 subunit by liver cells was then examined by immunohistochemistry. Five different antibodies, directed against separate epitopes, were used and yielded similar results. In normal liver tissue, LIF receptor (LIF-R) expression was detected as a continuous sinusoidal staining and in the stroma of portal tracts (Fig. 7A). In the cirrhotic liver, the sinusoidal staining was enhanced, whereas a very faint staining was observed in fibrous septa (Fig. 7B). Staining adjacent sections with LIF receptor antibody and with an antibody to CD31 (endothelial cells in the cirrhotic liver were labeled) showed a large degree of colocalization (Figs 7C,7D).
Figure 7 Detection of LIF receptor by immunohistochemistry. (a): LIF-R expression in normal liver is observed in portal tracts (arrows) as well as along sinusoids (arrowheads); (b): Sinusoidal staining is highly increased in cirrhotic liver (arrows); (c) and (d): consecutive sections of a cirrhotic liver analyzed for LIF-R (c) or CD31 (d) expression. No labeling was seen when the antibodies were replaced by a species-matched control antibody.
In a subsequent step, cultured human liver myofibroblasts were examined for their membrane expression of gp190 using flow cytometry. Adherent cells were released by action of EDTA and subjected to anti-gp190 labeling. No detectable levels of gp190 were observed with any of the 5 antibodies, although gp130 expression could be detected with the B-R3 antibody. In order to detect a low-level expression of functional LIF-R, myofibroblasts were exposed for 15 minutes to 10 ng/ml recombinant LIF; then, STAT-3 phosphorylation was examined by Western blot. No consistent effects were seen in 7 separate experiments. When a very weak signal was occasionally seen, it was not inhibited by 2 separate blocking antibodies to LIF-R (data not shown). Finally, production of soluble receptor was never detected in myofibroblast supernatants either.
Discussion
In this study, we demonstrate for the first time that LIF is expressed at low levels in normal human liver, whereas it is greatly increased in fibrotic liver, in a localization consistent with that of activated myofibroblasts. The slightly diffuse staining is suggestive of extracellular matrix deposition consistent with the expression of the M-type isoform of LIF. Experiments using cultured human liver myofibroblasts confirmed that these cells secreted extremely high levels of LIF in the range of 0.1–1 μg/106 cells/48 h. These levels are similar to those produced by activated lymphocytes, a classic source of LIF, and suggest that liver myofibroblasts may be a major source of LIF during chronic liver diseases. Our results are in agreement with data obtained in the rat showing that non-parenchymal cells, possibly activated stellate cells (i.e., myofibroblasts), express LIF [6]. Another study also reported an increased expression of LIF in peri-ductular cells, following bile duct ligation in IL-6 knock-out mice [7]; this location likely qualifies those cells as myofibroblasts. LIF expression by liver myofibroblasts is also reminiscent of its expression by kidney mesangial cells, a close relative to liver myofibroblasts, that we have previously reported [8].
On the other hand, and in contrast with mesangial cells [9], liver myofibroblasts do not appear to express cell surface LIF-specific gp190 receptor subunit. This is based on results obtained from immunohistochemistry, flow cytometry, as well as functional experiments. This indicates that LIF cannot exert an autocrine effect on liver myofibroblasts. However, we show that myofibroblasts express the IL-6 family common transducing subunit gp130. In this regard, others have shown that human liver myofibroblasts are responsive to oncostatin-M [10], indicating the presence of its functional alternative receptor consisting of gp130 and the specific OSMRβ chain.
Nonetheless, LIF receptor expression was detected by immunohistochemistry in human liver, in a peri-sinusoidal location. Similar results were obtained with 5 different antibodies directed to several epitopes of gp190. The pattern of continuous sinusoidal staining and the colocalization experiments are in favor of an expression in sinusoidal endothelial cells. However, we can not exclude staining of the sinusoidal domain of hepatocytes. In any case, these data indicate that cells close to LIF-producing myofibroblasts express LIF receptors and could thus respond to LIF in a paracrine fashion.
This study led to the discovery of new LIF transcripts resulting from a direct splicing of exon 1 to exon 3. This was observed for both LIF-D and LIF-M. Those transcripts were present at much lower levels than full-length transcripts, as suggested by RT-PCR and by the fact that they do not appear on Northern blot; thus, their biological relevance can be questioned. Whether s-LIF-D or s-LIF-M transcripts are translated also remains hypothetical. In the case of s-LIF-D, initiation at the AUG within exon D would result in a reading-frame shift following the 6th amino-acid (aa) and a termination at aa 88, the resulting protein bearing no homology with LIF. There are, however, several in-frame CUG codons within exon 3. Initiation at CUG 113 would result in the synthesis of a 125 aa polypeptide, recapitulating the sequence of the C-terminal part of LIF. Similar considerations apply to s-LIF-M that, in any case, does not contain an initiating AUG in exon 1. It should be emphasized that the lack of an AUG codon does not preclude the translation of the classical forms of LIF-M or LIF-T [3,5]. More experiments are needed to know whether these new transcripts are translated.
LIF secretion was dose-dependently decreased by IL-4, a known inhibitor of LIF secretion in other cell types [11,12]. IL-4 is also known to up-regulate collagen synthesis in human liver myofibroblasts and could thus be a pro-fibrogenic mediator in the liver [13]. Whether LIF expression is relevant to liver fibrogenesis needs to be assessed. LIF could affect extracellular matrix remodeling since it regulates the expression of several matrix proteinases and their inhibitors in various cell types [14,15]. In addition, LIF could play a role in the pathophysiology of chronic liver diseases through action on endothelial cells and on hepatocytes. Regarding endothelial cells, and depending on the model, both pro-angiogenic [16] and anti-angiogenic effects [17] have been described. Especially interesting is the demonstration that LIF can stimulate the adhesion of neutrophils to endothelial cells [18]; indeed, neutrophils are involved in the pathogenesis of liver diseases such as alcoholic liver disease. As already mentioned, the effects of LIF on human hepatocytes are still being debated [2].
Conclusions
For the first time, we show the expression of LIF in human liver myofibroblasts, as well as of two new isoforms of mRNA. Hepatic stellate cells and activated myofibroblasts have already been shown to synthesize a number of mediators involved in the control of inflammation, such as monocyte chemotactic-1 protein [19], or platelet-activating factor [20]. Expression of LIF by myofibroblasts and of its receptor by adjacent cells suggest a potential LIF paracrine loop in human liver that may play a role in the regulation of intra-hepatic inflammation and reinforces the concept of a major role of liver myofibroblasts in the regulation of intra-hepatic inflammation [21].
Methods
Tissue samples
Histologically normal/subnormal liver samples were obtained from macroscopically normal location in hepatectomy specimens, taken at a distance from a focal nodular hyperplasia (n = 5); a hemangioma (n = 1); or a colon cancer metastasis (n = 1). Cirrhotic specimens (n = 11) were obtained from patients undergoing liver transplantation for cirrhosis with associated hepatocellular carcinoma. In 10 out of 11 cases, the patients underwent liver transplantation. The cirrhosis etiologies were viral hepatitis C (n = 4); viral hepatitis B + D (n = 2); alcoholic (n = 4); or a combination of viral hepatitis B + C + alcoholic (n = 1).
Tissue sampling and processing
A portion of fresh tissue samples was routinely formalin-fixed and paraffin-embedded for diagnosis and a portion immediately frozen in liquid nitrogen-cooled isopentane and stored at -80°C. Five μm-thick serial frozen sections of each sample were air-dried on Super Frost/Plus slides (Menzel Glaser, Germany) and processed for immunohistochemistry. The procedures were in accordance with the European guidelines for the use of human tissues.
Materials
Culture medium and additives, recombinant human epidermal growth factor (EGF) and Moloney Murine Leukemia Virus reverse transcriptase were from Gibco-BRL (Life Technologies, Cergy-Pontoise, France). Taq polymerase and the pGEM-Teasy plasmid were from Promega (Madison, WI). The Qiagen RNeasy minikit was from Qiagen (Courtaboeuf, France). The [α32P]dCTP, Hybond N+ membrane, ECL reagent, and the Ready-to-go DNA labeling kit were from Amersham (Les Ulis, France). Ultrahyb solution was from Ambion (Austin, TX). Recombinant human IL-4 was a gift from Schering-Plough (Kenilworth, NJ). Anti-gp130 mAb B-R3 was from Diaclone (Besançon, France), anti-gp80 mAb M91 was from Coulter-Immunotech (Marseille, France), anti-phospho-STAT-3 (Tyr705) was from Cell Signaling Technology (Beverly, MA). All other chemicals were from Sigma (St Quentin Fallavier, France).
Cell culture
Human hepatic myofibroblasts were obtained from explants of non-tumoral liver resected during partial hepatectomy and characterized as previously described [22,23]. Myofibroblasts were routinely grown in DMEM containing 5% fetal calf serum, 5% pooled human AB serum and 5 ng/ml EGF. For studies of LIF secretion, cells were grown to confluence, made quiescent in serum and EGF-free Waymouth medium for 2 days and then exposed to agonists for 2 days. The results were normalized according to the DNA content of the monolayer [24].
Detection of LIF in culture supernatants
ELISA
Human LIF was measured using an ELISA based on two specific monoclonal antibodies, exactly as described previously [25]. A standard curve was obtained with recombinant glycosylated CHO-derived human LIF. The detection limit of the assay is 20 pg/ml, and LIF can be quantified at concentrations up to 1.2 ng/ml, without sample dilution. This ELISA is not sensitive to soluble receptor binding to the LIF molecule.
Biological assay
The Ba/F3 proliferation assays were performed, as described previously [26], using the Ba/F3 gp190 + gp130 transfectant cell line which expresses the two human LIF receptor chains (gp190 and gp130) and responds to all cytokines sharing gp190. LIF-dependent Ba/F3 cells were washed three times with RPMI to remove LIF which is required to maintain the cell line; then, cells (5 × 103 per well, in 50 μl, in duplicates) were incubated in the presence of 50 μl of three-fold dilutions of cytokines or supernatant, as indicated. After three days at 37°C, 0.015 ml of a 5 mg/ml solution of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (Sigma, Saint-Quentin Fallavier, France), in PBS, was added to each well. After 4 hours at 37°C, 0.11 ml of a mixture of 95 % isopropanol + 5 % formic acid was added to the wells, and the absorbance values were read at 570 nm, in a Titertek Multiskan microplate reader (Labsystems, Les Ullis, France). The blank consisted of eight wells containing the cells incubated with the Ba/F3 culture medium without any added cytokine.
Detection of LIF mRNA by Northern blot
Total RNA was isolated using the Qiagen RNeasy minikit. For Northern blot, 2 μg RNA were separated on a 1.0% agarose gel containing ethidium bromide in MOPS buffer. Running buffer and gel contained 0.2 M formaldehyde. The RNAs were transferred onto a Hybond N+ membrane by downward capillary transfer in running buffer. Examination of the stained membrane under UV light was used to confirm the quality of loading and transfer. The probe used was a 730 bp cDNA containing the whole coding sequence of human LIF [27]. Probes were labeled with [α32P]dCTP, by random priming using the Ready-to-go kit. Hybridization was performed using the Ultrahyb solution. The blots were washed in stringent conditions (0.1X SSC, 0.1% SDS at 65°C).
RT-PCR and cloning
One μg of total RNA was reverse-transcribed using MMLV-RT. An aliquot was used for PCR. Thirty five cycles were performed, each consisting of 94°C, 30 s; 60°C, 30 s; and 72°C, 30 s. PCR was performed in 50 μl of a reaction buffer containing 50 mM KCl, 10 mM Tris-HCl (pH 9.0), 1% Triton X-100, 1.5 mM MgCl2, 0.4 mM dNTPs, 0.2 mM primers, and 1.25 units of Taq polymerase. Then, an aliquot of the reaction was analyzed by agarose gel electrophoresis. The primers used are listed in Table 1 and are also positioned on the LIF sequence in Figure 5. When indicated, PCR products were directly cloned in the pGEM-Teasy plasmid and sequenced on both strands (Genome Express, Meylan, France).
Detection of LIF and LIF receptor expression
Antibodies and immunoperoxidase histochemistry
A commercially available polyclonal antibody against human LIF (R&D Systems, Minneapolis, Minnesota, USA), and different monoclonal antibodies against gp190, previously described [28], were used at concentrations optimised on control tissues. For colocalization experiments, mouse monoclonal antibodies against α-smooth muscle actin (Dako A/S, Glostrup, Denmark), and CD 31 (Dako) were used. For immunohistochemistry, frozen sections were incubated with the antibodies diluted in phosphate-buffered saline, pH 7.4, containing 4% bovine serum albumin. After washing, the epitopes were detected with the Envision+ system HRP detection and revealed with liquid diaminobenzidine (Dako). As negative control, we used either a clarified mouse myeloma ascites (Cappel Research Products, Durham, USA) or a rabbit non-immune immunoglobulin fraction (Dako), at the same concentration as the respective antibodies. Sections were examined with a Zeiss Axioplan 2 microscope (Carl Zeiss Microscopy, Jena, Germany). Images were acquired with an AxioCam camera (Carl Zeiss Vision, Hallbergmoos, Germany) by means of the AxioVision image processing and analysis system (Carl Zeiss Vision).
Flow cytometry
For each staining, 2 × 105 cells were incubated for 30 min at 4°C with saturating concentrations (10 μg/ml) of the indicated antibody in 0.1 ml of PBS supplemented with 1 % bovine serum albumin (BSA) and 0.1 % human polyclonal IgG (w/v, both from Sigma). Then, cells were washed twice with the same buffer and incubated for 30 min at 4°C with the FITC-conjugated goat anti-mouse IgG. After washing with PBS, the cells were resuspended in 0.14 ml of PBS containing 1% formaldehyde (v/v) and analysed by flow cytometry with a three color FACScalibur flow cytometer (Becton-Dickinson, Mountain View, CA) equipped with the CellQuest software. Control stainings used the second antibody only.
ELISA (soluble receptor)
The sandwich ELISA assay for soluble gp190 measurement has already been described [28]. It uses mAb 6G8 as the capture mAb, and biotinylated 10B2 mAb as the tracing mAb. Both mAb recognize distinct epitopes specific to the ectodomain of gp190. The assay has a detection limit of 0.5 ng/ml.
Immunodetection of phosphorylated STAT-3
Confluent cultures of myofibroblasts were left for 2 days in serum-free medium, and subsequently exposed to recombinant human LIF for 15 minutes [29]. Then, cells were lyzed in modified RIPA buffer in the presence of protease and phosphatase inhibitors, as described [30]. Identical amounts of proteins were analyzed by Western blot with an antibody against phospho-STAT-3. The blots were stripped and rehybridized with an antibody against total STAT-3.
Authors' contributions
TH performed most of the cell culture and RT-PCR experiments and cloned the new LIF variants. AD and NS performed the immunohistochemistry experiments and prepared the corresponding figures. JLT provided the monoclonal antibodies to LIF-R and participated in the design of the experiments showing the secretion of active LIF. SD performed the LIF ELISA assays, the biological activity testing and flow cytometry experiments. VN performed the experiments looking at STAT-3 phosphorylation. JFB provided the human liver samples. JFM was involved in the coordination of the project and in the critical reading of the manuscript. JR conceived the study and was the main coordinator and responsible for the redaction of the manuscript. All authors read and approved the final manuscript.
Acknowledgments
Supported by grants from Ligue contre le Cancer Aquitaine-Dordogne et Charentes and from Association pour la Recherche sur le Cancer.
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| 15566573 | PMC538256 | CC BY | 2021-01-04 16:38:23 | no | Comp Hepatol. 2004 Nov 26; 3:10 | utf-8 | Comp Hepatol | 2,004 | 10.1186/1476-5926-3-10 | oa_comm |
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Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-251555507810.1186/1476-7120-2-25ReviewThe biological effects of diagnostic cardiac imaging on chronically exposed physicians: the importance of being non-ionizing Andreassi Maria Grazia [email protected] Laboratory of Cellular Biology and Genetics, CNR Institute of Clinical Physiology, Pisa, Italy2004 22 11 2004 2 25 25 9 11 2004 22 11 2004 Copyright © 2004 Andreassi; licensee BioMed Central Ltd.2004Andreassi; 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.
Ultrasounds and ionizing radiation are extensively used for diagnostic applications in the cardiology clinical practice. This paper reviewed the available information on occupational risk of the cardiologists who perform, every day, cardiac imaging procedures. At the moment, there are no consistent evidence that exposure to medical ultrasound is capable of inducing genetic effects, and representing a serious health hazard for clinical staff. In contrast, exposure to ionizing radiation may result in adverse health effect on clinical cardiologists. Although the current risk estimates are clouded by approximations and extrapolations, most data from cytogenetic studies have reported a detrimental effect on somatic DNA of professionally exposed personnel to chronic low doses of ionizing radiation. Since interventional cardiologists and electro-physiologists have the highest radiation exposure among health professionals, a major awareness is crucial for improving occupational protection. Furthermore, the use of a biological dosimeter could be a reliable tool for the risk quantification on an individual basis.
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Introduction
Over the last 30 years, medical cardiology imaging has rapidly grown, becoming an essential part of the cardiology clinical practice. Imaging procedures include conventional imaging tests such as echocardiography, radionuclide imaging, and angiography as well as a newer imaging techniques such as emission computed tomography and magnetic resonance imaging which promise to expand diagnostic capabilities [1]. These techniques widely differ not only for what concerns costs, availability and technical information, but they also differ in environmental and health hazards.
Many cardiac procedures can deliver high radiation doses to the clinical staff [2]. This exposure may represent a significant health risk, resulting in deleterious clinical implications which can affect not only the personnel involved, but also their progeny [3-5]. Unfortunately, many physicians are unfamiliar with radiation biology or the quantitative nature of the risks and, frequently, ultrasound and ionizing radiation risks are misunderstood [6-9]. The purpose of this paper is to discuss the published evidence on health effects of cardiac imaging procedures employing ultrasound and ionizing radiation.
Ultrasound imaging
Ultrasound imaging, also called sonography, is a method of obtaining human body images through the use of high frequency sound waves. Ultrasounds are mechanical vibrations with frequencies above the human limit of audibility. The use of ultrasounds in order to obtain images for medical diagnostic purposes, typically employs frequencies ranging from 2 MHz to about 12 MHz [10]. Ultrasound does not use ionizing radiation, and it is the preferred image modality for monitoring both pregnant women and their embryos or fetus [10]. In contrast to ionizing radiation, which can damage biological materials by dislodging electrons from atoms and molecules, ultrasounds do not cause ionisation. They usually interact with human tissue primarily by generating heat, but also non-thermal effects which are ascribed to cavitation (i.e. micro-bubble) [11]. The process of cavitation includes ultrasounds mechanical effects which lead to hydrodynamic breaks of hydrogen bonds and oscillation of hydrogen ions, and chemical effects produced by the occurrence of free radicals in intercarionic space in the process of cavitation (Figure 1). Theoretically, these free radicals may interfere with DNA, causing chromosomal damage. Indeed, ultrasounds of diagnostic intensities induced detectable DNA damage in animal cells [12,13]. Currently, there is a body of studies on human DNA damage from exposure to therapeutic and diagnostic ultrasounds [14-20]. In particular, Stella et al. [15] reported that therapeutic ultrasound induce a significant increase in sister chromatid exchanges (SCEs) in human lymphocytes after treatment both in vitro and in vivo. In the same study, no increase in chromosomal aberrations was observed during and after ultrasound therapy [15]. Subsequently, some reports on human cells indicated that ultrasound was not able to induce SCEs or chromosomal damage (Table 1). Thus, there is at present no indication that exposure to medical ultrasound is capable of inducing genetic effects and representing a serious health hazard for clinical staff. However, very little information is available on the genetic effects of individuals occupationally exposed to chronic ultrasound. Medical staff can be exposed to hand-transmitted ultrasound waves in the work-place.
Figure 1 At high acoustic pressure, ultrasound is capable of causing rapid bubble which grow and collapse among them (a) and cells (b). This mechanism results in the production of sufficient energy to disrupt chemical bonds and produce reactive free radicals, that may interfere with DNA.
Table 1 Summary of studies on genetic effects of medical ultrasounds
Author, Year (Ref) Assay System Endpoint Exposure Result
Miller et al., 1983 (14) Human lymphocytes exposed in vitro SCE 2 MHz
SPPA intensity 100 W/cm2 Negative
Stella et al., 1984 (15) Human lymphocytes exposed in vitro SCE
CA 1 W/cm2; 0.860 MHz; for 40–160 sec Positive/ Negative
Barnett et al., 1987 (16) Human lymphocytes exposed in vitro SCE 3.1 MHz
SPPA intensities from 15 to 135 W/cm2. Negative
Carrera P et al., 1990 (17) Chorionic villi exposed in vitro
Chorionic villi from exposed pregnant women SCE 2 MHz at 1, 2, 3 h
Diagnostic US for 20 min (in vivo exposure Negative
Miller et al., 1991 (18) Human lymphocytes from exposed patients SCE 4 patients underwent therapeutic US
4 healthy persons underwent sham-therapeutic US Negative
Martini et al., 1991 (19) Lymphocyte and lymphoblastoid cells exposed in vitro SCE 5 MHz for 20 sec, 1 min, 5 min, and 20 min Negative
Sahin O et al., 2004 (20) Human lymphocytes from exposed patients MN 10 patients underwent 10 session of US therapy at 1 MHz for 10 min and 10 control subjects underwent sham-therapeutic US Negative
Garaj-Vrhovac and Kopjar, 2000 (22) Human lymphocytes from cardiologists working with Doppler ultrasound CA
SCE
MN Unit working with colour Doppler US (transducer frequencies 2.5–7.5 MHz.
SPPA intensity 60–110 W/cm2. Positive
SCE: sister-chromatid exchange; MN: Micronuclei; CA: Chromosomal aberrations; SPPA: Spatial Peak Pulse Average
Indeed, ultrasound sources do not transmit acoustic energy into air, and only low level ultrasound reaches medical personnel through handling of the probe [21]. Probably, occupational exposure to ultrasound occurs during training procedures [21]. In fact, medical personnel often apply diagnostic ultrasound to themselves during training or during technique demonstrations [21]. Consequently, ultrasound is not harmful like the other types and sources of radiation. However, a recent investigation indicated that medical personnel from a cardiology unit working with colour Doppler ultrasonic equipment had an increased genotoxic damage compared to the control subjects [22]. Therefore, this observation requires further studies in order to determine if chronic exposure to ultrasound might induce genotoxic effects.
Ionizing radiation
Ionizing radiation is known to cause harm. High radiation doses tend to kill cells, while low doses tend to damage or alter the genetic code (DNA) of irradiated cells. The biological effects of ionizing radiation are divided into two categories: deterministic and stochastic effects. Deterministic effects, such as erythema or cataract, have a threshold dose below which the biological response is not observed [23-25]. Some interventional procedures with long screening times and multiple image acquisition (e.g. percutaneous coronary intervention, radio-frequency ablation, etc) may give rise to deterministic effects in both staff and patients [26,27].
A stochastic effect is a probabilistic event and there is no known threshold dose. The likelihood of inducing the effect, but not the severity, increases in relation to dose and may differ among individuals.
In fact, the effect of low doses of radiation -less than 50 mSv- do not cause an immediate problem to any body organ, but spread out over long periods of time after exposure. The biological effects are at DNA level and they may not be detected [23-25]. The cell has repair mechanisms against damage induced by radiation as well as by chemical carcinogens. Consequently, biological effects of low dose radiation on living cells may result in three outcomes: (1) injured or damaged cells repair themselves, resulting in no residual damage; (2) cells die; or (3) cells incorrectly repair themselves resulting in a biological change (Figure 2). Such biological changes include the development of cancer and genetic defects in the future children of exposed parents. At present, however, the effects of low-level exposure remain uncertain [28]. The associations between radiation exposure and the development of cancer are mostly based on populations exposed to relatively high levels of ionizing radiation (e.g., Japanese atomic bomb survivors). Since extraordinary large studies are required to quantify the risks of very low doses of radiation, it is unlikely that we will be able to precisely quantify cancer risk in human populations at doses below 10 mSv [28]. For instance, an epidemiological study of more than 5 million people would be needed to quantify the effect for a 10 -mSv dose or less [28]. Our inability to quantify risk does not, however, imply that this risk is negligible. Furthermore, the small (and often not so small) individual risk applied to a large number of individuals, and by protracted exposures, translates into a significant public health problem). As such, the international scientific community has adopted a prudent approach and acknowledged the fact that any level of exposure could potentially lead to biological effects. A linear, no-threshold dose response relationship is used by the IRCP in order to describe the relationship between radiation dose and the occurrence of cancer [29]. This dose-response model suggests that any increase in dose, no matter how small, results in an incremental increase in risk.
Figure 2 Radiation damage of DNA. Damaged DNA is screened through the process of DNA repair and mismatch correction. DNA lesions that escape repair, has the ability to produce mutations, which lead to the development and the progression of both cancer and human diseases even decades after exposure.
Genetic effects are the result of a mutation produced in the reproductive cells of an exposed individual that are passed on to their offspring. These effects may show up as birth defects or other conditions in the future children of the exposed individual and succeeding generation. Indeed, studies with laboratory animals have provided a large body of data on radiation-induced genetic effects [30]. Recently, these effects have been also observed in studies of people exposed to radiation from Chernobyl disaster, radiation workers and medical radiologists who have received doses of radiation [31-33]. However, no conclusive evidence exists yet [34,35].
Radiation exposure to cardiologists
The use of radiation in medicine is the largest source of man-made radiation exposure. According to the latest estimation of the United Nations, an average of 2.4 mSv/year comes from natural sources [24]. In western countries, the exposure dose from medical radiation corresponds to 50 to 100% of the total natural radiation. In 1997, the German Federal Office for Radiation Protection reported 136 million x ray examinations and 4 million nuclear medicine diagnostic tests, resulting in a mean effective dose of 2.15 mSv per person per year [36]. Cardiac and interventional procedures account for a large percentage of nuclear and radiological examinations [36]. Of all radiological examinations, 28% are arteriographies and interventions. An additional 2% derive from chest X-rays and 37% from CT: many of them are cardiological referrals. Regarding nuclear medicine, 22% are cardiological scan. These percentages are likely higher now, since the use of cardiac and interventional procedures is increasing.
Cardiac ionizing procedures expose both patients and medical staff to the highest radiation levels in diagnostic radiology, and recently, as the number of diagnostic and interventional cardiac catheterisation procedures has greatly increased, serious radiation induced skin injuries and an excess of cataract development have been reported in exposed staff [37-39]. Furthermore, it has been suggested that fluoroscopic procedures may be a health hazard and increase the risk for brain tumours in interventional cardiologists [40].
Today, interventional cardiologists represent, indeed, the most important group of exposed among professionally exposed physicians [41,42].
As known, the limit on effective dose for exposed workers should be 100 mSv in a consecutive five year period, subject to a maximum effective dose of 50 mSv in any single year. Radiation dose limits to adult occupational workers provided by the International Commission on Radiological Protection (ICRP) are shown in table 2.
Table 2 Recommended occupational dose limits by International Commission on Radiological Protection (ICRP).
TISSUE INJURY OCCUPATIONAL DOSE LIMITS/YEAR
whole body 20 mSv 2 rem
Lens of the eye 150 mSv 15 rem
Skin, hands, feet, and other organs 500 mSv 50 rem
As a matter of fact, the head dose sustained by cardiologists may reach 60 mSv per year, and may in some cases exceed the occupational limit of 150 mSv per year recommended for the lens of the eye [41].
However, the correlation between occupational doses and staff radiological risks is not simple, and it is very dependent on equipment, the specialist, and protocols followed throughout the procedure [43]. Many factors can influence occupational doses for the same radiation dose imparted during cardiac procedure. One of the most important factors is that protection tools are available in catheterisation laboratories and are appropriately used [43]. In addition, another likely reason is a lack of knowledge, information and training in radiation protection [43].
Importantly, a recent survey showed that that most of cardiologists do not correctly evaluate the dose exposure, the medico-legal regulation, the environmental impact and individual bio-risks of the radiological investigations [9]. As shown in table 3, this surprising lack of knowledge of both dose and clinical risk of commonly performed ionising test examinations, is not at all restricted to cardiologists, and seems to be democratically spread across all specialties – from surgeons to orthopaedics, to paediatricians [6-9].
Table 3 Doctors' knowledge of radiation dose and risk for medical ionising testing
Author, year (Ref) Physicians Radiological Awareness Evaluation Results
Shiralkar S et al., 2003 (6) British physicians Radiation doses for common radiological investigations. 97% of doctors underestimates dose.
5% believes that US use ionising radiation.
8% believes thatMRI use ionising radiation.
Finestone A et al., 2003 (7) Istraeli orthopaedists Mortality risk of radiation-induced carcinoma from bone scan scintigraphy Mortality risk was identified correctly by less than 5% of respondents.
Lee CI et al., 2004 (8) Emergency department (ED), physicians and radiologists Radiation dose and possible risks associated with CT scan Almost all doctors were unable to accurately estimate the dose.
Only 9% ED physicians believed that there was increased risk.
Correia MJ et al., 2005 (9) Adult and paediatric cardiologists Environmental impact, individual bio-risks, dose exposure and medico-legal regulation of medical ionising testing Only 11%, 5%, 29% and 42% of physicians correctly identified environmental impact, individual bio-risks, dose exposure and legal regulation, respectively.
CT = computed tomography; MRI = magnetic resonance imaging; US = ultrasound
Probably, this unawareness has its root in the difficult perception of a long-term risk associated to radiation exposure. In particular, the perception of cancer risk, which can have a latency period of many years after exposure, is often elusive. Furthermore, the exact risk at very low doses to a specific individual is further complicated by many factors, such as carcinogenic agents in our environment, cigarette smoke, diet and genetic background.
However, a recent study has estimated that from 0.6% to 3% of all cancers are due to medical X-rays [44]. These figures are impressive but may largely underestimate the true risk, since they are referred to radiological data concerning the 1991–1996. Taking into account current radiological activities, medical radiation is likely to account for at least 20% of cancer in developed countries [45].
With regard to occupational exposure for radiologists and radiotherapists, available epidemiological studies have been recently reviewed by Yoshinaga et al [46]. An excess risk of leukaemia associated with occupational radiation was found among early workers employed before 1950, when radiation exposures were high. In addition, several studies provided evidence of a radiation effect for breast and skin cancer. To date, there is no clear evidence of an increased cancer risk in medical radiation workers exposed to current levels of radiation doses. However, given a relatively short period of time for which the most recent workers have been followed up and in view of the increasing uses of radiation in modern medical practices, it is important to continue to monitor the health status of medical radiation workers [46].
To the fatal cancer risk, one must add the risk of non-fatal cancer and major genetic damage transmitted to the offspring. It is relevant to underline that the long-term damage may not include only cancer but also other major degenerative diseases, including atherosclerosis [47,48]. However, it is important to realize that many difficulties are involved in designing epidemiological studies that can accurately measure the increases in health effects due to low exposures to radiation as compared to the normal rate of cancer. Studies with very large sample size are required in order to quantify the risks of very low doses of radiation. An alternative strategy could be based on the measure of biological effects by using biomarkers as predictors of delayed health outcomes [49].
Biomarkers in the assessment of radiation exposure
Damage to deoxyribonucleic acid (DNA), which carries the genetic information in chromosomes in the cell nucleus, is considered to be the main initiating event by which radiation damage to cells results in the development of cancer and hereditary disease. Four biomarkers (Figure 3) -analysis of structural chromosome aberrations, micronucleus assay, sister chromatid exchange analysis and comet assay- in peripheral lymphocytes are currently employed in order to study human exposure to environmental carcinogens [50]. Among these, the test of chromosomal aberrations in peripheral blood lymphocytes has the most abundant literature validating that a high frequency of chromosomal breakage is a strong predictor of cancer risk in healthy subjects [51,52].
Figure 3 Biomarkers of DNA damage in human lymphocytes: a) Structural chromosomal aberrations (CA) are typical of cancer cells, probably as a manifestation of genetic instability. b) Micronuclei (MN) can originate from chromosome breaks or whole chromosomes that fail to engage with the mitotic spindle when the cell divides. Therefore, the micronucleus test can be considered just as a real "biological dosimeter" for evaluating both numerical and structural chromosome aberrations. c) Sister chromatid exchanges (SCEs) represent symmetrical exchanges between sister chromatids; generally they do not result in chromosomal alterations of the genetic information. c) The Comet assay is an especially sensitive method for detecting DNA single-strand breaks and oxidative DNA damage in individual cells. The entity of the DNA damage is proportional to the length of the comet.
During the last years, the micronucleus assay has become popular since it is fast and inexpensive, and it is considered to be a "biological dosimeter" for exposure to ionizing radiation [53].
The importance of cytogenetic study of peripheral lymphocytes in subjects exposed to ionizing radiation has been reported for more than 20 years, especially in radiologists [54-68]. The available evidence suggests that chronic exposure to low dose radiation has a genotoxic effect on somatic DNA of professionally exposed workers (Table 4). This effect seems to be cumulative over time, although the majority of these studies failed to establish a dose-effect relationship for low doses. The absence of increase of somatic DNA damage in relation to the dose might be explained by various factors. Dosimetry records may underestimate the real dose exposure if the badges are not properly worn. The potential combined effect of other genotoxic exposures would also induce DNA damage, enhancing the effect of radiation exposure [63]. Moreover, genetic susceptibility may account for the inter-individual differences to radiation sensitivity. Such possible susceptibility may recognize sources of variability (genetic polymorphism) in people's DNA repair gene sequence [69]. However, it is interesting to underline that, in a group of radiologists, it has been documented an important parallelism between the decrease of the exposure to ionizing radiation in the hospitals and a reduction in the frequency of chromosome aberrations over the most recent decades [58] (Figure 4). This decrease was the result of an efficient protection policy among radiologists. Unfortunately, this is not the case for invasive cardiologists who need to know very well both the long-term risks and the doses involved in the large amount of examinations they prescribe and/or perform every day [40,41].
Table 4 Cytogenetic studies in hospital workers
Author, year (ref) Exposed Subjects, n Non-exposed Subjects, n Endpoint Results Exposure Correlation with dose (Yes/No)
Bigatti et al, 1988, (54) 63 (physicians, nurses and technicians) 30 (ward nurses and office personnel) CA Positive < legal limit. No
Barquinero et al, 1993, (55) 26 (hospital workers) 10 (healthy individuals) CA Positive 1.6–42.71 mSv No
Paz-y-Mino et al, 1995, (56) 10 (hospital workers) 10 (healthy individuals) CA Positive 1.84 mSv/year. No
Vera et al, 1997, (57) 20 (medical staff working at an X-ray department) 20 (general population) CA
MN Positive <25 mSv/year. No (Major DNA damage in subjects exposed to both ultrasound and X-ray)
Bonassi et al., 1997, (58) 871 (hospital workers from 4 laboratories) 617 (healthy individuals) CA Positive Available only partially and variable. Yes/No
Rozgaj et al, 1999, (59) 483 (radiologists, pneumologists, technicians) 160 (healthy individuals) CA Positive <20 mSv/year No
Undeger et al., 1999, (60) 30 (technicians) 30 (nurses, technicians, office personnel) Comet Positive 50 mSv/ year. No
Cardoso et al, 2001, (61) 8 (workers in X-rays, radiotherapy and nuclear medicine sectors) 8 (healthy individuals) CA
MN
SCE Positive 63.2 mSv/life No
Maluf et al, 2001, (62) 22 (hospital workers) 22 (non-exposed workers) MN
Comet Positive 0.2 – 121. mSv No
Maffei et al, 2002, (63) 37 (physicians, technicians) 37 (non-exposed workers MN Negative/ Positive 35 mSv /life No
Bozkurt et al, 2003, (64) 16 (nuclear medicine) 16 (non-exposed physicians) SCE Positive 3.39 mSv/year. Yes
Garaj-Vrhovac and Kopjar, 2003, (65) 50 (physicians, 25 technicians, 10 nurses) 50 (healthy students and office employees) Comet Positive 0–8.5 mSv/year. No
Maffei et al, 2004, (66) 34 (physicians, technicians) 35 (non-exposed workers) CA Positive 1.81–141.77 mSv/life. Yes
Zakeri et al., 2004, (67) 71 (cardiologists, nurses and technicians) 36 (healthy individuals) CA
MN Positive 3.0 mSv/year No
Andreassi et al, 2004, (67) 31 interventional cardiologists 31 clinical cardiologists MN Positive 4 mSv/year No
SCE: sister-chromatid exchange; MN: Micronuclei; CA: Chromosomal aberrations;
Figure 4 a) Decrease in exposure to ionizing radiation in hospital radiologists over the most recent decades and b) a similar time-related reduction in the frequency of chromosome-type aberrations (redrawn from ref. 58)
As matter of fact, our results and a recent monitoring of personnel working in angiocardiography laboratories in Iranian Hospitals showed a high frequency of chromosome aberrations in cardiologists s and technicians compared to unexposed subjects [68,69].
Taken together, these evidences highlight that the use of a biological dosimeter could complement the data obtained by physical dosimetry and reduce the uncertainties of low-dose radiation risk assessment [70]. The analysis of chromosome aberrations is the gold standard endpoint for radiation biological dosimetry. Limitations and strengths on biodosimetry have been fully discussed in the IAEA Report 405 [70]. A possible limitation is the response to high radiation dose (> 4 Sv) where cell death and delays in progression through the cycle represents a pitfall for estimation of acute irradiation particularly when non-uniform or partial body irradiation have occurred.
Moreover, the method is laborious, time consuming and requires expert skills. Scoring of micronuclei has been proposed as an alternative to conventional chromosome aberrations analysis, being more sensitive and faster [71]. Although micronuclei method has been improved, inter-laboratories discrepancies have emphasized the need for better standardization [53].
However, in many countries the application of cytogenetic dosimetry has yet medical-legal recognition, and it is complementary to physical dosimetry. On the other hand, the usefulness of biomarkers as early biological effects, with special concern for the prediction of cancer, has been recently emphasized [72]. Therefore, the application of biodosimetry- that measures true cellular injury resulting from that radiation- could greatly enhance health risk, identifying susceptible individuals and enhancing the possibility of preventive measures, especially in occupational settings with a high volume of radiological activities (Figure 5).
Figure 5 Illustration of potential use of biomarkers as early predictors of clinical disease. The evaluation of genetic effects such as chromosomal damage could be used to anticipate delayed health outcomes, providing a greater potential for preventive measures.
Conclusion
Occupational exposure can occur in cardiological procedures which employ ultrasound and ionizing radiation. Today, there are no consistent adverse biological effects on operators caused by exposures to ultrasound. However, it is clearly necessary to continually monitor both the potential risks and safety of ultrasound exposure. In contrast, exposure to ionizing radiation may result in adverse health effect on both cardiologists directly and on their progeny. Although the current risk estimates are clouded by approximations and extrapolations, most data from cytogenetic studies have reported an enhanced DNA damage in hospital workers exposed to chronic low doses of ionizing radiation. The occupational dose of interventional cardiologists, and electrophysiologists tend to be higher compared to other medical specialists as a result of the recent increasing use of interventional techniques. On the other hand, physicians are dramatically unaware of dose, long-term risks and populations health impact caused by the use of medical ionizing radiation. Thus, a major awareness appears to be crucial in order to improve both one's knowledge on the appropriateness of protective tools and also in trying to reduce the number of unnecessary procedures. The use of a biological dosimeter could be a reliable tool for risk quantification on an individual basis.
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| 15555078 | PMC538257 | CC BY | 2021-01-04 16:38:29 | no | Cardiovasc Ultrasound. 2004 Nov 22; 2:25 | utf-8 | Cardiovasc Ultrasound | 2,004 | 10.1186/1476-7120-2-25 | oa_comm |
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J Negat Results BiomedJournal of Negative Results in Biomedicine1477-5751BioMed Central London 1477-5751-3-61556373110.1186/1477-5751-3-6ResearchImmunological parameters in girls with Turner syndrome Stenberg Annika E [email protected]én Lisskulla [email protected] Carl GM [email protected] Malou [email protected] Dept. of Otorhinolaryngology, Karolinska University Hospital, Stockholm, Sweden2 Dept. of Woman and Child Health, Karolinska University Hospital, Stockholm, Sweden3 Dept. Clin. Chemistry, Engelholm hospital, Engelholm, Sweden2004 25 11 2004 3 6 6 23 10 2002 25 11 2004 Copyright © 2004 Stenberg et al; licensee BioMed Central Ltd.2004Stenberg 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.
Disturbances in the immune system has been described in Turner syndrome, with an association to low levels of IgG and IgM and decreased levels of T- and B-lymphocytes. Also different autoimmune diseases have been connected to Turner syndrome (45, X), thyroiditis being the most common.
Besides the typical features of Turner syndrome (short stature, failure to enter puberty spontaneously and infertility due to ovarian insufficiency) ear problems are common (recurrent otitis media and progressive sensorineural hearing disorder).
Levels of IgG, IgA, IgM, IgD and the four IgG subclasses as well as T- and B-lymphocyte subpopulations were investigated in 15 girls with Turners syndrome to examine whether an immunodeficiency may be the cause of their high incidence of otitis media. No major immunological deficiency was found that could explain the increased incidence of otitis media in the young Turner girls.
Antibodieslymphocytesimmunoglobulinshearingotitis media
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Introduction
Recurrent otitis media is often a problem in children with Turner syndrome (TS) [1,2]. More than 60% of the Turner girls (60–80%) aged 4–15 years suffer from repeated attacks of acute otitis media, as compared to 5% of children (aged 0–6 years) in the normal population [3,4]. These problems among the Turner girls are more extensive and last longer (up in their teens) than in an non Turner population. Frequent insertions of myringeal tubes are often necessary and in order to try to prevent chronic ear problems regular and frequent controls are necessary. However, sequelae like chronic otitis media are frequently seen, even if controls have been meticulous. A sensorineural hearing loss is also common among these patients, with a typical dip in the mid frequencies, declining over time. This sensorineural dip has been identified already in 6-year-old Turner girls [3]. Later in life (~35 years) a progressive high frequency hearing loss is added to the dip, leading to more prominent hearing problems and hearing aids often become necessary [2,5,6]. The cause of the associated ear and hearing problems is not known but the ear problems later in life could be influenced by the loss of estrogen.
TS is caused by the presence of only one normally functioning X-chromosome. The other sex chromosome can be missing (45, X) or abnormal and mosaicism is often present. Occurring in one of every 2000 female births, TS is one of our most common sex chromosome abnormalities [7]. TS is characterized by short stature, no spontaneous puberty and infertility due to ovarian dysgenesis with no estrogen production [8]. Mental retardation is not connected to the syndrome. Since the early 80's, treatment is given with growth hormone from birth and estrogen therapy to induce puberty.
Immunological disturbances have previously been described in TS, with an association to reduced levels of serum IgG and IgM, increased IgA and decreased levels of circulating T- and B-lymphocytes. However, the results have not been conclusive [9-12].
In the normal population children with IgG2 deficiency commonly develop recurrent acute otitis media. It is believed that these infections are secondary to impaired antibody response, rather than Eustachian tube dysfunction [13]. As immunological derangements seem to be common in TS, an immunological deficiency could be a potential cause to parts of the ear problems.
The aim of this study was to investigate immunoglobulin and lymphocyte subpopulations in girls with Turners syndrome to examine whether an immunodeficiency may be the cause of their high incidence of otitis media. Immunotherapy would then be a possible treatment.
Materials and methods
Subjects
Blood samples from patients with the diagnosis TS, genetically confirmed, were investigated according to the Swedish ethical record no 88–265.
Analyses regarding immunoglobulin- and lymhpocyte subpopulations were performed in 15 girls, aged 5–17 years (median age 11 years), randomly selected from all girls in this age group with TS attending the Karolinska Hospital, Stockholm (total 29 patients). Of these 53% (n = 8) had suffered from repeated attacks of otitis media. All TS girls had been treated with growth hormones and their karyotypes were: 45, X (n = 8); 45, X/46, XX (n = 4); 45, X/46, X, i(Xq) (n = 2); and 45, X/46, X, r(X) (n = 1) (r = ring chromosome).
A medical history was attained, focusing on autoimmune diseases, previous and current ear diseases and other infectious diseases, ear operations, and hearing problems.
Lymphocyte subpopulations
Leukocyte counts (109/L) were analysed in a Coulter MicroDiff II (Beckman-Coulter). The differential leukocyte (lymphocytes, monocytes and granulocytes) counts and percentages were obtained by 2-color FACS-analysis with CD14/CD45 markers. The number and percentage of lymphocyte subpopulations were obtained by standardized 2- or 3-color FACS-analysis on Epics XL or Elite flowcytometer (Beckman-Coulter) using commercial reagents. CD19+ was marker for B-cells and CD3+ for T-cells, CD3+CD4+ for helper T-cells, CD3+CD8+ for cytotoxic T-cells, CD56+CD3- for NK-cells and HLA-DR+ for activated T-cell subsets. The ratio of CD4+/CD8+ was also calculated. The monoclonal antibody clones used were: UCHT1 (CD3+), SFCI12T4D11/T4 (CD4+), SFCI21Thy2D3/T8 (CD8+), 116/Mo2 (CD14+), 89B/B4 (CD19+), KC56 (CD45+), NKH1 (CD56+) and 9-49/I3 (HLA-DR+), all from Cytostat, Beckman-Coulter. All FACS-analyses were performed at the routine laboratory, Department of Clinical Immunology, Karolinska Hospital and the results were compared to age-related in-house and published reference ranges (5 to 95 percentiles) [14] except for CD56+CD3- for which an adult reference was used (10–90 percentile).
Complement and antibodies
Hemolytic complement (classical and alternative pathways), IgA antibodies to gliadin and endomysium, IgG antibodies to pneumococcal polysaccharide and tetanus toxoid antigen, the serum concentrations (g/L) of circulating IgA, IgG, IgM, IgD, IgG1, IgG2, IgG3 and IgG4 as well as the Gm(23)-allotyping of IgG2 were analysed by standard methods and compared to age related reference ranges used at the routine laboratory, Department of Clinical Immunology, Karolinska Hospital, Stockholm.
Statistical analysis
Medians of continuous parameters were compared between groups by Mann-Whitney U-test and correlations were performed by Spearman rank analysis. A two-tailed p < 0.05 was considered significant.
Results
Lymphocyte subpopulations
The leukocyte counts as well as the absolute counts and percentages of lymphocytes, monocytes, and granulocytes were within normal limits for all 15 Turner girls. Likewise most girls had normal counts and percentages of lymphocyte subpopulations as compared to the 5 to 95% percentiles age-related reference ranges (Fig. 1a and 1b) including activated CD4+ and CD8+ T-cells (HLA-DR+). However, the CD4+/CD8+ ratio was in the lower range (girls aged ≥10), with one girl having a very low ratio (0.6).
Figure 1 1a and b Percentages (Fig 1a) and absolute counts (Fig 1b) of lymphocyte subpopulations in 15 girls with Turner's syndrome divided into two age groups. Group A aged <10 years (n = 4) and group B aged ≥10 years (n = 11). Girls with recurrent otitis media are illustrated with open symbols (n = 8) and those who are otitis free with filled symbols (n = 7). The horizontal lines indicate medians and the shaded boxes the 5 to 95 percentiles of age-related reference ranges except for CD56+CD3- cells for which the 10 to 90 percentiles reference range of adults was used.
Complement and Immunoglobulin levels
Hemolytic complement (classical and alternative pathway) was within normal limits for all 15 Turner girls.
The serum concentrations of IgG, IgA, IgM, IgD and the four IgG subclasses were for most Turner girls within the age-related 95% confidence intervals (Fig. 2). The exceptions were one girl with elevated IgM (2.3 g/L), five with elevated IgD (0.1–0.23 g/L), two with elevated IgG1 (10.2 and 10.8 g/L), one with low IgG2 (0.4 g/L) and two girls with low IgG4 (<0.01 g/L).
Figure 2 Immunoglobulin levels in 15 Turner girls. The shaded boxes indicate the 95% confidence interval for the 5–20 years age group. Girls with recurrent otitis media are illustrated with open symbols (n = 8) and those who are otitis free with filled symbols (n = 7).
The frequency of homozygous G2m(23)-negative Turner girls was 33% (5/15).
Antibodies
Normal levels of IgG antibodies to tetanus toxoid and polysaccharide antigen were detected among most Turner girls, except for two respectively one, having too low levels. Slightly elevated IgA antibodies to gliadin were observed in 3 (20%) girls, whereas no IgA antibodies to endomysium could be detected in any of the 15 girls.
Age
When comparing girls aged <10 years (n = 4) and ≥10 years (n = 11) the following parameters were found to be influenced by age with decreased values among the older girls: total counts of leukocytes (p = 0.0093), lymphocytes (p < 0.05), monocytes (p = 0.0093), granulocytes (p = 0.015), CD19+ (p = 0.0053) and CD4+HLA-DR+ (p = 0.035), as well as the percentage of CD19+ (p = 0.023). Also IgG2 increased with age (p = 0.05). These findings are in line with the reference literature for the normal population [14].
Recurrent Otitis Media
The girls with TS were divided into two groups according to their history of recurrent otitis media. As age influenced some of the parameters we only considered girls ≥10 years old (n = 11). Significant increases in absolute counts of lymphocytes (p = 0.004), CD3+ T-cells (p = 0.0087), CD4+ T-cells (p = 0.012) and CD4+HLA-DR+ (p = 0.05) as well as in the percentage of CD3+ T-cells (p = 0.05) in otitis prone (n = 5) compared to otitis free (n = 6) Turner girls was shown. No such differences were noticed for any immunoglobulin levels, antibody titers, CD4+/CD8+-ratio or CD8+, CD19+, CD56+CD3- lymphocyte subpopulations.
Karyotype
Any apparent influence, of the different karyotypes, on any of the parameters studied was not observed within the group.
Discussion
In this study no major derangement in the immune status was found among the girls with TS. Normal levels of most lymphocyte- and immunoglobulin subpopulations were registered. The few outliers noted must be considered as a normal individual variation.
However, as described in an earlier study of Turner girls, the present study confirmed a CD4+/CD8+ ratio in the lower range [12], supposedly as a consequence of a slightly increased CD8+ population. Although, the patients were few, we noticed some differences between the otitis prone and otitis free Turner girls. The elevated counts of lymphocytes, CD3+, CD4+ cells and CD4+HLA-DR+ cells seen among the otitis prone girls, probably reflects a secondary effect of an activated immune system involving T-helper cells, rather than any immune deficient state. Moreover, the levels of IgG antibodies to pneumococcal polysaccharide antigen, which are important in the defense of bacteria, were normal. A homozygous lack of the IgG2m(23) allotype was seen in 33% of the girls, which is the same frequency as in the normal population [15]. A negative IgG2m(23) allotype have been correlated to an impaired immune response to haemophilus influenzae vaccination with subnormal levels of IgG2. In the study group a negative IgG2m(23) allotype was not correlated to a positive history of recurrent otitis media, neither could the different karyotypes be associated to the levels of immunoglobulin- or lymphocyte subpopulations. Perhaps the cause of the repeated attacks of otitis media in Turners syndrome is not to be found in the periphery, but rather more locally. Even if earlier computed tomography scans of the temporal bone have not shown any abnormalities [2], the Eustachian tube may be dysfunctional and/or the cell system might be underdeveloped. Recently new aspects on the growth of the temporal bone have been proposed, with a hypothesis that the loss of X-chromosome material leads to a prolonged cell cycle and otic growth disturbances during fetal life [16]. The SHOX-gene located on the p-arm of the X-chromosome has been found to code for growth and could potentially also code for growth of the skull base and temporal bone where the middle ear is located. [17]. As the girls investigated were 5–17 years old, transient hypogammaglobulinemia in the first years is still possible. However, the girls suffered otitis media up in their teens.
Our findings of normal immunoglobulin- and lymphocyte subpopulations are not entirely in concordance with some earlier studies, where a reduction of circulating IgM and IgG as well as T- and B-lymphocytes has been observed [9,10]. However, in these studies the values were not dramatically decreased, but rather within the lower range of the normal reference values. On the other hand, some other studies have not shown low T- and B-lymphocyte counts [11] or low concentrations of immunoglobulins [12], agreeing with the present study. In the normal population there is a difference between IgG and IgM levels in women and men with decreased values in men [12], but this difference cannot be found in newborns or children. Earlier there have been suggestions that the difference is caused by the amount of X chromosome material, as men with 47, XXY have higher values than men with normal karyotype (46, XY) and women with 47, XXX have even higher values than normal women (46, XX) [18]. There have also been suggestions that the sex hormones influence the immune system and that the lack of estrogens might influence the immune response negatively [11]. As most of the girls studied were prepubertal, the influence from sex hormones should not be as important. In some earlier studies the age span has been wider and the size of the study groups relatively small. There have also been discussions that the regular treatment with growth hormones may influence the immune system. However, in a previous study no major effects on the immunoglobulin levels or lymphocyte subpopulations could be demonstrated in Turner girls treated with growth hormones [12].
In conclusion, we did not find any major immunological deficiency in immunoglobulins or lymphocyte subpopulations that could explain the increased incidence of otitis media observed in girls with TS. Therefore, treatment with immunotherapy is not an option in this patient group. Further studies are warranted to elucidate local pathology, both from an immunological and anatomical point of view.
Authors' contributions
AES participated in the design of the study, performed the statistical analysis and drafted the manuscript. LS participated in the design of the study and collected the blood samples. CGMM performed the statistical analysis. MH participated in the design and coordination of the study and collected the blood samples.
All authors read and approved the final manuscript.
Acknowledgements
This work was supported by grants from the Swedish Medical Research Foundation, grant 00720 and the Sven Jerring foundation.
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Stenberg AE Nyhlén O Windh M Hultcrantz M Otological problems in children with Turner'sTurner's syndrome Hear Res 1998 124 85 90 9822905 10.1016/S0378-5955(98)00113-0
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Masin JS Hostoffer RW Arnold JE Otitis media following tympanostomy tube placement in children with IgG2 deficiency Laryngoscope 1995 105 1188 1190 7475873
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| 15563731 | PMC538258 | CC BY | 2021-01-04 16:37:33 | no | J Negat Results Biomed. 2004 Nov 25; 3:6 | utf-8 | J Negat Results Biomed | 2,004 | 10.1186/1477-5751-3-6 | oa_comm |
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-2-611553588810.1186/1477-7525-2-61ResearchThe development of a new measure of quality of life for young people with diabetes mellitus: the ADDQoL-Teen McMillan Carolyn V [email protected] Rachel J [email protected] Jessica [email protected] Nicola JH [email protected] Clare [email protected] Department of Psychology, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, UK2 National Children's Bureau, 8 Wakley St, London, EC1V 7QE, UK2004 9 11 2004 2 61 61 9 7 2004 9 11 2004 Copyright © 2004 McMillan et al; licensee BioMed Central Ltd.2004McMillan 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 study evaluated the psychometric properties of the ADDQoL-Teen, an innovative individualised, patient-centred questionnaire measuring perceived impact of diabetes mellitus on quality of life (QoL) of teenagers. Respondents rate all 30 life domains for frequency, and personally applicable domains for 'bother'. Two overview items measure present QoL and diabetes-dependent QoL. ADDQoL-Teen design was based on the ADDQoL (for adults with diabetes).
Methods
Interviews and discussion groups were conducted with 23 teenagers aged 13–16 years, during work to design the ADDQoL-Teen. The new questionnaire was then completed by 152 young people, (mean age 16.4 ± 2.4 years), attending diabetes clinics at six UK centres.
Results
Five domains detracted from the measure's reliability and factor structure, four of which were analysed separately and one deleted. The 25-domain ADDQoL-Teen had high internal consistency reliability [Cronbach's alpha = 0.91, (N = 133)] and could be summed into an overall Average Weighted Impact score. There were two subscales: a 10-item Impact-Self subscale (measuring impact of diabetes and its treatment on the individual) and a 15-item Impact-Other subscale (measuring impact on interactions with others and the external world). Both subscales had good internal consistency reliability, [Cronbach's alpha coefficients of 0.82 (N = 142) and 0.88 (N = 138) respectively]. Domains reported as most severely (and negatively) impacted by diabetes were (mean weighted impact ± SD): lie in bed (-3.68 ± 3.41), interrupting activities (-3.5 ± 3.23), worry about the future (-3.45 ± 3.28), career (-3.43 ± 3.15) and sweets (-3.24 ± 3.24), (maximum range -9 to +3). Analysis of the overview items showed that although 72.5% considered that their present QoL was good or brilliant, 61.8% felt that having diabetes had a negative impact on QoL, but 35.6% reported no impact and 2.6% reported a positive impact on QoL.
Conclusions
The ADDQoL-Teen is a new measure of perceived impact of diabetes and its treatment on QoL of teenagers. It will help healthcare professionals and parents consider QoL issues as well as medical outcomes when caring for young people with diabetes. It may be used in clinical trials and for routine clinical monitoring in a context of continuing evaluation.
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Background
Increasing numbers of children are being diagnosed with diabetes mellitus [1] and, once diagnosed, these children and their families face major changes to their lives. However, the emphasis from health professionals is often on control of blood glucose levels and far less consideration is given to the impact of diabetes and the complex daily treatment regimen on each child's quality of life (QoL) and the child's perceptions of the disorder and its management. QoL is an outcome of diabetes management that is important in its own right and the significance of interacting biopsychosocial factors in the management of chronic disorders is recognised [2]. Thus both psychological and physiological effects of diabetes need to be measured. Measures of the impact of diabetes on the QoL of children are needed, to provide healthcare professionals with information to help protect the QoL of their patients. Such information can be used not only in research, to measure the impact of educational interventions, care provision and treatment regimens on QoL, but also in consultations where completed questionnaires can form the basis of structured discussions between the child, their parents and healthcare professionals. Professionals can be encouraged to be more patient-centred, and help children to overcome the negative impact of diabetes and its treatment on their QoL.
Adults may not be able to assess a child's point of view adequately, so children themselves should rate their own QoL wherever possible [3-5], and a more child-centred approach helps clinicians to treat patients successfully [4] and has produced data which are both valid and informative [6,7]. The questions asked in diabetes-specific adult measures such as the ADDQoL [8,9] are usually too abstract for younger children and/or inappropriate. Existing paediatric diabetes-specific QoL measures do not allow each child to say which aspects of diabetes matter to them personally: they are not sufficiently child-centred or individualised. For example the Diabetes Quality of Life Measure for adults [10] was simplified and modified to be suitable for adolescents [11], but children were not involved in the generation of items. The PedsQL [12], whilst completed by the children themselves, does not use an individualised approach, i.e. it is not possible for the individual to indicate the relevance or importance of a specific aspect of life to his or her QoL.
This paper describes the design and subsequent psychometric validation of a new teenager-centred, individualised measure of the impact of diabetes on the QoL of teenagers, the ADDQoL-Teen. The ADDQoL-Teen follows the philosophy underpinning the individualised ADDQoL measure for adults, but ideas in the teenager version are more specific and concrete than the broader, more abstract concepts of the adult version.
Methods
I. Design of the ADDQoL-Teen questionnaire
Four hospitals in the Greater London area participated in the research to design the questionnaire, following Ethical Committee approval. To help identify QoL issues for inclusion in the ADDQoL-Teen measure, clinic sessions were observed, health professionals consulted, and a literature review undertaken. Semi-structured interviews using open-ended questions were then conducted with 10 teenagers with diabetes, and discussions took place with 13 teenagers in small groups of 2–4 teenagers each. The views of 23 young people, aged 13–16 years, were obtained in all. The groups were single-sex as teenagers might be inhibited from talking freely about sensitive issues with members of the opposite sex present.
This qualitative research identified important QoL issues that formed the content of 30 items in the new ADDQoL-Teen questionnaire. The items were designed to reflect the teenagers' own perceptions of life with diabetes and measure their individual feelings about the importance of the issues in their everyday lives, rather than being based on researchers' or professionals' opinions. The questionnaire items, response choices and format were based on comments from the teenagers to ensure that the items were child-centred and had face validity for the teenagers themselves. The design of a questionnaire for teenagers was part of a wider study to design child-centred questionnaires for children with diabetes in three age ranges including 5–8 years (ADDQoL-Junior) and 9–12 years (ADDQoL-Junior Plus) [13].
Description of the ADDQoL-Teen questionnaire
In order to produce an individualised questionnaire, the ADDQoL measure of the impact of diabetes on QoL of adults [8] measures the impact of diabetes on each aspect of life and the importance of that aspect for the QoL of the individual. Design of the ADDQoL was, in turn, influenced by the generic individualised interview measure, the SEIQoL (the Schedule for the Evaluation of Individual Quality of Life) [14]. In the ADDQoL, adults' impact ratings for each applicable aspect of life (domain) are multiplied by importance ratings to provide a weighted impact score for each domain. In the new ADDQoL-Teen, however, teenagers are asked about the frequency ('a' stem) with which diabetes impacts on each aspect of life, and then how much that particular domain bothers them ('b' stem). The majority of stem 'a'/frequency items are in the format: Do you ever ..... because of your diabetes? and stem 'b'/bother items in the format: Does it bother you when ..... because of your diabetes? (See example in Fig. 1). Multiplying frequency and bother ratings for each domain gives a domain weighted impact score. Each item provides an assessment in stem 'a' of whether the aspect of life described is relevant to the teenager, and thus contains a 'no' response option as well as multiple 'yes' response options. Stem 'a'/frequency scoring is 3, 2, 1, 0 (from Yes – a lot..... No – I do not). Stem 'b'/bother has response options scoring from -3, -2, -1, 0 (Yes – it bothers me very much..... No – it does not bother me, it's OK) and a positive response option (No – it does not bother me, I like it) scoring 1.
Figure 1 Example of an ADDQoL-Teen domain item.
The ADDQoL-Teen questionnaire has 30 items dealing with specific life domains, in which the wording of item stems and response choices is appropriate to teenagers. Table 1 contains a full description of the wording of each 'a'/frequency stem as well as the item abbreviations that will be used throughout this article. The majority of items have a negative sense but there are three items (7: extra things, 13: out of fix, and 30: holidays) that have a positive sense. The 'b'/bother stem is scored differently for these three positive items. For example, the responses to item 7b (How do you feel about having extra things because of your diabetes?) are scored 3, 2, 1, 0, -1 (from I like having them very much......I don't like having them).
Table 1 ADDQoL-Teen item wording and abbreviations
No: Abbreviation Overview item
A present QoL In general, I feel my quality of life is.....
B diabetes-dependent QoL Does diabetes usually make your quality of life worse or better?
Full item as in the 'a'/frequency stem
1 others fuss Do you ever feel people fuss or worry about you because of your diabetes?
2 sweets Do you ever feel you want to eat sweets but don't because of your diabetes?
3 drink Do you ever want to drink something but you don't drink it because of your diabetes?
4 eat Do you ever want to eat something but you don't eat it because of your diabetes?
5 insulin Do you take insulin?
6 bleed Do you ever bleed or have any bruises or lumpy bits where you take your insulin?
7 *extra things Do you ever have extra things, like snacks, money, treats or days out because of your diabetes?
8 interrupt do Do you ever find diabetes interrupts what you are doing, like watching TV, working at home or school, playing computer games or any other activities?
9 finger tests Do you have finger prick blood tests?
10 control Do you ever feel you want to take more control of diabetes on your own, with less help from other people?
11 moody Do changes in your blood sugars ever make you feel moody?
12 unwell Do you ever feel unwell because of your diabetes, like having a headache or pain, or feeling tired, sick or dizzy?
13 *out of fix Do you ever find that having diabetes gets you out of a fix, or gets you out of doing something you don't want to do?
14 sleep away Do you ever get asked to sleep away from home or at a friend's house, but you don't because of your diabetes?
15 wake nights Do you ever wake up in the night feeling hypo with low blood sugar?
16 lie in bed Do you ever want to have a lie in bed, but you don't because of your diabetes?
17 miss events Do you ever miss a party, a school trip, going out or any other event because of your diabetes?
18 low BG Do you ever feel your blood sugar is too low?
19 high BG Do you ever feel your blood sugar is too high?
20 worry future Do you ever worry about the future, like getting married, having children or your future health because of your diabetes?
21 career Do you ever feel that having diabetes will make a difference to your future job or career?
22 different Do you ever feel 'different' because of your diabetes?
23 not allowed Are you ever told that things are 'not allowed' because of your diabetes?
24 family life Do you ever feel that diabetes makes a difference to life with your family or the people you live with?
25 responsibility Do you ever find you are expected to take more responsibility than you would like because of your diabetes?
26 play sport Do you ever find that having diabetes makes any difference to playing sport?
27 go toilet Do you ever find that you need to go to the toilet too often because of your diabetes?
28 social life Do you ever find you need to fit diabetes into your social life, like carrying equipment, planning when to eat, or where to take insulin when away from home?
29 clinic visits Do you go to a diabetes clinic?
30 *holidays Have you ever been to B.D.A holidays or weekends away, or made new friends because of your diabetes?
*positive item.
B.D.A: British Diabetic Association (now known as Diabetes UK).
Finally there are two overview/global items: QA: present QoL and QB: diabetes-dependent QoL. QA (In general, I feel my quality of life is --- brilliant---good---OK---not OK---bad) is scored 3, 2, 1, -1, -2 respectively. QB (Does diabetes usually make your quality of life worse or better? ---a lot worse---a fair bit worse---a bit worse---neither worse nor better---better) is scored -3, -2, -1, 0, 1. There is a free comments section at the end of the questionnaire where respondents are asked if there is anything else they would like to say about their life with diabetes.
Weighting the items and summation to an Average Weighted Impact score
Negative items: The 'a'/frequency ratings in categories scoring 1, 2, and 3 are multiplied by the corresponding 'b'/bother ratings to give a weighted score from -9 to +3 (maximum negative to maximum positive impact of diabetes on a domain). Zero scores on the 'a'/frequency rating are ignored as these items are not applicable to the individual and no 'b'/bother rating is made. The overall ADDQoL-Teen Average Weighted Impact score (ADDQoL-Teen AWI) can be calculated by summing weighted impact scores for all applicable domains before dividing by the number of domains applicable to the individual teenager. ADDQoL Teen AWI varies from -9 to +3, the maximum negative to maximum positive weighted impact of diabetes on overall QoL.
Positive items: items 7, 13, and 30 have weighted scores from -3 to +9 (maximum negative to maximum positive impact of diabetes on that domain). The weighting procedure for positive items is similar to that for negative items.
Overview items: QA and QB are not included in the calculation of AWI, but analysed individually.
II. Study to determine the psychometric properties of the ADDQoL-Teen
Patient recruitment
In order to determine the psychometric properties of the 30-item ADDQoL-Teen, at least 150 completed questionnaires were needed, as factor analyses ideally require five or more respondents per item [15]. Young people with Type 1 diabetes mellitus (N = 78) were recruited to an interview study conducted by the National Children's Bureau [16], and also completed the questionnaire. Another 74 young people were recruited to complete the questionnaire only. Six UK centres were involved, (Centres A to F), of broad geographical spread, and serving diverse communities. Recruitment was undertaken by diabetes specialist nurses.
The criteria for inclusion were: the patient was expected to move from paediatric to adult care in the following year or the patient had moved from paediatric to adult care in the previous year. Moving from paediatric to adult care was defined as moving out of the care of the paediatric team. Depending on the size of the caseload in each research area, the nurse either included all patients or a random sample that fitted the criteria in the sampling frame. Ethical Committee approval was obtained for the study to be conducted at all the centres.
Statistical analyses
The 'No – I do not' response option and loss of data
None of the data from any respondent who selected a No – I do not response option (i.e. not applicable, hereafter referred to as "N/A") would normally be included in factor and reliability analyses, as the SPSS statistical package treats N/A responses as missing. Furthermore, if the SPSS default of listwise deletion of missing data is used, all cases that have any missing values across all 30 items are lost to analysis. Results of reliability and factor analyses are therefore reported below with SPSS set to pairwise deletion of missing data, and N/A responses to read as zero, to avoid considerable loss of data. This procedure has been fully described for the original development of the ADDQoL for adults [8].
Homogeneity of the patient sample
There was a risk of systematic differences in responses from the six UK centres creating artefactual correlations within a data set combined to provide sufficient numbers for the psychometric analyses. To check that the final sample was sufficiently homogenous ADDQoL-Teen weighted item scores were converted to standardised z scores for each subgroup, and then recombined. All questionnaire items were forced onto one factor in a Principal Components Analysis of (1) raw weighted scores and (2) recombined z scores, and results compared (a procedure used previously in the original development of the ADDQoL [8]).
Normality issues
Normality of distributions was investigated through histograms, box plots and standardised z(skew) values, whereby acceptable z(skew) values between ±2.58 indicate normality [17]. The ADDQoL-Teen is not a questionnaire where a normal spread of scores and normal distributions would be expected. Respondents were expected to report predominantly negative effects of diabetes with few indicating that diabetes had some positive effects on their lives. The ADDQoL-Teen identifies individuals with extreme responses – the ones most affected by their health condition. Although normality of data is desirable for factor analyses, finding transformations for skewed variables, where N/A was set to zero, that did not adversely affect normal distributions of other items in the questionnaire, proved difficult. The assumption was made that if reliability were high, the factor analysis robust, and the number of respondents sufficiently high, then a degree of non-normality was tolerable. Factor analyses were conducted on data with reflect and inverse transformations, but reliability of non-transformed variables is reported.
Internal consistency reliability
Cronbach's alpha coefficient [18] was determined in reliability analyses. Nunnally [19] regarded an alpha of 0.9 as the minimum acceptable for making decisions about individuals, but 0.8 adequate for comparing groups. Others consider that an acceptable minimum alpha can be 0.7 – 0.8, or even lower for short subscales [20]. In the present analyses a minimum alpha of 0.9 was regarded as ideal, but alphas above 0.8 were considered very acceptable. Acceptable corrected item-total correlations were those ≥0.2 [21].
Factor structure
Factor structure was explored with Principal Components Analysis, using Varimax rotation. A forced one-factor solution was obtained to confirm the validity of calculating the ADDQoL-Teen AWI score, and unforced analysis to investigate the existence of any subscales. Salient loadings were taken as ≥0.4, higher than the recommended minimum 0.3 [22], erring on the side of caution in an effort to reduce the risk of spurious loadings that owed their origin to non-normality of item distributions, and also to avoid multiple loadings.
Bonferroni correction
In exploratory investigations of correlations and subgroup differences in responses, the Bonferroni correction for familywise error was adopted (i.e. alpha was set initially to 0.05/n where n was the number of variables within a "family") and then the Holm's sequential Bonferroni procedure for multiple tests was applied [23].
Assessing tolerance of missing data
To assess the effects of respondent missing data on the measure's reliability, reliability analyses were run sequentially deleting the strongest item each time, (i.e. deleting the item having the lowest "alpha if item deleted" and therefore contributing most to the internal consistency reliability of the scale, as described elsewhere [24].
Analysis was conducted using SPSS for Windows (Release 9).
Results
Patient sample
Diabetes services have introduced age-appropriate clinics for teenagers with diabetes to help their transition from paediatric to adult care [16]. Some services offer adolescent clinics for 14–16 year olds, followed by transition clinics run jointly by paediatric and adult services. Other services run young person's clinics for young people up to 25 or 30 years. The ADDQoL-Teen was originally designed for age range 13–16 years, with the intention that it could be completed by those aged 17–18 years, many of whom would still be attending school. However, 28 of the 152 young people who completed the questionnaire in this study fell outside the 13–18 year age range, of whom 21 were older and 7 younger, and 31 individuals were aged 17 or 18 years. Table 2 shows the ages of the sample, broken down by clinic. There were 72 males (47% of the sample), [mean age 16.79 ± 2.64; range: 12.8 to 24.0 years] and 80 females (53%), [mean age 15.96 ± 2.17, range: 10.4 to 22.7 years]. Over three-quarters (78%) of the sample were attending school or sixth form college at the time of questionnaire completion, 7% were at university, 12% working, and the remainder unemployed. In order to have sufficient data for the psychometric analyses and to evaluate the questionnaire for the wider age range, it was decided to use the data for all 152 respondents, even those who fell outside the 13–18 age range. Key analyses were re-run on the subset of 13–18 year olds to check that results were similar to those from the full data set, although sample size (124) in this age range was less than optimal.
Table 2 The sample of 152 young people who completed the ADDQoL-Teen
Hospital Male Female Paediatric clinic Adult clinic Mean age Total
A 16 13 21 8 16.58 ± 1.28 29
B 21 25 38 8 15.80 ± 2.12 46
C 11 13 17 7 18.27 ± 3.50 24
D 8 14 16 6 14.92 ± 1.27 22
E 10 11 11 10 15.11 ± 1.29 21
F 6 4 10 - 19.44 ± 1.32 10
Total 72 80 113 39 16.36 ± 2.43 152
Questionnaire completion rates
Completion rates were very high, providing an indication of the acceptability of the questionnaire to respondents: 'a'/frequency stem items (99.6%); 'b'/bother stem items (99.4%); overview items (98.0%).
Homogeneity of the patient sample
Initial analyses demonstrated that the six subgroups (recruited from six centres) could be treated as one for the purposes of reliability and factor analyses where a larger N is desirable. Percentage variance accounted for by the loadings on the forced 1-factor analyses was very similar: raw weighted scores (27.52%); standardised z scores for each hospital subgroup recombined (25.62%). Regression analysis found no significant difference between the two sets of loadings: the correlation of 0.987 was close to a perfect 1 (p < 0.001), the constant (0.027) close to zero [t (28) = 1.83, p > 0.05] and the slope (0.916) also close to 1, [t (28) = 32.8, p < 0.0001].
Descriptive statistics
Frequency analyses of domains indicated that from 1 to 82% of respondents used the No – I do not (N/A) response option (Table 3). The items with the highest frequency of N/A responses were 17: miss events (82%), 14: sleep away (80%) and 30: holidays (72%). Thus the great majority of respondents did not consider that they missed any events, or sleeping over at a friend's house as a result of their diabetes, nor had their diabetes resulted in going away on British Diabetic Association (B.D.A, now known as Diabetes UK) holidays or weekends or making new friends. The areas of greatest importance/frequency of feeling (Response: Yes – a lot in the 'a'/frequency stem) were: 5: insulin (69%), 9: finger tests (49%), 28: social life (26%), and 2: sweets (25%). The areas where the highest percentage of young people considered they were very much bothered were: 12: unwell (25%), 6: bleed, 8: interrupt do and 19: high BG (all 21%), (Table 3). The positive response option (No – it does not bother me, I like it) was used by up to 19% of respondents: 19% liked going to the diabetes clinic, 7% liked finger prick blood tests, 5% liked taking insulin, and 3% liked other people fussing or worrying about them because of their diabetes (items 29, 9, 5, and 1 respectively).
Table 3 Descriptive statistics of ADDQoL-Teen domain items
No: Abbreviation N % N/A % 'a'/frequency: Yes, a lot‡ % 'b'/bother: very much§ Weighted impact: Mean ± SD** Median [range]
1 others fuss 148 2.6 21.1 9.9 -2.16 ± 2.52 -2 [-9 to 3]
2 sweets 112 25.3 24.7 16.0 -3.24 ± 3.24 -2 [-9 to 0]
3 drink 80 46.4 6.0 5.3 -2.11 ± 2.31 -1 [-9 to 0]
4 eat 117 21.9 16.6 14.7 -2.62 ± 3.09 -1 [-9 to 1]
5 insulin 151 0.7 69.1 14.5 -2.21 ± 3.27 0 [-9 to 3]
6 bleed 142 6.0 15.9 21.2 -2.39 ± 2.70 -1.5 [-9 to 1]
7 *extra things 87 42.8 7.9 - 1.78 ± 2.80 0 [-2 to 9]
8 interrupt do 90 40.0 14.7 20.7 -3.50 ± 3.23 -2 [-9 to 0]
9 finger tests 149 0.7 49.0 19.3 -2.16 ± 3.21 -1 [-9 to 3]
10 control 87 41.3 16.0 5.4 -1.85 ± 2.90 -1 [-9 to 2]
11 moody 125 16.7 19.3 18.4 -2.89 ± 3.04 -2 [-9 to 3]
12 unwell 131 13.2 13.8 24.5 -2.93 ± 2.87 -2 [-9 to 3]
13 *out of fix 85 44.1 11.2 - 1.47 ± 2.78 1 [-3 to 9]
14 sleep away 30 80.3 2.0 4.6 -2.23 ± 2.75 -1 [-9 to 3]
15 wake nights 111 27.0 4.6 18.4 -2.08 ± 2.22 -1 [-9 to 2]
16 lie in bed 77 48.3 16.6 14.7 -3.68 ± 3.41 -2 [-9 to 3]
17 miss events 27 82.2 2.6 5.9 -2.67 ± 3.10 -1 [-9 to 1]
18 low BG 120 20.5 6.6 11.3 -1.71 ± 2.18 -1 [-9 to 3]
19 high BG 130 13.2 9.9 20.7 -2.77 ± 2.77 -2 [-9 to 1]
20 worry future 93 38.2 17.1 13.9 -3.45 ± 3.28 -2 [-9 to 3]
21 career 100 34.9 15.8 20.4 -3.43 ± 3.15 -2 [-9 to 2]
22 different 81 46.1 9.9 12.6 -2.72 ± 2.93 -1 [-9 to 0]
23 not allowed 105 30.9 10.5 20.4 -3.16 ± 2.99 -2 [-9 to 2]
24 family life 89 40.8 9.2 13.2 -2.60 ± 3.11 -1 [-9 to 3]
25 responsibility 93 37.7 11.9 6.7 -1.83 ± 2.85 -1 [-9 to 3]
26 play sport 96 36.8 11.2 8.6 -2.17 ± 2.86 -1 [-9 to 3]
27 go toilet 97 36.2 8.6 11.8 -2.14 ± 2.63 -1 [-9 to 3]
28 social life 135 10.6 25.8 17.9 -2.84 ± 3.28 -1 [-9 to 0]
29 clinic visits 148 2.0 23.0 2.6 -0.30 ± 2.08 0 [-9 to 3]
30 *holidays 43 71.5 2.6 - 2.40 ± 2.83 2 [-1 to 9]
*positive item.
**max possible range negative items [-9 to 3] and positive items [-3 to 9].
‡valid % of Yes – a lot in response to the 'a'/frequency stem.
§valid % of Yes – it/they bother/s me very much in response to the 'b'/bother stem.
As expected, all negative items showed negative weighted impact of diabetes on the domains, whereas positive items indicated positive impact of diabetes on domains. The most severe negative impact of diabetes was felt (in descending order of impact, means in brackets) for 16: lie in bed (-3.68), 8: interrupt do (-3.5), 20: worry future (-3.45), 21: career (-3.43) and 2: sweets (-3.24) (Fig. 2). The least severe negative impact of diabetes was felt for 29: clinic visits (-0.3), 18: low BG (-1.71), 25: responsibility (-1.83) and 10: control (-1.85). Diabetes had the most positive impact on 30: holidays (2.4) (noting that this item was only applicable to 28% of respondents) and 7: extra things (1.78). Overview items found that although the majority (72.5%) considered that their present QoL was good or brilliant (mean 1.79), 61.8% felt that having diabetes had a negative impact on QoL (mean -0.83), but 35.6% considered it had no impact on QoL, and 2.6% that the disorder had a positive impact on QoL (Table 4).
Figure 2 Mean weighted impact scores of the domains of the 25-item ADDQoL-Teen for the whole sample and 13–18 year age group.
Table 4 Descriptive statistics of ADDQoL-Teen overview items
No: Abbreviation N Mean ± SD Median [range]
A present QoL 149 1.79 ± 0.97* 2 [-2 to 3]
B diabetes-dependent QoL 149 -0.83 ± 0.88** -1 [-3 to 1]
*max possible range [-2 to 3]; **max possible range [-3 to 1].
Preliminary factor and reliability analyses of the 30-item ADDQoL-Teen
Preliminary factor and reliability analyses were conducted to determine the number of items in the scale that could be summed into the overall ADDQoL-Teen AWI score. Full results are not provided, but these analyses resulted in the decision not to include five items (items 7, 13, 14, 29, 30) in the summation of an overall scale AWI, for the following reasons. The three positive items (7: extra things, 13: out of fix, 30: holidays) had unsatisfactory loadings, (<0.4), in a forced 1-factor analysis of the 30-item scale. It was decided to omit them from summation of AWI and, as further reliability and factor analyses did not indicate that they formed a subscale, to analyse each of them as separate items. Item 29: clinic visits had a relatively low corrected item-total correlation (0.218), reduced the reliability of the whole scale, and had an unsatisfactory forced 1-factor loading (<0.2). Indeed a high percentage reported that they were not bothered by attending clinic (57%) or that they liked it (19%). Item 29 can also be analysed separately. However, item 14: sleep away had an unsatisfactory forced 1-factor loading (<0.4), and although it contributed to the overall scale reliability, a very high percentage (80%) regarded the domain as N/A and, of those for whom it was applicable, only 8% found that it impacted a lot or a fair bit on their QoL. As the domain was covered by 17: miss events, it was decided to delete item 14 from the scale.
All further analyses below were conducted on the 25-item scale.
The 25-item ADDQoL-Teen
Internal consistency reliability
Cronbach's alpha was close to the ideal level of 0.9 (0.913, N = 133). All corrected item-total correlations were >0.37, i.e. well above the acceptable minimum. None of the 25 items would increase the alpha coefficient if deleted from the scale (Table 5).
Table 5 Reliability analysis of 25-item ADDQoL-Teen (whole sample)
Item Scale mean if item deleted Scale variance if item deleted Corrected item-total correlation Alpha if item deleted
1: others fuss -44.67 1412.16 0.4881 0.9102
2: sweets -44.33 1357.63 0.6016 0.9079
3: drink -45.60 1429.77 0.5125 0.9103
4: eat -44.56 1363.22 0.5800 0.9084
5: insulin -44.50 1334.19 0.6447 0.9070
6: bleed -44.37 1391.05 0.5024 0.9099
8: interrupt do -44.56 1364.32 0.5632 0.9088
9: finger tests -44.59 1385.38 0.4451 0.9114
10: control -45.51 1426.39 0.3727 0.9121
11: moody -44.35 1376.36 0.5240 0.9096
12: unwell -44.19 1391.50 0.4842 0.9103
15: wake nights -45.15 1423.99 0.5022 0.9102
16: lie in bed -44.75 1404.51 0.3802 0.9127
17: miss events -46.22 1442.99 0.4281 0.9114
18: low BG -45.28 1430.58 0.4483 0.9109
19: high BG -44.33 1392.77 0.5171 0.9096
20: worry future -44.64 1377.43 0.5054 0.9100
21: career -44.42 1364.17 0.5803 0.9084
22: different -45.20 1362.36 0.7117 0.9062
23: not allowed -44.55 1361.46 0.6364 0.9073
24: family life -45.16 1372.68 0.6134 0.9078
25: responsibility -45.50 1415.25 0.4332 0.9111
26: play sport -45.26 1396.84 0.5138 0.9097
27: go toilet -45.38 1422.21 0.5002 0.9102
28: social life -44.09 1349.43 0.5856 0.9083
Alpha = 0.9129, standardised item alpha = 0.9144 (N = 133).
Factor structure
A forced 1-factor Principal Components Analysis indicated that all but one item (16: lie in bed) loaded at ≥0.4 (Table 6). However, whilst item 16 loaded slightly low (0.389) it contributed to overall scale reliability and there did not seem sufficient reason to remove it from the scale, especially as descriptive analysis showed this domain to be the most severely impacted by diabetes (mean weighted impact -3.68 ± 3.41). Thus both reliability and factor analyses of the 25-item scale gave support for the calculation of an AWI score by summing the weighted scores of applicable items.
Table 6 Forced 1-factor analysis of 25-item ADDQoL-Teen (whole sample)
Item Loading
1: others fuss 0.455
2: sweets 0.631
3: drink 0.543
4: eat 0.639
5: insulin 0.620
6: bleed 0.581
8: interrupt do 0.622
9: finger tests 0.475
10: control 0.403
11: moody 0.555
12: unwell 0.498
15: wake nights 0.554
16: lie in bed 0.389
17: miss events 0.447
18: low BG 0.455
19: high BG 0.534
20: worry future 0.500
21: career 0.590
22: different 0.745
23: not allowed 0.650
24: family life 0.646
25: responsibility 0.415
26: play sport 0.546
27: go toilet 0.486
28: social life 0.630
% variance 30.4%
Subscales
An unforced Principal Components Analysis with Varimax rotation found seven factors (not shown). Items referring to other people/the external world loaded on Factor 1 (e.g. 1: others fuss, 21: career, 22: different, 26: play sport, 28: social life). Factor 2 was concerned with consumption of food and drink (2: sweets, 3: drink, 4: eat). Items concerning the effects of diabetes and its treatment on the individual loaded on the remaining five factors in a pattern that was difficult to interpret and with some items double loading. The scree plot indicated two factors.
A forced 2-factor analysis gave the clearest factor structure (Table 7). Factor 1 contained items that related to the way diabetes and its treatment affected interactions with others and the "external world". It included 1: others fuss, items 2, 3, 4 (consumption of food and drink), and 28: social life. Item 27: go toilet double loaded, loading slightly higher (0.358) on this factor than on Factor 2 (0.329). Factor 2 contained items connected with diabetes, its treatment and effects on the individual, e.g. 5: insulin, 6: bleed, 9: finger tests, and 11: moody. Item 25: responsibility double loaded, slightly higher on Factor 2 (0.295) than on Factor 1 (0.293). The factors accounted for 20.7% and 17.0% of the variance respectively.
Table 7 Forced 2-factor analyses of the whole sample compared with the 13–18 age group
Whole sample 13–18 years
Impact-Other Impact-Self Impact-Other Impact-Self
1: others fuss .571 .545
2: sweets .593 .277 .559 .285
3: drink .530 .518
4: eat .611 .268 .645 .274
5: insulin .354 .540 .341 .542
6: bleed .285 .560 .575
8: interrupt do .511 .359 .451 .499
9: finger tests .480 .471
10: control .482 .389
11: moody .598 .302 .519
12: unwell .757 .714
15: wake nights .270 .538 .273 .534
16: lie in bed .452 .501
17: miss events .516 .578
18: low BG .602 .664
19: high BG .721 .697
20: worry future .570 .527
21: career .495 .329 .547 .272
22: different .664 .371 .637 .382
23: not allowed .670 .654
24: family life .691 .713
25: responsibility .293 .295 .325
26: play sport .463 .298 .496 .365
27: go toilet .358 .329 .412
28: social life .648 .610
Loadings >0.25 are shown, with all loadings >0.4 in bold typeface.
The best solution seemed to be that the 25-item scale had two subscales, one relating to the effects of diabetes and its treatment on interactions with others and the external world (the "Impact-Other" subscale) and the second to effects on the individual (the "Impact-Self" subscale). Item 25: responsibility double loaded slightly higher on Factor 2 than on Factor 1, and it was decided to retain this item in the Impact-Self subscale because taking responsibility for diabetes and its treatment will rest increasingly on the individual child as he/she grows older.
Domains of others fuss, miss events, career, different, not allowed, family life, play sport and social life, on the Impact-Other subscale, clearly relate to interactions with the others and the external world. The consumption of food and drink very often occurs in a social context. Frequent visits to the toilet (27: go toilet) or having to stop an activity to inject insulin (8: interrupt do) may cause embarrassment socially as well as being annoying for the individual. The association of the other items on this scale with the external world is also explicable: item 10: control refers to the individual taking control of diabetes, with less help from other people; and having to get up early in the morning to test/inject may be a major issue, particularly for teenagers, again making the young person with diabetes feel different from others (16: lie in bed).
Eight of the ten items of the Impact-Self subscale clearly relate to the effects of diabetes and its treatment on the individual (domains of insulin, bleed, finger tests, moody, unwell, wake nights, high BG and low BG. As pointed out above, taking responsibility for treatment may have greater impact on the individual child with increasing age (item 25), at the same time the child with diabetes may worry about his/her own future (item 20).
Internal consistency reliability of the 15-item Impact-Other subscale was very satisfactory (Cronbach's alpha = 0.883, N = 138), but falling short of the optimal alpha of 0.9. All corrected item-total correlations were satisfactory (>0.38) and only one item (16: lie in bed) would increase alpha if deleted, and then only by 0.001. Similarly the 10-item Impact-Self subscale also had very satisfactory reliability (alpha = 0.818, N = 142). All corrected item-total correlations were satisfactory (>0.38) and no item would increase alpha if deleted. These analyses confirmed the reliability of the subscales, and gave support for summing the subscale items into their respective subscale total scores.
Dealing with missing data
The whole 25-item scale was found to be reliable at alpha ≥ 0.9 with maximum one item of missing data and reliable at alpha ≥ 0.8 with up to 10 items of missing data. We recommend that AWI is calculated as the mean of the completed domains with no more than one item of missing data, if the desired alpha level is 0.9, or up to 10 missing values, if the desired alpha level is set at 0.8, which is very acceptable for most research purposes involving group comparisons. The scale is reliable at >0.7 with up to 15 items missing data but we do not advise calculating AWI with this number of missing items, as questionnaire content may well be distorted. The 15-item Impact-Other subscale was reliable at alpha ≥ 0.8 with maximum four items of missing data, but the 10-item Impact-Self subscale was reliable at alpha ≥ 0.8 with no item of missing data. Higher levels of reliability (alpha ≥ 0.9) are required of measures that are being used to compare an individual's scores across time [19] and for such purposes the full scale score would be needed with no more than one applicable item missing (excluding N/A items).
ADDQoL-Teen AWI and subscale scores
Analysis of the data for the whole sample found that mean overall ADDQoL-Teen AWI was -2.39 ± 1.68, mean Impact-Other was -2.44 ± 1.86 and mean Impact-Self was -2.31 ± 1.86, (maximum possible range -9 to 3) implying that young people perceived that diabetes had a negative impact on their QoL, on interactions with others and the external world, and on themselves.
Sex differences
There were no significant sex differences in ADDQoL-Teen AWI and subscale scores after a Bonferroni correction requiring significance of p = 0.017 or less for that family of variables. However, the sex difference in Impact-Self approached significance (p = 0.028) on a Mann-Whitney test, with female respondents tending to show greater perceived negative impact of diabetes on self-related factors (-2.6 ± 1.85) than did male respondents (-1.99 ± 1.84). Considering the 25 ADDQoL-Teen items as another group (with Bonferroni correction requiring minimum significance of p = 0.002), sex differences in 6: bleed reached significance. Female respondents showed significantly greater perceived negative impact of having bleeding or bruising at site of insulin injection (-3.01 ± 2.92) than did males (-1.65 ± 2.21) [U = 1764.5, p = 0.002, 2-tailed]. Sex differences also approached significance for 20: worry future (p = 0.011), and 22: different (p = 0.043) and, considering the three positive items as another family of variables, for 7: extra things (p = 0.026). Compared with males, females showed a tendency towards greater perceived negative impact of diabetes on feeling different from peers, worries about the future, but greater positive impact on getting extra things because of their diabetes.
Correlations with age
Small but significant positive correlations with age were found for AWI and the two subscales (Table 8) indicating lessening impact of diabetes on overall QoL as measured by the ADDQoL-Teen, lessening impact of diabetes on relationships with others and external world (Impact-Other) and on self-related factors (Impact-Self) with increasing age. There were also significant positive correlations with age for the two overview items, indicating improving present QoL with increasing age, and lessening impact of diabetes on QoL. Moderate correlations were found between ADDQoL-Teen AWI and the overview item QB: diabetes-dependent QoL (rho = 0.49), and a smaller correlation, as expected, with overview item QA: present QoL (rho = 0.34).
Table 8 Correlations between ADDQoL-Teen AWI, subscales, overview items and age at completion of questionnaire (whole sample)
Age AWI
ADDQoL-Teen AWI 0.21 (p = 0.01)
Impact-Other subscale 0.22 (p = 0.006) 0.90 (p < 0.001)
Impact-Self subscale 0.16 (p = 0.043) 0.85 (p < 0.001)
QA: present QoL 0.19 (p = 0.02) 0.34 (p < 0.001)
QB: diabetes-dependent QoL 0.26 (p = 0.002) 0.49 (p < 0.001)
N (range) 149 – 152 149 – 152
All correlations are 2-tailed non-parametric Spearman's rho, and significant after Bonferroni corrections applied.
The 13–18 year age group
The mean age of those in the 13–18 year age group was 15.82 ± 1.47, a little less than that of the whole sample (16.36). Mean weighted impact scores of the younger group were very similar to those of the full sample (Fig. 2). The most negatively impacted domains, in descending order (mean ± SD) were: 20: worry future (-3.53 ± 3.36), 16: lie in bed (-3.51 ± 3.32), 8: interrupt do (-3.48 ± 3.33), and 23: not allowed (-3.4 ± 3.12). A forced 1-factor analysis of the scores of the 124 teenagers in the 13–18 age range on all 30 items, found support for excluding the same five items from the scale as described above for the whole sample (i.e. the three positive items, and items 14 and 29). All 25 ADDQoL-Teen items loaded >0.4 on a forced 1-factor analysis except 10: control and 25: responsibility (loading at 0.356 and 0.394 respectively, full results not shown).
Regression analysis found no significant difference between the forced 1-factor loadings for the subset of 13–18 year olds and those for whole sample (N = 152). The correlation of 0.954 was close to 1, the constant (0.027) was close to zero [t (23) = -0.71, p > 0.05] and the slope (1.04) was also close to 1, [t (23) = 15.21, p < 0.001]. This high correlation indicated that data from the whole sample could substitute for that from the narrower age range. Table 7 compares loadings obtained from the forced 2-factor analyses of the 13–18 year age group with those of the whole sample. The loadings are very similar, except that the double loading of 8: interrupt do is higher on Impact-Self with the 13–18 year group, perhaps implying that the younger age group may have less responsibility for deciding on whether to interrupt an activity because of their diabetes, and this is seen as impacting more on the self than on others; and 10: control loads less than optimally (0.389) on Impact-Other in the 13–18 age group. 27: go toilet loads >0.4 on Impact-Other in the 13–18 age group, but double loads with the wider age range.
Cronbach's alpha of the whole 25-item scale was 0.9132, (N = 106) and only 10: control would marginally increase alpha if deleted (0.9133). All corrected item-total correlations were satisfactory. The scale was found to be reliable at 0.9 with up to two items missing and reliable at 0.8 with up to 10 items missing. The 15-item Impact-Other subscale had good internal consistency reliability (Cronbach's alpha = 0.887, N = 111) and was reliable at 0.8 with up to four items missing. The 10-item Impact-Self subscale was reliable (alpha = 0.805, N = 114) if no items were missing. All corrected subscale item-total correlations were satisfactory.
Note: Cronbach's alpha for the sample in the 13–18 age range was only marginally lower (by 0.005) than that for the narrow 13–16 age range (0.918, N = 76), again indicating that the addition of respondents aged 17–18 years is not harmful to the questionnaire's reliability.
Free comments section
The free comments section at the end of the ADDQoL-Teen was used by 49 young people in all. The majority of respondents' comments emphasised a response that they had already made to a questionnaire item. The following areas were mentioned by at least four individuals and are not directly covered in the questionnaire. Consideration will be given to adding further items to cover these new areas in the future:
• The effect of diabetes on patient's lives, and having to organise/plan life around diabetes and its treatment (nine respondents).
• Other people, including healthcare professionals, not understanding diabetes and its effects on the young person's life (five respondents).
• Concerns about weight, and difficulty losing weight (four respondents).
Although 17–18 year olds were not included in the focus groups at the questionnaire design stage, analysis of the free comments showed that only five of the 49 respondents offering comments fell outside the narrower age range of 13–16 years: four young people were aged 17, and one was within a few days of their 13th birthday. However, each of these four 17 year olds commented on a different aspect of life with diabetes not already covered by the questionnaire (i.e. there was no salient aspect missing from the questionnaire on which all four commented). If the questionnaire was not suitable for those aged 17–18, and was missing important domains for these older respondents, it is very likely that a greater number of older respondents would have taken the opportunity to comment at this point. We can be reassured therefore that the questionnaire is suitable for the older age group (17–18 years), even though the measure was not specifically piloted with them.
Discussion
The ADDQoL-Teen is a new child-centred, individualised questionnaire measuring the impact of diabetes and its treatment on the QoL of teenagers. The items not only reflect the concerns of teenagers with this condition, as expressed in interviews and focus groups, but also use teenagers' wording where possible. Twenty-five of the life domains form a scale with excellent internal consistency reliability. Summation of the weighted impact scores from the applicable items into a single score, the ADDQoL-Teen AWI, gives a measure of the Average Weighted Impact of diabetes on the QoL of the individual. There are two subscales: the 15-item Impact-Other subscale, measuring the impact of diabetes and its treatment on interactions with others and the external world, and the 10-item Impact-Self subscale, measuring the impact of diabetes and its treatment on the individual. Both subscales have good internal consistency reliability. The two overview items (QA and QB) provide global measures of an individual's present QoL, and the perceived impact of diabetes and its treatment on their QoL respectively and, as expected, QB has a higher correlation with AWI than QA, as both QB and AWI measure impact of diabetes on QoL. Of the original 30 items, one item, concerning sleeping away from home, was deleted from the scale as it detracted from scale reliability and factor structure, and was not applicable to the great majority of respondents. Four items, three of which concerned potential positive aspects of diabetes such as getting extra things like snacks or treats, either did not load well with the 25 items in the single scale, or detracted from reliability, but can be analysed individually.
Despite the majority describing their present QoL as good or brilliant, young people perceived overall negative impact of diabetes on QoL (AWI), including negative impact on interactions with others and the external world (Impact-Other), and on themselves (Impact-Self). However, interesting information can also be gleaned by analysing frequencies of individual domains. Domains reported as most severely (and negatively) impacted by diabetes were lie in bed, interrupt do, worry future, career and sweets. These show the particular concerns of young people about not being able to stay in bed in the morning like many of their contemporaries, owing to the demands of the diabetes treatment regimen, and the way that this treatment regimen interrupts their normal day-to-day activities. Respondents were also looking to the future and were concerned about their career prospects, getting married, having children, and their longer-term health. The impact of diabetes on consumption of carbohydrates was most notable in relation to eating sweets. The usefulness of the questionnaire's bi-polar scale was indicated by the numbers of individuals who chose a positive response: almost a fifth of respondents liked attending their diabetes clinic, and perhaps a surprising number liked taking insulin or doing finger prick blood tests (5% and 7% respectively). It was also interesting to note that concerns about having a low blood glucose level had the least negative impact on QoL of any of the domains, although this aspect of diabetes is of major concern to healthcare professionals.
Some sex differences were found. Girls and young women showed significantly greater perceived impact of experiencing bleeding or bruising at the site of insulin injection, and there was a non-significant tendency for females to show greater perceived negative impact than males with respect to feeling different from peers, and worries about the future, but greater positive impact on getting extra things because of their diabetes. With increasing age, correlations indicated reduced perceived negative impact of diabetes on overall QoL (AWI), on relationships with others and the external world (Impact-Other) and on self-related factors (Impact-Self). Present QoL also improved with increasing age. The moderate correlation between ADDQoL-Teen AWI and the overview item QB: diabetes-dependent QoL was too low (rho = 0.49) to allow the single overview item to replace the 25-item scale for most purposes.
Content validity was also good: relatively few respondents mentioned new domains in the free comments section at the end of the questionnaire. However, consideration will be given in the future to adding further items to cover new areas: organising life around diabetes, other people's understanding of the condition, and concerns about excess weight.
The teenagers involved in interviews and focus groups during work to design the questionnaire lived in and around London. However, the respondents in the questionnaire study were from six areas in Britain, and there were clear indications of acceptability to all in terms of very high completion rates, and that neutral, non-regional vocabulary had been chosen. In order to have sufficient data for the psychometric analyses it was necessary to use the data for all 152 respondents, even those who fell outside the age range for which the questionnaire was originally designed (13–16 years). Nevertheless, completion rates indicated the acceptability of the questionnaire to a much wider age range than that for which it was originally intended. This is a valuable outcome, as there is considerable variability in age range at paediatric, adolescent, transition and adult diabetes clinics between different diabetes services in the UK. Indeed the mean age of respondents from one of the centres in the present study, a paediatric clinic, was 19.4 years.
The questionnaire can also be recommended for 13–18 year olds, as analyses performed on the subset of data for this age group found results very similar to those for the full data set. Although the sample size (124) in the 13–18 year age range was less than optimal, the factor structure was clear and very similar to that of the wider age range, and the full 25-item scale and two subscales also had very good internal consistency reliability. There appeared to be some slight differences in mean weighted impact scores between the two groups (Fig. 2). The negative impact of not being allowed to do things because of diabetes was higher in the 13–18 year age group (who had a lower mean age), as was the negative impact on diabetes on eating, and of having to take more responsibility than they would like. As might be expected, those in the 13–18 age group also perceived greater negative impact of not being allowed to do things because of their diabetes, and also for high blood glucose levels (glycaemic control often deteriorates in adolescence [25]). Not being able to lie in bed was the most negatively impacted of all domains for the whole sample, and the second most extreme response for the younger age group. Both groups were concerned about the future and the effects of diabetes on their careers. Moreover, there was no evidence from analysis of free comments that the measure was unsuitable for 17–18 year olds, as only four representatives of this age group took the opportunity to comment here, and no aspect was mentioned by more than one of these older respondents. We would not recommend that the measure is used above the age of 18, unless the cognitive development of the young person seemed to indicate that the equivalent adult measure, the ADDQoL, were unsuitable. However, if a hospital has young people over 18 years in its adolescent clinic, clinicians might welcome a measure that has been found in practice to be suitable for young people above this age cut-off when conducting studies on their patients.
Although physiological measures are used to monitor the treatment of children and young people with diabetes, there are no child-centred, individualised psychological instruments currently in use in paediatric clinics that measure the impact of diabetes on children's everyday lives and on their QoL. The ultimate aim of QoL measurement is to improve patients' QoL wherever possible, by taking into account the impact of the treatment regimen and the effects of diabetes on their experience of daily living. Use of the ADDQoL-Teen would facilitate understanding of these issues and would provide healthcare professionals with valuable information about the psychosocial effects of diabetes on teenagers' everyday lives, which will help them consider psychological issues as well as medical outcomes when caring for teenagers with diabetes. Children (and parents) are faced with the day-to-day responsibility for the management of diabetes, and any improvements in QoL, whilst welcome in themselves, may also mean that these young patients will be more likely to follow the planned treatment regimen which will, in turn, help improve control of blood glucose levels and contribute to a reduction of long-term complications of diabetes.
Conclusions
The internal consistency reliability and some aspects of the validity of the new child-centred, individualised ADDQoL-Teen have been established for young people with diabetes, and the measure may be recommended for use with individual patients. The new questionnaire should help health professionals to consider psychological issues as well as medical outcomes when caring for young people with diabetes. The instrument is also expected to be useful in evaluating new treatments and educational interventions for diabetes in clinical trials.
Authors' contributions
CB and RJH designed the ADDQoL-Teen, with RJH conducting the interviews and focus groups with teenagers that informed the design of the measure. JD and NJHM conceived and designed the interview study and JD conducted the interviews and collated the questionnaires. CVM carried out the psychometric and statistical analyses of questionnaire data and drafted the manuscript. CB contributed to the interpretation of psychometric analyses, decision-making regarding item selection, and manuscript preparation. All authors read and approved the final manuscript.
ADDQoL-Teen copyright
For access to and a licence to use the ADDQoL-Teen, contact the copyright holder, Clare Bradley PhD, Professor of Health Psychology, Health Psychology Research, Royal Holloway, University of London, Egham, Surrey, TW20 0EX.
Email: [email protected]
Acknowledgements
Initial qualitative work was supported by a small grant from the British Diabetic Association (now Diabetes UK) to Ms Rachel Wilson (now Honeyford), Prof. Clare Bradley and also to Emeritus Professor of Psychology, Margaret Christie, who advised in the early days of designing the questionnaire. Carolyn McMillan was funded by a grant from the National Children's Bureau. The authors acknowledge the valuable assistance from the clinic teams in recruiting patients. We also acknowledge and thank the participants themselves for their essential contributions.
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| 15535888 | PMC538259 | CC BY | 2021-01-04 16:38:11 | no | Health Qual Life Outcomes. 2004 Nov 9; 2:61 | utf-8 | Health Qual Life Outcomes | 2,004 | 10.1186/1477-7525-2-61 | oa_comm |
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Virol JVirology Journal1743-422XBioMed Central London 1743-422X-1-71554833310.1186/1743-422X-1-7ResearchGenome structure and transcriptional regulation of human coronavirus NL63 Pyrc Krzysztof [email protected] Maarten F [email protected] Ben [email protected] der Hoek Lia [email protected] Department of Human Retrovirology, University of Amsterdam, Meibergdreef 15, 1105 AZ, Amsterdam, The Netherlands2004 17 11 2004 1 7 7 29 10 2004 17 11 2004 Copyright © 2004 Pyrc et al; licensee BioMed Central Ltd.2004Pyrc 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
Two human coronaviruses are known since the 1960s: HCoV-229E and HCoV-OC43. SARS-CoV was discovered in the early spring of 2003, followed by the identification of HCoV-NL63, the fourth member of the coronaviridae family that infects humans. In this study, we describe the genome structure and the transcription strategy of HCoV-NL63 by experimental analysis of the viral subgenomic mRNAs.
Results
The genome of HCoV-NL63 has the following gene order: 1a-1b-S-ORF3-E-M-N. The GC content of the HCoV-NL63 genome is extremely low (34%) compared to other coronaviruses, and we therefore performed additional analysis of the nucleotide composition. Overall, the RNA genome is very low in C and high in U, and this is also reflected in the codon usage. Inspection of the nucleotide composition along the genome indicates that the C-count increases significantly in the last one-third of the genome at the expense of U and G. We document the production of subgenomic (sg) mRNAs coding for the S, ORF3, E, M and N proteins. We did not detect any additional sg mRNA. Furthermore, we sequenced the 5' end of all sg mRNAs, confirming the presence of an identical leader sequence in each sg mRNA. Northern blot analysis indicated that the expression level among the sg mRNAs differs significantly, with the sg mRNA encoding nucleocapsid (N) being the most abundant.
Conclusions
The presented data give insight into the viral evolution and mutational patterns in coronaviral genome. Furthermore our data show that HCoV-NL63 employs the discontinuous replication strategy with generation of subgenomic mRNAs during the (-) strand synthesis. Because HCoV-NL63 has a low pathogenicity and is able to grow easily in cell culture, this virus can be a powerful tool to study SARS coronavirus pathogenesis.
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Background
Until recently only two human coronaviruses were known – human coronavirus (HCoV) 229E and HCoV-OC43, representatives of the group 1 and 2 coronaviruses, respectively. Both were identified in 1960s and are generally considered as common cold viruses. An outbreak of severe acute respiratory syndrome (SARS) in the spring of 2003 led to the rapid identification of SARS-CoV [1,2], which is considered to be a distinct member of the group 2 coronaviruses [3] or the first member of group 4 coronaviruses [4,5]. We identified earlier this year another human pathogen from this family: HCoV-NL63 [6], a variant that belongs to group 1 together with HCoV-229E and PEDV. These recent findings may be striking, as since the 1960's not a single new HCoV had been described. The genome features of SARS-CoV and its transcription strategy have been described in detail [1,5,7]. Here, we present such an analysis for HCoV-NL63.
HCoV-NL63 is a member of the coronaviridae family that clusters together with arteri-, toro- and roniviruses in the order of the nidovirales. Coronaviruses are enveloped viruses with a positive, single stranded RNA genome of approximately 27 to 32 kb. The 5' two-third of a coronavirus genome encodes a polyprotein that contains all enzymes necessary for RNA replication. The expression of the complete polyprotein requires a -1 ribosomal frameshift during translation that is triggered by a pseudoknot RNA structure [8,9]. The polyprotein undergoes autocatalytic cleavage by the viral papain-like proteinase and a chymotrypsin-like proteinase. The 3' one-third of a coronavirus genome encodes several structural proteins such as spike (S), envelope (E), membrane (M) and nucleocapsid (N) that, among other functions, participate in the budding process and that are incorporated into the virus particle. Some of the group 2 viruses encode a hemagglutinin esterase (HE) [10,11]. Non-structural protein genes are located between the structural genes. These accessory genes vary significantly in number and sequence among coronavirus species. Their precise function is unknown, but several reports indicate that they can modulate viral pathogenicity [12].
Coronavirus replication is a complex, not yet fully understood mechanism [13,14]. The 5' end of the genomic RNA contains the untranslated leader (L) sequence with the Transcription Regulation Sequence (TRS) in the downstream part. The L TRS is very similar to sequences that can be found in front of each open reading frame (body TRSs). The RNA-dependent RNA-polymerase has been proposed to pause after a body TRS of a particular gene is copied during (-) strand synthesis, subsequently switching to the L TRS and thus adding a common L sequence to each sg mRNA [15]. This discontinuous transcription mechanism is based on base-pairing of the nascent (-) strand copy RNA with the (+) strand L TRS. The nested set of (-) strand sg mRNAs are subsequently copied into a set of (+) strand sg mRNA. Other factors besides the sequence similarity between body and L TRS influence the efficiency of transcription. The level of transcription of a particular gene has been reported to be inversely related to the distance of a particular TRS to the 3' end of the genome [16-19].
In this study, we analyzed the genome structure of HCoV-NL63. First, we focus on the unusual nucleotide composition of the RNA genome. We describe in detail the bias in the nucleotide composition and its influence on the codon usage of this virus. We provide a possible mechanistic explanation for a shift in nucleotide bias at two-third of the HCoV-NL63 genome that is based on the RNA replication mechanism. Second, we describe in detail the different sg mRNAs generated during HCoV-NL63 replication and their relative abundance.
Results
Nucleotide content of the HCoV-NL63 genome
We described previously that the newly identified HCoV-NL63 virus has a typical coronavirus genome structure and gene order [6]. The nucleotide composition of the genomic (+) strand RNA of several coronaviridae members is presented in Figure 1, demonstrating a common pattern with U as the most abundant nucleotide and G and in particular C as underrepresented nucleotides. HCoV-NL63 has the most extreme nucleotide bias among the coronaviridae, with 39% U and only 14% C. As a general trend, U and C seem to compete directly, because the genomes with the lowest C-count (HCoV-NL63, HCoV-OC43 and BCoV) have the highest U-count and vice versa (Figure 1).
Figure 1 Nucleotide content of coronaviridae RNA genomes. We arranged the viruses based on their C-count, which ranges from 14% (HCoV-NL63) to 20% (SARS-CoV).
To investigate if all coding regions of HCoV-NL63 display a similarly strong preference for U and against C, we also plotted the nucleotide count for the individual genes and 5' and 3' non-coding regions (Figure 2). The typical nucleotide bias is observed in all genome segments. The highest U-count is found in the ORF3 and E genes (43%) and the lowest C-count in the 1a/1b genes and the 3' UTR (13%, 14% and 14%, respectively). The N gene is most moderate in its nucleotide bias, with 21% C and 32% U, confirming the "competition" idea that was already suggested by inspection of Figure 1.
Figure 2 Nucleotide content of individual HCoV-NL63 genes and the 5'/3' untranslated regions (UTR).
We plotted the nucleotide distribution along the genome (Figure 3) to determine whether there is any significant variation. We observed that local changes in A-count are inversely linked to changes in G-count. This is most striking in the 20400–26000 nt region, where three A peaks are mirrored by three G anti-peaks. Although the typical bias is maintained along the genome, the most notable variation occurs in the last one-third of the genome, where an increase in C and a decrease in G content is apparent. This region encodes the structural proteins.
Figure 3 Nucleotide distribution along the HCoV-NL63 genome. The change in the C- and G-count at two-third of the genome is statistically significant for all tested coronaviruses (HCoV-NL63, HCoV-229E, SARS-CoV, HCoV-OC43) with p < 0.01 for C-count and p < 0.05 for G-count in Mann-Whitney U test for two independent samples.
Recently, Grigoriev reported an interesting feature within coronaviral genomes that is visible when the cumulative GC-skew is plotted [20,21]. Cumulative GC skew graph is a way to visualize the local G:C ratio along the genome, discarding the local fluctuations. A biphasic pattern was described that separates the 1a/1b polyprotein genes and the structural genes. The cumulative GC-skews for HCoV-NL63 and four other coronaviruses: HCoV-OC43, HCoV-229E, PEDV and SARS-CoV are presented in Figure 4. In the 1a/1b genes, the G:C ratio reaches high levels, whereas for all coronaviruses, including HCoV-NL63, the 3' end of the genome displays a flattening of the curve, as the G:C ratio reaches value ~ 1 or less. Grigoriev proposed that this biphasic pattern is due to the discontinuous transcription process [20]. He suggested that the frequent deamination of cytosine on the (-) strand RNA results in a decrease of G on the (+) strand in the region encoding the structural genes. In the discussion section we will present an alternative mechanistic explanation.
Figure 4 Cumulative GC-skew diagrams for several coronaviral RNA genomes. The vertical bar indicates the border between the 1a/1b and the structural genes.
HCoV-NL63 codon usage
The bias in the nucleotide count led us to compare the codon usage of HCoV-NL63 with that of human mRNA (Table 1). The codon usage of HCoV-NL63 differs markedly from that of human mRNAs. Third-base choices in the four-codon families (Thr, Pro, Ala, Gly, Val) provide a convenient example of this contrasting codon usage. For instance, the Gly codons in human mRNAs prefer C (34%) over G (25%), A (25%) and U (16%). In contrast, HCoV-NL63 prefers U (83%) over A (7%), C (8%) and G (2%). This result strongly suggests that the codon usage is shaped directly by the unusual nucleotide composition of the viral genome, that is a high U-count and a low G/C-count. All HCoV-NL63 genes, except for the E gene, follow this trend (Table 1). The coronaviral addiction to the U nucleotide is most prominent in the "free" third position of degenerate codons. For the complete genome, the U-count at the third position is up to 58% whereas the A-count is 20%, G-count is 13% and C-count is only 9% (Figure 5). This illustrates that the U-pressure mainly affects the %C and %G.
Figure 5 Nucleotide composition of the first, second and third codon positions in the HCoV-NL63 genome.
Table 1 Codon usage of HCoV-NL63 compared with that of human genes
Amino acid Codon Humana HCoV-NL63 1ab (20190) S (4071nt) ORF3 (678nt) E (234nt) M (681nt) N (1134nt)
Arg CGA 0.62b 0.16 0.12 0.22 0.44 1.28 0.00 0.26
CGC 1.07 0.28 0.21 0.37 0.00 0.00 0.88 1.06
CGG 1.16c 0.06 0.04 0.15 0.00 0.00 0.00 0.00
CGU 0.46 1.57 1.49 1.40 1.77 0.00 2.20 3.44
AGA 1.17 0.78 0.76 0.74 0.88 0.00 1.32 1.06
AGG 1.17 0.47 0.49 0.22 0.00 1.28 0.00 1.32
Leu CUA 0.70 0.39 0.31 0.29 2.65 1.28 0.88 0.26
CUC 1.97 0.36 0.24 0.74 0.88 2.56 0.44 0.26
CUG 4.01 0.21 0.18 0.37 0.44 1.28 0.00 0.00
CUU 1.30 2.97 2.84 2.87 4.42 3.85 5.73 2.91
UUA 0.74 2.68 2.75 2.51 2.65 3.85 4.41 0.79
UUG 1.28 2.95 2.97 3.02 3.54 2.56 2.20 2.38
Ser UCA 1.20 1.49 1.38 1.92 1.33 0.00 0.44 2.91
UCC 1.76 0.33 0.30 0.59 0.00 1.28 0.00 0.26
UCG 0.45 0.08 0.07 0.00 0.44 0.00 0.00 0.26
UCU 1.49 3.06 2.76 3.98 2.21 0.00 3.52 5.82
AGC 1.94 0.34 0.27 0.59 0.00 0.00 0.88 0.79
AGU 1.21 2.47 2.51 2.36 2.21 1.28 3.08 2.12
Thr ACA 1.49 1.72 1.89 1.33 0.88 1.28 2.64 0.26
ACC 1.91 0.44 0.40 0.52 0.00 1.28 0.44 1.06
ACG 0.62 0.19 0.13 0.37 0.44 0.00 0.88 0.00
ACU 1.30 3.58 3.28 5.53 4.87 1.28 1.76 2.65
Pro CCA 1.68 1.03 1.01 1.11 0.44 2.56 0.44 1.59
CCC 2.00 0.18 0.15 0.22 0.00 0.00 0.00 0.79
CCG 0.70 0.10 0.07 0.22 0.00 0.00 0.44 0.00
CCU 1.74 2.10 1.93 1.92 3.10 1.28 2.20 5.29
Ala GCA 1.60 1.51 1.66 1.33 0.00 2.56 1.32 0.26
GCC 2.83 0.64 0.55 1.11 0.88 0.00 0.44 0.79
GCG 0.75 0.14 0.13 0.22 0.00 0.00 0.00 0.26
GCU 1.86 3.38 3.39 3.02 3.98 2.56 2.64 4.76
Gly GGA 1.64 0.46 0.49 0.44 0.00 0.00 0.44 0.26
GGC 2.26 0.48 0.34 1.18 1.33 0.00 0.44 0.00
GGG 1.65 0.14 0.13 0.07 0.00 0.00 0.44 0.53
GGU 1.08 5.19 5.56 4.35 4.42 1.28 4.41 3.44
Val GUA 0.71 1.04 1.19 0.59 0.00 1.28 0.88 0.79
GUC 1.46 0.89 0.77 0.96 2.65 2.56 1.76 0.79
GUG 2.86 0.77 0.68 1.03 0.88 1.28 2.20 0.26
GUU 1.10 7.18 7.41 6.63 7.52 3.85 7.49 5.29
Lys AAA 2.40 3.25 3.70 1.33 2.65 2.56 1.32 3.70
AAG 3.22 2.25 2.29 1.77 1.77 0.00 1.76 4.23
Asn AAC 1.92 1.27 1.05 2.28 0.44 0.00 0.88 2.38
AAU 1.67 4.88 4.73 6.19 3.54 3.85 3.96 4.50
Gln CAA 1.2 1.97 1.89 2.58 1.77 5.13 0.44 1.59
CAG 3.44 1.03 0.76 1.69 0.00 0.00 2.20 3.70
His CAC 1.50 0.39 0.39 0.52 0.44 0.00 0.00 0.27
CAU 1.07 1.50 1.6 1.03 0.88 1.28 2.64 1.06
Glu GAA 2.89 2.20 2.35 1.55 3.10 1.28 1.32 2.12
GAG 4.00 1.12 1.17 0.66 0.44 0.00 1.32 2.38
Asp GAC 2.55 1.19 1.2 1.03 2.21 1.28 1.32 0.79
GAU 2.2 4.27 4.67 3.10 1.33 2.56 0.88 5.56
Tyr UAC 1.54 0.89 0.82 1.03 1.77 1.28 1.32 1.06
UAU 1.21 3.8 3.83 4.13 5.75 5.13 3.52 0.79
Cys UGC 1.26 0.30 0.31 0.29 0.44 0.00 0.00 0.26
UGU 1.03 3.01 3.28 3.02 1.33 2.56 1.76 0.00
Phe UUC 2.04 0.68 0.58 0.74 1.77 2.56 0.88 1.06
UUU 1.72 5.76 6.02 4.94 7.52 8.97 5.29 2.65
Ile AUA 0.73 1.30 1.29 1.62 1.33 2.56 0.88 0.26
AUC 2.11 0.33 0.30 0.44 0.00 0.00 1.32 0.26
AUU 1.58 3.76 3.77 3.68 3.98 7.69 3.08 3.17
a data obtained from GenBank Release 142.0 [28].
b all values represent the percentage of a specified codon.
c the highest value for each codon group is typed bold.
Identification of the HCoV-NL63 TRS elements
The 5' end of HCoV-NL63 genome RNA contains the L sequence of 72 nucleotides that ends with the L TRS element. This TRS has a high similarity to short sequences that are located in front of each open reading frame (S-ORF3-E-M-N) [22]. We previously identified the L TRS and body TRS of the N gene using a cDNA bank [6], which allowed us to predict the body TRS of the other genes. To confirm these predictions, we amplified and sequenced all sg mRNA fragments with a general L primer and gene-specific 3' primers in an RT-PCR protocol.
Inspection of sg mRNA junctions indicated that they are indeed composed of the part of the HCoV-NL63 genome that is directly downstream of a particular body TRS, with its 5' end derived from the leader sequence. Apparently, strand transfer occurred on the 5' end of the body TRS, as indicated in Figure 6. The most conserved TRS region was defined by multiple sequence alignment as AACUAAA (gray box). This core sequence is conserved in all sg mRNA, except for the E gene that contains the sub-optimal TRS core AACUAUA (Figure 6). Interestingly, the E gene contains a 13-nucleotide sequence upstream of the core sequence with perfect homology to the L sequence. Perhaps the upstream sequence compensates for the absence of an optimal TRS core during discontinuous (-) strand synthesis. This would suggest that these sequences are copied during (-) strand synthesis, and that the actual strand transfer within the E sequences occurred after copying of the core TRS and the next 13 nucleotides. Evidence for such a "delayed" strand transfer is provided by the junction analysis of the M and N sg mRNAs, which clearly demonstrates that the nucleotides directly upstream of the core TRS are derived from the body TRS element and not from the leader.
Figure 6 Body-leader junctions of all HCoV-NL63 sg mRNAs. Shown on top is the leader (L) sequence and below the specific sequences upstream of the structural genes. The fusion of 5' L sequences to 3' sg RNA is indicated by the boxes. Sequence homology between the strands near the junction is marked by asterisks, the conserved AACUAAA TRS core is highlighted in gray.
Analysis of the subgenomic mRNAs of HCoV-NL63
To determine whether the predicted sg mRNAs encoding the S-ORF3-E-M-N proteins are produced in virus-infected cells, we performed Northern blot analysis on total cellular RNA (Figure 7). We used a (-) strand N gene probe that anneals to both genomic RNA and all sg (+) strand mRNAs. We included RNA from MHV-infected cells to obtain discrete size markers. Six distinct mRNAs are produced in HCoV-NL63 infected cells. The sizes of the RNA fragments were estimated and these values nicely fit the size of the genomic RNA and the five predicted sg mRNAs. All HCoV-NL63 ORFs that have the potential to encode viral proteins are indeed transcribed into sg mRNAs (Figure 7).
Figure 7 The left panel shows the Northern blot analysis of HCoV-NL63 RNA in infected LLC-MK2 cells. RNA of HCoV-NL63 (NL63 lane) was compared with RNA of MHV strain A59 (MHV lane). Non-infected LLC-MK2 cells are included as a negative control (control lane). MHV RNA bands represent the complete genome (1) and sg mRNAs 2a (2), S (3), 17.8 (4), 13.1 and E (5), M (6), N (7). HCoV-NL63 RNA includes the complete genome (1) and sg mRNAs for S (2), ORF3 (3), E (4), M (5) and N (6). The right panel shows the MHV and HCoV-NL63 genome organization and the HCoV-NL63 sg-mRNAs.
To determine the expression level of each subgenomic RNA, we measured the intensity of the signals. When plotted as a function of the genome position (Figure 8), there appears a correlation between the relative distance of a gene to the 3' terminus and its RNA expression level, with the exception of the E gene.
Figure 8 Expression levels of the HCoV-NL63 genomic and sg mRNAs.
Discussion
We analyzed the nucleotide composition of the HCoV-NL63 genomic (+) RNA, which was found to exhibit a typical coronavirus pattern with an abundance of U (39 %) and shortage of G (20%) and C (14%). In fact, HCoV-NL63 has the most pronounced nucleotide bias among the coronaviridae.
There is a significant fluctuation in the nucleotide count among the HCoV-NL63 genes. For instance, ORF3 and M appear as extreme U-rich and A-poor islands. It is possible that the unique nucleotide composition of some structural genes reflects their evolutionary origin, perhaps suggesting that some of these functions were acquired recently from another viral or cellular origin by gene transfer. These properties mimic the pathogenicity islands of prokaryotic genomes [23]. Consistent with this gene transfer hypothesis is the observation that there is a lot of variation in the number and identity of the 3' genes among coronaviridae.
Inspection of the nucleotide composition along the genome indicates a bi-phasic pattern. The 5' two-third of the genome encoding the 1ab polyprotein has a stable nucleotide count with the typical U>A>G>C order, but rather striking differences are observed in the 3' one-third of the genome that encodes the structural proteins (Figure 2). Most notably, the C-count increases significantly at the expense of G and U. Grigoriev recently reported the typical nucleotide bias of coronaviral genomes and the switch in nucleotide count at two-thirds of the genome [20]. He performed an analysis based on cumulative GC-skew, and suggested that the drop in GC-ratio is in fact due to a decrease in G-count. However, inspection of the HCoV-NL63 nucleotide composition indicates that the switch is due to a sudden increase in C-count, with a slight drop in G-count. Inspection of other coronaviral genomes confirms that C goes up (with highest significance in group 1 coronaviruses) and G goes down (with highest significance in group 2 coronaviruses) at two-third of the viral genome (results not shown). Grigoriev presented a possible mechanistic explanation. He suggested that the 3'-terminal one-third of the viral genomic (-) strand is more likely to be single stranded because (-) sg mRNA synthesis on the (+) strand template frequently disrupts the protective duplex in that region. This would make this part of the (-) strand genome more vulnerable to C to U transitions, which would eventually lead to a decrease of the G-count on the (+) strand. This scenario explains the G decrease, but obviously is not consistent with the local increase in C-count. We therefore propose an alternative mechanism that is also dictated by the viral transcription strategy. The central 1a/1b portion of the viral (+) strand genome is less likely to be annealed to complementary (-) strand during viral replication because most (-) strand RNAs are sub-genomic, which lack this 1a/1b domain. The 1a/1b portion of the genome thus becomes more vulnerable to C to U deamination, which correlates with the high U-count and the low C-count. Obviously, there may be many other cellular conditions and viral properties like higher amount of secondary structures on the 3' part of the genome that could have shaped the coronavirus genome over an evolutionary timescale, but this scenario explains the switch in nucleotide count at two-thirds of the viral genome.
We show that U-counts reach the highest values and C-counts the lowest values at the third position of the HCoV-NL63 codons (Figure 5). Analysis of the synonymous codon usage indicates that codons with a high U and A content are preferred over C and G rich codons (Table 1). Thus, the peculiar genome composition has a direct effect on the codon usage of HCoV-NL63, and possibly even an indirect effect on the amino acid composition of coronaviral proteins by affecting the non-synonymous codon usage [24-26]. The synonymous codon usage of HCoV-NL63 clearly differs from that in human cells. Thus, the genome may have been shaped by cytosine deamination over an evolutionary timescale, but it is possible that the translational machinery has restricted this genome drift because of the availability of tRNA molecules.
Inspection of the viral genome sequence led us to predict that the 1ab polyprotein is expressed from the genomic RNA and the 3' structural proteins and ORF3 from 5 distinct sg mRNAs. This was confirmed experimentally. We observed that sg mRNAs are more abundant when the corresponding TRS is located closer to the 3' end of the genome. The exception is formed by the E sg mRNA, which is relatively underexpressed. This may correlate with the low expression level of this protein. The general trend of increased gene expression along the genome has been reported previously for other coronaviruses [19]. A possible mechanistic explanation is that the viral polymerase density is reduced along the genome or that the polymerase becomes less susceptible to execute a transfer from body TRS to L TRS during extended (-) strand synthesis. Fine-tuning of the efficiency of the strand-transfer processes may be modulated by many other features, including the local sequence and structure of the core body TRS and its flanking regions. It was reported previously [27] that the core of the L TRS of group 1 coronaviruses is presented in the single stranded loop of a mini-hairpin. We found similar motifs in HCoV-NL63 (results not shown). Although not excessively stable, this structural motifs is predicted to fold as part of the complete leader sequence, and it may participate in the strand transfer process.
The core sequence AACUAAA is conserved in the L TRS and all body TRSs, except for the E gene that has a single mismatch AACUAUA. The presence of a sub-optimal core sequence may in fact explain the lower than expected expression level of this sg mRNA (Figure 8). But there is another striking feature of the E body TRS: it has 13 additional upstream nucleotides in common with the leader TRS. If one assumes that strand transfer does not occur at the core sequence but up to 13 nucleotides further upstream, this sequence homology will result in additional base pairing interactions that may stimulate the strand transfer process. Thus, the extended TRS homology may compensate for the sub-optimal core element. A remarkably similar scenario of sub-optimal core and extended TRS is apparent in the E gene sequence of PEDV (results not shown). A further indication that the viral polymerase frequently copies beyond the core sequence is provided by the actual sequence of the M and N sg mRNAs, which apparently have copied the TRS nucleotide that flanks on the 5' side the core element of body TRS.
Methods
Genome Analysis
The nucleotide content of different Coronaviridae family members was assessed using BioEdit software. The nucleotide distribution was determined using a Microsoft Excel datasheet (300 nucleotide (nt) window and 10-nt step). Codon usage was assessed using DNA 2.0 software. Data was processed in Microsoft Excel datasheet and all statistical analysis was performed with SPSS 11.5.0 software. The level of significance of the nucleotide bias was established for 300-nt non-overlapping windows with the non-parametric Mann-Whitney U test for two independent samples. Cumulative GC-skew graphs were generated as described previously [20] with the value in step n defined as the sum of (G-C)/(G+C) from step 0 to n (200-nt sliding window, 10-nt step).
Viral RNA isolation
HCoV-NL63 RNA was obtained from virus-infected LLC-MK2 cells (2 × 107) after 6 days of culture (virus passage 7). Mouse Hepatitis Virus (MHV) RNA was obtained by infecting 2 × 107 LR7 cells with MHV strain A59. The medium was removed and the cells were dissolved in 15 ml TRIzol® and RNA was isolated according to the standard TRIzol® procedure. RNA was subsequently precipitated with 0.8 volume of isopropanol, dried and dissolved in 50 μl H2O. Integrity of the RNA was analyzed by electrophoresis on a non-denaturating 0.8% agarose gel. RNA was stored at -150°C.
RT-PCR
The cDNA used for sequencing and probe construction was made by MMLV-RT on viral RNA with 1 μg of random hexamer DNA primers in 10 mM Tris pH 8.3, 50 mM KCl, 0.1% Triton-X100, 6 mM of MgCl2 and 50 μM of each dNTPs at 37°C for 1 hour. The single stranded cDNA product was made into double-stranded DNA in a standard PCR reaction with 1.25 U of Taq polymerase (Perkin-Elmer) per reaction with appropriate primers (see below).
Northern Blot
Gel electrophoresis of viral RNA was performed on a 1% agarose gel with 7% of formaldehyde at 100 Volt in 1×MOPS buffer (40 mM MOPS, 10 mM sodium acetate, pH 7.0). Transfer onto a positively charged nylon membrane (Boehringer Mannheim) was done overnight by means of capillary force. RNA was linked to the membrane in a UV crosslinker (Stratagene). For generation of the HCoV-NL63 probe, the RT-PCR product was further amplified with 5' primer N5PCR1 (CTG TTA CTT TGG CTT TAA AGA ACT TAG G) and 3' primer N3PCR1 (CTC ACT ATC AAA GAA TAA CGC AGC CTG). Similarly, the MHV probe was amplified with 5' primer MHV_UTR-B5' (GAT GAA GTA GAT AAT GTA AGC GT) and 3' primer MHV_UTR-B3' (TGC CAC AAC CTT CTC TAT CTG TTA T). Labeling of the probes was done in a standard PCR reaction with specific 3' primers (N3PCR1 and MHV_UTR-B3') in presence of [α-32P]dCTP. Prehybridization and hybridization was done in ULTRAhyb buffer (Ambion) at 50°C for 1 and 12 hours, respectively. The membrane was then washed at room temperature with low-stringency buffer (2×SSC, 0.2% SDS) and at 50°C in high stringency buffer (0.1×SSC, 0.2% SDS). Images were obtained using the STORM 860 phosphorimager (Amersham Biosciences) and data analysis was performed with the ImageQuant software package. The size of sg mRNA fragments of HCoV-NL63 were estimated from their migration on the Northern blot using the sg mRNA of MHV as size marker.
Sequence analysis of TRS motifs
The L/body TRS junctions were PCR-amplified from an HCoV-NL63 cDNA bank. We performed 35 cycle PCR with the 5' L primer (L5 – TAA AGA ATT TTT CTA TCT ATA GAT AG) and gene specific 3' primers (S gene – SL3' – ACT ACG GTG ATT ACC AAC ATC AAT ATA; ORF3 – 4L3' – CAA GCA ACA CGA CCT CTA GCA GTA AG; E gene – EL3' – TAT TTG CAT ATA ATC TTG GTA AGC; M gene – ML3' – GAC CCA GTC CAC ATT AAA ATT GAC A; N gene – 3-163-F15 – ATT ACC TAG GTA CTG GAC CT). The PCR products were analyzed by electrophoresis on a 0.8% agarose gel and products of discrete size were used for sequencing using the BigDye terminator kit (ABI) and ABI Prism 377 sequencer (Perkin Elmer). Sequence analysis was performed by Sequence Navigator and AutoAssembler 2.1 software.
Sequences
The complete genome sequence of HCoV-NL63 [6] is deposited in GenBank (accession number: NC_005831). sg mRNA sequences are deposited in GenBank under the accession numbers: AY697419-AY697423. The GenBank accession number of the sequences used in this genome analysis are: MHV (mouse hepatitis virus, strain MHV-A59): NC_001846; HCoV-229E: NC_002645; HCoV-OC43 strain ATCC VR-759: NC_005147; PEDV (porcine epidemic diarrhea virus, strain CV777): AF353511; TGEV (transmissible gastroenteritis virus, strain Purdue): NC_002306; SARS-CoV isolate Tor2: NC_004718; IBV (avian infectious bronchitis virus, strain Beaudette): NC_001451; BCoV (bovine coronavirus, isolate BCoV-ENT): NC_003045.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
KP carried out the viral RNA isolation, RT-PCR, sequencing of sg mRNAs, Northern blot evaluation and all computer analysis done in this study; MFJ carried out the full genome sequencing; all authors participated in writing the manuscript. Lv/dH and BB are the principal investigators
Acknowledgements
We thank Berend Jan Bosch and Peter Rottier for providing MHV infected cells and Alexander Nabatov and Barbara van Schaik for technical support.
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| 15548333 | PMC538260 | CC BY | 2021-01-04 16:38:33 | no | Virol J. 2004 Nov 17; 1:7 | utf-8 | Virol J | 2,004 | 10.1186/1743-422X-1-7 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1851556938810.1186/1471-2105-5-185Methodology ArticleNoise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer Aris Virginie M [email protected] Michael J [email protected] Jeff [email protected] James J [email protected] Patricia [email protected] Michael [email protected] Peter P [email protected] Center for Applied Genomics, Public Health Research Institute, Newark, NJ 07103, USA2 Center for Computational Biology, New Jersey Institute of Technology, Newark, NJ 07103, USA3 Dept of Microbiology and Molecular Genetics, UMDNJ-New Jersey Medical School, Newark, NJ 07103, USA4 Current address: Ortho-Clinical Diagnostics a Johnson & Johnson Company, Raritan, NJ 08869, USA2004 29 11 2004 5 185 185 6 7 2004 29 11 2004 Copyright © 2004 Aris 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 major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups.
Results
We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation.
Conclusions
This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.
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Background
DNA microarrays have become a useful tool in biomedical research as they can be used to determine the relative expression of thousands of genes in a given sample. Such expression profiles could predict genetic predisposition to disease, serve as a set of diagnostic markers, define better drug treatments options for existing diseases (pharmacogenomics), or mark the precise nature of disease progression. A major limitation of this technology is the lack of uniform data mining strategies. This study integrates complementary approaches to more effectively analyze Affymetrix GeneChip microarray data derived from several different types of solid tumors. If the noise is consistent and reproducible it can be filtered from the data and some false positives can be eliminated. There are two principal sources of noise in microarray experiments: biological noise and technical noise. Biological noise consists of variation among patients and tumor locations, variation in the cellular composition of tumors, heterogeneity of the genetic material within tumor due to genomic instability. Technical noise consists of differences in sample preparation and experiment variables which include nonspecific cross hybridization, differences in the efficiency of labeling reactions and production differences between microarrays. Biological noise cannot be corrected but it can be accounted for with statistics using replicates of the treatments or conditions. However, the noise derived from experimental techniques is reproducible and its boundaries can be modeled. It has been observed that in differential gene expression comparisons of any given gene, there is a greater variance in the fold-change calculation at lower signal intensities [1,2], and when comparing replicate samples, lower expression values tend to have greater variance in signal intensity. This suggests that larger errors can occur when lower signals are used to compute fold-changes in differential comparisons. Fold change, computed in this way, can lead to extraneous inclusions in lists of significantly up-regulated or down regulated genes. For example, a fold change of two calculated from intensities of 25 and 50 may not be as trustworthy as a fold-change of two determined between intensity values of 2,000 and 4,000. Thus, the purpose of error boundary modeling is to reduce the influence of less trustworthy fold-change calculations in the analysis of differential microarray data. The efficacy of coupling a noise boundary model to an analysis method has been previously shown for two color cDNA arrays [3-6].
The principal concerns when using microarray data derived from different labs to identify cancer markers is that chip-to-chip normalization cannot eliminate differences in cRNA synthesis and labeling, hybridization protocols, scanner settings and image processing software. Variable RNA quality can influence the amount of individual cRNAs generated. The laser power on scanners can differ causing saturation of high intensity probe sets and may have a more variable estimation of the very low expressed transcripts.
Two studies [7,8] have successfully classified different types of cancer by their molecular profile on microarrays using hierarchical clustering and support vector machine (SVM) techniques. Both studies found that their markers comported a high number of genes whose expression differed among the normal tissues of origin. The approach taken in this paper is that the cancer samples are compared first to their normal tissue and then the most discriminating genes for each cancer vs. normal comparison are compared between cancers. This circumvents normalization problems due to lab-specific parameters (scanner settings, labeling, hybridization variables) and tissue specific artifacts, as each cancer biopsy is compared to its corresponding normal tissue processed by the same research group, in the same environment. These environmental parameters and artifacts are assumed to be similar for the normal and cancer biopsies and should cancel out. This allowed the selection of the genes that best discriminated between the normal and tumor samples. These classifiers were then evaluated to see if they were specific to the different types of cancers. Since gene expression measurements of individual Affymetrix GeneChips probe sets frequently do not follow a normal distribution, a non-parametric analysis was used.
The commonly used t-test tests to see if two populations have a different mean but does not test the overlap of the populations. Selecting markers with minimal overlap in their expression between the normal and tumor states would improve their predictive value. We developed a method to find such markers using an un-weighted voting scheme. This non-parametric method for marker selection was chosen so that no assumptions on the shape of the data distribution were required. The computed noise boundary makes the selection criteria more stringent, eliminating many false positives signals and highlighting genes that are differentially expressed most consistently in comparisons between a cancer and its corresponding normal tissue. This integrative approach can yield a signature of distinct transcripts distinguishing a variety of solid tumors.
The objectives of this work were three fold: 1). develop a noise boundary for GeneChip data, 2). develop an algorithm for selecting markers with minimal overlap in their expression between the normal and tumor states, 3). integrate the analysis of previously published data from different sources.
Results and discussion
Noise boundary model
The noise boundary model was created to evaluate the reliability of calculated fold changes. Data from normal tissue biopsies obtained from public data sets were used to infer the noise boundary to be used when comparing the normal tissue to their corresponding cancer biopsies. The cancer biopsies were not used in designing the noise model as they are likely to be more variable than normal tissue. Three different signal extraction methods, MAS5 [9,10], dChip [11,12] and RMA [13] were compared and MAS5 was chosen as it showed stable results and is widely used. A complete analysis of the three methods is included in the supplemental data [see Additional file 2].
A fold-change threshold boundary was drawn for each comparison between normal tissues for each of the cancers studied to model the noise inherent to the method. The data was first sorted according to the average intensity of the values of the probe-sets for two replicate chips. If there is no noise in the technique or the biology, one should expect to have all the fold changes be 1. However when plotting the fold changes against the average intensity for the probe-sets we observed that the data formed a volcano plot with considerable scattering for low intensity and a progressive tapering of the fold changes at high intensities. As there is a lot of noise in estimating the low end expression a cut-off is needed to eliminate part of that noise. Then, as the samples were biological replicates, we assumed that most of the genes were not differentially expressed; a certain percent of the genes should not change and a percentile cut off was set up to eliminate spurious variations. The data was then binned into fixed width bins including 200 expression values. For modeling purposes, the percentile was plotted against the inverse of the average bin intensity to reveal a linear relationship that can be characterized with a slope and intercept. A sensitivity analysis to optimize the noise boundary percentile and low intensity cut off parameters was performed and is presented in the supplemental data [see Additional file 2]. The low intensity cutoff was set to 100 and bins with a mean expression value lower than 100 were excluded. The 80th percentile of the fold-change, chosen as the noise boundary, was calculated for each bin. Figure 1 shows the 80th percentile error boundaries for the five different tissues as a function of the inverse of the average bin intensity. To decrease the effect of saturation on the regression, the gene expression values in the top 8% were eliminated (this correspond to the 5 highest bins intensities in figure 1). The noise boundary was found to be tissue dependant and the slope and intercept were calculated for each tissue (Table 1). For a fold change to be considered reliable, it has to be greater than the noise boundary threshold for the same average intensity:
Nonparametric microarray data analysis: Er Algorithm
In microarray experiments, the number of replicates is often small and the distributions of gene expression are not normal for all genes. For the same difference in mean, depending on the distribution of the data, the overlap of two distributions can be dramatically different. Ideal markers would be genes with no overlap in their distribution; the consistency of change is therefore more significant than the amplitude. To address the problem of low numbers of replicates and multiple testing on 12,000 genes, the noise boundary model was incorporated with non-parametric data mining. The noise boundary eliminates noise that is proportional to the probe intensity measured. The combination of the non-parametric voting scheme with the noise model will be referred to as the directional change assessment algorithm. For each transcript, the ratio of expression intensities (fold change) of each cancer biopsy to each normal biopsy was determined. Those ratios were recorded and evaluated against the noise boundary model. If a ratio was above the ratio given by the noise boundary, the direction of the fold-change as increased (+) or decreased (-) expression was recorded. If the ratio was below the value given by the noise boundary for the average of the intensities, then the fold-change was considered insignificant and assigned a no change (0) direction.
For each probe-set
For each sample ci in the cancer class
For each sample nj in the normal class
If ci>nj then r = ci/nj else r = nj/ci
If r>noise_boundary ((ci+nj)/2) And If ci>nj
Pos_score = Pos_score + 1
If r>noise_boundary ((ci+nj)/2) And If ci ≤ nj
Neg_score = Neg_score + 1
Else NoChange_score = NoChange_score + 1
We designed an index, called event ratio to summarize the overlap in distribution between the cancer and normal samples. This Er index is described by:
Where the #comparisons is equal to the number of cancer samples multiplied by the number of normal samples. This method counts direction and evaluate the overlap of the distributions normalized to the number of comparisons. The Er index ranges from 0 to 1. As the direction of change for a gene becomes more consistent, Er approaches 1. Conversely, if the Er score is close to 0.5, then the gene is inconsistent with regard to its directionality and thus cannot be considered a reliable marker for disease classification. As the score approaches 0, the transcript direction and fold change cannot be reliably estimated as it is within the noise level of the technique. The software is available at .
To test the validity of this approach, the samples were shuffled 100 times between the categories (cancer and normal) and the Er computation was repeated. Each time the data was shuffled, the probe sets were sorted by descending Er score and the probe set information was discarded and replaced by its rank. The average and standard deviation of the ranks was then computed and compared to the results obtained for the cancer versus normal biopsies. For all the comparisons performed, higher Er scores were obtained in the case of cancer versus normal classifications than with randomly shuffled sets. An illustration of the results obtained with the breast cancer versus normal biopsies can be seen in figure 2. The average Er score per rank converged rapidly, and was consistent after shuffling the dataset 50 times (figure 3).
Comparison of the Noise Boundary-Er Algorithm to standard analysis techniques
To compare the Er algorithm including the noise model to other commonly used analysis methods, the replicate set from the Latin square dataset was used [14]. In this dataset fourteen specific RNAs were exogenously added to the hybridization mixture in two fold increasing concentrations. The T-test performed on this data identified all fourteen genes as well as 161 presumed false positives with a significant p-value (below 0.01). Therefore, the percentage of true positives is only 8% of the genes found significant in the result. This reflects the multiple-testing problem when using the t-test in this way. If twelve thousand tests are performed simultaneously on 12,000 genes with a type I error of 0.01 (the test is falsely considered significant one time every 100 tests), we can expect 120 (= 12,000*0.01) probe sets to be below the p-value 0.01 simply by chance. The Hochberg/Simes [15] method addresses this issue. They both found 16 genes to have a significant fold change with 11 of the 14 true positives (approx 69% true positives in the result). Another correction technique for multi-testing is the Bonferroni [15,16] method which found seven genes to be significant including six out of the fourteen true positives (approx. 86% true positives in the result). The SAM [17] method found 21 significant genes including 12 out of the 14 true positive (57% true positives in the result) for a delta of 1.54, and a Pi0Hat of 0.96. Although they were able to identify 85% of the exogenously added transcripts, their false positive rate was underestimated. SAM estimated a median false positive rate of 4.58%, but found 9 out of 21 probe-sets to be significant while they were not exogenously added (false positive rate of 43%). The Er model described above with a cut-off of 0.9 identified 12 genes with 8 of them being true positives (66% true positives in the result). These data suggest that the Er model is well within the separation levels of those standards techniques. However the results and performance of the different techniques might be dataset dependant. The replicate set from the Latin square dataset has little inherent noise. Even the chips at different control concentrations can be considered as technical replicates as only 14 out of 12,000 genes were supplemented. Using the noise model to remove noise from a noisier dataset might prove even more useful. It would be interesting to compare those methods on multiple datasets, but at the time of this study, this is the only dataset with an absolute knowledge of true and false positives. It is worth noting that the supplemented RNAs were added at a fold variance in concentration but that the actual intensity found averaged only 1.53 fold. All those methods greatly decrease the number of false positives compared to the t-test alone but some true positives were also missed. This is partly due to the fact that the control RNAs were added at concentrations testing the limits of detection.
Cancer-specific biomarkers
The Er model was used to compare each cancer biopsy to its corresponding normal tissue. In the absence of error modeling, the directional change algorithm identified 1,910 probe-sets that had an Er score above 0.9 in ovarian cancer, 1,355 in breast cancer, 1,730 in oral cancer and 322 in prostate cancer. Incorporation of error modeling dramatically reduced the number of probe-sets with Er scores above 0.9 to 272 for ovarian, 177 for breast, 129 for oral cancer and 2 for prostate cancer [see Additional file 3]. For lung cancer biopsies, the distinct sub-classes were compared against normal tissues and 15 probe-sets with an Er value above 0.9 in all comparisons were uncovered.
The advantage of determining Er scores for differentially expressed cancer transcripts is that it provides a statistical metric that can be used to underscore markers that are unique to a particular cancer. Although the Er is not a statistical test and an Er score can vary in its significance depending on the number of samples studied, we selected genes with a high Er index in one cancer type (Er> = 0.9) and low in the others (Er<0.6). As Affymetrix HG-U95A and Hu133A contain different probe-set numbers for the same gene, the SOURCE software [18] from Stanford University was used to match the probe set to their cluster ID using the UniGene Build 167. Cluster IDs were then matched between chip types using Microsoft Access. No universal marker encompassing all the cancer vs. their normal tissue was found. This result is consistent with the result from Ramaswamy et al. [7] using 14 common tumor types including breast, prostate, ovarian and lung cancer. Nonetheless, caveolin-1 (CAV1) was found down regulated in 90% of breast, ovarian, and lung tumors, and in 80% of the prostate cancers. This gene is also down-regulated in large diffuse B-cell lymphoma [19], is associated with a region of the chromosome 7 q31 frequently deleted in tumors [20], and has been shown to have a tumor suppressing activity when restored [21,22].
The number of genes found to be reliable markers varied greatly between cancer types [see Additional File 1]. Prostate and lung cancer had the smallest number of such markers and were the 2 datasets with the most samples. The only prostate marker identified, SIM2, is a transcription factor involved in regulation of transcription during development [23]. This gene has also been found differentially expressed in colon and pancreatic cancer [24], and an antisense inhibition of SIM2-s expression in a colon cancer cell line restored growth inhibition and apoptotic cell death [24].
Two genes, AGER and MARCO, were found to be under expressed in all the lung cancer types compared to other cancer types. The advanced glycosylation end product-specific receptor (AGER or RAGE) has been previously reported down-regulated in non small cell lung carcinoma [25]. AGER is a receptor for amphoterin which mediates cell differentiation [26], and is highly expressed in lungs. Down-regulation of AGER may be a critical step in lung tumor formation as it is down regulated in all the different subtypes of lung cancer studied here. On the other hand, AGER seems to be up-regulated in pancreatic cancer and its level correlates to the metastatic potential of the cancer cell line [27]. The second gene specific to lung cancer is MARCO which is expressed by alveolar macrophages in the lung and is involved in inflammation and pathogen clearance [28,29]. A decrease of MARCO RNA in the sample may be due to a decrease in the number of macrophages inside the tumor compared to the normal tissue.
Thirty nine probe sets were found to have an Er score above 0.9 in ovarian cancer and lower than 0.6 for the other cancers. Two of these genes, Janus kinase 1 (JAK1) and a zinc finger homeobox (ZFHX1B), which are involved in the TGF β signaling pathway regulating cell growth, were down regulated. PAX8, a gene important in development which had been identified in an earlier study [30], was found to have consistently increased expression. Three genes involved in cell growth or maintenance, MLLT2, PRSS11, FOXO3A, were down regulated.
Breast cancer profiles have several interesting features. First, 16 ribosomal protein genes have decreased expression: L34 is involved in translational control [31], S27 in signal transduction, and RPS4X in development and cell cycle control. As the genes coding for those ribosomal proteins are located on different chromosomes the down regulation of these ribosomal proteins could be due to methylation of the ribosomal DNA [32,33]. All of the markers for breast cancer are down regulated except for inosine monophosphate dehydrogenase 1 (IMPDH1), increased by two fold, which is involved in the biosynthesis of purine nucleotides. Breast cancer has distinct sub-groups which some are hormone dependant for growth, others being very aggressive with an Her-2 amplification. The cancer samples in this study [8] are likely to be a mix of these subtypes. This might explain why the well known markers for a particular sub group do not appear in those results. However, the particular sub-classification of those 16 breast cancer samples is not known [8].
In oral cancer [see Additional File 1], many genes involved in differentiation of epithelial cells are found to be specific markers for this cancer. Keratin 4 and 13 (KRT4 and 13), and the small proline-rich protein 1B (SPPR1B) involved in epidermal differentiation, are all down regulated, as well as cellular adhesion genes desmoglein 1 and 3 (DSG1 and 3). The matrix metalloproteinase 13 (MMP13) gene encoding collagenase was specifically up regulated in this cancer. In the original study the up regulation of MMP1 and down regulation of KRT4 was confirmed by RT-PCR [34].
Conclusions
The method described here provides improved non-parametric approaches to microarray data analysis. After applying the noise boundary model, markers were selected according to their consistency for up-regulation or down-regulation using a voting scheme comparing normal versus cancer biopsies. Tissue-specific expression differences were eliminated by comparing the cancer samples to the normal biopsies from the same tissue. The genes with the greatest differential expression between cancer and normal biopsies were then compared between cancer types. This differs from previous studies [7,8,35] which directly compare results among different cancers. Groups of markers with consistent differential expression among ovarian, breast, prostate and lung cancer were found. Many of these markers are related to de-differentiation of the tissue and were highly specific to their tissue of origin. Also, tumors arising from cells with the same embryogenic origin tend to have the same genes required for cancer progression. This confirms a previously described oncodevelopmental connection [36].
Methods
All of the microarray data used in this analysis was derived from RNA isolated from biopsies and hybridized on Affymetrix GeneChips HG-U95A, HG-U95Av2 or HG-U133A. All the research groups used the same standard procedure for labeling the cRNA, hybridization and scanning the GeneChips [37]. The datasets were obtained from several different sources: Data from 24 breast cancer biopsies were from Su et al.[8], and the three corresponding normal breast tissue biopsies were provided by Garret Hampton from the Genomics Institute of the Novartis Research Foundation. For prostate cancer, the dataset was derived from 21 tumors and 8 normal biopsies [38] whereas the ovarian cancer dataset originated from 14 tumor and four normal biopsies [39]. Finally, the lung cancer dataset consisted of biopsies from 61 samples of lung adenocarcinoma, 20 lung carcinoids, six small cell lung cancer, 21 squamous lung cancers, and 17 normal lung tissues [40]. Out of the 61 adenocarcinoma samples, 19 were replicates and 52 were sub-divided into five categories according to Bhattacharjee et al.(2001) [40]: seven in cluster 1, nine in cluster 2, 15 in cluster 3, 13 in cluster 4, and eight samples of colon metastasis. The Oral cancer dataset consisted in 4 normal and 16 oral cancer biopsies [34]. The directional change assessment and the noise model algorithms were programmed using Python, and the comparison for markers was performed with Excel.
Authors' contributions
VA and PT conceived the study. VA carried out the analysis and drafted the manuscript. MC performed the programming. JC helped with the evaluation of raw image analysis methods. MR supervised the programming and analysis. PS and JD participated in the study design and management.
Supplementary Material
Additional File 2
Assessing the noise level and trust threshold for differential expression on Affymetrix GeneChips. This document compares the noise from MAS5, RMA and dChip and presents the sensitivity analysis for the noise model parameters using the Latin square replicate data set.
Click here for file
Additional File 3
Gene markers for prostate, breast, ovarian, oral, and lung cancer. This file presents the Top Er scores for each cancer studied when compared to its normal tissue. There is a result table with gene information for each cancer on separate tabs.
Click here for file
Additional File 1
Gene markers that distinguish between prostate, breast, ovarian, oral, and lung cancer. This file presents the Er scores of genes expression levels that are consistently up or down in a given cancer compared to its normal tissue (Er>0.9) but not in any of the other four cancers (Er<0.6).
Click here for file
Acknowledgments
We thank Garret Hampton from the Genomics Institute of the Novartis Research Foundation for providing microarray data from three normal breast biopsies, and Gokce Toruner for his critical review of the paper.
This work is supported by NIH grant CA83213 from the National Cancer Institute. The Center for Applied Genomics is supported in part with R&D Excellence grant 00-2042-007-21 from the New Jersey Commission on Science and Technology.
Figures and Tables
Figure 1 This figure represents the 80th percentile for each of the five normal tissues plotted against the inverse of the average bin intensity. Bins with an average intensity below the cutoff of 100 (above 0.01 in the figure) were not displayed as they are below the minimum intensity cutoff.
Figure 2 Comparison of the Er score of the 500 top ranked probe sets for breast cancer versus normal breast biopsies. Er score for the real breast cancer vs. normal biopsies (red line), Average Er score of the 500 top ranked probe sets of the 100 shuffling sets (blue line), one standard deviation away form the average shuffled sets (orange line).
Figure 3 Average Er scores for the breast shuffled sets depending on the number of shuffling. The average Er score and a standard deviation above and below are represented for 10, 30, 50, 80, 100, 150 and 200 shuffling of the dataset. We can see that the average Er score converges rapidly after 50 shuffling of the data set.
Table 1 Average slopes and intercepts for the different tissue type. This table displays the average slope and intercept of the regression of the 80th percentile of the bins by the inverse of the average expression per bin. The bin size was 200 and the minimum intensity cutoff was 100.
Average Slope Stdev Average Intercept Stdev
lung normal 96 29 1.42 0.15
Breast Normal 139 33 1.24 0.06
Ovarian Normal 154 45 1.48 0.12
Prostate Normal 61 26 1.61 0.26
Oral Normal 89 12 1.55 0.22
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| 15569388 | PMC538261 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Nov 29; 5:185 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-185 | oa_comm |
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BMC Evol BiolBMC Evolutionary Biology1471-2148BioMed Central London 1471-2148-4-491555507010.1186/1471-2148-4-49Research ArticleDiversity and specificity in the interaction between Caenorhabditis elegans and the pathogen Serratia marcescens Schulenburg Hinrich [email protected] Jonathan J [email protected] Department of Evolutionary Biology, Institute for Animal Evolution and Ecology, Westphalian Wilhelms-University, Hüfferstr. 1, 48149 Münster, Germany2 Centre d'Immunologie de Marseille Luminy, INSERM/CNRS/Université de la Méditerranée, Case 906, 13288 Marseille Cedex 9, France2004 22 11 2004 4 49 49 17 9 2004 22 11 2004 Copyright © 2004 Schulenburg and Ewbank; licensee BioMed Central Ltd.2004Schulenburg and Ewbank; 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
Co-evolutionary arms races between parasites and hosts are considered to be of immense importance in the evolution of living organisms, potentially leading to highly dynamic life-history changes. The outcome of such arms races is in many cases thought to be determined by frequency dependent selection, which relies on genetic variation in host susceptibility and parasite virulence, and also genotype-specific interactions between host and parasite. Empirical evidence for these two prerequisites is scarce, however, especially for invertebrate hosts. We addressed this topic by analysing the interaction between natural isolates of the soil nematode Caenorhabditis elegans and the pathogenic soil bacterium Serratia marcescens.
Results
Our analysis reveals the presence of i) significant variation in host susceptibility, ii) significant variation in pathogen virulence, and iii) significant strain- and genotype-specific interactions between the two species.
Conclusions
The results obtained support the previous notion that highly specific interactions between parasites and animal hosts are generally widespread. At least for C. elegans, the high specificity is observed among isolates from the same population, such that it may provide a basis for and/or represent the outcome of co-evolutionary adaptations under natural conditions. Since both C. elegans and S. marcescens permit comprehensive molecular analyses, these two species provide a promising model system for inference of the molecular basis of such highly specific interactions, which are as yet unexplored in invertebrate hosts.
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Background
By definition, parasites have a negative effect on host fitness. Since parasites usually show a shorter generation time than their hosts, they are also able to adapt rapidly to newly arising host genotypes. Both characteristics together select for hosts with efficient counter-adaptations. Subsequently, parasites are favoured if they can circumvent these host countermeasures. Such interactions may result in a co-evolutionary arms race, consisting of repeated cycles of the emergence of new parasite offences and host countermeasures. Hence, parasite-host interactions can lead to extremely rapid evolutionary change [1,2]. As such, they are thought to be responsible for much of the complexity found in the immune system of animals [3]. They are also likely to account for the evolution of diverse genetic mechanisms, which aid in generating fast changes, including sexual reproduction and recombination [4,5]. They may also affect the evolution of other life-history traits, such as reproductive rate, longevity, or competitive ability, which compete for available resources with defence and virulence traits in host and parasite, respectively [6,7].
Co-evolutionary arms races between hosts and parasites (meaning here eukaryotic organisms, bacterial pathogens, and viruses that gain a fitness advantage from infecting and harming a host) are in many cases assumed to be determined by negative frequency dependent selection. In particular, rare parasite and host genotypes should be at an advantage because commonness facilitates evolution of host or parasite counter-adaptations, respectively [4,5]. Such frequency dependent dynamics rely on two important conditions: i) natural genetic variation in both host resistance and parasite virulence, and ii) natural genotype-specific interactions between hosts and parasites [1,4]. Empirical evidence for the presence of both of these prerequisites is still rare, especially for invertebrate hosts [8,9]. They include various associations between snails and trematodes (e.g., Potamopyrgus antipodarum versus Microphallus [10] or Bulinus globosus versus Schistosoma [11]), the association between the waterflea Daphnia magna and its microparasite Pasteuria ramosa [8], between Drosophila melanogaster and its parasitoid Asobara tabida [12], between the bumble bee Bombus terrestris and the trypanosome Crithidia bombi [13], or between the copepod Macrocyclops albidus and the cestode Schistocephalus solidus [14]. Clearly, more data is needed to determine the importance of parasite-mediated co-evolutionary arms races in nature.
In this study, we evaluated differences in host resistance and parasite virulence, both defined in a broad sense and reciprocally as the effect of an infection on host condition (i.e. alive, morbid, or dead). In particular, we tested the presence of genetic variation and also the presence of strain- and genotype-specific interactions during the infection of the nematode Caenorhabditis elegans (Nematoda: Rhabditidae) by the Gram-negative bacterium Serratia marcescens (Enterobacteriaceae). C. elegans has recently been established as a model to study parasite-host interactions and in particular the genetics of host defence [15-17]. It is a soil inhabitant found in almost all temperate regions of the world. It seems to be common in decomposing material, where it feeds on diverse microorganisms [18,19]. About 50 natural strains are currently available. These strains are genetically very diverse, even when isolated from populations at a single location [18,19]. They also differ in many life-history traits, including their response towards the potential parasite Bacillus thuringiensis [20]. The parasites that C. elegans encounters under natural conditions have not yet been unambiguously identified. The ubiquitous soil-dwelling bacteria Pseudomonas aeruginosa, B. thuringiensis and S. marcescens are all likely candidates [17]. For our study, we chose S. marcescens, recently adopted as a model to study the genetic basis of virulence [15,21], as a pathogen as it is able to produce a persistent infection and it is likely to benefit from the infection [22], thus behaving as a true parasite of the nematode.
Results
In our main experiment, we compared the consequences of infection of eight different natural C. elegans strains with 5 different S. marcescens strains plus one control (heat-killed bacteria of S. marcescens Db11, a strain for which the genome sequence is now complete). The C. elegans strains were isolated from Münster, in Northwest Germany, and belong to four different microsatellite genotypes [18]. The S. marcescens strains originate from different locations around the world. The interaction between the two species was examined with the help of a survival assay, in which the survival of individual worms was monitored in the presence of a defined concentration of bacteria [20]. The survival assay was performed in 96-well plates on five occasions (runs). During each run, all possible bacterial and worm strain combinations were assayed in parallel, resulting in a total of 16 data points per factor combination per run and 80 data points per factor combination in total.
80 out of a total 3840 cases (2.08%) had to be excluded because of errors during automated worm-transfer (either no worm or more than one worm per well), resulting in between 75 and 80 usable data points per combination of worm and bacterial strains. The number of valid cases did not differ significantly among these factor combinations (likelihood ratio test [LRT], χ2 = 0.904, d.f. = 35, P > 0.999). In the control (worms with dead S. marcescens Db11), only 12 out of 625 were not found in the category "alive" (1.92%). Of these, 9 were morbid and 3 were dead. The recorded number of live worms per strain did not differ significantly from 100% (LRT, χ2 = 0.455, d.f. = 7, P > 0.999). It also did not differ significantly among the worm strains (LRT, χ2 = 0.416, d.f. = 7, P > 0.999). These results show that the experimental set-up itself does not cause significant levels of dead or morbid worms and that it does not have a different effect on different worm strains.
The different C. elegans strains show substantial differences as to their ability to survive in the presence of pathogenic S. marcescens (Fig. 1). In general, the strains MY6 and MY18 were most resistant, whereas MY14 and MY15 were most susceptible. Moreover, the strains with identical microsatellite genotypes generally produce similar but not identical levels of resistance. This suggests that these strains bear additional genetic differences, which were not resolved by microsatellite genotyping. At the same time, the different S. marcescens strains differ considerably in their effect on C. elegans (Fig. 1). Here, strain Sm2170 was most virulent, whereas strains Sma3 and Sma13 generally produced the fewest cases of mortality and morbidity. Since S. marcescens strains were grown under identical conditions and since some of them are already known to differ in phenotype (e.g. red pigmentation), the observed differences are most likely determined genetically. Most interestingly, the interaction between specific worm and bacterial strains seems to differ across the table. For instance, C. elegans strain MY10 is more susceptible to S. marcescens strain Sma13 than to ATCC274, whereas the opposite is true for C. elegans strain MY20 (Fig. 1). Similarly, host strain MY15 is more susceptible to pathogen strain ATCC274 than to strain Db11, whereas the pattern is reversed for almost all other host strains (Fig. 1).
Figure 1 Treatment response for the different bacterial and worm strain combinations of the main experiment. The response is expressed as host condition (values for the whole experiment), such that the black area refers to the proportion of dead worms, grey to the proportion of morbid, and white to the proportion of live worms. For C. elegans, both strain (bottom line) and genotype (top line) designations are given. For S. marcescens, only strain names are listed.
In general consistency with these observations, ordinal logistic regression (OLR) analysis indicates a significant effect of the factors bacterial strain, worm strain or genotype, the interaction between the two, and also experimental run on the treatment response (Table 1). The two respective models employed are significantly better than models without any predictors (model including worm strain as factor: LRT, χ2 = 1285.63, d.f. = 199, P < 0.0001; model including worm microsatellite genotype as factor: LRT, χ2 = 854.55, d.f. = 99, P < 0.0001). However, they are both significantly worse than the respective saturated models (model including worm strain: LRT, χ2 = 491.32, d.f. = 199, P < 0.0001; model including worm genotype: LRT, χ2 = 442.94, d.f. = 99, P < 0.0001). The latter test examines whether the model employed considers a sufficient number of factors or factor combinations to explain the variation found in the data. The results suggest that the model is not sufficiently complex. We decided against employing more complex models (e.g. consideration of host genotype nested in host strain in a single model), because the response variable is ordinal with only three categories (alive, morbid, dead), such that a larger number of predictor variables in the model would most likely lead to highly increased random error in the regression analysis. Thus, as an alternative, we analysed the data using association tests.
Table 1 Ordinal logistic regression analysis of the importance of different factors in the main experiment.
Source χ2 d.f. P
Consideration of worm strains as a factor
Bacteria 272.78 4 < 0.0001
Worm 188.11 7 < 0.0001
Bacteria*Worm 127.15 28 < 0.0001
Run [Bacteria, Worm] 835.27 160 < 0.0001
Consideration of worm genotypes as a factor
Bacteria 193.77 4 < 0.0001
Worm 169.87 3 < 0.0001
Bacteria*Worm 34.21 12 0.0006
Run [Bacteria, Worm] 477.14 80 < 0.0001
Ordinal logistic regression was based on a model, which contained bacterial strain, worm strain (alternatively worm genotype), the interaction between the two and run nested within both bacterial strain and worm strain/genotype as factors. The importance of different factors was assessed with the likelihood ratio test. Significant probabilities after Dunn-Sidák correction are given in bold.
Two-way associations were analysed with the LRT. The results show a significant effect of either of the different factors on worm condition (Table 2). The relevance of these associations was further examined by taking into account a second predictor variable using the Cochran-Mantel-Haenszel (CMH) test of conditional independence. All previously identified associations remained significant, irrespective of the second predictor variable considered (Table 2). The only exception refers to the case where the factor worm strain was corrected by the factor worm genotype, suggesting that the observed variation among C. elegans strains is due to differences in genotypic composition. The remaining results indicate that the significant effect from one of the factors on the treatment response is independent of the significant effect from one of the other factors. This finding is consistent with the presence of an interaction effect from the factors bacterial strain and nematode strain/genotype, as above suggested by OLR.
Table 2 Association analysis of the impact of different factors on worm condition in the main experiment .
Factor Test χ2 d.f. P
Single factor effects
Bacteria LRT 291.05 8 < 0.0001
Worm strain LRT 154.84 14 < 0.0001
Worm genotype LRT 136.29 6 < 0.0001
Run LRT 186.33 8 < 0.0001
Factor effects in consideration of one of the others (in brackets)
Bacteria (Worm strain) CMH 196.74 4 < 0.0001
Bacteria (Worm genotype) CMH 196.44 4 < 0.0001
Bacteria (Run) CMH 192.08 4 < 0.0001
Worm strain (Bacteria) CMH 146.21 7 < 0.0001
Worm strain (Run) CMH 139.83 7 < 0.0001
Worm strain (Worm genotype) CMH 10.32 7 0.1713
Worm genotype (Bacteria) CMH 135.50 3 < 0.0001
Worm genotype (Run) CMH 129.34 3 < 0.0001
Run (Bacteria) CMH 52.56 4 < 0.0001
Run (Worm strain) CMH 51.09 4 < 0.0001
Run (Worm genotype) CMH 50.89 4 < 0.0001
The associations were assessed with the likelihood ratio test (LRT) or the Cochran-Mantel-Haenszel (CMH) test. Bold probabilities are significant after Dunn-Sidák correction.
In the second experiment, we specifically addressed the presence of an interaction between two bacterial strains (Db11, ATCC274) and four host strains (MY8, MY10, MY14, MY15), the latter belonging to two different host genotypes. For this experiment, all factor combinations were included in each 96-well plate and in one experimental run. Only 7 out of 384 cases had to be excluded for the reasons given above (1.82%), resulting in 46 to 48 data points per factor combination. Again, the number of valid cases did not differ among factor combinations (LRT, χ2 = 0.093, d.f. = 3, P = 0.996). In the control treatment of this experiment, all animals were alive.
The second experiment confirmed the presence of variation in host resistance and pathogen virulence, although the overall level of virulence was lower than in the main experiment (Fig. 2). Subsequent OLR revealed a significant effect from the factor worm strain or worm genotype, and also the interaction between the bacterial strain and either worm strain or genotype. The effect of bacterial strains was significant before Dunn-Sidák adjustment of significance levels (due to multiple testing), but insignificant afterwards (Table 3). For these OLR analyses, the models employed were significantly better than a model without any predictors (model including worm strain as factor: LRT, χ2 = 62.29, d.f. = 7, P < 0.0001; model including worm microsatellite genotype as factor: LRT, χ2 = 59.12, d.f. = 3, P < 0.0001). Moreover, they were not significantly worse than the respective saturated models (model including worm strain: LRT, χ2 = 4.62, d.f. = 7, P = 0.7060; model including worm genotype: LRT, χ2 = 1.32, d.f. = 3, P = 0.7238), suggesting that they contained sufficient details to explain the observed variation.
Figure 2 Treatment response for the different bacterial and worm strain combinations of the second experiment. The black area denotes the proportion of dead worms, grey the proportion of morbid, and white the proportion of live worms.
Table 3 Ordinal logistic regression analysis of the importance of different factors in the second experiment.
Source χ2 d.f. P
Consideration of worm strains as a factor
Bacteria 4.89 3 0.0270
Worm 33.20 1 < 0.0001
Bacteria*Worm 26.89 3 < 0.0001
Consideration of worm genotypes as a factor
Bacteria 4.80 1 0.0284
Worm 31.97 1 < 0.0001
Bacteria*Worm 24.50 1 < 0.0001
Ordinal logistic regression was based on a model, which contained bacterial strain, worm strain (alternatively worm genotype), and the interaction between the two as factors. The importance of different factors was assessed with the likelihood ratio test. Bold probabilities indicate significance after Dunn-Sidák correction.
Subsequent performance of association tests generally corroborated the OLR analyses: The different predictor variables had a significant effect on the treatment response (LRT analysis in Table 4). With two exceptions, this was still true after correcting for one of the other predictors (CMH tests in Table 4). One of the exceptions refers to the factor bacterial strain, which no longer produced a significant effect if corrected by any of the other factors. This is in agreement with results from the OLR analysis. The other case shows that the factor worm strain becomes insignificant if corrected by worm genotype, which confirms the findings for the main experiment (see above). Consequently, the results clearly demonstrate that there are significant differences among host genotypes and, most importantly, that there are significant strain- or genotype-specific interactions between the two species.
Table 4 Association analysis of the impact of different factors on worm condition in the second experiment.
Factor Test χ2 d.f. P
Single factor effects
Bacteria LRT 10.31 2 0.0058
Worm strain LRT 32.80 6 < 0.0001
Worm genotype LRT 30.46 2 < 0.0001
Factor effects in consideration of one of the others (in brackets)
Bacteria (Worm strain) CMH 4.34 1 0.0372
Bacteria (Worm genotype) CMH 4.41 1 0.0358
Worm strain (Bacteria) CMH 29.21 3 < 0.0001
Worm strain (Worm genotype) CMH 0.93 3 0.8193
Worm genotype (Bacteria) CMH 28.37 1 < 0.0001
The associations were assessed with the likelihood ratio test (LRT) or the Cochran-Mantel-Haenszel (CMH) test. Bold probabilities are significant after Dunn-Sidák correction.
Discussion
We here provide evidence for the presence of i) genetic differences in resistance among natural C. elegans strains, ii) genetic differences in virulence among natural S. marcescens strains, and also iii) strain- or genotype-specific interactions between the two. The first of these points is generally consistent with our previous results on the presence of strain-specific differences in resistance of C. elegans towards Bacillus thuringiensis [20]. However, in the previous study, we compared C. elegans strains from different locations across the world, whereas in the present study all strains derive from the same place (the town of Münster in Northwest Germany) [18]. Previous microsatellite genotyping demonstrated that these strains are genetically extremely diverse [18]. Our present results highlight the fact that genetic diversity translates into phenotypic differences in resistance. Importantly, as these differences are present in one population, they could provide the basis for and/or represent the outcome of evolution under natural conditions. These conclusions are restricted to the host C. elegans, because the S. marcescens strains considered did not come from the same location.
The observed strain- and genotype-specific interactions represent an important precondition for negative frequency dependent selection. As such, they may contribute to the emergence of co-evolutionary arms races [1,4]. The relevance of our results for the association between C. elegans and S. marcescens in the wild must currently be considered unclear. To date, it is unknown whether the two species indeed co-exist under natural conditions, even though it is strongly suggested by the fact that both – especially S. marcescens – are common soil inhabitants [19,23]. If they do co-exist, they clearly show the potential to engage in co-evolutionary interactions. In fact, in this case, the observed specificity may represent a signature of past counter-adaptations. Our results would then also suggest that such highly specific interactions are widespread among invertebrate hosts; they are currently only known in a few arthropods and molluscs (see the background section for examples).
The situation is clearly different if the two species do not share the same natural habitat. In this case, the observed specificity must be the result of independent adaptations of parasite and host strains to other environmental conditions. Pleiotropy of such adaptations should then have produced the specific C. elegans-S. marcescens interactions as a side effect. For example, the C. elegans strains may have adapted differently towards environmental toxins. If the underlying detoxification mechanisms are also employed in the defence against pathogens, then this may result in the observed differences in resistance. Such mechanisms could indeed be of relevance in the interaction with S. marcescens, for which at least one toxin (hemolysin ShlA) was previously suggested to contribute to pathogenesis in C. elegans [22]. Moreover, such mechanisms may also account for highly specific interactions, even if the two species did co-exist in the wild, underlining the idea that past co-evolutionary events cannot be reliably deduced from the observation of specific interactions without further information (e.g. historical records of co-existence in nature or congruent phylogenies of host and parasite strains).
Whatever its origin, the finding of high specificity in the interaction has further implications. The molecular basis of highly specific resistance is currently unexplored in invertebrate hosts. It could be due to the presence of different alleles of a certain cell surface protein targeted by specific parasite effector molecules. Such cell surface proteins have been suggested to be important for the interaction between C. elegans and Bt toxin, the main virulence factor of B. thuringiensis [24,25]. As a non-exclusive alternative, specificity may be a consequence of the inducible immune system as recently suggested for the specific interactions between the copepod M. albidus and the cestode S. solidus [14] or the waterflea D. magna and its microparasite P. ramosa [26]. The presence of an inducible system was recently demonstrated for C. elegans in response to S. marcescens [27], the fungus Drechmeria coniospora [28], and also the Bt toxin of B. thuringiensis [29]. Considering that diverse molecular tools are available for C. elegans, this nematode may in the future provide a valuable model system to dissect the molecular basis of specificity in invertebrate-pathogen interactions.
Similarly, the observed highly specific virulence was previously unknown for S. marcescens. This bacterium is considered to be an opportunistic pathogen with a broad host range [23]. Hence, it should mainly possess unspecific virulence factors, which are effective against a large number of different taxa. Interestingly, some of the genes previously identified to contribute to pathogenesis in C. elegans also mediate virulence in other hosts (Drosophila melanogaster; mice), whereas other genes do not [22]. This already indicates some degree of specificity. Our results may now provide the basis for a molecular genetic characterisation of virulence factors that vary in their specific effects against different strains of a single host species. This information may potentially be of great value for understanding pathogenicity of S. marcescens in humans, where this bacterium has become a growing health problem, primarily in nosocomial infections [30].
Conclusions
Based on the analysis of natural isolates of the nematode C. elegans and its potential microparasite S. marcescens, our study provides evidence for i) genetic variation in host susceptibility and parasite virulence, and also ii) strain- and genotype-specific interactions between the two. These two factors represent an important precondition for frequency dependent selection and as such for the emergence of co-evolutionary arms races. Such highly specific interactions were previously unknown for C. elegans or S. marcescens. Moreover, they have not as yet been reported for invertebrates other than molluscs and arthropods. At least for C. elegans, the observed variation was found among strains from the same population, such that it could indeed be of relevance for evolutionary changes under natural conditions. Taken together, these findings suggest that there is widespread potential for co-evolutionary interactions in animal hosts. Both C. elegans and S. marcescens represent important model organisms in biological research for which a diversity of manipulative techniques is available. Therefore, the association between these two species may in the future provide a valuable tool for the comprehensive analysis of such highly specific interactions.
Methods
We compared eight different natural C. elegans strains with 5 different natural S. marcescens strains plus one control. The C. elegans strains were isolated by HS and co-workers in 2002 from Münster, North-West Germany [18]. They are available from the Caenorhabditis Genetics Centre under strain numbers MY6, MY8, MY10, MY14, MY15, MY17, MY18, MY20 [31]. Some of these strains bear different genotypes: strains MY6 and MY18 have genotype EU4; MY8, MY10 and MY20 genotype EU3; MY14 and MY15 genotype EU2; and MY17 genotype EU5 [18]. Maintenance of worms, including feeding, worm transfer, synchronisation of cultures and cryo-preservation followed standard procedures [32]. These C. elegans strains had all been cryo-preserved within 5 generations after isolation [18]. They were then thawed only few generations before the start of the experiments to ascertain that they were subjected to selection towards laboratory conditions for the shortest possible time. One generation before the start of the experiment, the worm cultures were always synchronised using NaOH/NaOCl-treatment [32].
The S. marcescens strain Db11 was originally isolated by H. Boman [33]; the strain Sm2170 was obtained from T. Watanabe [34]; and strains ATCC274 [35], Sma3, Sma13 from G. Salmond (Cambridge, UK). These strains are known to differ in pigmentation, which is thought to correlate with virulence: Db11, Sma3, and Sma13 have no pigments, whereas ATCC274 and Sm2170 are pigmented [22]. They were also already shown to differ in virulence towards the main C. elegans strain N2 when tested on solid agar, whereby virulence varied in the following order (from high to low): Sm2170 > ATCC274 > Db11 > Sma3 = Sma13 [22]. Note, however, that the time-course of infection in liquid medium is much more rapid than on solid medium and that the underlying mechanisms of pathogenesis in the two cases are not identical, at least for Db11 (JJE and E. Pradel, unpublished observations). One day before the start of the experiment, the bacteria were grown in Luria Broth (LB) for about 18 h at 37°C. Their OD was then adjusted to a value of 0.1 by addition of LB. An OD 0.1 corresponds to a cell count of approximately 2 × 108 per ml. As a control, we used heat-killed bacteria of strain Db11 (incubation at 70°C for 15 min). These dead bacteria were previously shown to have lost their deleterious effects on C. elegans [22].
The interaction between C. elegans and S. marcescens was assessed using a simple survival assay [20]. For this, individual worms were confronted with a defined concentration of the pathogens in NGM solution and their survival checked after 24 h. The experiment was performed in 96-well plates. Each plate always contained the five different S. marcescens strains and the control, randomised across the plate. Only one C. elegans strain was examined per plate. Eight plates were analysed in parallel, each with one of the eight different C. elegans strains. Five runs of this set-up were performed, whereby the order in which strains were analysed during each run was randomised. This set-up resulted in a maximum of 80 data points per bacterial strain – worm strain combination.
For a specific run, 50 μl NGM solution (without any bacteria) were first added to each well of the 96-well plates using a multi-channel pipette. Thereafter, individual worms were transferred to each well using the COPAS automated worm-sorter (Union Biometrica Inc.). For worm-sorting, we used synchronised L4 stage worms. Success of worm transfer was monitored. If no worm or more than one worm was transferred to a particular well, then it was excluded from further analysis. After worm transfer was completed, 50 μl NGM solution with pathogens were added to each well with a multi-channel pipette. These 50 μl contained 45 μl NGM solution and 5 μl of bacteria in LB with an OD of 0.1, resulting in a total of approximately 1 × 107 bacterial cells per well at the start of the survival assay. This concentration was found in a pilot study to permit detection of differences in survival among worm strains after 24 h. After this time period, the condition of the worms was recorded using the following three categories: i) alive (clearly visible body movements; in some cases only after being touched with a small pipette tip), ii) morbid (touching them with a small pipette tip resulted in retarded, very slow movements), iii) dead (no movements, even after being touched with a small pipette tip).
After completion of the experiment, we re-assessed the interaction between two bacterial strains (Db11 and ATCC274) and four worm strains (MY8, MY10, MY14, MY15; the first two and the last two have identical microsatellite genotypes). The general set-up was the same as above. In this case, a total of four 96-well plates were studied at the same time. In contrast to the above experiment, each 96-well plate contained both the different worm and bacterial strains, randomised across the plate. This set-up results in a maximum of 48 data points per factor combination. In addition to the above, we included two 96-well plates as a control. These contained heat-killed Db11 bacteria and the four worm strains randomised across plates (48 data points per worm strain). After exposure to the pathogens, we confirmed that worms were indeed infected with the bacteria by analysis of some of the animals (N = 40) using differential interference contrast microscopy and a fluorescent microscope (DMIRBE, Leica).
The statistical analysis was performed with the program JMP version 5.0 (SAS Institute Inc.). Based on the hierarchical order of the categorical response variable (0, dead; 1, morbid; 2, alive), we used an ordinal logistic regression analysis (OLR) [36,37]. For the main experiment, we included bacterial strain, worm strain, the interaction between the two, and also run nested within both bacterial and worm strain as factors in the model. The whole analysis was repeated using worm microsatellite genotypes instead of worm strain in the model. For the second experiment, which was performed on a single occasion instead of separate runs, a full factorial model was employed, including bacterial strain, worm strain (alternatively, microsatellite genotype), and the interaction between the two as factors.
For the main experiment, the lack of fit test was significant, indicating that the chosen model may not be sufficiently complex to explain the variation in the data (see results section). Therefore, we additionally employed association tests based on the inferred frequency counts for the different factor combinations. We specifically assessed the association between the treatment response (condition of the worms) and either of the following factors: bacterial strain, worm strain, worm microsatellite genotype, and run. The significance of the association was inferred using the likelihood ratio test (LRT) [36,38]. We further used the Cochran-Mantel-Haenszel test (CMH) to assess the conditional independence between the treatment response and one of the above factors in consideration of a second factor from the above list [36]. The response variable was treated as ordinal, while the predictors were treated as nominal (ordinal-nominal conditional association test) [36]. Multiple testing was accounted for by adjusting the significance level using the Dunn-Sidák procedure [38].
List of abbreviations used
CMH, Cochran-Mantel-Haenszel test; LB, Luria broth; LRT, likelihood ratio test; NGM, nematode growth medium; OD, optical density; OLR, ordinal logistic regression.
Authors' contributions
HS designed the study, carried out the experiments, analysed the data and wrote the first draft of the manuscript. JJE participated in the design of the study, the interpretation of the data and revised the manuscript.
Acknowledgements
We would like to thank Aurélie Blanc for her help with the experiments, and Nils Anthes, Carole Couillault, Susanne Fetzner, Markus Haber, Martin Hasshoff, Léo Kurz, Nico Michiels, Anne Millet, Elizabeth Pradel, Nathalie Pujol, Manuela Schüngel, Claus-Peter Stelzer for advice on this project and/or comments on the manuscript. Worm sorting was carried out using the facilities of the C. elegans functional genomics Platform of the Marseille-Nice Génopole. HS was supported by a grant from the German Science Foundation (grant SCHU 1415/1-1), JJE by institutional grants from the CNRS and INSERM.
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| 15555070 | PMC538262 | CC BY | 2021-01-04 16:29:02 | no | BMC Evol Biol. 2004 Nov 22; 4:49 | utf-8 | BMC Evol Biol | 2,004 | 10.1186/1471-2148-4-49 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-431556373610.1186/1471-2180-4-43Methodology Article16S rRNA gene based analysis of Enterobacter sakazakii strains from different sources and development of a PCR assay for identification Lehner Angelika [email protected] Taurai [email protected] Roger [email protected] Institute for Food Safety and Hygiene, Vetsuisse Faculty, University of Zurich, CH-8057 Zurich, Switzerland2004 25 11 2004 4 43 43 20 9 2004 25 11 2004 Copyright © 2004 Lehner 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
E. sakazakii is considered to be an opportunistic pathogen, implicated in food borne diseases causing meningitis or enteritis especially in neonates and infants. Cultural standard identification procedures for E. sakazakii include the observation of yellow pigmentation of colonies and a positive glucosidase activity. Up to now, only one PCR system based on a single available 16S rRNA gene sequence has been published for E. sakazakii identification. However, in our hands a preliminary evaluation of this system to a number of target and non-target strains showed significant specificity problems of this system. In this study full-length 16S rRNA genes of thirteen E. sakazakii strains from food, environment and human origin as well as the type strain ATCC 51329 were sequenced. Based on this sequence data a new specific PCR system for E. sakazakii was developed and evaluated.
Results
By phylogenetic analysis of the new full-length 16S rRNA gene sequence data obtained we could show the presence of a second phylogenetic distinct lineage within the E. sakazakii species. The newly developed 16S rRNA gene targeting PCR system allows identification of E. sakazakii strains from both lineages. The assay's ability to correctly identify different E. sakazakii isolates as well as to differentiate E. sakazakii from other closely related Enterobacteriaceae species and other microorganisms was shown on 75 target and non-target strains.
Conclusion
By this study we are presenting a specific and reliable PCR identification system, which is able to correctly identify E. sakazakii isolates from both phylogenetic distinct lines within the E. sakazakii species. The impact of this second newly described phylogenetic line within the E. sakazakii species in view of clinical and food safety aspects need further investigation.
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Background
E. sakazakii is considered to be an opportunistic pathogen, implicated in food borne diseases causing meningitis or enteritis especially in neonates and infants [1,2]. Mortality rates of 20 – 50% are reported for patients who contract the disease [3]. The survivors often suffer from severe neurological disorders. A recent study on the occurrence of E. sakazakii in production environments from food (milk powder, chocolate, cereal, potato, pasta) factories and households isolated the organism with varying frequency from nearly all environments examined, strongly indicating, that this is a widespread organism [4]. Up to now, E. sakazakii has been isolated from a wide range of foods, including cheese, meat, vegetables, grains, herbs and spices and UHT milk, but most of the literature concentrates on the presence the organism in dried infant formula milk [5-8]. In 1990, Clark et al. [9] were the first to prove a clear epidemiologic correlation between E. sakazakii isolated from patients and dried infant formula involved in 2 hospital outbreaks using a combination of typing methods. Although the levels of contamination seemed low, the authors could show, that with an initial concentration of 1 CFU/ml, reconstituted formula stored at room temperature would take approx. 10 hrs to reach 107 cells per 100 ml and even sooner in formula held on 35 – 37°C. Since then, many case reports have described this epidemiologic correlation [10,11].
Cultural standard identification procedures for E. sakazakii include the observation of yellow pigmentation of colonies and the testing for glucosidase activity. Based on this latter biochemical activity commercially selective chromogen media have recently been developed such as the Druggan-Forsythe-Iversen agar, DFI (commercially available under: Chromogenic Enterobacter sakazakii agar, Oxoid CM1055, Oxoid, UK) and the Enterobacter Sakazakii Isolation Agar, ESIA (AES Laboratoire, France).
Besides, molecular assays have often proven to be useful as they offer a alternative means to rapidly and specifically identify organisms from a wide variety of sources. Although PCR detection methods are now widely used in identification of microorganisms, only one PCR system, based on a single available full-length 16S rRNA gene sequence of type strain ATCC 29544 [GenBank: AB004746], was published up to now for E. sakazakii [12]. However, in our hands a preliminary evaluation of this system on a number of target and non-target strains showed significant specificity problems inherent in this assay. Therefore, the aim of this study was to provide more full-length 16S rRNA sequence data from E. sakazakii strains of different origin. Finally, based on this new sequence data a specific PCR system was developed for E. sakazakii detection.
Results and discussion
As a starting point, the full-length 16S rRNA sequences of thirteen E. sakazakii strains from various sources (five fruit powder isolates, three human isolates, two production environment isolates, one milk isolate, one baby food isolate, one milk powder isolate) and of E. sakazakii type strain ATCC 51329 were determined. This sequence data has been deposited in the GenBank [AY752936 – AY752943, AY803186 – AY803192]. Thereafter, a sequence comparison was performed between the 16S rRNA genes of the fourteen E. sakazakii strains and the E. sakazakii type strain ATCC 29544 by calculation of a distance matrix using the ARB programme. Thirteen E. sakazakii strains used in the study exhibited 99.4 to 100% sequence similarity to the E. sakazakii type strain ATCC 29544 [GenBank: AB004746]. Meanwhile similarity between strain ATCC 51329 and the strain ATCC 29544 was significantly lower (97.9%).
Phylogenetic affiliation of the newly obtained sequences to tree_jul04_1450 comprising >28.000 almost full-length (>1449 nucleotides) 16S rRNA gene sequences revealed the presence of two phylogenetically distinct lineages within the E. sakazakii species. In figure 1 a subtree based on the data mentioned above is shown. From this analyses it can be observed, that thirteen of the fourteen newly sequenced strains cluster together with the E. sakazakii type strain ATCC 29544, whereas the E. sakazakii strain ATCC 51329 forms a second branch within this group. The subtree was calculated using the TREEPUZZLE tool within the ARB package exhibiting a "consensus tree" from different calculation methods. The value on each branch is the percent occurrence of the branching order in 500 bootstrapped trees. The 92% bootstrap value for the E. sakazakii ATCC 51329 branch strongly supports the theory about the second lineage.
Further qualitative inspection of the sequences revealed that, besides of the presence of a number of single base substitutions, at least four "hot spots" of polymorphisms can be observed along the E. sakazakii 16S rRNA gene (E. coli positions 187 to 193; 455 to 477, 590 to 600 and 1132 to 1141). Within these regions substitutions ranging from two to seven bases are present in all strains investigated and are most pronounced in E. sakazakkii strain ATCC 51329, thus providing the bases for the formation of the second phylogenetic branch. In table 1, the sequence polymorphisms in the above mentioned regions are shown for all E. sakazakii strains investigated in the study in relation to the E. sakazakii typestrain ATCC 29544, E. cloacae typestrain ATCC13047 and E. coli type strain ATCC 11775. Recently, a number of E. sakazakii partial sequences were deposited into the public database [13]. Addition of these partial sequences (mostly the first 500 bp of the 16S RNA gene) to the phylogenetic tree further confirms the presence of the two lineages within the E. sakazakii species (data not shown). However, for a reliable pylogenetic analyses determination of at least 1000 nucleotides is recommended [14]. The impact of this second phylogenetic line within the E. sakazakii species in view of clinical and food safety aspects needs further evaluation.
In a second step, based on our new sequence information, we designed a specific primer pair for the amplification of the 16S rRNA gene of strains within the two phylogenetic lineages of E. sakazakii. They were first evaluated using PROBE-MATCH tool of the ARB software. Additionally, BLAST searches were performed against the non-redundant database (nr) of EMBL/GENBANK. Both primers seemed to be specific. Amplification conditions in the assay were optimized by taking an annealing temperature of 60°C for 1 min.
We then sought to evaluate the utility of the PCR assay next. In particular the assay's ability to correctly identify different E. sakazakii strains as well as to differentiate E. sakazakii from other closely related Enterobacteriacae was assessed. This was done by applying the assay to purified genomic DNA template from isolates of 45 known E. sakazakii strains form different origin and 28 non E. sakazakii strains. A gel-based example of the screen is shown in figure 2, where E. sakazakii isolates and non-target microorganisms were analyzed. The rest of the results from the screen are summarized in table 2. The new PCR assay was able to correctly confirm all the E. sakazakii isolates tested. There were no amplification products observed when the non E. sakazakii strains controls were similarly analyzed.
For comparison the various strains were also similarly analyzed using the PCR system described by Keyser et al. [12]. The results are summarized in table 2 and confirm our preliminary evaluation data where we found significant specificity problems with this system. A gel-based example of the screen is shown in figure 2, where E. sakazakii isolates and non-target microorganisms were analyzed. First the system was not able to detect all the E. sakazakii strains. By using the PROBE_MATCH tool included in the ARB software it can be retrieved, that one of the primers (Esak3) used in the Keyser system exhibits four mismatches at the 3 prime end to the 16S rRNA gene sequence of E. sakazakii strain ATCC 51329, thus providing a possible explanation for the negative amplification result with this strain. Moreover, this system also gives positive results with some non-target organisms (e.g. E. cloacae, S. liquefaciens, S. fucaria, S. Enteritidis).
To determine the detection limit of the newly developed assay, PCR was performed on decreasing amounts of purified DNA template from representatives of the two E. sakazakii lineages (strain ATCC 51329 and strain ATCC 29544). In both strains a detection limit of 10 pg was determined (figure 3). Furthermore the influence of non-specific DNA background on the assay's detection limit and efficiency was also investigated. The target DNA template concentration was held constant at 10 pg while increasing amount of non-target DNA, purified templates from B. cereus ATCC 10876, L. acidophilus ATCC 13651, S. aureus ATCC 25923 and E. faecium DSM 2918 were used. There were no significant influences on assay's performance observed in the presence of up to 200 ng non-target DNA in the reaction mixture (data not shown).
Conclusions
By phylogenetic analysis of the new provided full-length 16S rRNA sequence data we could show the presence of a second phylogenetic distinct lineage within the E. sakazakii species. Based on this new sequence data we have developed a 16S rRNA targeting PCR assay, which allows identification of E. sakazakii strains from both lineages. The assay's ability to correctly identify different E. sakazakii isolates as well as to differentiate E. sakazakii from other closely related Enterobacteriaceae species and other non-target microorganisms was shown. This PCR system provides a valuable tool for rapid identification of this food borne pathogen.
Methods
Bacterial strains
Overall, 47 E. sakazakii strains, with isolates from human, food and environmental origin, as well as 28 non E. sakazakii strains from selected species were included in this study (Table 2). In a first step, the 16S rRNA genes of fourteen E. sakazakii strains (E. sakazakii ATCC 51329, E. sakazakii 1084 fruit powder isolate, E. sakazakii 954 fruit powder isolate, E. sakazakii 858 fruit powder isolate, E. sakazakii 759 fruit powder isolate, E. sakazakii 236 fruit powder isolate, E. sakazakii FSM393 baby food isolate, E. sakazakii FSM33 milk isolate, E. sakazakii 265 milk powder isolate, E. sakazakii FSM468 production environment isolate, E. sakazakii 266 production environment isolate, E. sakazakii ES4 human isolate, E. sakazakii ES11 human isolate, E. sakazakii ES Vo7/24922 human isolate) were sequenced. Afterwards, all strains were used for validation of the specificity of the new developed PCR identification system.
The strains were grown on blood agar plates under appropriate conditions. DNA was extracted from the grown colonies using the DNeasyR Tissue Kit (Qiagen AG, Switzerland) in accordance with the suppliers' protocol.
16S rRNA amplification and direct sequencing
For 16S rRNA gene amplification, reaction mixtures (total volume 50 μl) containing primers 616V (5' AGA GTT TGA TYM TGG CTC 3') and 630R (5' CAK AAA GGA GGT GAT CC 3') [15] at 10 pmol each were prepared by using the Expand High Fidelity PCR system (Roche, Rotkreuz, Switzerland): 10 × Expand High Fidelity buffer (without MgCl2), 2.5 mM MgCl2, 200 μM dNTPs each and 3 U Expand High Fidelity enzyme mix.
The amplification was performed in a T3 thermocycler (Biometra, Germany). The PCR conditions were: 2 min at 95°C, 10× (94°C, 15 s; 52°C, 30 s; 72°C, 90 s) followed by 15× (94°C, 15 s; 52°C, 30 s; 72°C, 129 s). Cycling was completed by a final elongation step at 72°C for 7 min. After PCR the reaction products were separated on a 1.5% agarose gel, stained with ethidium bromide and visualized under UV light. In cases where sequencing was desired, the correct size (approx 1500 bp) products were excised from the gel and purified using the MinElute™ gel extraction kit (Qiagen, Switzerland). The products were thereafter sequenced. Sequencing reactions were performed using a modified Sanger method and the Big-Dye chemistry from Applied Biosystems on an ABI 3730 capillary DNA Analyzer (Applied Biosystems, USA) employing the same primer pair as for amplification of the 16S rRNA gene (616V/630R) and additional internal primers for "walking reactions". Sequencing was performed by Microsynth (Balgach, Switzerland).
Phylogenetic analysis, tree construction and design of specific primer
16S rRNA gene sequences of fourteen newly sequenced strains were added to an alignment of about 28'000 almost full length small subunit rRNA sequences by using the alignment tool of ARB program package [16]. Alignments were refined by visual inspection. Phylogenetic analyses were performed by using distance matrix and the TREEPUZZLE tool of the ARB program. Primer design was accomplished by applying the PROBE DESIGN tool included in the software package ARB on special data structures (PT-Servers) derived from the ssu-rRNA database "ssu_jan04.arb".
The following specific E. sakazakii S16 rRNA gene targeting primers Esakf (5' GCT YTG CTG ACG AGT GGC GG 3') and Esakr (5' ATC TCT GCA GGA TTC TCT GG 3') were designed and synthesized (Mirosynth, Balgach, Switzerland). This primer pair binds to conserved regions (E. coli position 88 – 107 (Esakf) and 1017 – 998 (Esakr)) in the S16 rRNA gene sequences giving an amplicon of 929 bp.
PCR reaction conditions
For amplification, reaction mixtures (total volume 50 μl) containing primer Esakf and Esakr at a concentration of 10 pM were prepared by using 10 × Taq reaction mixture, 2 U Taq polymerase (Promega, Madison, WI),) and 200 μM dNTPs each. Thermal cycling was carried out by using an initial denaturation step of 94°C for 2 min, followed by 29 cycles of denaturation at 94°C for 30 sec. annealing temperature for 1 min and elongation at 72°C for 1 min 30 sec. Cycling was completed by a final elongation step at 72°C for 5 min. Amplification conditions were optimized by gradually increasing the annealing temperature in the assay from 52°c to 64°C. The reaction products were resolved on a 1.5% agarose gel followed by ethidium bromide staining and examination under UV light.
Accession number of 16S rRNA sequences
E. sakazakii ATCC 51329 [GenBank: AY752937], E. sakazakii 1084 fruit powder isolate [GenBank: AY803192], E. sakazakii 954 fruit powder isolate [GenBank: AY752938], E. sakazakii 858 fruit powder isolate [GenBank: AY752936], E. sakazakii 759 fruit powder isolate [GenBank: AY752939], E. sakazakii 236 fruit powder isolate [GenBank: AY752943], E. sakazakii FSM393 baby food isolate [GenBank: AY752941], E. sakazakii FSM33 milk isolate [GenBank: AY752940], E. sakazakii 265 milk powder isolate [GenBank: AY803191], E. sakazakii FSM468 production environment isolate [GenBank: AY752942], E. sakazakii 266 production environment isolate [GenBank: AY803190], E. sakazakii ES4 human isolate [GenBank: AY803186], E. sakazakii ES11 human isolate [GenBank: AY803187], E. sakazakii ES Vo7/24922 human isolate [GenBank: AY803189].
Authors' contributions
AL and TT carried out the experimental part of the study. RS carried out the conception of the study. All authors participated in production and approval of the manuscript.
Acknowledgements
We would like to thank S. Schumacher for her excellent technical assistance, Dr. J. Marugg (Nestle SA, Research and Development, Switzerland) and Dr. R. Zbinden (Departement of Medical Microbiology, University of Zurich) for providing some strains used in this study.
Figures and Tables
Figure 1 Phylogenetic tree comprising the the 16S rRNA gene sequence data of E. sakazakii strains obtained in this study in comparison to E. sakazakii type strain ATCC 29544, the E. cloacae type strain ATCC 13047 and the E. coli type strain ATCC 11775. The scale bar represents ten nucleotide substitutions per 100 nucleotides.
Figure 2 Agarose gel analyses of selected target and non-target strains after amplification of the DNA using the PCR system established by Keyser et al. [10] and the system developed in this study. Lane 1, 6, 10: MWM (Roche XIV), lane 2: E. sakazakii ATCC 29004, 3: E. sakazakii ATCC 51329, 4: E. sakazakii fruit powder isolate, 5: E. cloacae, wild strain amplified with the Keyser PCR system; lane 7 – 10 same strains amplified with the system developed in this study.
Figure 3 Defining the detection limit of the PCR system. Decreasing amounts of purified E. sakazakii strain ATCC 51329 genomic DNA target (100 ng – 1 pg) were amplified by PCR. Lane 1, DNA 100 bp marker; In lanes 2 to 5, 1 ng, 100 pg, 10 pg, 1 pg of E. sakazakii DNA was used per reaction.
Table 1 Polymorphism "hot spots" along the 16S rRNA gene of the E. sakazakii strains analyzed in the study in correspondence to E. sakazakii type strain ATCC 29544, E. coli type strain ATCC 11775 and E. cloacae type strain ATCC13047
E. sakazakii 16S rRNA gene sequence polymorphism positions1
strain Sequence accession no. source I (187–193) II (455–477) III (590–600) IV (1132–1141)
E. coli ATCC 11775 X80725 GCAAGCA GAGTAAAGTTAATACCTTTGCTC TTGTTAAGTCA CGGTCCG-GCC
E. cloacae ATCC 13047 AJ251469 GCAAGAC TGTTGTGGTTAATAACCGCAGCA CTGTCAAGTCG CGGTCCG-GCC
E. sakazakii ATCC 29544 AB004647 human TACGGAC TGTTGTGGTTAATAACCGCAGCA TGATTAAGTCA CGGTTCG-GCC
E. sakazakii ES 11 AY803187 human TWCGGAC YGYTGTGGTTAATAACCACAGCA CTGTTAAGTCA CGGTTCG-GCC
E. sakazakii ES 4 AY803186 human TWCGGAC TGYTGTGGTTAATAACCACAGCA CTGTTAAGTCA CGGTCCG-GCC
E. sakazakii FSM 266 AY803190 environment TACGGAC CGTTGTGGTTAATAACCGCAGCG CTGTTAAGTCA CGGTTCG-GCC
E. sakazkaii 1084 AY803192 fruit powder TACGGAC CGTTGTGGTTAATAACCACAGCG YKRTTAAGTCA CGGTTCG-GCC
E. sakazakii ES Vo7/24922 AY803189 human TACGGAC TGTTGTGGTTATTAACCRCAGCA YKRTTAAGTCA CGGTTCG-GCC
E. sakazakii FSM 265 AY803191 milk powder TTCGGAC CGTTGTGGTTAATAACCGCAGCG YKRTTAAGTCA CGGTTCG-GCC
E. sakazakii ATCC 51329 AY752937 GCAAGAC GGTTAAGGTTAATAACCTTGGCC CTGTCAAGTCG CACATCATGGT
E. sakazakii 858 AY752936 fruit powder TACGGAC TGTTGTGGTTAATAACCGCAGCA TGATTAAGTCA CGGTCCG-GCC
E. sakazakii 759 AY752939 fruit powder TACGGAC TGTTGTGGTTAATAACCACAGCG CTGTTAAGTCA CGGTYCG-GCC
E. sakazakii 954 AY752938 fruit powder TWCGGAC YGYTGTGGTTAATAACCACAGCR YTGTTAAGTCA CGGTYCG-GCC
E. sakazakii 236 AY752943 fruit powder TWCGGAC YGYTGTGGTTAATAACCACAGCR YKGTTAAGTCA CGGTYCG-GCC
E. sakazakii FSM 468 AY752942 environment TACGGAC YGYTGTGGTTAATAACCACAGCA YTRTTAAGTCA CGGTTCG-GCC
E. sakazakii FSM 393 AY752941 baby food TACGGAC CGTTGTGGTTAATAACCGCAGCG TKRTTAAGTCA CGGTCCG-GCC
E. sakazakii FSM 33 AY752940 milk TTCGGAC TGTTGTGGTTAATAACCACAGCA CTGTTAAGTCA CGGTTCG-GCC
1 E. coli positions according to Brosius et al. [17]
Table 2 Bacterial strains used in the study and results of the PCR identification system by Keyser et al. [12] in comparison to the system established in this study
No of strains species origin PCR result (primers from this study) PCR results (Keyser primer set)
1 E. sakazakii ATCC 29004 + +
1 E. sakazakii ATCC 51329 + negativ
1 E. sakazakii ATCC 29544 + +
10 E. sakazakii fruit powder + +
1 E. sakazakii milk powder + +
2 E. sakazakii baby food + +
1 E. sakazakii milk + +
16 E. sakazakii production environment + +
14 E. sakazakii human + +
1 E. cloacae LMG 2783 - -
1 E. cloacae LMG 3008 - -
1 E. cloacae food - -
1 E. cloacae food - positive
1 E. cloacae clinical - -
1 P. dissolvens LMG 2683 - -
1 P. agglomerans LMG 1286 - -
3 P. agglomerans food, clinical, cosmetics - -
1 E. hermanii wild strain - -
1 E. coli ATCC 25922 - -
1 E. coli wild strain - -
1 S. liquefaciens cosmetics - positive
1 S. fucaria production environment - positive
1 K. oxytoca cheese - -
1 K. pneumoniae wild strain - -
1 P. mirabilis DSM 788 - -
1 S. sonnei ATCC 29930 - -
1 S. Enteritidis wild strain - positive
1 P. aeruginosa ATCC 15442 - -
1 L. acidophilus ATCC 13651 - -
1 S. aureus ATCC 25923 - -
1 S. agalactiae ATCC 33019 - -
1 B. cereus ATCC 10876 - -
1 E. faecium DSM 2918 - -
1 L. monocytogenes wild strain - -
1 M. luteus ATCC 9341 - -
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| 15563736 | PMC538263 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Nov 25; 4:43 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-43 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-241557162710.1186/1465-9921-5-24ResearchGene expression profiling reveals novel TGFβ targets in adult lung fibroblasts Renzoni Elisabetta A [email protected] David J [email protected] Sarah [email protected] Xu [email protected] Piersante [email protected] George [email protected] Athol U [email protected] Srihari [email protected] Andrew G [email protected] Christopher P [email protected] Andrew [email protected] Jeremy D [email protected] Carol M [email protected] Kenneth I [email protected] Bois Roland M [email protected] Interstitial Lung Disease Unit, Royal Brompton Hospital, Imperial College of Science, Technology and Medicine, Emmanuel Kaye Building, 1B Manresa Road, SW3 6LR, London, UK2 Division of Academic Rheumatology, Royal Free Hospital, London, U.K3 Centre for Cardiovascular Biology and Medicine, Guy's, King's, and St. Thomas' School of Biomedical Sciences, King's College London, UK4 Division of Respiratory Diseases, University of Siena, Siena, Italy5 MRC Clinical Science Centre, Hammersmith Campus, Imperial College London, UK6 Dept of Pathology, Royal Brompton Hospital, London, UK2004 30 11 2004 5 1 24 24 5 9 2004 30 11 2004 Copyright © 2004 Renzoni et al; licensee BioMed Central Ltd.2004Renzoni 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
Transforming growth factor beta (TGFβ), a multifunctional cytokine, plays a crucial role in the accumulation of extracellular matrix components in lung fibrosis, where lung fibroblasts are considered to play a major role. Even though the effects of TGFβ on the gene expression of several proteins have been investigated in several lung fibroblast cell lines, the global pattern of response to this cytokine in adult lung fibroblasts is still unknown.
Methods
We used Affymetrix oligonucleotide microarrays U95v2, containing approximately 12,000 human genes, to study the transcriptional profile in response to a four hour treatment with TGFβ in control lung fibroblasts and in fibroblasts from patients with idiopathic and scleroderma-associated pulmonary fibrosis. A combination of the Affymetrix change algorithm (Microarray Suite 5) and of analysis of variance models was used to identify TGFβ-regulated genes. Additional criteria were an average up- or down- regulation of at least two fold.
Results
Exposure of fibroblasts to TGFβ had a profound impact on gene expression, resulting in regulation of 129 transcripts. We focused on genes not previously found to be regulated by TGFβ in lung fibroblasts or other cell types, including nuclear co-repressor 2, SMAD specific E3 ubiquitin protein ligase 2 (SMURF2), bone morphogenetic protein 4, and angiotensin II receptor type 1 (AGTR1), and confirmed the microarray results by real time-PCR. Western Blotting confirmed induction at the protein level of AGTR1, the most highly induced gene in both control and fibrotic lung fibroblasts among genes encoding for signal transduction molecules.
Upregulation of AGTR1 occurred through the MKK1/MKK2 signalling pathway. Immunohistochemical staining showed AGTR1 expression by lung fibroblasts in fibroblastic foci within biopsies of idiopathic pulmonary fibrosis.
Conclusions
This study identifies several novel TGFβ targets in lung fibroblasts, and confirms with independent methods the induction of angiotensin II receptor type 1, underlining a potential role for angiotensin II receptor 1 antagonism in the treatment of lung fibrosis.
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Background
Transforming Growth Factor beta (TGFβ) is a multifunctional cytokine that regulates a variety of physiological processes, including cell growth and differentiation, extracellular matrix production, embryonic development and wound healing [1]. Altered expression of TGFβ plays a crucial role in organ fibrosis, hypertrophic scarring, cancer, autoimmune and inflammatory diseases [2].
In the lung, TGFβ is consistently linked with progressive fibrosis [3-5]. Increased expression of TGFβ has been reported in a variety of fibrotic lung diseases [6,7,3], including idiopathic pulmonary fibrosis (IPF), a relentlessly progressive fibrotic lung disease with a median survival from diagnosis of only two years [8], and pulmonary fibrosis associated with systemic sclerosis, one of the leading causes of death in scleroderma patients [9]. Animal models also support a central role played by TGFβ in lung fibrosis. Intra-tracheal adenovirus-mediated TGFβ gene transfer causes severe lung fibrosis extending to the periphery of the lungs [5]. Mice lacking alphavbeta 6, an integrin which is crucial to the release of active TGFβ from latent extracellular complexes, develop lung inflammation but are strikingly protected from bleomycin-induced lung fibrosis [10]. IL-13 overexpression induces lung fibrosis which is mediated via TGF-β1 induction and activation [11]. Experimental inhibition of TGFβ with neutralizing antibodies, soluble receptors, or gene transfer of the TGFβ inhibitor Smad7, inhibits fibrosis in animal models [12-14].
Lung fibroblasts are the main cell type responsible for excessive extracellular matrix synthesis and deposition in fibrosing lung disorders [15]. TGFβ modulates fibroblast function through several mechanisms, including induction of extracellular matrix protein synthesis and inhibition of collagen degradation [1]. However, knowledge of TGFβ targets in adult lung fibroblasts is still limited to a small number of genes. Oligonucleotide array technology allows the simultaneous assessment of thousands of genes providing a global gene expression profiling of the response to a stimulus. The response to TGFβ has been investigated using oligonucleotide microarrays in keratinocytes [16] as well as in dermal [17] and in a human fetal lung fibroblast line [18], but not in primary human adult lung fibroblasts. Fibroblastic responses are likely to vary with the origin and developmental state of the cells [19], and a detailed study of TGFβ responses in adult lung fibroblasts is needed to gain further insights into the fibroproliferative process in the lung.
We therefore quantified gene expression by oligonucleotide microarrays of adult lung fibroblasts (derived from biopsies of normal and both idiopathic and scleroderma-associated pulmonary fibrosis) in response to TGFβ, and identified several novel TGFβ targets among the wide variety of genes regulated by this cytokine. Of these, we particularly focused on angiotensin II receptor type 1, the most highly TGFβ-induced gene among those encoding for signal transduction molecules.
Methods
Cell culture
Primary adult lung fibroblasts were cultured from three control samples (unaffected lung from patients undergoing cancer-resection surgery) and from open-lung biopsy samples of lung fibrosis patients, three with idiopathic pulmonary fibrosis (IPF) [8] and three with pulmonary fibrosis associated with the fibrotic disease systemic sclerosis [9]. Independent reviews of the clinical (SV, ER) and histopathologic diagnosis (AGN) were performed. All the idiopathic pulmonary fibrosis biopsies were characterized by a usual interstitial pneumonia pattern (UIP), whereas all of the scleroderma-associated pulmonary fibrosis were classified as non-specific interstitial pneumonia (NSIP) [8]. Verbal and written consent was given by all subjects; authorization was given by the Royal Brompton Hospital Ethics Committee. Fibroblast culture conditions were as previously described [20]. At confluence, lung fibroblasts (all between passages 4–5) were serum-deprived for 16 hours, and exposed to either 4 ng/ml of activated TGF-β1 (R&D Systems) or serum-free culture medium for four hours. The concentration and time point of TGFβ used in our experiments was determined from ongoing studies within our laboratory, in which a 4 hour treatment with TGFβ 4 ng/ml was found to show significant induction of selected known direct TGFβ target genes, including CTGF.
RNA isolation and gene array analysis
At the end of the treatment period with or without TGFβ, total RNA was harvested (Trizol, Life Technologies), quantified, and integrity was verified by denaturing gel electrophoresis.
Preparation of RNA samples for chip hybridization followed Affymetrix (Affymetrix, Santa Clara, California) protocols. Each RNA sample derived from an individual fibroblast line was hybridized on a separate microarray chip. Hybridization of cRNA to Affymetrix human U95Av2 chips, containing approximately 12,000 well characterized human genes, signal amplification and data collection were performed using an Affymetrix fluidics station and chip reader, following Affymetrix protocol. Scanned files were analyzed using Affymetrix Version 5.0 software (MAS5). Chip files were analyzed by scaling to an average intensity of 150 per gene, as recommended by Affymetrix. Reproducibility was assessed using two pairs of RNA samples from the same control line, TGFβ-treated/untreated; the concordance correlation coefficients were of 0.979 and 0.983, respectively.
TGFβ response was analyzed by using a combination of the MAS5 Affymetrix change algorithm and of ANOVA models. According to Affymetrix criteria, in each TGFβ-treated/medium only pair, genes were defined as differentially regulated (either up or down) by TGFβ only when identified as significantly increased (I) or decreased (D) as determined by the Affymetrix change algorithm, with a change p value<0.001, and were detected as Present (according to the "absolute call"obtained by an Affymetrix algorithm) at least in the samples with the highest count (i.e. medium only in the case of D and TGFβ in the case of I). Genes were defined as TGFβ-responsive in normal human lung fibroblasts when they fulfilled all of the following three conditions: a) they were detected as TGFβ-regulated by Affymetrix criteria (see above) in at least two of the three control pairs; b) they showed a mean fold change after TGFβ of at least 2 (or lower than 0.5) in control fibroblasts; c) either a two-way ANOVA including only control fibroblasts detected a significant (p < 0.05) increase or decrease in control fibroblasts after TGFβ or they were also found to be responsive in at least four of the six fibrotic fibroblast lines and a significant effect (p < 0.05) of treatment (with TGFβ) was detected by a repeated measure ANOVA model including all the samples and adjusting for individual samples, disease, and interaction between treatment and disease. All statistical analyses were performed on log transformed data to reduce inequalities of variance. Thus, the latter ANOVA model could detect genes which were equally up- or down-regulated in normal and fibrotic fibroblasts, taking advantage of the larger number of samples, while the first model (equivalent to a paired t test) could detect changes possibly occurring in controls but not in fibrotic cell lines.
Except for unknown genes, all gene symbols and names are given according to the nomenclature proposed by the Human Genome Organization (HUGO) Gene Nomenclature Committee.
Real time-PCR
Real time PCR (RT-PCR) was performed to confirm selected novel TGFβ targets in lung fibroblasts. Adult lung fibroblast lines [three control and three fibrotic (IPF)] were treated with or without TGFβ (4 ng/ml) for four hours. Total RNA was isolated from treated and untreated samples using Trizol (Life Technologies) and the integrity of the RNA was verified by gel electrophoresis. Total RNA (1 microgram) was reverse transcribed in a 20 μl reaction volume containing oligonucleotide dTs (dT18) and random decamers (dN10) using M-MLV reverse transcriptase (Promega) for 1 hour at 37°C. The cDNA was diluted to 100 μl with DEPC-treated water and 1 μl was used per real-time PCR reaction. A set of eight standards containing a known concentration of target amplicon was made by PCR amplification, isolation by gel electrophoresis through a 2% agarose gel followed by gel purification using QIAquick PCR purification spin columns (Qiagen). The concentration of the amplicon was measured by spectrophotometry and diluted in DEPC-treated water containing transfer RNA (10 μg/ml) to make standards of 10 fold dilutions from 100 pg/ μl to 0.01 fg/ μl. The target was measured in each sample and standard by real-time PCR using FastStart DNA Master SYBR Green (Roche Applied Science) as described by the manufacturer, in half the reaction volume (10 μl). Samples and standards were amplified for 30 to 40 cycles with the appropriate primers (Molecular Biology Unit, KCL School of Biological Sciences) at least in duplicate. The amount of target in the sample in picograms was read from the standard curve and values were normalised to 28S ribosomal RNA (pg of target/pg of 28S ribosomal-RNA). The oligonucleotide primer sequences are listed (5'-3'): angiotensin II receptor type1 (AGTR1) primers: forward TGC TTC AGC CAG CGT CAG TT and reverse GGG ACT CAT AAT GGA AAG CAC; SMAD specific E3 ubiquitin protein ligase 2 (SMURF2): forward AAC AAG AAC TAC GCA ATG GGG and reverse GTC CTC TGT TCA TAG CCT TCT G; nuclear receptor co-repressor 2 (NCOR2): forward CAG CAG CGC ATC AAG TTC AT and reverse GTA ATA GAG GAC GCA CTC AGC; bone morphogenetic protein 4 (BMP4) primers: forward CTA CTG GAC ACG AGA CTG GT and reverse GAG TCT GAT GGA GGT GAG TC.
The results were analyzed using Student's paired t-test after logarithmic transformation, and statistical significance was taken as a p value of <0.05.
Western blot analysis of TGFβ-induction of angiotensin II receptor 1
Lung fibroblasts were grown to confluence in DMEM with 10% FCS. At confluence, lung fibroblasts (all between passages 2–5) were serum-deprived overnight, and exposed to either 4 ng/ml of activated TGF-β1 (R&D Systems) or serum-free culture-medium with the addition of 0.1% BSA for 24 hours. To determine the signalling pathways through which TGFβ induces AGTR1, lung fibroblasts were treated with specific inhibitors 30 minutes before treatment with TGFβ. These included the dual MKK1/MKK2 inhibitor U0126 (10 μM) and predominant MKK1 inhibitor PD98059 (50 μM), known to inhibit MKK2 only weakly [21], as well as the p38 MAPK inhibitor SB 202190 (30 μM). Cell layer lysates were examined. Cell protein (10 μg/sample) was heated to 99°C for 5 min, loaded into sample wells, resolved on a 12% tricine SDS-polyacrylamide gel (Novex, San Diego, CA), and run at 120 V for 2 h. The separated proteins were transferred onto nitrocellulose membranes at 30V for 90 minutes. Membranes were blocked by incubation for one hour with 5% non-fat milk in phosphate buffered saline (PBS) containing 0.1% Tween 20. They were then washed and incubated overnight at 4°C in a 1:500 dilution of rabbit anti-angiotensin II receptor 1 polyclonal antibody (Santa Cruz Biotechnology), followed by a three-time wash in PBS and incubation in 1:1000 goat anti-rabbit biotinylated IgG (Vector Laboratories, Peterborough, UK) for 60 min at room temperature. Membranes were washed three times in PBS, and the signal was amplified/detected by using the ECL protocol as described by the manufacturer (Amersham plc, Little Chalfont, UK). Films were analysed by laser scanning densitometry on an Ultrascan XL (LKB-Wallac, UK). Data were analyzed by using Student's paired t test after log transformation and a p value<0.05 was considered significant.
Immunohistochemistry
The distribution of staining for AGTR1 was assessed by immunohistochemistry in surgical lung biopsies from four patients with idiopathic pulmonary fibrosis (IPF), meeting the diagnostic criteria of the American Thoracic Society/European Respiratory Society Consensus Classification [8], and in control biopsies (normal periphery of resected cancer) from three patients undergoing cancer resection surgery. Paraffin-embedded sections were dewaxed with xylene, hydrated and heated in the microwave at 120 degrees for 30 minutes in citrate buffer (10 mM pH 6.0).
Slides were then briefly rinsed in PBS, blocked with 10% normal goat serum for 20', incubated with rabbit polyclonal anti-human AGTR1 antibody (N-10, 1:50, Santa Cruz Biotechnology, Santa Cruz, Calif) for one hour at room temperature. After washing with PBS, sections were incubated with biotinylated goat anti-rabbit IgG diluted in PBS (1:200) for 30 minutes, rinsed, and finally incubated with Vectastain Elite STR-ABC reagent (Vector Laboratories) for 30 minutes. After washing, sections were visualized using 3-amino-9-ethylcarbazole chromogen and H2O2 as substrate (SK-4200; Vector Laboratories). Sections were then washed in tap water, counterstained with Carrazzis hematoxylin, and mounted with Gelmount (Biomeda, Foster City, CA) for examination using an Olympus BH-2 photomicroscope. Controls included an exchange of primary antibodies with goat matched antibodies. To confirm staining specificity, sections were also incubated with either nonimmune rabbit IgG control or secondary antibody only.
Results
Microarray analysis of TGFβ-response in primary adult lung fibroblasts
According to the criteria outlined in the methods, a four hour treatment with TGFβ was found to regulate 129 transcripts in human lung fibroblasts. TGFβ-responsive transcripts included genes with roles in gene expression, matrix formation, cytoskeletal remodelling, signalling, cell proliferation, protein expression and degradation, cell adhesion and metabolism. A complete list of TGFβ-regulated genes is provided (see Additional file 1). The complete set of gene array data has been deposited in the Gene Expression Omnibus database with GEO serial accession number GSE1724 .
We did not observe a substantial degree of difference in the response to TGFβ between the two fibrotic groups (idiopathic pulmonary fibrosis and scleroderma-associated pulmonary fibrosis) and control lung fibroblasts. Once the criteria outlined in the methods section and the p-value for interaction with treatment had been taken into account, there were no significant differences in the response to TGFβ among the three groups except for two genes, KIAA0261 (probe N: 40086_at), an unknown gene more upregulated in IPF (median fold change 2.2) than in scleroderma-associated pulmonary fibrosis (1.5) and in controls (1.3), and BTG1 (probe N: 37294_at), which was only slightly more downregulated in scleroderma-associated pulmonary fibrosis (fold change:0.4) than in IPF (0.6) and in controls (0.7). As both the number of genes and the magnitude of the differences were minimal, they were not considered meaningful and were not investigated further. Among genes responding significantly to TGFβ in control lung fibroblasts, as assessed by ANOVA analysis, none changed in opposite directions in either of the fibrotic groups. All the genes that responded significantly in the control group alone, were also TGFβ-responsive when analysis was extended to include the fibrotic cell lines. Furthermore, none of these genes responded differently to TGFβ between the two fibrotic groups, which are thus presented together in Tables 1 and 2.
Table 1 Transcription factor genes regulated by TGFβ in control and fibrotic lung fibroblasts (LF)
Gene Symbol Affymetrix Probe N Control LF* Fibrotic LF* Gene name
BHLHB2 40790_at 6.0 5.1 basic helix-loop-helix domain containing, class B, 2
CBFB 41175_at 2.9 2.8 core-binding factor, beta subunit
EGR2 37863_at 52.0 3.3 early growth response 2 (Krox-20 homolog, Drosophila)
ETV6 38491_at 2.0 2.6 ets variant gene 6 (TEL oncogene)
FOXO1A 40570_at 3.8 6.0 forkhead box O1A (rhabdomyosarcoma)
JUNB 2049_s_at 3.7 4.2 jun B proto-oncogene
JUNB 32786_at 4.4 3.0 jun B proto-oncogene
LRRFIP1 41320_s_at 2.1 1.5 leucine rich repeat (in FLII) interacting protein 1
MKL1 35629_at 2.7 2.6 megakaryoblastic leukemia (translocation) 1
MSC 35992_at 2.4 1.7 musculin (activated B-cell factor-1)
NCOR2 39358_at 2.2 2.2 nuclear receptor co-repressor 2
NPAS2 39549_at 2.4 3.1 neuronal PAS domain protein 2
NR2F2 39397_at 0.4 0.5 nuclear receptor subfamily 2, group F, member 2
NRIP1 40088_at 2.3 1.8 nuclear receptor interacting protein 1
RUNX1 393_s_at 2.3 2.6 runt-related transcription factor 1 (aml1 oncogene)
RUNX1 39421_at 3.1 2.3 runt-related transcription factor 1 (aml1 oncogene)
RUNX1 943_at 2.2 2.7 runt-related transcription factor 1 (aml1 oncogene)
SKI 41499_at 2.5 2.1 v-ski sarcoma viral oncogene homolog (avian)
SMURF2 33354_at 2.2 2.2 E3 ubiquitin ligase SMURF2
SRF 1409_at 2.1 1.9 serum response factor
SRF 40109_at 2.2 2.0 serum response factor
TCF21 37247_at 0.2 0.4 transcription factor 21
TCF8 33439_at 2.8 1.8 transcription factor 8 (represses interleukin 2 expression)
TIEG 224_at 2.2 2.1 TGFB inducible early growth response
TIEG 38374_at 3.2 2.7 TGFB inducible early growth response
ZFP36L2 32587_at 0.3 0.4 zinc finger protein 36, C3H type-like 2
ZFP36L2 32588_s_at 0.3 0.3 zinc finger protein 36, C3H type-like 2
ZNF365 35959_at 14.2 2.5 zinc finger protein 365
*Mean fold change in mRNA abundance in TGFβ treated/untreated control and fibrotic lung fibroblasts (LF), respectively. Fibrotic lung fibroblast ratios represent the average values of idiopathic and scleroderma-associated pulmonary fibrosis lung fibroblasts.
Table 2 TGFβ-regulated signalling and ECM/cytoskeletal genes in control and fibrotic lung fibroblasts
Gene Symbol Affymetrix Probe N Control LF* Fibrotic LF* Gene name
Signal transduction
ACVR1 39764_at 2.2 1.7 activin A receptor, type I
ADM 34777_at 0.3 0.4 adrenomedullin
AGTR1 346_s_at 3.8 3.2 angiotensin II receptor, type 1
AGTR1 37983_at 5.1 5.9 angiotensin II receptor, type 1
BDKRB2 39310_at 0.4 0.4 bradykinin receptor B2
BMP4 1114_at 0.2 0.2 bone morphogenetic protein 4
BMP4 40333_at 0.1 0.3 bone morphogenetic protein 4
DYRK2 40604_at 3.0 3.0 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2
DYRK2 760_at 2.9 3.3 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2
DYRK2 761_g_at 3.3 2.2 dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 2
MLP 36174_at 2.4 1.7 MARCKS-like protein
PLK2 41544_at 0.4 0.6 polo-like kinase 2 (Drosophila)
RRAD 1776_at 3.0 5.2 Ras-related associated with diabetes
RRAD 39528_at 3.6 5.1 Ras-related associated with diabetes
SMAD3 38944_at 0.4 0.4 SMAD, mothers against DPP homolog 3 (Drosophila)
SMAD7 1857_at 2.3 2.2 SMAD, mothers against DPP homolog 7 (Drosophila)
SOCS1 41592_at 0.1 0.1 suppressor of cytokine signaling 1
SPRY2 33700_at 2.0 1.8 sprouty homolog 2 (Drosophila)
STK38L 32182_at 3.7 3.8 serine/threonine kinase 38 like
TGFBR3 1897_at 0.3 0.5 transforming growth factor, beta receptor III (betaglycan)
TNFRSF1B 1583_at 0.4 0.6 tumor necrosis factor receptor superfamily, member 1B
TNFRSF1B 33813_at 0.4 0.4 tumor necrosis factor receptor superfamily, member 1B
TSPAN-2 35497_at 4.2 5.0 tetraspan 2
Extracellular matrix remodelling/Cytoskeletal
COL4A1 39333_at 2.2 2.0 collagen, type IV, alpha 1
COMP 40161_at 2.7 5.3 cartilage oligomeric matrix protein
COMP 40162_s_at 5.0 18.9 cartilage oligomeric matrix protein
CTGF 36638_at 4.8 6.1 connective tissue growth factor
CYR61 38772_at 4.4 3.5 cysteine-rich, angiogenic inducer, 61
ELN 31621_s_at 4.9 3.7 elastin
ELN 39098_at 8.4 11.6 elastin
PLAUR 189_s_at 2.7 2.8 plasminogen activator, urokinase receptor
PLOD2 34795_at 2.5 1.8 procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2
SERPINE1 38125_at 3.7 4.0 serine (or cysteine) proteinase inhibitor, clade E, member 1
SERPINE1 672_at 6.0 5.5 serine (or cysteine) proteinase inhibitor, clade E, member 1
TIMP3 1034_at 2.0 1.5 tissue inhibitor of metalloproteinase 3
TIMP3 1035_g_at 2.4 1.6 tissue inhibitor of metalloproteinase 3
TPM1 36790_at 2.3 1.7 tropomyosin 1 (alpha)
TPM1 36791_g_at 2.7 2.1 tropomyosin 1 (alpha)
TPM1 36792_at 2.5 2.0 tropomyosin 1 (alpha)
Mean fold change in mRNA abundance in TGFβ treated/untreated control and fibrotic lung fibroblasts (LF), respectively. Fibrotic lung fibroblast ratios represent the average of idiopathic and scleroderma-associated pulmonary fibrosis lung fibroblasts.
For the purpose of this study, we will concentrate on genes involved in transcriptional regulation, cytoskeletal/extracellular matrix organization, and signal transduction (Tables 1 and 2).
Control of transcription
TGFβ regulated a wide array of transcription factors (Table 1), including the known TGFβ target JUNB. Other TGFβ targets in lung fibroblasts identified by this study included Smad co-activators RUNX1 and CBFB, recently implicated in the targeted subnuclear localization of TGFβ-regulated Smads [22,23]. Transcriptional regulators involved in cell cycle control/cell differentiation induced by TGFβ included FOXO1A, NPAS2, and TIEG (TGFβ-inducible early growth response), while ZFP36L2, a zinc finger transcription factor linked to cell proliferation induction, was repressed by TGFβ. Serum response factor (SRF) and MKL1 were also induced by TGFβ. Transcriptional repressors induced by TGFβ included Ski, which together with Sno interacts with Smad molecules to inhibit transcription and may contribute to terminating TGFβ response [24] and TCF8, a previously reported TGFβ target in fetal lung fibroblasts [18]. Other transcriptional co-repressors upregulated by TGFβ were nuclear co-repressors NCOR2 (or SMRT) and BHLHB2, which repress transcription by recruiting histone deacetylases [25], and musculin (MSC).
Cytoskeletal/Extracellular matrix organization
Most genes in this category were known TGFβ targets. As expected, transcripts involved in promoting extracellular matrix formation and cell adhesion such as connective tissue growth factor (CTGF) were upregulated, while we observed inhibition of bone morphogenetic protein 4 (BMP4), a member of the TGFβ superfamily whose activity has recently been shown to be inhibited by CTGF through direct binding [26].
TGFβ also induced matrix genes including elastin (ELN), collagens (COL4A1), plasminogen activator inhibitor (PAI1 or SERPINE1) and PLOD2, an enzyme which stabilizes collagen cross-links (Table 2). Tissue inhibitor of matrix metalloproteinase 3 (TIMP3) was upregulated by TGFβ. Genes involved in cytoskeletal organization induced by TGFβ included known target tropomyosin (TPM1). Interestingly, smoothelin, a smooth muscle gene recently reported to be highly induced by TGFβ in fetal lung fibroblasts [18], was also induced by TGFβ in this study, but at a slightly lower fold ratio than that chosen for the selection criteria (1.8).
Control of signal transduction
Among signalling molecules (Table 2), known targets included upregulation of SMAD7 and downregulation of SMAD3 [18,16]. Novel targets in lung fibroblasts included SMURF2, a recently identified E3 ubiquitin ligase, which negatively regulates TGFβ signalling by targeting both TGFβ receptor-Smad7 complexes and Smad2 for ubiquitin-dependent degradation [27,28]. At the investigated timepoint, TGFβ downregulated the accessory receptor betaglycan, a membrane anchored proteoglycan which increases the affinity between TGFβ and type I and II receptors. Interestingly, TGFβ upregulated activin A type I receptor, a receptor for TGFβ family member activin, whose stimulation induces fibroblast-mediated collagen gel contraction [29]. Members of the Ras family of GTPases, ARHB and RADD (Ras-related GTP-binding protein), involved in cytoskeleton remodelling, were also upregulated by TGFβ. TGFβ also induced Dickkopf1 (DKK1), a potent inhibitor of Wnt/beta-catenin signalling.
Of particular interest was the novel observation that TGFβ upregulated angiotensin II receptor 1 (AGTR1) in lung fibroblasts; conversely, the gene encoding for vasodilatory peptide adrenomedullin (ADM) was inhibited by TGFβ.
Validation of selected TGFβ-induced genes by real time RT-PCR
Several of the genes regulated by TGFβ confirmed previously published findings, thus validating our methods, including JUN-B, SMAD7, connective tissue growth factor, elastin, and SERPINE1 [17,18,16,30]. To further consolidate our analysis, we selected a small group of novel TGFβ targets to be confirmed by RT-PCR in both control and fibrotic lung fibroblasts. These novel fibroblast TGFβ-responsive genes included potential key candidates in the regulation by TGFβ of lung tissue fibrosis and included angiotensin II receptor type 1 (AGTR1), SMURF2, a gene involved in terminating TGFβ signalling, NCOR2, a transcriptional co-repressor and BMP4, a member of the TGFβ family. Compared to untreated samples, we confirmed that TGFβ upregulated AGTR1 (ratio = 2.4; p = 0.002), SMURF2, (ratio = 1.8, p = 0.003), NCOR2 (ratio 1.4; p = 0.004), and downregulated BMP4 (ratio = 0.4; p = 0.009), with no difference in the response between control and fibrotic fibroblasts (Figure 1).
Figure 1 Independent verification of microarray results by measurement of gene expression with real time-PCR. TGFβ treatment (4 ng/ml) for four hours induces expression of mRNA for angiotensin receptor 1 (panel a), nuclear receptor co-repressor 2 (NCOR2) (panel c) and SMURF2 (panel d) as well as inhibition of bone morphogenetic protein 4 (panel b) in three control lung fibroblast cell lines (dashed lines) and three fibrotic lung fibroblasts (solid lines).
Induction of angiotensin II receptor type 1 by TGFβ
We focused on AGTR1 protein because, as shown by microarray analysis, it was the most highly TGFβ-induced gene among signaling molecules in both control and fibrotic fibroblasts (Table 2). To verify whether AGTR1 mRNA upregulation corresponded to an increase in protein levels, we performed Western analysis on primary human adult lung fibroblasts exposed to TGFβ or medium alone in serum-free conditions for 24 hours. The intensity of the angiotensin II receptor 1 immunoreactive band was significantly increased in TGFβ-treated fibroblasts compared to those treated with medium alone (2.4 fold; p < 0.001) (Figure 2). To identify the signalling pathways through which TGFβ induces AGTR1, we evaluated whether the ability of TGFβ to induce AGTR1 expression in lung fibroblasts was blocked by specific signaling pathway inhibitors. A 30 minute preincubation with the dual MKK1/MKK2 inhibitor U0126 significantly inhibited TGFβ induction of AGTR1 protein (p < 0.01), whereas predominant MKK1 inhibitor PD98059 and p38 MAPK inhibitor SB202190 had no significant effect (Figure 2).
Figure 2 TGFβ treatment induces angiotensin II receptor 1 (AGTR1) protein expression in adult lung fibroblasts; the induction is mediated by MKK1/MKK2. Representative Western Blot (top) and average values (± SD) of angiotensin II receptor type 1 protein expression in lung fibroblasts treated with TGFβ (4 ng/ml)with or without 1/2 hour pre-incubation with of one the following signalling inhibitors: U0126, PD98059, SB202190. A 24 hour treatment with TGFβ induced an upregulation of AGTR1 protein (mean: 2.4 fold, **p < 0.001, Student's paired t-test). The induction of AGTR1 by TGFβ was specifically blocked by MKK1/MKK2 inhibitor U1026 (*p < 0.01 compared with TGFβ-induced AGTR1, Student's paired t-test), but not by predominant MKK1 inhibitor PD98059 or p38 inhibitor SB202190). The results are representative of three independent experiments on both control and fibrotic cell lines. As a loading control, Western analysis with an anti-GAPDH antibody was also performed.
AGTR1 expression in idiopathic pulmonary fibrosis lung biopsies
We assessed staining for AGTR1 in lung biopsies from four patients with idiopathic pulmonary fibrosis and compared it to that of three control lungs. In particular we aimed to evaluate AGTR1 staining in fibroblastic foci, aggregates of fibroblasts/myofibroblasts in close contact with alveolar epithelial cells. Both in control and in idiopathic pulmonary fibrosis lung biopsies, AGTR1 immunoreactivity was observed in alveolar epithelial cells and alveolar macrophages. In addition, the fibroblasts within the fibroblastic foci present in idiopathic pulmonary fibrosis biopsies stained positive for the receptor (Figure 3).
Figure 3 Angiotensin II receptor 1 staining in lung biopsies from control patients (A) and from patients with idiopathic pulmonary fibrosis (B). Immunohistochemistry for the angiotensin II receptor 1 (AGTR1), counterstained with haematoxylin. AGTR1 positive staining is seen in alveolar macrophages, in epithelial cells and in fibroblastic foci (arrows) in usual interstitial pneumonia biopsies (panel B). Epithelial cells and alveolar macrophages express AGTR1 in control lung biopsies (panel A).
Discussion
In this study we report, for the first time, the transcriptional profile in response to TGFβ in adult primary human lung fibroblasts both from control and from fibrotic lungs. Our analysis of the response to TGFβ focused on TGFβ gene targets involved in transcription and signalling, identifying a series of genes previously unknown to respond to TGFβ in lung fibroblasts. These included angiotensin II receptor 1, providing further insights into links between TGFβ and angiotensin in the pathogenesis of fibrosis [31,32].
Although gene expression profiling in response to TGFβ has been investigated previously, earlier work has been confined to skin fibroblasts [17], keratinocytes [16], and a human fetal lung cell line [18], which is likely to respond differently to TGFβ from the adult lung fibroblast. Our data cannot be directly compared with the fetal lung fibroblast profiling because of methodological disparities, chiefly due to differences in the timing of the RNA collection. However, even restricting the comparison to results obtained at similar time points, we found a significant dissimilarity. Among transcription factors, only JUNB and TCF8 were upregulated by TGFβ both in fetal [18] and in adult lung fibroblasts, while all others differed between the two cell types. Interestingly, in this study, TGFβ caused an induction of both MKL1 and serum response factor, while neither were upregulated in fetal lung fibroblasts. The recently reported cooperation between these two transcription factors in determining smooth muscle cell differentiation [33] suggests that they may play a similar role in lung fibroblasts and suggests differences between fetal and adult lung fibroblasts in the transcriptional programs involved in the TGFβ-induced acquisition of the myofibroblastic phenotype.
In this study, we did not observe a substantial difference in the response to TGFβ between lung fibroblasts from two patterns of fibrotic lung disease and control lung fibroblasts. In vivo heterogeneity between interstitial lung fibroblasts may occur in fibrotic and normal lung, obscuring the demarcation between normal and abnormal phenotypes, when cell lines are isolated using standard techniques [34,35]. This may explain discrepancies among studies on growth rate and resistance to apoptosis in fibroblasts derived from fibrotic lungs [34,36]. In particular, the fibroblasts/myofibroblasts forming the fibroblastic foci, observed to be linked to disease progression [37], could differ from the remaining fibroblasts found in the interstitium. The issue of sampling a population of homogeneous lung fibroblasts will be the subject of further investigation by using laser microdissection techniques targeting fibroblastic foci coupled with new technologies to amplify RNA from limited quantities of tissue [38]. Further, it is possible that the absence of striking differences in the response to TGFβ between disease groups and controls is due to a loss of the pro-fibrotic phenotype in vitro, even though the gene expression patterns of different passages of the same fibroblast line have been observed to cluster together, indicating that the in vitro phenotypes are stable through several passages in culture [19]. Further, we ensured that RNA was extracted from all fibroblast lines at comparable passages. Thus, even though our study cannot exclude the presence of subtle differences in the response to TGFβ, we have observed that, overall, fibrotic lung fibroblasts retain the capacity to respond to TGFβ, which could therefore be targeted by pharmacological means.
Among the novel TGFβ targets identified by microarray analysis in lung fibroblasts, we focused our attention on the induction of angiotensin II receptor type 1 (AGTR1), as its involvement is likely to significantly amplify the pro-fibrotic actions of TGFβ. The ligand for this receptor is angiotensin II, a vasoactive peptide which has been linked to fibrogenesis in the kidney and in the heart [39,40]. Recent studies have indicated that a local renin-angiotensin system could also be involved in the development of lung fibrosis [41,42]. Elevated angiotensin converting enzyme levels have been found in bronchoalveolar lavage (BAL) fluid from patients with idiopathic pulmonary fibrosis [41]. Compared to controls, lung fibroblasts from patients with idiopathic pulmonary fibrosis produce higher levels of angiotensin II, shown to induce apoptosis in alveolar epithelial cells through AGTR1 [31,43]. Blockade of angiotensin II or of AGTR1 attenuates lung collagen deposition in animal models of lung fibrosis [42,32]. Interestingly, the modulation of AGTR1 could be cell specific, as suggested by the report that TGFβ reduces AGTR1 expression in cardiac fibroblasts [44].
In addition to Smad molecules, the classic signalling pathway used by TGFβ family members, TGFβ also signals through the mitogen-activated protein kinase (MAPK) signalling pathways [16]. In this study, TGFβ was found to induce AGTR1 via mitogen-activated protein kinase kinase (MKK1/MKK2). The finding that the MKK1/MKK2 inhibitor U0126, but not the MKK1 inhibitor PD98059, was able to suppress TGFβ-induced AGTR1 expression, suggests that both MKK1 and MKK2 must be antagonized in order to inhibit transcription.
The functional effects of AGTR1 stimulation in lung fibroblasts are only partially known. Although two isoforms of angiotensin II receptor exist, AGTR1 and AGTR2, the effects described so far of angiotensin II on lung fibroblasts are ascribed to the type 1 receptor. AGTR1 has been found to mediate mitogenesis in human lung fibroblasts [45] and extracellular matrix synthesis in lung [46] as well as in cardiac and dermal fibroblasts [47]. Whereas angiotensin II is known to induce TGFβ [46], the regulation of AGTR1 by TGFβ has not, to our knowledge, been previously reported in lung fibroblasts. Our data support the concept of a positive feed back loop by which TGFβ potentiates the pro-fibrotic actions of angiotensin II by increasing AGTR1 expression, providing a mechanism for the attenuation of the proliferative response to angiotensin II by TGFβ blockade [45]. Thus, cooperation and amplification of pro-fibrotic effects between TGFβ and AGTR1 are likely to be implicated in lung fibrosis. Interestingly, adrenomedullin, a multifunctional vasodilatory peptide that downregulates angiotensin II-induced collagen biosynthesis in cardiac fibroblasts [48], was inhibited by TGFβ, confirming a previous report [49], and suggesting that TGFβ exerts a complex regulation over vasoactive peptides and/or their receptors in lung fibroblasts.
AGTR1 was found to localize to fibroblasts within fibroblastic foci in IPF/UIP biopsies. An increase in AGTR1 staining has been reported in the fibrotic regions surrounding the bronchioles in chronic obstructive pulmonary disease [50]. The finding that AGTR1 localizes to fibroblastic foci in IPF biopsies supports the potential relevance of the angiotensin system in this disease and suggests that the pro-fibrotic role of AGTR1 in IPF is not limited to epithelial cells [31]. Further studies are needed to assess the functional effects of AGTR1 stimulation in lung fibroblasts and to evaluate the biological role of AGTR1 in lung fibrosis.
Conclusions
Our findings confirm that in response to TGFβ, both control and fibrotic lung fibroblasts are potent effector cells expressing a very wide range of genes that are likely to contribute to the fibrotic process. In particular, we have shown that TGFβ has the capacity to influence the expression of angiotensin II receptor type 1 both at the mRNA and at the protein level. In view of the known induction of TGFβ by angiotensin II [45], our findings support the existence of a self-potentiating loop between TGFβ and angiotensin II, resulting in the amplification of the pro-fibrotic effects of both systems. Future treatment strategies could be based on the disruption of such interactions.
Authors' contributions
EAR participated in the design and interpretation of the study, carried out the cell culture work and participated in the microarray work, performed immunohistochemistry staining, and drafted the manuscript. DJA participated in the design and coordination of the study and in the preparation of the manuscript, SH performed the RT-PCR assays, XSW carried out the Western Blot analysis, PS performed the statistical analysis and participated in the interpretation of results and preparation of the manuscript, GBG participated in the microarray work, AUW participated in the interpretation of results, SV participated in cell line selection and clinical characterization, AGN reviewed fibrotic lung biopsies and interpreted immunohistochemistry staining, CD and CMB contributed towards the overall organizational setup for the study of lung fibroblast lines and participated in the interpretation of results, AL and JDP participated in the preparation of the manuscript, KIW conceived of the study and participated in the design, RdB participated in study design, interpretation and coordination. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Complete list of genes regulated by a four hour treatment with TGFβ in control and fibrotic fibroblasts This data set contains all the genes up- or down-regulated by a four hour treatment with TGFβ (according to the criteria described in the methods) in control and fibrotic lung fibroblasts. Fibrotic lung fibroblast fold ratios are the average of the fold ratios for lung fibroblasts from idiopathic pulmonary fibrosis and pulmonary fibrosis associated with systemic sclerosis. Genes are sub-grouped into functional classes. Affymetrix probe set numbers, approved gene symbols, gene names and GenBank accession numbers are provided in the table.
Click here for file
Acknowledgments
We are grateful to Helen Causton, Laurence Game, Nicola Cooley and Helen Banks of the CSC/IC Microarray Centre for expert help with Affymetrix microarray experiments. We thank Carmen Fonseca, Paul Beirne and Alan Holmes for their technical expertise and for their review of the manuscript. This work was supported by the Royal Brompton & Harefield Clinical Research Fund, by the Raynaud's and Scleroderma Association Trust, the Scleroderma Society, the Welton Foundation, the Rosetrees Charitable Trust, and by the Arthritis and Research Council.
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| 15571627 | PMC538264 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Nov 30; 5(1):24 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-24 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-461556939410.1186/1475-2875-3-46ResearchReduced bio-efficacy of permethrin EC impregnated bednets against an Anopheles gambiae strain with oxidase-based pyrethroid tolerance Etang Josiane [email protected] Fabrice [email protected] Pierre [email protected] Lucien [email protected] Institute of Medical Research and Studies of Medicinal Plants (IMPM), Ministry of Scientific Research and Technique, P.O. Box 6163, Yaoundé, Cameroon2 Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale (OCEAC), Tel. +237-223-22-32, Fax. +237-223-00-61 BP. 288, Yaoundé, Cameroun3 Centre de Recherches Entomologiques de Cotonou (CREC), 06 B. P. 2064, Cotonou, Bénin4 World Health Organization, Head office, Geneva, Switzerland5 World Health Organization, Regional Office for Africa, P.O. Box BE 773, Harare, Zimbabwe2004 29 11 2004 3 46 46 27 9 2004 29 11 2004 Copyright © 2004 Etang et al; licensee BioMed Central Ltd.2004Etang 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
Insecticide-treated nets (ITNs) are an integral component of malaria control programmes in Africa. How much pyrethroid resistance in malaria vectors will impact on the efficacy of ITNs is controversial. The purpose of this study was to evaluate knockdown and killing effects of ITNs on a metabolic-based resistant or tolerant malaria vector strain.
Methods
Bio-efficacy of 500 mg/m2 permethrin EC treated bednets was assessed on the OCEAC laboratory (OC-Lab) strain of Anopheles gambiae s.s.. This strain is resistant to DDT and tolerant to pyrethroids, with elevated mixed function oxidases. The Kisumu reference susceptible strain of A. gambiae s.s. was used as control. Nets were impregnated in February 1998 and used by households of the Ebogo village. Then they were collected monthly over six months for Bio-assays (WHO cone test). Knockdown and mortality rates were compared between the OC-Lab and the Kisumu strains, by means of the Mantel-Haenszel chi-square test.
Results
During the whole trial, permethrin EC knockdown rates were impressive (mostly higher than 97%). No significant difference was observed between the two strains. However, the mortality rates were significantly decreased in the OC-Lab strain (40–80%) compared with that of the Kisumu strain (75–100%). The decrease of killing effect on the OC-Lab strain was attributed to permethrin EC tolerance, due to the high oxidase metabolic activity.
Conclusion
These data suggested an impact of pyrethroid tolerance on the residual activity of ITNs. More attention should be given to early detection of resistance using biochemical or molecular assays for better resistance management.
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Background
Malaria is the most important vector-borne disease in Africa. It is estimated that 80 to 90% of the 300 million annual cases and one million deaths occur on this continent [1]. The sharp rise of its incidence in the past decades resulted in dramatic economic consequences for African countries [2]. The global strategy adopted by the World Health Organization (WHO) in 1992 recommended an integrated management of the disease, including selective vector control [3]. Selective vector control is defined as: application of site-specific targeted use of different and cost-effective vector control methods alone or in combination to reduce human-vector contact.
Insecticide-treated nets (ITNs) are one of the main vector control tools against malaria. They are as effective as indoor residual spraying (IRS) [4] and strongly advocated for malaria prevention [5,6]. Implementation does not systematically require vector control services that no longer exist in many countries. At this time, insecticides belonging to the pyrethroid family are the only compounds available for the impregnation of materials. They strike mosquitoes with knockdown and killing effects at dosages far below the threshold of mammalian toxicity [7].
However, the emergence of pyrethroid resistance in the Anopheles gambiae complex and the Anopheles funestus group, the most important malaria vectors in Africa, is a threat to the effectiveness of ITNs [8-11]. This resistance is based on several mechanisms that could segregate according to their operational impact on vector biology and control. Some modifications of insecticide effects associated with reduced sensitivity of the sodium ion channel along nerve axons due to kdr mutation have been reported in A. gambiae s.s. from West and East Africa [12,13]. In addition, there is strong evidence for metabolic-based resistance mechanisms in African malaria vectors [14,15]. Three major enzyme families (esterases, glutathione S-transferases and cytochrome P450 oxidases) are involved in insect detoxification. Elevation of their activity usually results in resistance to insecticides such as pyrethroids. In Cameroon, elevated esterase, oxidase or glutathione S-transferase activities were reported as the main resistance mechanisms in many populations of the A. gambiae complex [16].
Malaria vector resistance to pyrethroids has been clearly demonstrated in Africa. However, its operational implication in terms of reducing efficacy of ITNs, especially in the case of metabolic-based resistance is not well documented. The aim of this study was to assess the knockdown and killing effects of ITNs on a metabolic-based pyrethroid resistance or tolerance strain of malaria vector. This study reports on the decrease of ITN's killing effect against a laboratory strain of A. gambiae s.s with a likely oxidase-based pyrethroid tolerance.
Methods
The study was undertaken in the entomology laboratory of the Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale (OCEAC), in Yaoundé (Cameroon).
Bednets impregnation and sampling
Bednets were made of white multifilament polyester fabric (75 denier; 156 meshes, 12 × 13 holes/inch2) manufactured by SiamDutch Mosquito Netting Co. Ltd. (Bangkok, Thailand). Two sizes of bednets were used : X-family (16.3 m2) and Family (13.13 m2). Both were strengthened on the lower part by a 20 cm sheeting border (made of more polyester filaments) to prevent tearing while being tucked in. They were impregnated with the target dosage of 500 mg/m2 permethrin EC and hung in households of the Ebogo village, for use during the period of March-September, 1998.
Ebogo-village (3°20 N, 11°20 E) is about 65 km far from Yaoundé (the capital city of Cameroon), in the equatorial forest. Anopheles moucheti is the main malaria vector there, with 307 infected bites/man/year [17]. This village was chosen for the implementation of ITNs because the people there were used to bednets, since a deltamethrin SC trial was conducted there in 1994.
A total of 50 permethrin EC impregnated bednets were distributed in the village, in addition to about 30 old nets that were retreated by the study team. All the new nets were identified by a code number. Immediately after impregnation, two nets were randomly chosen and brought to the laboratory. Then, two others were collected each month from the Ebogo households and systematically replaced by unused ones. Replacement nets and old ones were properly identified so that they could not later be collected from the field and used for bio-assays. People were asked not to wash their bednets during the trial.
Laboratory procedure
Netting section
In the laboratory, netting portions were isolated from the lower part of bednets collected from the field and wrapped in aluminum sheets. Each sample was identified by a code number and kept at 4°C until Bio-assays were performed (less than one month).
Mosquito strains
The bio-efficacy of treated nets was assessed on the OC-Lab strain of A. gambiae s.s., originated from Yaoundé and laboratory-reared for about 15 years without insecticide selection. The Kisumu susceptible reference strain of A. gambiae s.s., originated from Kenya and provided by LIN/IRD Montpellier, was used as a control.
The OC-Lab. strain is known to be strongly resistant to DDT and tolerant to pyrethroids, response to WHO susceptibility test [18] performed in 1997 is given in table 1. We registered 26 per cent mortality rate to 4 per cent DDT, 78–95 per cent mortality rates to 0.25 percent permethrin (former diagnostic concentration), 0.025 per cent deltamethrin (former diagnostic concentration) and 0.2 per cent cyfluthrin. With these diagnostic concentrations, the time of knockdown for 50 per cent mosquitoes during exposure to insecticide-impregnated papers was 2–5 fold increased compared with that of the Kisumu strain. However, mortality rates to 1.0 per cent permethrin, 0.05 per cent deltamethrin (revised current diagnostic concentrations) was higher than 98 per cent, with knockdown time ratio less than 2 fold.
Table 1 Kisumu susceptible and OCEAC Laboratory strains of Anopheles gambiae s.s. response to WHO susceptibility test.
Strains Insecticides No TKd50(CI) TKd95(CI) Tkd50R (CI) Mt ST.
Kis. 4% DDT 100 18.8 (17.6–20.0) 28.7 (25.8–33.7) -- 100 S
1.0% permethrin 99 9.2 (8.6–9.7) 14.3 (13.2–16.0) -- 100 S
0.25% permethrin 100 12.4 (11.2–13.7) 28.8 (24.8–35.4) -- 94.1 T
0.05% deltamethrin 89 9.4 (8.4–10.2) 17.2 (15.6–20.0) -- 100 S
0.025% deltamethrin 100 8.9 (8.1–9.7) 19.7 (17.5–23.0) -- 100 S
0.2% cyfluthrin 120 8.6 (8.0–9.1) 15.5 (14.1–17.7) -- 100 S
OC-Lab. 4% DDT 100 9.9 (86.1–119.6) 268.9 (195.0–465.5) 5.2 (4.0–6.7) 26 R
1.0% permethrin 101 12.2 (11.5–12.8) 17.5 (16.3–19.5) 1.3 (0.9–1.7) 98.7 S
0.25% permethrin 125 45.7 (13.6–153.3) 109.2 (3.3–3593.0) 3.7 (0.6–22.9) 78.7 R
0.05% deltamethrin 100 16.8 (13.2–21.3) 36.2 (23.7–55.5) 1.8 (1.1–2.9) 100 S
0.025% deltamethrin 125 24.9 (23.6–26.5) 41.2 (37.5–46.9) 2.8 (2.3–3.4) 94.8 T
0.2% cyfluthrin 108 17.8 (12.7–24.8) 34.1 (32.5–37.3) 2.1 (1.3–2.9) 94.4 T
Kis.: Kisumu strain, OC-Lab.: OCEAC Laboratory strain, No: Number of tested mosquitoes, Tkd50: knockdown time in minutes for 50% tested mosquitoes, Tkd95: knockdown time in minutes for 95% tested mosquitoes, CI: confidence interval at 95%, Tkd50 R: Tkd50 OC-Lab strain / Tkd50 Kisumu strain, Mt.: Mortality rate 24 h post exposure, ST: status, S: indicates susceptibility, T: suspects resistance to be confirmed (or tolerance), R: suggests resistance.
Biochemical analysis of esterase, mixed function oxidase and glatathione S-transferase enzyme systems using microtitre plates and spectrophotometer as described by Penilla et al. [19] and Brogdon et al. [20] revealed elevation of mixed function oxidases activity in the OC-Lab strain (Figure 1). For this enzyme system, the activities (mean ± standard deviation) of the OC-Lab and Kisumu strains were 0.049 ± 0.018 and 0.027 ± 0.011 nmol cytochrome unity equivalent/mg protein (rank-sum normal statistic with correction Z = -3.924, p < 0.001). Esterase and glutathione S-transferase levels were lower in the OC-Lab strain than in the Kisumu strain. For esterases, two substrates were used, α-naphtyl acetate and paranitrophenyl acetate. With α-naphtyl acetate, the activities of the OC-Lab and Kisumu strains were 0.057 ± 0.011 and 0.117 ± 0.083 μmol α-naphtol produced/min/mg protein (rank-sum normal statistic with correction Z = 6.302, p < 0.001).
Figure 1 Oxidase levels in Kisumu and OCEAC laboratory strains of Anopheles gambiae s.s. through biochemical assays. KISUMU: Pattern of cytochrome P450 UE/mg protein in individuals of the Kisumu susceptible laboratory strain, OC-Lab: Pattern of cytochrome P450 UE/mg protein in individuals of the OCEAC laboratory strain.
With paranitrophenyl acetate, the activities of the OC-Lab and Kisumu strains were 0.002 ± 0.008 and 0.053 ± 0.132 μmol p-nitrophenol produced/min/mg protein (rank-sum normal statistic with correction Z = 7.028, p < 0.001). For glutathione S-transferases, the activities of the OC-Lab and Kisumu strains were 0.013 ± 0.024 and 0.087 ± 0.104 μmol GSH conjugated/min/mg protein (rank-sum normal statistic with correction Z = 3.172, p = 0.001).
PCR analysis [21] showed that individuals of the OC-Lab strain belonged to the M molecular form of A. gambiae s.s. and those of the Kisumu strain to the S molecular form. Individuals of the OC-Lab. strain which survived to WHO susceptibility test were screened by PCR [22], all of them appeared free of kdr Leu-Phe mutation.
Bio-assays
After treatment, ten batches of five unfed females (2–5 days old) of the OC.Lab strain and those of the Kisumu strain were exposed under WHO's plastic cones to netting from newly treated nets for three minutes. Ten other batches of each mosquitoe strain were exposed to netting from untreated nets as control. Mosquitoes were then transferred in white cups and the knockdown rates were recorded at 60 minutes post-exposure. They were then supplied with a 15% glucose solution and held under laboratory conditions, at 80 per cent relative humidity and 27°C (± 2°C) temperature. The mortality rates were recorded after 24 hours.
Bio-assays were also performed on used nets. Between March and September 1998, 100 females of A. gambiae s. s. from the OC-Lab strain and 100 specimens from the Kisumu strain were tested each month (from M0 to M6). For the control, 50 specimens from the OC-Lab and 50 others from the Kisumu strain were exposed to untreated netting.
Each month knockdown and mortality rates of the OC-Lab strain and the Kisumu strain were then compared by means of the Mantel-Haenszel chi-square test.
Results
Efficacy of freshly treated nets
Table 2 indicates knockdown and mortality rates in mosquitoes after exposure to nettings from freshly treated and untreated nets.
Table 2 Kisumu and OCEAC Laboratory strains of Anopheles gambiae s.s. response to permethrin EC freshly treated nets.
Variables Nets Kisumu strain OC-Lab strain X2 p
No % No %
Kd rates Untreated 50 0 50 0
Permethrin EC (500 mg/m2) 100 100 100 97 3.04 0.08
Mt rates Untreated 50 0 50 2
Permethrin EC (500 mg/m2) 100 89 100 68 13.06 <0.001
Kd: Knockdown rates 60 minutes post-exposure, Mt: Mortality rates 24 hours post-exposure, No: number of tested mosquitoes, p: Probability at 5%.
Knockdown Rates
No knockdown effect was observed in mosquitoes exposed to netting from untreated nets (control), either in the Kisumu strain or in the OC-Lab strain. With netting from permethrin EC freshly treated nets (M0), more than 95 per cent of mosquitoes from both strains were knocked down 60 minutes post-exposure. The difference between the two strains was not significant at the five per cent level (p = 0.08, df = 1).
Mortality rates
With netting from untreated nets, mortality rate in each strain did not exceed 2 per cent. Using netting from permethrin EC freshly treated nets, the mortality rate in the OC-Lab strain did not exceed 70 per cent, while about 90 per cent mosquitoes of the Kisumu strain were killed, difference between the two strains was highly significant (p < 0.001, df = 1).
Efficacy of treated bednets during domestic utilization
Knockdown rates
During the whole trial, no knockdown effect was observed in mosquitoes exposed to netting from untreated nets, either in the Kisumu strain or in the OC-Lab strain, while most of the mosquitoes exposed to netting from treated nets were knocked down during the 60 minutes post-exposure. The profile of knockdown rate variations during the six month evaluation is given in Figure 2. No significant difference was observed between the Kisumu strain and the OC-Lab strain (p > 0.05, df = 1). For both strains, the knockdown rate was mostly higher than 90 per cent, except in the fifth month during which about 70 per cent were registered.
Figure 2 Knockdown rates in Kisumu and OCEAC laboratory strains of Anopheles gambiae s.s. to permethrin EC used nets. OC-Lab: Permethrin EC treated net knockdown rates on the OCEAC laboratory strain, KISUMU: Permethrin EC treated net knockdown rates on the Kisumu susceptible laboratory strain.
Mortality rates
The mortality rates in the control netting were constantly lower than five per cent for both strains. Conversely, numerous mosquitoes exposed to netting from treated nets were killed during the 24 hours post-exposure. The graph of the mortality rate variations during the six month evaluation is given in Figure 3. During the first five months, the killing effect was higher in the Kisumu strain than in the OC-Lab strain. The decrease of net efficacy on the OC-Lab strain was significant during the first three months (p < 0.001, df = 1). From the fourth to the sixth month, there was no longer a significant difference between the two strains (0.13 <p < 0.57, df = 1).
Figure 3 Mortality rates in Kisumu and OCEAC laboratory strains of Anopheles gambiae s.s. to permethrin EC used nets. OC-Lab: Permethrin EC treated net mortality rates on the OCEAC laboratory strain, KISUMU: Permethrin EC treated net mortality rates on the Kisumu susceptible laboratory strain, * stars indicate months during which the mortality rates were significantly lower in the OCEAC strain than in the kisumu strain.
Discussion
DDT resistance in the OC-Lab. strain of A. gambiae s.s. which originated from Yaoundé city was not selected in the laboratory. The selective pressure was performed in the field several years prior to the collection of the strain. DDT was used in Yaoundé for residual indoor spraying during the 1950's [23]. Furthermore, Desfontaines et al. [24] reported the intensive use of household insecticides containing mixture of compounds such as pyrethrins and pyrethroids (coils, mats, etc ...) in this city for protection against mosquito bites. The first susceptibility tests on the OC-Lab strain were performed in 1997 using WHO's protocol. Samples were tested for 4.00 per cent DDT, 0.25 per cent and 1.00 per cent permethrin, 0.025 per cent and 0.05 per cent deltamethrin, then 0.20 per cent cyfluthrin. Susceptibility tests were carried out with these ranges of pyrethroid dosage because the OC-Lab strain had to be used for the evaluation of cyfluthrin bio-efficacy in phase III of the World Health Organization Pesticide Scheme (WHOPES). This strain was found resistant to 4.00 per cent DDT and 0.25 per cent permethrin, tolerant to 0.025 per cent deltamethrin and 0.20 per cent cyfluthrin, but susceptible to 1.00 per cent permethrin and 0.05% deltamethrin. It was also shown that the kdr Leu-Phe mutation was not involved in this case of DDT resistance or pyrethroid tolerance. Bio-assays using WHO cone test with a cyfluthrin EW 50 mg active ingredient per m2 of netting resulted in 35 per cent mortality rate versus 95 per cent rate for the Kisumu strain. The difference in knockdown rates was not significant (95–100 per cent for both strains). It was seen that the strain was not suitable for that trial. In fact, the cyfluthrin bio-efficacy was assessed with the Kisumu susceptible reference strain [25]. Subsequently, biochemical analysis revealed over-production of mixed function oxidases in the OC-Lab strain and the same metabolic-based resistance was reported in wild populations of A. gambiae s.l. from cotton and rice fields in northern Cameroon [16], which is a threat for the efficacy of treated nets in this area. It was, therefore, essential to investigate pyrethroid-treated material effectiveness against a metabolic-based resistant malaria vector population. With these rationales, the OC-Lab strain was found suitable for a laboratory trial compared with a susceptible reference strain of A. gambiae s. s., such as the Kisumu strain. The study was not carried out with field mosquitoes because the cotton fields are actually located 1,000 km from the laboratory, it would be difficult to collect sufficient field samples for bio-assays.
From current data, the insecticide activity of treated nets on the Kisumu reference strain was clearly demonstrated, despite some breakdowns observed after the third month. The decrease of knockdown and mortality rates at this period may be related to bad conditions of net utilization. Previous reports have underlined the impact of external factors such as dirt and fume on the bio-efficacy of treated nets [26-28]. However, the activity of permethrin in this study was similar to that usually reported in field trials [29,30]. Conversely, nets were less effective against the OC-Lab strain, especially in term of mortality rate. These data are consistent with those previously obtained with cyfluthrin.
The decrease of knockdown rate prior to that of mortality rate is known as one of the major modifications of pyrethroid effects associated with kdr mutation [12,31]. The contrast between knockdown and mortality rates in this trial is relevant to the involvement of metabolic detoxification in insecticide resistance which does not systematically induce the decrease of knockdown effect.
In Cameroon, ITNs were found effective in reducing malaria transmission and morbidity during the early 1990s [32,33] and, until now, they have been strongly advocated by the national malaria control programme. Therefore, the emergence of pyrethroid resistance in the A. gambiae complex [34] is of a particular concern for the efficacy of interventions. Generally, insecticide resistance has a major impact in reducing efficacy of IRS programmes. Detoxification through mixed function oxidases was reported to delay the deltamethrin IRS programme against A. funestus populations from northern Kwazulu/Natal [10]. By the same token, high activities in glutathione S-tranferases, esterases and mixed function oxidases resulted in the failure of the IRS programme against A. albimanus in southern Mexico [19]. ITNs tested in laboratory and experimental huts in West Africa were found partially effective against DDT or pyrethroid resistant populations of A. gambiae s.s. with kdr gene frequency higher than 70% [35]. Nevertheless, the epidemiological impact at community level was similar to that observed in areas with susceptible vectors (Henry, personal communication). The lessening of pyrethroid exito-repellent and irritancy effects against knockdown resistant mosquitoes allowed their contact with treated nets and resulted in killing many of them. ITNs could, therefore, work positively against pyrethroid-resistant malaria vectors with kdr gene. Considering the genetic diversity of pyrethoid resistance mechanisms, the efficacy of ITNs against knockdown resistant populations could not be extrapolated to vector populations with elevated monoxygenases activity.
From the current study, it has been seen that the knockdown rates were not decreased in the permethrin-tolerant strain; likewise pyrethroid properties (excito-repellent and irritancy) may not be impeded against metabolic-based resistance mosquitoes. There is a converging suggestion that the impact of insecticide resistance on the efficacy of ITNs used as personal protection tools might not be limited when resistance is due to high metabolic detoxification. Conversely, the decrease of mortality rates puts forward a potential limited impact of ITNs when used at community level as vector control intervention aiming at mass reduction of vector density.
Conclusions
Current data call attention to early detection of resistance as one of the key guidelines for insecticide resistance management. Susceptibility tests are the entry point for insecticide resistance studies. However, whether resistance is detected or not, it is necessary to go through biochemical or molecular assays for detection of resistance genes which may not have a great impact on the level of resistance when they stand at very low frequency. In order to preserve efficacy of relevant tools such as ITNs against malaria vectors, it would be easy to control insecticide resistance when the occurrence of the involved gene is at low rather than at high frequency.
Moreover, the impact of insecticide resistance on vector control interventions is a complex phenomenon that depends not only on the resistance itself (mechanisms, gene frequency, etc...), but also includes vector behaviour, environment and insecticide properties such as the excito-repellent effect. Drawing a general conclusion on the efficacy of ITNs in areas with metabolic-based resistant vector populations needs further investigation in experimental huts to study their behaviour and at community level to assess epidemiological impact.
Authors' contributions
JE participated in the conception of the study, carried out field and laboratory procedures, analysed and interpreted data, drafted and revised the manuscript.
FC carried out biochemical assays, participated in data analysis and interpretation. PG participated in the design of the study and revising it critically. LM conceived the study, participated in its design, coordinated, revised and gave final approval of the version to be published.
Acknowledgements
Technical support for biochemical and molecular characterization of mosquito strains was provided by the WHO Multilateral Initiative on Malaria (MIM) network for "the study of factors conditioning the evolution of pyrethroid resistance in Anopheles gambiae s.l. in Africa". The authors are grateful to households of the Ebogo village for their acceptance and Mr. Jean-Claude Toto for technical support in the field. The study was founded by the Agence Francophone pour l'Enseignement Supérieur et la Recherche (AUPELF-UREF).
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| 15569394 | PMC538265 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 29; 3:46 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-46 | oa_comm |
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BMC Oral HealthBMC Oral Health1472-6831BioMed Central London 1472-6831-4-31555507210.1186/1472-6831-4-3Research ArticleSocio-demographic factors and edentulism: the Nigerian experience Esan Temitope Ayodeji [email protected] Adeyemi Oluniyi [email protected] Patricia Adetokunbo [email protected] Ayodeji Omobolanle [email protected] Department of Restorative Dentistry, Obafemi Awolowo University, Ile-Ife, Nigeria2 Department of Restorative Dentistry, Obafemi Awolowo University, Ile-Ife, Nigeria3 Department of Restorative Dentistry, University of Lagos, Lagos, Nigeria4 Department of Preventive Dentistry, Obafemi Awolowo University Teaching Hospitals complex, Ile-Ife, Nigeria2004 22 11 2004 4 3 3 26 2 2004 22 11 2004 Copyright © 2004 Esan et al; licensee BioMed Central Ltd.2004Esan 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 rate of total edentulism is said to be increasing in developing countries and this had been attributed mainly to the high prevalence of periodontal diseases and caries. Several reports have shown that non-disease factors such as attitude, behavior, dental attendance, characteristics of health care systems and socio-demographic factors play important roles in the aetiopathogenesis of edentulism. The aim of this study was to assess the relationship between socio-demographic factors and edentulism.
Methods
A total of 152 patients made up of 80 (52.6%) males and 72 (47.4%) females who presented in two prosthetic clinics located in an urban and a rural area were included in the study. The relationship between gender, age, socio-economic status and edentulism in this study population was established.
Results
No significant relationship between gender and denture demand was noted in the study. The demand for complete dentures increased with age while the demand for removable partial dentures also increased with age until the 3rd decade and then started to decline. A significant relationship was found between denture demand and the level of education with a higher demand in lower educational groups (p < 0.001). In addition, the lower socio-economic group had a higher demand more for prostheses than the higher group.
Conclusions
The findings in this study revealed a significant relationship between socio-demographic variables and edentulism with age, educational level and socio-economic status playing vital roles in edentulism and denture demand.
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Background
Edentulism (partial or total) is an indicator of the oral health of a population [1]. It may also be a reflection of the success or otherwise of various preventive and treatment modalities put in place by the health care delivery system [2]. Many patients also regard edentulism as self-mutilating and may be a strong incentive to seek dental treatment [3].
While the rate of total edentulism is decreasing in developed countries, the reverse is the case with developing countries and this had been attributed mainly to the high prevalence of periodontal diseases and caries [5-7].
Previous studies have also shown that several non-disease factors such as attitude, behavior, dental attendance, characteristics of health care system and socio-demographic factors play important roles in the aetiopathogenesis of edentulism [3].
Some studies reported that the incidence of edentulism correlated with educational levels and income status, with those in the lower levels exhibiting higher risks of becoming totally edentulous [8,9]. In addition, a study done in a rural area of Eastern Guatemala showed that social and environmental influence such as poverty, lack of proper education and inadequate diet contributed to widespread premature and heavy losses of permanent teeth [10].
Although, Hoover and McDermount [11] reported a higher prevalence of edentulism in males than females, Marcus et al observed that the prevalence of edentulism had no relationship with gender [12]. They also observed that there was an inverse relationship between the level of education, income and edentulism.
Studies among Nigerians have linked some of these socio-demographic factors with the prevalence, pattern and rate of dental diseases [13,14] but there has been no report on the influence of these on edentulism.
The aim of this study therefore was to assess the relationship between socio-demographic variables with types of edentulism.
Methods
All patients that attended and were treated in the removable prosthetic units of Obafemi Awolowo University Teaching Hospitals Complex (OAUTHC), Ile-Ife (a rural area located in the south west of Nigeria) between the months of March and May year 2002 and Lagos University Teaching Hospitals (LUTH), Lagos (an urban area also located in south west Nigeria) between December 2002 and March 2003 were included in the study. Information such as age, gender, occupation and level of education attained were documented. The types of partial denture received following treatment at the clinics were also documented
There has not been a consensus on various socio-economic classifications in Nigeria because of the unstructured nature of the society. Therefore, for the purpose of this study, a standard occupational classification system designed by Office of population Census and Surveys, London (OPCS 1991) [15] modified based on local reality was used and patients were classified into three socio-economic groups:
Class 1 = Skilled worker e.g professionals and managerial officers and retirees of this cadre.
Class 2 = Unskilled workers e.g. Artisans and traders
Class 3 = Dependants. e.g. Retirees of class 2, those not on pensions, house wives of class 2 cadre, students whose parents are unskilled workers
Data was analysed using SPSS for Windows version 10.0, (SPSS Inc Chicago Illinois, USA). Analysis included frequency, cross tabulations, calculation of means. Association between discrete variables was tested by Chi-Square and the level of significance was set at 5%.
Results
One hundred and fifty two patients attended the prosthetic clinics during the study period. Eighty (52.6%) were males while 72 (47.4%) were females (Table 1). Their ages ranged from 8 to 84 years. The median age was 22.00 years, while the mean age was 41.8 (±19.5) years. The mean age for Ile-Ife study population was 41.3 (±20.46) years, while that of Lagos was 39.9 (±17.56) years.
Table 1 Distribution by gender.
Gender Ile-Ife Lagos Total
No % No % No %
Male 48 48.0 32 61.5 80 52.6
Female 52 52.0 20 38.5 72 47.4
Total 100 100.0 52 100.0 152 100.0
χ2 = 2.515, df = 1, P = 0.113.
There were no statistically significant age (p = 0.312) and gender (p = 0.113) differences between the populations from the two centers. (Tables 1 and 2).
Table 2 Denture demand by age and center/clinic
Age group Ile-Ife Lagos Total
N % N % N %
≤20 18 18.0 5 9.6 23 15.13
21–40 35 35.0 22 42.3 57 37.5
41–60 25 25.0 17 32.7 42 27.63
≥61 22 22.0 8 15.4 30 19.74
Total 100 100.0 52 100.0 152 100.00
χ2 = 3.568, df = 3, P = 0.312
There was a highly significant difference in the educational status of patients seen at LUTH and OAUTHC with patients seen at LUTH being of higher educational levels than patients seen at OAUTHC. (χ2 = 7.50 df = 3, P < 0.001) (Figure 1).
Figure 1 Distribution of patients according to educational level.
In terms of socioeconomic status, 28 (18.42%) patients belonged to class I; 43(28.29%) patients belonged to class II while 81(53.29%) belonged to class III. There was no statistically significant difference in the socio-economic status of patients from the two centers ((χ2 = 5.70, df = 2, p = 0.057). (Figure 2)
Figure 2 Socio-economic status distribution.
In both centers, 134 patients (88.2%) received removable partial dentures, 13 patients (8.6%) received complete dentures while 5 patients (3.3%) received either upper or lower complete dentures. There was no significant difference in the demand for different types of dentures between the study locations. (P = 0.315). (Table 3).
Table 3 Demand for various types of denture by study location.
Types of dentures Ile-Ife Lagos Total
N % N % N %
Complete 11 11.0 2 3.8 13 8.6
Lower or upper complete denture 3 3.0 2 3.8 5 3.3
Removable partial denture 86 86.0 48 92.3 134 88.2
Total 100 100.0 52 100.0 152 100.0
χ2 = 3.568, df = 3, P = 0.312
For the purpose of analysis, complete and lower/upper complete denture columns were merged.
However, there was a significantly higher demand for removable partial dentures than any other type of prostheses. (P < 0.01). (Table 3)
It was observed that as the age increased, the proportions demanding for complete dentures also increased. In addition, those in age group 21–40 years demanded more for removable partial denture than any other age groups. While those above 61 years asked more for removable complete dentures than removable partial dentures. (Table 4).
Table 4 Types of denture demand within each age group.
Age group Denture demanded TOTAL
Complete Partial
no % no % no
≤20 1 4.3 22 95.7 23
21–40 2 3.5 55 96.5 57
41–60 5 11.9 37 88.1 42
≥61 10 33.3 20 66.7 30
Total 18 11.8 134 88.2 152
Likelihood-ratio χ2 = 16.579, df = 3, P = 0.001
No significant relationship between gender and pattern of denture demand (p = 0.812) was noted and no statistically significant difference was noted in the pattern of denture demand between the two centers (p = 0.277). (Table 5 and Figure 3).
Table 5 Demand for various prostheses in relation to gender.
Type of Denture male Female Total
Complete denture 6 7 13
Complete upper or Lower denture 3 2 5
Partial denture 71 63 134
Total 80 72 152
χ2 = 0.57, df = 1, p = 0.812
For the purpose of analysis, complete and lower/upper complete denture columns were merged.
Figure 3 Types of prostheses demanded by centers
The lower educational groups demanded more for complete dentures among those asking for complete denture, while those with higher level of education asked more for removable partial denture. (P < 0.001). (Table 6). More over, those with tertiary level of education constituted the majority of the study population. (Table 6)
Table 6 Demand for dentures according to educational level
Educational Level Complete Denture Partial Denture Total
No % No % No %
Nil 11 61.1 12 8.9 23 15.1
Primary 3 16.7 8 5.9 11 7.2
Secondary 0 0.0 51 38.1 51 33.6
Tertiary 4 22.2 63 47.0 67 44.1
TOTAL 18 100.0 134 100.0 152 100.0
Fishers exact test P < 0.001
For the purpose of analysis, secondary and tertiary educational levels' rows were merged. Also complete denture and either upper or lower complete denture column were merged
Among the patients that were completely edentulous, there was no significant difference in the demand for complete dentures between those with lower educational status and those with higher educational status. P = 0.276 (Table 7)
Table 7 Relationship between age group, educational level and completely edentulous state
Age group Educational Level
Nil Primary Secondary/Tertiary
≤20 1(9.1%) - -
21–40 2(18.2%) - -
41–60 4(33.4%) 1(33.3%)
>60 4(33.4%) 2(66.7%) 4(100%)
Likelihood-ratio χ2 = 7.515, df = 6 P = 0.276
It was noted that the lower the socio-economic status the higher the demand for dentures. This picture was independent of rural or urban dwelling. (Tables 8 and 9). However, 28.3 % of those in Class II who need dentures asked for complete as opposed to 3.6% in Class I and 8.6% of those in Class III.
Table 8 Relationship between edentulous state and socio-economic status.
Socio-economic status Edentulous state Total
Partial complete
No % No % No %
Class I 27 96.4 1 3.6 28 18.4
Class II 33 76.7 10 23.3 43 28.3
Class III 74 91.4 7 8.6 81 53.3
Total 134 88.2 18 11.8 152 100
χ2 = 7.992, df = 2, P = 0.018
Table 9 Socio-economic status distribution by centers
Socio-economic Class Lagos center Ife center TOTAL
NO % NO % No %
Class I 8 15.4 20 20.0 28 18.4
Class II 21 40.4 22 22.0 43 28.3
Class III 23 44.2 58 58.0 81 53.3
TOTAL 52 100.0 100 100.0 152 100.0
χ2 = 5.70, df = 2, p = 0.057
Discussion
Tooth loss could occur as a result of caries, periodontal diseases, trauma, tooth impaction, orthodontic reasons, hypoplasia, over eruption, supernumerary teeth, attrition, neoplastic and cystic lesions [5-7].
Many studies have consistently shown the role of specific diseases like dental caries and periodontal disease as a major cause of tooth loss [7,13,14]. This same picture was noted in similar Nigerian studies [5,6].
Okoisor further established that the disease factors responsible for tooth loss was age related; with caries and periodontal diseases being the major causes of tooth mortality in children and adult respectively [5].
However, none of the studies done in Nigeria evaluated the role of other factors such as education, socio-economic status, gender, location of patients, dental attitude and behavior in the etiology of edentulism.
The older age groups in this study required more of removable complete dentures than the younger age groups while the younger age groups required more of removable partial dentures. This is in agreement with the study done by Marcus et al [12]. Although there was an over representation of age groups >61 and 21–40 in our study population, the percentages of these age groups in Nigerian population are 4% and 30% respectively in both urban and rural areas [16]. Hence, these age groups have risk factors that might be responsible for their needing dentures.
This age related changes may not be unconnected with the deteriorative physiological changes noticed after adolescence and which gets worse with increase in age, a situation which is changing rapidly in the developed countries due to improved social infrastructure and functional health system [17,18].
Most studies have also shown significant gender difference in edentulism with more males becoming edentulous than females [11,19]. This has been attributed to the fact that males are more active than females and do not pay much attention to oral care. A significant gender difference was not seen in this study although variation in site presentation was observed. In Lagos, an urban area, more males actually demanded for prostheses. However, in Ile-Ife, a rural area more females demanded for prostheses. This is in agreement with the studies done by Eklund and Burt [8] and Marcus et al [12]. Although no statistically significant difference was noted in the rural-urban gender presentation, a larger qualitative study alongside a quantitative study may be able to adduce possible reasons for this interesting observation.
Majority of our study population belonged to the higher education status. This is because those with higher level of education are more informed about their health needs and may seek dental treatments earlier and more often than those of lower educational status who may only seek dental treatment when there is apparent morbidity. In addition, those of higher educational status are likely to be richer than those of lower educational status. Hence, they are able to afford the cost of dental treatments from time to time.
Our study showed that the need for complete dentures decreased with increasing level of education (p < 0.001), hence the likelihood of retaining teeth in the mouth becomes higher as the educational level increases. Although the educational status of the patients from the two centers differ, independent analysis of these centers still showed the same significant effect of educational status on the pattern of denture demand. This is in agreement with the findings of Brodeur et al, where the proportion of completely edentulous adults decreased from 26% in 1980 to 20 % in 1993 due to improved income and educational status [1].
The association between edentulism and educational status may be as a result of improved dental health awareness, increased utilization of oral health facilities, proper oral hygiene habits acquired during learning process and peer group influence.
Interestingly, about 23.3 % of those in class II who need dentures asked for complete dentures as opposed to 3.6% in class I and 8.6% in class III. The reason for this may be as a result of the fact that they may not be able to afford the exorbitant cost of restorative procedures hence they wait until they have lost their set of teeth to have a complete removable denture which is cheaper.
The present study showed that people with low socio-economic status demanded for more dentures than the high socio-economic group. Studies have long established a gradient relationship between socio-economic status and health [20]. In so many intricate ways, socio-economic status tends to affect health behaviors, the environment and social influences an individual is exposed to. Hunter and Arbona found that environmental influences such as land hunger, family poverty, and inadequate diet are of paramount importance in the cause of tooth loss [10]. They concluded, "Periodontal disease drives the poorest of the poor to spend disproportionately large sums on pain killers and destructive traditional medicine".
The importance of socio-economic status is further reflected in the urban-rural variance noted in this study population's demand for denture. The Ile-Ife study group, a rural population with a lower socio-economic status, demanded for more dentures than the Lagos study group.
However, this study population was an all-inclusive hospital based sample; the result may not be representative of the population at large. Hence, its use can only be limited to the study population. A randomized population based survey may be able to present a better picture among Nigerians.
This study observed that edentulism is due to a combination of various factors. Poor education, a risk factor for poverty, has been identified as a major factor in edentulism. So also is the socio-economic status of the patient. These two factors, which are non-disease factors, affect the mortality of teeth arising from disease factors. There is therefore a need for oral health policy formulators to focus on improving the educational and socio-economic status of its citizens (a down stream approach) rather than the present emphasis on disease control (an up stream approach) in oral health care delivery.
On the other hand, with increasing level of literacy, and positive social changes in Nigeria, Prosthodontists should brace up to face the challenges that may arise from increased removable partial denture demand and decreased demand for complete dentures. This is because, with increase in level of education and socio-economic status of patients, the demand for removable partial dentures is likely to increase while dentists may be confronted with a significant increase in the number of difficult edentulous mouths requiring treatment.
In addition to addressing the non-disease factors, dental education should be targeted at the uneducated populace, the rural dwellers and low-income groups to reduce the rate of total edentulism.
Conclusion
No gender relationship with denture demand was noted this study. In addition, the demand for complete dentures increased with age. There was a statistically significant inverse relationship between educational levels and demand for dentures. There was more demand for prostheses among the lower socio economic groups.
Pre-publication history
The pre-publication history for this paper can be accessed here:
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Environ HealthEnvironmental Health1476-069XBioMed Central London 1476-069X-3-111552212210.1186/1476-069X-3-11ResearchMercury exposure, malaria, and serum antinuclear/antinucleolar antibodies in amazon populations in Brazil: a cross-sectional study Silva Ines A [email protected] Jennifer F [email protected] Andrew [email protected] Andre [email protected] Ana Maria [email protected] Elizabeth CO [email protected] Souza Jose M [email protected] CL [email protected] Noel R [email protected] Ellen K [email protected] The Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe Street, Room E6642, Baltimore, Maryland, 21201 USA2 Department of Epidemiology, University of Maryland Medical School, Baltimore, Maryland, USA3 Institute Evandro Chagas (IEC), Fundaçao Nacional da Saúde, Belem do Pará-66090, Brazil2004 2 11 2004 3 11 11 18 2 2004 2 11 2004 Copyright © 2004 Silva et al; licensee BioMed Central Ltd.2004Silva 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
Mercury is an immunotoxic metal that induces autoimmune disease in rodents. Highly susceptible mouse strains such as SJL/N, A.SW, B10.S (H-2s) develop multiple autoimmune manifestations after exposure to inorganic mercury, including lymphoproliferation, elevated levels of autoantibodies, overproduction of IgG and IgE, and circulating immune complexes in kidney and vasculature. A few studies have examined relationships between mercury exposures and adverse immunological reactions in humans, but there is little evidence of mercury-associated autoimmunity in humans.
Methods
To test the immunotoxic effects of mercury in humans, we studied communities in Amazonian Brazil with well-characterized exposures to mercury. Information was collected on diet, mercury exposures, demographic data, and medical history. Antinuclear and antinucleolar autoantibodies (ANA and ANoA) were measured by indirect immunofluorescence. Anti-fibrillarin autoantibodies (AFA) were measured by immunoblotting.
Results
In a gold mining site, there was a high prevalence of ANA and ANoA: 40.8% with detectable ANoA at ≥1:10 serum dilution, and 54.1% with detectable ANA (of which 15% had also detectable ANoA). In a riverine town, where the population is exposed to methylmercury by fish consumption, both prevalence and levels of autoantibodies were lower: 18% with detectable ANoA and 10.7% with detectable ANA. In a reference site with lower mercury exposures, both prevalence and levels of autoantibodies were much lower: only 2.0% detectable ANoA, and only 7.1% with detectable ANA. In the gold mining population, we also examined serum for AFA in those subjects with detectable ANoA (≥1:10). There was no evidence for mercury induction of this autoantibody.
Conclusions
This is the first study to report immunologic changes, indicative of autoimmune dysfunction in persons exposed to mercury, which may also reflect interactions with infectious disease and other factors.
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Background
Mercury has been recognized as a significant environmental and public health problem for more than 40 years, primarily for its effects on the developing nervous system, as expressed in tragic episodes of human poisoning in Japan and Iraq [1]. Awareness of the effects of mercury on the immune system has increased in the last decade [2,3].
In rodent models exposure to inorganic and organic mercury has a range of immunotoxic effects, functionally associated with decreased cell-mediated immunity and the induction of autoimmunity [4]. These effects vary with strain [5-7]. Both inorganic and organic forms of mercury are immunotoxic, although they differ quantitatively and qualitatively in their effects on the immune system; methylmercury may require metabolism into inorganic species to induce immunotoxic effects, such that the effects of methylmercury are delayed and reduced in appearance [6]. Ethylmercury (C2H5Hg+), the active compound in thimerosal and other medical compounds, induces in a dose-dependent pattern all the features of systemic autoimmunity that have been described after exposure to mercuric chloride (HgCl2) [8]. Mercury can enter the body through inhalation, as elemental mercury (Hg0), through dermal or eye contact, as ethylmercury, and by absorption through the gastrointestinal track, primarily as methylmercury (CH3Hg+) through ingestion of contaminated fish [1]. Inhaled Hg0 vapor easily crosses the pulmonary alveolar membranes to enter the circulatory system, where it is primarily bound to red blood cells, and is rapidly distributed to the central nervous system, and the kidneys [9]. Mercury absorbed through skin contact is oxidized in the liver to Hg2+ by glutathione [10]. After entering the blood stream, mercury is distributed to all tissues, including the brain, kidney, lungs, hair, nails, liver, fetus, milk, etc [1,10].
In the literature, no cases of frank autoimmune disease have been reported in persons exposed to mercury, occupationally or environmentally [3]. A few studies have examined relationships between mercury exposures and adverse immunological reactions, particularly in connection with mercury amalgam, but these are controversial [1]. At relatively high levels of occupational exposure, changes in immunoglobulins have been reported, but not consistently [3,11-13]. Nephropathy described in workers with either acute or chronic exposures to Hg0 vapor may involve deposition of autoantibodies to basement membrane proteins in the glomerulus [3,14]. In a study of chloralkali workers, circulating anti-laminin antibodies were found in some workers as well as autoantibodies against glomerular basement membrane and circulating immune complexes, but no significant increases in antinuclear autoantibodies (ANA) were found [12]. No studies of immune parameters have been conducted in the large longitudinal studies of children exposed to methylmercury via fish consumption in the Seychelles or in the Faeroe Islands [1,15,16]. In a cross-sectional study of a maritime population of children with exposure to polychlorinated biphenyls and methylmercury via seafood consumption, numbers of naïve T-cell subsets (CD4+CD45RA), T-cell proliferation, and plasma IgM were decreased, while IgG levels were increased, relative to controls [17].
The goal of this study was to test the hypothesis that exposures to methylmercury and/or inorganic mercury may have effects on specific markers of mercury-induced autoimmunity, that is, ANA and antinucleolar (ANoA) autoantibodies, and in a subset of subjects anti-fibrillarin (AFA) autoantibodies. ANoA autoantibodies, a marker found in some human autoimmune diseases [18], have been reported to be elevated by mercury in mice [19]. More recently, Pollard et al. have proposed that ANoA antibodies targeting the nucleolar 34-KDa protein fibrillarin may be specific biomarkers of mercury-induced immunotoxicity [20,21]. Mercury-induced ANoA in mice reacts with a conserved epitope of fibrillarin [20,21], which is indistinguishable from the AFA response seen in scleroderma. A recent case-control study reported that severely affected scleroderma patients with AFA were more likely to have higher levels of mercury in urine, as compared either to less severely affected cases without AFA, or controls, suggesting an etiologic role for mercury in this autoimmune disease [22]. However, the sample size was small and levels of mercury were low in all subjects.
We were able to conduct this study in collaboration with an ongoing epidemiological surveillance of mercury exposures in Amazonian Brazil, where populations are exposed to both inorganic and organic mercury associated with gold mining activities [23,24]. In Amazonian Brazil, as in many other regions of the world, elemental mercury is used in liquid form for amalgamation of gold particles in placer deposits [23,25]. The gold miners are directly exposed to inorganic mercury and residents of downstream communities are exposed to methylmercury via consumption of fish. Extensive work has been done on many of these populations, documenting a range of exposures among miners and fish consumers [24-26], many well above the levels found in populations in North America and Europe, and well in excess of the levels found in the Seychelles and Faeroes cohorts [1], although lower than those reported in Minamata [27].
In this study we analyzed autoantibodies and mercury exposures in three populations from the state of Pará, Brazil. These groups were exposed to different types of mercury in different settings, with exposure to other risk factors not all of which were determined. Therefore, these may contribute to the observed differences among communities, in addition to mercury exposures. We report here that exposures to mercury are associated with significant increases in the prevalence of elevated serum ANoA.
Methods
To test our hypothesis we examined three separate populations, selected from ongoing studies of mercury exposures and health status being conducted by FUNASA (Fundaçao Nacional de Saúde), under the leadership of Dr Santos of the Evandro Chagas Institute. The communities in our study were chosen from this surveillance database on the basis of differences in exposures to mercury and other risk factors in Pará, Brazil. At Rio-Rato, a garimpo or gold mine site, most of the population was directly involved in gold extraction and refining, resulting in relatively high but often episodic exposures to inorganic mercury, similar to those described by us and others [25,26]. This site is in the lower Tapajós River watershed, an area of high malaria transmission [28]. At Jacareacanga, a riverine community on the Tapajós River several hundred km downstream from the region of active gold mining in Pará, the inhabitants consume fish known to be contaminated with methylmercury [24,29,30]. There is little autochthonous malaria in this town but many people have histories of malaria because of contact with the nearby region [31]. Finally, at the village of Tabatinga, located on the lower Amazon River east of the Tapajós, the population has no direct or indirect contact with gold mining, and fish collected in this village have levels of methylmercury [24] within the guidelines for safe consumption recommended by the WHO and the US FDA [1]. Tabatinga has had no prevalent malaria over the past ten years, according to data from FUNASA (personal communication JM Souza).
Study design
The overall design of the mercury surveillance studies conducted by Dr Santos is a community based, cross sectional survey of Brazilian populations in Amazonia, focused on the states of Pará, Amazonas, Acre, and Rondônia, including gold mining sites, riverine communities, and populations without exposure to mercury. The study design is described in detail by Santos and colleagues [29,32]. In all studies, a census was first conducted at each site to determine sampling strategy. Subjects were then contacted by house-to-house survey and enrolled in proportion to the population in terms of age and gender. Overall, between 80 and 90% of contacted persons consented to participate at each site. Information was collected by interview, administered in Portuguese by trained personnel, to provide information on demographics (age, gender, educational attainment), diet (with particular emphasis on fish), birthplace, current/previous occupation (including use of mercury), income, health status, reproductive history (women), drug and alcohol use, past/current malaria, number of people per household, time residing at the site, and medical history. A short clinical examination was conducted, and samples of hair, blood, urine, and stool were taken for laboratory analyses, including mercury levels in hair and urine. Malaria was assessed by questionnaire to determine past history of malaria (self reported), as well as by thick smears taken to determine prevalent malaria. All smears were read by trained technicians. Data on past malaria were stratified using Baird as reference [33], in which he determined the minimum number (4) of prior malaria infections associated with acquisition of functional immunity (i.e., no disease and/or parasitemia after biting). The study was approved by the institutional review board of the IEC and FUNASA. The University of Maryland Medical School and the Johns Hopkins Medical Institutions Institutional Review Boards also approved the analyses conducted in this study.
Mercury exposure
Subjects' exposure to mercury was determined in two ways. First, information was gathered by questionnaire on occupational history (contact with and use of mercury in gold mining), and/or fish consumption (by weekly frequency and predominant types of fish consumed). Second, mercury concentrations were measured in biologic compartments. For persons in Tabatinga and Jacareacanga with chronic exposure via fish consumption, hair mercury (μg Hg/g of hair) was used as the exposure biomarker as recommended [1]. Hair samples were collected in 2 cm lengths (from the scalp) and analyzed using standard methods of atomic absorption spectrophotometry by cold vapor technique in the laboratory of Dr Santos, which participates in the international QA/QC program with the Université de Quebec [34]. For persons with occupational exposures to inorganic mercury, in Rio-Rato, urine mercury was used as the biomarker (μg Hg/L of urine, no data on creatinine was available). This is the standard method utilized by Santos and others for assessing occupational exposures to inorganic mercury and generally reflects relatively recent exposures [1,35].
Immunologic outcomes
Blood samples were collected by venipuncture and sera were separated on site by centrifuge, aliquoted and immediately frozen on liquid nitrogen for transport by air to the IEC in Belem (Pará). Aliquots of frozen serum were stored at -80°C and then transferred on dry ice to Baltimore via air transport accompanied by Dr Silbergeld. All analyses were done under blinded conditions.
Detection of ANA/ANoA
The serum samples were stored at -80°C until analysis. Each aliquot was thawed and 10 μL taken for analysis by indirect immunofluorescence (IIF) microscopy using commercially available slides prepared from human epithelial cells (HEp-2) as substrate (INOVA Diagnostics) following the methods of Burek and Rose [36]. The slides were stored in the dark at 4°C until they were analyzed by a blinded reader (IAS or AG). Randomly selected slides were re-checked by an experienced immunologist (CLB).
Detection of AFA
The antigen proteins were obtained from rat liver nuclei [20], and the proteins were separated by 15% SDS-PAGE. Preparations were first fractionated by SDS-PAGE and subsequently transferred to nitrocellulose and immunoblotted. Briefly, nitrocellulose was blocked in PBS/0.1% Tween-20/5% dry milk for 2 h at room temperature. Incubation with primary antibody (serum samples) (1/50 in blocking solution) was performed at room temperature for 1 h, followed by 3 washing steps of 10 min each in PBS/0.1% Tween-20. Secondary antibody (horseradish peroxidase-conjugated goat anti-human IgG) (Caltag Lab, CA) was used at a dilution of 1/2000 in blocking solution for 1 h at room temperature followed by 3 washes of 10 min each in PBS/0.1% Tween-20. Bound antibody was detected using chemiluminescence. The 34 KDa protein was detected by molecular weight using serum of scleroderma (SC) patients as a positive standard. The SC serum revealed one band at the expected molecular weight of 34 KDa.
Data analyses
The concentration of serum autoantibodies is expressed in terms of the dilution factor at which fluorescence could still be detected. Detection of autoantibodies at a serum dilution of ≥1:40 is considered "positive" for most clinical uses [37]. However, detectability at dilutions between 1:10 and 1:40 can also have health implications [37] and may be relevant as biomarkers of mercury exposure. Since we are studying the autoantibodies as biomarkers of immunotoxicity rather than as indicators of disease, we present our findings at both dilutions, ≥1:40 and ≥1:10.
Statistical analysis
Means for continuous variables (median for variables with skewed distributions) and percents for categorical variables were computed for the descriptive analysis in our data. Chi Square test was used to compare categorical variables and Student's t-test was used for continuous variables. In Jacareacanga we stratified hair mercury levels based on World Health Organization guidance (≤8 or ≥8 μg/g hair). In Tabatinga we stratified exposure by the observed median level (5.57 μg/g) since most hair mercury concentrations were below 8 μg/g. In Rio-Rato we used urine mercury levels based on WHO guidance (≤5 or ≥5 μg/L). We used the mean age for each population to stratify by age. Logistic regression modeling was used to evaluate the effect of mercury exposures on prevalence of ANA and ANoA (for 1:10 and 1:40 cutoffs), while controlling for age, sex, prevalent malaria, past history of malaria, and occupation. All data were analyzed using the SAS v.8.1 statistical package.
Results
Because of substantial differences among the populations and sites, we present the results for each site separately.
Tabatinga
Tabatinga is a typical riverine community in the lower Amazon. The community sample consisted of 98 adults, with 73% females, and a mean age of 44 years, (Table 1). This community has no occupational exposures to inorganic mercury, and the fish consumed have relatively low methylmercury contamination. The distribution of hair mercury is shown in Figure 1A; the majority of the persons had hair mercury levels below 8 μg/g. The median hair mercury concentration of 5.57 μg/g is higher than that reported in European and North America populations, which may reflect the very high frequency of fish consumption rather than excessive fish contamination [38]. No present malaria cases were found, and only 10% reported any past malaria (Table 2). Otherwise, the population was in good health.
Table 1 Demographic characterization of the 3 populations
Current Occupation (%) Prev. Occupation
Populations N Age [Mean] Sex (%) F/M Gold Mine Fisherman Others Students Gold Miner (%)
Tabatinga-adults 98 44 73/27 0 1.1 98.9 0 0
Jacareacanga 140 25 54/46 0 2.2 72.4 25.4 9.4
Rio-Rato 98 30 35/65 54 0 46 0 N/A
N/A = data not available from original survey.
Figure 1 Distribution of mercury levels Population distributions are shown for (A) Tabatinga and (B) Jacareacanga in μg Hg/g hair and (C) Rio-Rato in μg Hg/L urine.
Table 2 Malaria and Hg data from the 3 populations Malaria status (prevalent and reported past infections) and mercury exposures in the three populations.
Malaria Hg (median) Hg
Populations Prevalent (%) History (%) Urine (microgram/L) Hair (microgram/g) range values
Tabatinga-adults N = 98 0 10.1 ND 6.4 1.19–16.96
Jacareacanga N = 140 0 69.6 ND 8 0.29–58.47
Rio-Rato N = 98 93.9 N/A 4 ND 0.01–81.37
N/A = data not available from original survey.
ND = analysis not completed in original survey.
The prevalence of detectable ANA and ANoA in the Tabatinga samples was very low (Table 3; Figure 2). Most measurements (90%) were not detectable even at the lowest (1:10) dilution. These data are similar to those recently reported for a referent population in Sao Paulo [39]. In the few subjects with ANA or ANoA detectable at ≥1:10, there was no relationship between ANA or ANoA for any of the variables studied.
Table 3 Percentages of detectable ANA and ANoA in serum from the 3 populations
ANA (%) ANoA (%) ANA + ANoA (%)
Populations <det ≥1:10 ≥1:40 <det ≥1:10 ≥1:40 <det ≥1:10 ≥1:40
Tabatinga-adults N = 98 92.9 7.1 2.0 97.9 2.1 0 100 0 0
Jacareacanga N = 140 89.3 10.7 3.6 82.0 18.0 13.0 100 0 0
Rio-Rato N = 98 45.9 54.1 51.0 59.2 40.8 36.7 89.0 11.0 10.0
<det = below detection level at lowest dilution.
ANA ≥1:10 or ANoA ≥1:10 percentages include ANA 1 1:40 or ANoA ≥1:40 percentages.
Figure 2 Detectable levels of serum autoantibodies Population distributions of (A, C, E) ANA and (B, D, F) ANoA are shown for (A&B) Rio-Rato, (C&D) Jacareacanga and (E&F) Tabatinga, at varying serum dilutions.
Jacareacanga
Jacareacanga is a riverine settlement of approximately 500 persons, located on the mid-Tapajós River. The 140 subjects consisted of 54% women and had a mean age of 25 years (Table 1). Fish are the primary protein source and piscivorous species sold at local markets have reported to have elevated concentrations of methylmercury [29,30]. No persons reported current employment in gold mining or refining, but some persons reported a history of such activities. The distribution of hair mercury is shown in Figure 1B. Median hair mercury levels were 8 μg/g (Table 2), substantially higher than that found in unexposed populations [1]. Fish consumption was the major predictor of hair mercury; previous occupation as a gold miner was also related to higher hair mercury concentrations. No subjects were positive for malaria by blood smear at the time of survey (Table 2). However, a majority reported a history of past malaria (Table 2). Among these subjects, 50% reported 2 or fewer infections, while the maximum number of past infections reported was 6.
As shown in Table 3, nearly 11% of the population had detectable ANA ≥1:10, and nearly 20% had detectable ANoA ≥1:10. In those subjects where ANA was detectable, most (96.4%) presented at low concentrations, while 13% had ANoA detectable at 1:40 (Figure 2). No subjects were positive for both autoantibodies. A significantly higher percentage of subjects with detectable ANoA (33%) had hair mercury levels greater than the median value of 8 μg/g. In the logistical model only mercury, from all the variables studied, was significantly correlated with the presence of ANoA (≥1:10) (Table 4). Individuals with higher hair mercury levels, who reported any past malaria, were more likely to have detectable concentrations of ANoA (40%) as compared to those with low mercury levels. In persons reporting fewer than 4 past malaria infections, hair mercury was positively correlated with the presence of detectable ANA (≥1:10; ≥1:40) and ANoA (≥1:10) (Table 5). In persons with low hair mercury, there was a positive correlation of number of past malaria infection with detectable ANA at either ≥1:10 or ≥1:40.
Table 4 Jacareacanga-odds ratio between risk factors and prevalence of ANoA ≥1:10 Logistical model for odds of detectable ANoA (≥1:10) and mercury exposure, gender, age, occupation, and malaria history, p < 0.05*.
Variable Odds ratio 95% Confidence interval p-value
Hg 3.27 1.28 – 8.37 0.014*
Gender 1.16 0.44 – 3.02 0.769
Age 0.93 0.36 – 2.39 0.871
Past-malaria 1.28 0.43 – 3.83 0.663
N past-malaria infections 1.18 0.39 – 3.55 0.772
Other occupations: gold miner 0.74 0.14 – 3.75 0.711
Table 5 Serum ANA and ANoA (Jacareacanga) stratified for mercury and past malaria infections P values obtained comparing Hg <8 with >8 μg/g hair (* p < 0.05) and number of malaria infections <4 with ≥4 (§p < 0.05).
# malaria infections <4 (%) # malaria infections ≥4 (%)
Hg >8:
ANA 1:10 3.61 17.65 §
ANA 1:40 0 11.76 §
ANoA 1:10 12.05 17.65
ANoA 1:40 8.43 17.65
Hg ≥8:
ANA 1:10 14.81 * 10.00
ANA 1:40 11.11 * 0
ANoA 1:10 33.33 * 30.00
ANoA 1:40 22.22 20.00
Rio-Rato
Rio-Rato is a gold mining community, where a small settlement has grown up around a still active mining site in the mid-Tapajós watershed. Approximately 2/3 of the population was male with a mean age of 30 years (Table 1). Educational and socioeconomic variables were low. Urine mercury levels (4 μg/L) were lower than those found in other mining populations in Amazonia [25,26,40] (Figure 1C). Only 6 had levels ≥25 μg/L, the median value found by us in another gold mine population [40]. This may have been due to the timing of our visit, during the dry season, when gold amalgamation activities were reduced. A high degree of variability in urine mercury levels among gold miners has been reported by others [26,41]. Because of this, we used exposure history to characterize mercury exposures in this population.
This region has a high rate of malaria transmission [28,31]. Over 90% of the Rio-Rato population had prevalent malaria, detected by thick film slide at the time of the survey (Table 2). No data on past malaria episodes were collected. Over half of the population had ANA detectable at ≥1:10, and nearly half had ANoA detectable at ≥1:10 (Table 3; Figure 2). At or above 1:40, the Rio-Rato population still presented with a high prevalence of elevated ANA (51%) and ANoA (36.7%) (Table 3; Figure 2). In 10% of the population, levels of both autoantibodies were detectable at 1:40. About a quarter had concentrations up to a dilution 1:160 and some persons in this sample had very high concentrations of autoantibodies, detectable up to a dilution of 1:320. The likelihood of ANoA detectable at 1:40 was significantly higher in those individuals with a longer history of work in gold mining (≥7 years compared to <4 years).
The presence of autoantibodies against the 34 KDa protein fibrillarin was determined by immunoblotting in those serum samples from Rio-Rato with ANoA detectable ≥1:10. Of 40 subjects, 3 had serum with detectable AFA, as shown in Figure 3.
Figure 3 AFA in Rio-Rato serum samples Photograph of denaturing gel electrophoresis of 3 AFA positive samples from 41 ANoA positive serum samples previously determined by IIF. Fibrillarin = 34 Kda protein.
Discussion
In this paper we report the first data on specific biomarkers of autoimmune dysfunction in persons exposed to inorganic mercury or methylmercury. One earlier study reported elevations in anti-laminin autoantibodies in workers exposed to mercury [12]; however, no correlation with mercury exposure was observed. While our data are limited by sample size, and are likely influenced by other variables in addition to mercury, the results are consistent with the experimental literature indicating that mercury can alter immune function and increase circulating levels of autoantibodies, including ANA and ANoA [5,6,42].
There was an overall qualitative correlation between mercury exposures and levels of ANA or ANoA, both by study site and within study sites. Our ability to compare these populations more directly was limited by differences in the original study design with respect to mercury exposure assessment (hair at Jacareacanga and Tabatinga, urine at Rio-Rato). Persons from Tabatinga, with the lowest range of mercury exposures, had lower prevalence of detectable ANA or ANoA, and in those few persons with detectable autoantibodies, the concentrations were low. Nonetheless, Tabatinga subjects had significantly elevated mercury levels, as compared to North America populations [1,38], which is probably attributable to their very high intake of locally caught fish, such that even though these fish had methylmercury levels below the US FDA or WHO guidance, these consumption rates resulted in elevated body burdens, as compared to North Americans eating fish much less frequently [43]. Persons from Jacareacanga were exposed to methylmercury from fish consumption. The median hair mercury levels in Tabatinga and Jacareacanga are relatively close, but the distribution of hair mercury in Jacareacanga shows that there are many persons with exposures well above those obtained in Tabatinga. In Jacareacanga subjects, higher prevalence of detectable ANoA was observed, mostly at low concentrations (1:10 or 1:40), but several had levels measurable in dilutions as high as 1:160. In Rio-Rato persons were highly exposed to inorganic mercury from gold mining activities, as well as methylmercury via fish consumption. Detectable levels of ANA and/or ANoA were prevalent and detectable at high concentrations (1:320).
It is possible that exposure to inorganic mercury may be more "autoimmunogenic" than exposure to methylmercury, as shown in mice models [6]. In contrast to studies in mice [20,21], we found little evidence that AFA, levels are specifically affected by mercury. This may indicate a difference between humans and mice. However, as shown in Figure 3, there appear to be many unidentified nuclear antigens observed in serum samples from this population, which were not observed in either the SC serum, or in studies of US control subjects (data not shown). It would be very pertinent to analyze sera from all these three populations, using a range of other nuclear antigens known to be targeted in autoimmune diseases [19,37].
We examined the serum ANA and ANoA results at both 1:10 and 1:40 dilutions. The results in Jacareacanga and Rio-Rato subjects are clearly different from studies of healthy individuals in the US and Brazil [39]. Interpretation must be cautious. Tan et al. [37] showed that many "healthy individuals" (31.7%) show detectable ANA at dilutions <1:40. This suggests that such a cutoff point for serum dilution may have relatively little diagnostic value. However, the purpose of this study was not to detect persons with latent autoimmune disease, but rather to use these antibody measurements as biomarkers to test the hypothesis that mercury exposures might induce autoimmune dysfunction. A recent publication on the prevalence of ANA in serum of normal blood donors in Brazil found no age-related differences in prevalence of detectable ANA among adults, and that very few subjects had detectable ANA at dilutions >1:40 [39]. None of the subjects in these populations were reported to have autoimmune disease or overt clinical disease of any type, except malaria, but only routine clinical assessments were done.
In Rio-Rato and Jacareacanga subjects, other risk factors were related to elevations in ANoA and ANA. In Rio-Rato, time spent at the site and in gold mining was positively correlated with likelihood of elevated ANoA. This variable, time spent at the site, may represent length of exposure to both mercury and malaria infection. In Jacareacanga, there was a positive relationship between malaria (any past reported cases) and likelihood of elevated ANoA. We examined these potential biological interactions among mercury, malaria, and autoimmune biomarkers further, because of studies demonstrating that repeated malaria infections are associated with increased levels of autoantibodies, including ANA, presumably due to cytotoxic damage and exposure of intracellular epitopes [44,45]. Other studies have shown that autoantibodies are produced in mice infected with malaria, which react with several nuclear antigens, namely RNA, soluble nuclear material and DNA [46]. Our data indicate that in persons with lower mercury exposures (less than the median of 8 μg/g hair), increasing number of past malaria infections (≥4) were associated with increased likelihood of ANA, but not ANoA. In persons with higher mercury exposure, increased malaria exposure did not further increase ANA. We do not, at present, have an explanation for these observations, except to speculate that higher mercury exposures may induce a strong autoimmune dysfunction, such that additional effects of malaria are not significant.
It is difficult to draw any firm conclusions from these analyses, since the malaria data in Jacareacanga were based upon unconfirmed self-reports. We could not test this hypothesis in the Rio-Rato group, since almost all subjects had prevalent malaria and extensive histories of past infection.
We have reported a suggested correlation between mercury exposures and number of past malaria infections among gold miners in another gold mine settlement, in Brazil, at Piranha [40]. We have also reported that exposure of mice to low levels of mercury both decreases host resistance to murine malaria (Plasmodium yoelii) and impairs acquisition of immunity to murine malaria in the Nussenzweig model [47]. Mercury may reset immunologic responses to malaria, to increase expression of autoantibodies through its documented effects to up regulate Th2 mediated immune responses [48,49].
Finally, we may speculate as to why we have been able to observe associations between mercury exposures and these biomarkers of autoimmune dysfunction in these populations, while most clinical or epidemiological studies of mercury and immunotoxicity have been negative or only weakly positive [11-13]. In these other studies, the cohorts were relatively small (between 44–70), and they were exposed only to elemental or inorganic mercury through working in mercury or chloralkali plants [11,13]. No reports of exposure to other risk factors, such as infectious diseases, were reported. In our study, some of the exposures were chronic in nature and included exposures to methylmercury (certainly for fish consumption in Amazonian populations, where fish form the major portion of the protein consumed) [30,50]. In contrast, the gold miners were likely to have relatively variable exposures to inorganic mercury, with episodes of very high inhalation exposure [25,26,41]. The role of genotype may also be important. In rodents, genotype clearly plays an important role in both modulating the immune response to inorganic mercury as well as toxicokinetics [5-7,51]. In susceptible mice, induction of genetically restrictive ANoA by mercury are linked to mouse MHC (H-2) haplotype s and q [7], while most other haplotypes confer relative resistance [52]. Non-MHC genes decide the strength of ANoA response in susceptible mice exposed to mercury [6,7]. In addition, mercury toxicokinetics differ among inbred mouse strains. As Nielson and Hultman [52] demonstrated, there is a correlation between mercury toxicokinetics and AFA production in mice. In this study, the populations in these communities represent a wide range of ethnicities, including Europeans, Africans, and indigenous groups of Amazonia. Their immunogenetics may include persons with increased susceptibility to mercury-induced autoimmune dysfunction.
Conclusions
Our study is the first to report a correlation between biomarkers (ANA and ANoA) and mercury exposure in humans. In addition, co-exposures to mercury and infectious diseases, including malaria, may set the stage for eliciting discernible alterations in immune function. Whether such co-exposures increase the risks of autoimmune disease will require further studies, which are underway.
List of abbreviations
ANA – antinuclear autoantibodies
ANoA – antinucleolar autoantibodies
AFA – anti-fibrillarin autoantibodies
IIF – indirect immunofluorescence
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
IA Silva carried out the analysis of autoantibodies, participated in the statistical analysis, and drafted the manuscript. JF Nyland helped in autoantibody analysis and technical editing of the manuscript; A Gorman helped in autoantibody analysis; AM Ventura, JM de Sousa, and ECO Santos carried out the field studies, malaria assessments, and mercury analysis. CL Burek and NR Rose guided the antibody analysis. EK Silbergeld participated in the design and coordinated the study; she also participated in the Jacareacanga field study, and in the draft of 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
We would like to thank Dr GT Strickland for his continued assistance and knowledge of malaria; Dr KM Pollard for supplying the SC patient serum and the protocol for extracting nuclei from rat liver. This study was supported by grants from the Pan American Health Organization, NIH-Fogarty (IAS), Heinz Family Foundation (EKS, IAS), W Alton Jones Foundation, the Portuguese Foundation for Sciences and Technology (IAS), and the Fundaçao Nacional da Saúde (FUNASA), Brazil.
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| 15522122 | PMC538267 | CC BY | 2021-01-04 16:36:31 | no | Environ Health. 2004 Nov 2; 3:11 | utf-8 | Environ Health | 2,004 | 10.1186/1476-069X-3-11 | oa_comm |
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Mol CancerMolecular Cancer1476-4598BioMed Central London 1476-4598-3-331557420010.1186/1476-4598-3-33ResearchCpG methylation of the FHIT, FANCF, cyclin-D2, BRCA2 and RUNX3 genes in Granulosa cell tumors (GCTs) of ovarian origin Dhillon Varinderpal S [email protected] Mohd [email protected] Syed Akhtar [email protected] CSIRO Health Sciences and Nutrition, Gate No 13, Kintore Avenue, PO Box 10041, Adelaide BC, Adelaide SA 5000, Australia2 Human Genetics Laboratory, Department of Biosciences, Jamia Millia Islamia, New Delhi, 100 025, India2004 1 12 2004 3 33 33 30 9 2004 1 12 2004 Copyright © 2004 Dhillon et al; licensee BioMed Central Ltd.2004Dhillon 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
Granulosa cell tumors (GCTs) are relatively rare and are subtypes of the sex-cord stromal neoplasms. Methylation induced silencing in the promoters of genes such as tumor suppressor genes, DNA repair genes and pro-apoptotic genes is recognised as a critical factor in cancer development.
Methods
We examined the role of promoter hypermethylation, an epigenetic alteration that is associated with the silencing tumor suppressor genes in human cancer, by studying 5 gene promoters in 25 GCTs cases by methylation specific PCR and RT-PCR. In addition, the compatible tissues (normal tissues distant from lesion) from three non-astrocytoma patients were also included as the control.
Results
Frequencies of methylation in GCTs were 7/25 (28 % for FHIT), 6/25 (24% for FNACF), 3/25 (12% for Cyclin D2), 1/25 (4% for BRCA2) and 14/25 (56%) in RUNX3 genes. Correlation of promoter methylation with clinical characteristics and other genetic changes revealed that overall promoter methylation was higher in more advanced stage of the disease. Promoter methylation was associated with gene silencing in GCT cell lines. Treatment with methylation or histone deacetylation-inhibiting agents resulted in profound reactivation of gene expression.
Conclusions
These results may have implications in better understanding the underlying epigenetic mechanisms in GCT development, provide prognostic indicators, and identify important gene targets for treatment.
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Background
Ovarian cancer is one of the most common cancers in women of all age groups. Among ovarian neoplasms, granulosa cell tumors (GCTs) are relatively rare, accounting for approximately 3% of all ovarian cancers. One common DNA modification is promoter hypermethylation associated with loss of expression of a tumor suppressor gene. During cancer development, there is a shift in methylation patterns and some promoter region CpG islands become methylated leading to silencing of the adjacent genes [1] and this process is considered to be a critical step in cancer development. The causes of methylation change in normal cells remain unknown. It has been hypothesised that fixation of methylation occurs when random seeding of methylation occurs in the promoter regions of silent genes [2]. Methylation seems to provide an ideal set of cancer specific markers for early detection of or for monitoring response to treatment. However, the use of methylation at a given site as a marker to detect low numbers of tumor cells relies on the background levels of methylation in normal tissues to be nearly zero.
The human FHIT gene is a member of the histidine triad gene family [3], the function of which remains unknown. FHIT gene has been shown to be hypermethylated in oesophageal, lung, breast, prostate, bladder, cervical, and oral cancers [4-9]. Recent studies indicate that FANC proteins interact with both BRCA1 and BRCA2 genes through a common pathway [10]. Methylation changes may disrupt the FANC-BRCA pathway and hence may be a marker change in the cancer development in granulosa cells of the ovary. Aberrant expression of cyclin D2 was also demonstrated in human ovarian granulosa cell tumors and testicular germ cell tumor cell lines [11]. In breast cancer, repression of expression was attributed to methylation of the cyclin D2 gene promoter region. Several studies have indicated that methylation of cyclin D2 and its mRNA and protein were absent in most breast cancer cell lines examined and in primary breast cancers although normal breast epithelial cells had abundant expression [12-14]. BRCA2 may play role in regulation of the cell cycle during proliferation and differentiation. To date, only one study has shown the absence of methylation in the promoter region of BRCA2 in breast cancers cell lines and other normal human breast, bladder, colon, and liver tissues [15]. RUNX3 is one of the genes with RUNT domain, which has been identified to have a tumor suppressor role that frequently shows loss of expression due to hemizygous deletion and hypermethylation in gastric cancers [16,17].
The role of epigenetic gene inactivation in GCTs of ovarian origin is yet not fully understood. Previously published reports on GCTs and its precursor lesions showed varying degree of promoter methylation of many tumour suppressor genes [18-20]. To investigate the role of promoter methylation in detail in ovarian tumorigenesis, we further evaluated CpG methylation of 5 more tumour suppressor genes in 25 GCTs and cell lines. We found 68% of GCTs patients exhibiting promoter methylation in at least one gene. The FHIT, FANCF and RUNX3 gene promoters were frequently methylated. Methylation status was correlated with histologic characteristics. We also found evidence that promoter methylation inactivates gene expression in GCTs and exposure to methylation and/or histone deacetylase (HDAC)-inhibiting agents reactivate the gene expression.
Results
We examined the hypermethylation status of a panel of 5 normally unmethylated tumor suppressor or cancer genes: FHIT, FANCF, Cyclin-D2, BRCA2 and RUNX3 in 25 ovarian GCTs and ovarian cell line DNAs using the MSP assay (Fig. 1). The frequency of promoter hypermethylation of the tumor suppressor gene loci included in the panel was FHIT 28%, FANCF 24%, Cyclin-D2 12%, BRCA2 4%, and RUNX3 56% of the 25 tumours (Table 1). DNA methylation and mRNA expression results in 5 ovarian cell lines are shown in Table 2. Fig. 2a and 2b shows representative examples of MSP of each gene.
Figure 1 Methylated profile of GCTs of ovarian origin in 25 patients for FHIT, FANCF, cyclin-D2 and BRCa2 genes. Results were scored as methylated (dark boxes) or unmethylated (light grey boxes).
Table 1 DNA methylation and mRNA expression in ovarian cancer cell lines
Cell line MSP mRNA expression
FHIT FANCF Cyclin-D2 BRCA2 RUNX3 FHIT FANCF Cyclin-D2 RUNX3
TOV-21G M M/U M U U + - + +
C13* M/U M M U M - + - -
OAW42 U M/U U U U + + + +
OAW28 U U U U U + + + +
OV-90 M/U U M U U + + - +
KGN M U U U M + + + -
Table 2 Association of grade classification (IA, IB and IC) and promoter methylation of different genes in granulosa cell tumours of ovarian origin
Genes Methylation Status Grade classification p-value* p-value† p-value** p-value‡ p-value#
IA IB IC
FHIT Methylated 3 1 3 1 0.0526 0.5856 0.2352 0.041#
Unmethylated 8 9 1
FANCF Methylated 2 1 3 0.6609 0.0312† 1 0.0769 0.041#
Unmethylated 9 9 1
Cyclin-D2 Methylated 0 0 3 0.23 0.0017† - 0.0088‡ 0.011#
Unmethylated 11 10 1
BRCA2 Methylated 0 0 1 1 0.16 - 0.2667 0.2857
Unmethylated 11 10 3
RUNX3 Methylated 7 3 4 0.6887 0.1052 0.1984 0.5165 0.0699
Unmethylated 4 7 0
*Fisher's exact test. IA type vs. IB and IC type; †Fisher's exact test. IA and IB type vs. IC type GCTs of ovarian origin;
**Fisher's exact test. IA type vs. IB; ‡Fisher's exact test. IA type vs. IC type; #Fisher's exact test. IB type vs. IC type GCTs of ovarian origin
Figure 2 (a) Representative examples of MSP of FHIT, FANCF, Cyclin-D2 and BRCA2 genes in cell lines and tumor samples; -ve, negative control (water blank PCR mixture without template); +ve, positive control (normal lymphocyte DNA treated with SssI methyl transferase). The unmethylated form of p16 was amplified as a control to check DNA integrity. (b) Representative examples of MSP with RUNX3 gene and its expression before and after treatment with 5'-AZA-dC in different ovarian tissue (both normal and cancerous) using RT-PCR. UT: untreated; T: treated with 5'-AZA-dC; T2–T25: tumour samples.
Hypermethylation was observed in all of the histological stages of cancer examined and in patients of all ages except BRCA2 which is seen in only one patient with stage IC. Eighteen tumors showed methylation of at least 1 gene, and 7 tumors showed no methylation of any of the 5 genes. A total of 28% of these tumors had one gene, 16% two genes, 8% three genes and 8% four genes hypermethylated (Fig 1). No methylation was observed in 15 normal ovarian tissue DNAs and 50 lymphocyte DNAs from females. Using statistical analysis, we examined methylation with regard to these cancer patient clinicopathological parameters of age and stage. None of these patients have any smoking history. FHIT methylation was only found in all three stages of the tumors but was significantly more pronounced in IC (P = 0.041 vs IB). Hypermethylation of FANCF was significantly more frequent in stage IC (P = 0.0312 when compared with IA and IB collectively; P = 0.041 vs IB). Cyclin-D2 methylation was found only in patients with IC type of cancer (P = 0.0017 vs. IA+ IB; P = 0.0088 vs. IA and P = 0.011 vs. IB; Table 1). BRCA2 methylation was found only in one patient with stage IC only cancer. RUNX3 methylation was found in all the three stages of these tumours. However, all the patients with stage IC showed methylation to associate with high stage but not at a statistically significant level. FANCF and Cyclin-D2 methylation was found to be more pronounced in older than younger patients. However, we are unable to amplify the modified DNA for either of the PCR products.
Methylation is known to inactivate the tumour suppressor genes. To test the hypothesis, we examined the expression of all these genes except BRCA2 by RT-PCR after cellular exposure to 5-Aza-2'-deoxycytidine (DAC). No FANCF mRNA in TOV-21G, Cyclin-D2 in C13* and OV-90, and RUNX3 in C13* and KGN was detected in untreated cell lines (Table 2). However, DAC treatment for 120 hours induced an increase in the detectable level of mRNA expression in these cell lines and the tumors samples that initially lacked the expression. We were unable to detect expression of RUNX3 gene in patient 11 that also shows no PCR products with either methylated or unmethylated PCR primers. Further studies involving this patient with markers specific for RUNX3 revealed the loss of heterozygosity (LOH) which may be responsible for the loss of expression (Data not given).
Discussion
Cancer cells especially of ovarian origin keep on accumulating genetic changes that allow them to evade various chemotherapeutic drugs and hence become increasingly dangerous. We try to answer some of the questions by exploring the role of methylation mediated gene silencing in five tumour suppressor gene in the present study. We asked a question whether hypermethylation of FHIT, FANCF, cyclin-D2, BRCA2 and RUNX3 resulted in the loss of gene expression. We performed reverse transcription-PCR to test the expression of all these genes on RNA extracted from tumour samples. With few exceptions, expression of mRNA correlated well with promoter hypermethylation of these genes. This could be partly due to complex process that controls gene expression in which the chromatin conformation, cofactors availability, repressor process and enhancer molecules all play a part. The present study shows that methylation is one of the important determinants, because in majority of the cases, expression of these genes in these tumours correlates with hypermethylation of the promoter sequences. We found a link between aberrant methylation of genes investigated and the clinicopathological features of stage IC, even though the sample size was small. One has to bear in mind that GCTs of ovarian origin constitute less than 5% of the total ovarian cancers. Similar reports have been reported for the FHIT methylation in highly malignant osteosarcomatous [21]. Taken together, our results demonstrate that promoter aberrant methylation of FHIT is an important mechanism for inactivation of this tumor suppressor gene in many malignancies. Cytogenetic investigation in these tumours revealed that chromosome 3 and 11 are found to carry deletions and reciprocal translocations along with the trisomy 14 and monosomy 22. It could be important because some of the tumour suppressor genes (FHIT, RASSF1A, RAR-β and FANCF) studied extensively in these tumours are in fact localised on these chromosomes.
FANC genes are essential in DNA repair pathways and recently, it has been shown that promoter hypermethylation of FANCF gene disrupts the FA-BRCA pathway, resulting in cisplastin resistance. We found FANCF promoter hypermethylation in 24% of the tumours. This is in line with the previous findings in squamous cell carcinomas of lung and oral cavity and, in cervical and ovarian cancer [22-25]. To test whether other epigenetic mechanisms such as partial methylation and histone deacetylation play a role, we examined the expression of all these genes except BRCA2 after treatment with DAC. DNA hypermethylation-mediated gene silencing is closely associated with histone modifications such as methyl-H3-K9. In this regard, DNA-demethylating agents 5'-aza-2'deoxycytidine (DAC) and trichostatin (TSA) reactivates expression of epigenetically silenced genes [26]. Although DNA hypermethylation is essential to maintain repressive state of histone code, histone modifications precede DNA hypermethylation in silencing specific genes [27,28]. In the present study, reactivation and/or increased expression of FHIT, cyclin-D2 and RUNX3 in C13*, FANCF in TOV-21G and cyclin-D2 in OV-90 cell line after exposure to DAC in the absence of promoter methylation suggests that key histone modifications, either by direct or indirect involvement of promoter methylation, also play a role in down-regulating FANCF gene expression in this cancer type. It is therefore assumed that FANCF silencing might be considered a candidate-mechanism underlying the state of genomic instability may be considered to be a rate-limiting in the origin of GCTs of ovarian origin. FANCF gene silencing has been shown to revert in vitro as a result of demethylation in some ovarian cell lines as seen in the present study also [23]. These reversible changes in the methylation status further complicate the tumour assessment that shows the contribution of FANCF methylation. The present finding along with the results from other cancer types suggests the presence of genomic instability due to the methylation mediated FANCF gene silencing. Cyclin D2 gene promoter is hypermethylated in 12% of GCTs, a number that is consistent with previous findings in other cancers [14,29,30]. We noted a trend that Cyclin D2 methylation is found only in IC type tumors.
The present study showed that hypermethylation of the RUNX3 gene promoter frequently occurred in ovarian GCTs. In human gastric cancers it has been shown that promoter hypermethylation and hemizygous deletion of the RUNX3 gene correlated with a significant reduction in expression, and the tumorigenicity of cell lines in nude mice was inversely related to their level of RUNX3 expression, indicating that RUNX3 is a tumor suppressor involved in the development of gastric cancers [17]. The presence of RUNX3 CpG island hypermethylation in GCTs, but not in normal ovarian tissue or peripheral blood suggests that RUNX3 hypermethylation might be associated with the genesis of this cancer type and this frequent methylation might serve as a biological marker. Loss of expression in RUNX3 gene in patient 11 which also lacks any PCR product with either of the unmethylated and methylated primers indicates that it could be due to the large deletion in the gene. However, studies involving the markers D1S234 and D1S199 showed the loss of heterozygosity which could to some extent explain the loss of expression in this patient. We could not confirm this due to the lack of further sample. However, the sample quality was good as indicated by the presence of band in GAPDH1 which was used as an internal control.
The histological examination of granulosa cells revealed that these are small, usually round to polygonal, but may be spindle-shaped with scanty amphophilic cytoplasm, containing only occasional small lipid droplets, and having indistinct cell borders. Granulosa cells regularly express inhibin and contain vimentin and smooth muscle actin intermediate filaments and, less commonly, cytokeratins. However, during their malignant transformation, theses cells exhibit one or more of so-called 'microfollicular', 'macrofollicular', 'trabecular' or 'insular' patterns. The microfollicular variant is characterized by multiple small rounded spaces formed by cystic degeneration in small aggregates of granulosa cells and often fragments of nuclear debris or pyknotic nuclei. These spaces, known as Call-Exner bodies, are found in only 30–50% of tumors. The granulosa cell nuclei are oriented somewhat radially around these structures. The nuclei are typically bland and often grooved in granulosa cell tumors and have few mitoses, but show variation in size and shape, and marked hyperchromatism.
There exist two different pathways that can contribute to the development of cancers; genome-wide hypomethylation may lead to the loss of chromosomes (as seen in these tumours) leading to chromosomal instability, whereas promoter methylation in tumour suppressor genes, which are responsible for gene silencing, can lead to the development of cancers in somatic cells. Therefore, the balance in DNA methylation is very important, and alteration in these may be protective in one pathway but deleterious in the other.
Conclusions
In view of the high mortality rates associated with ovarian cancer, a better understanding of the molecular mechanisms underlying tumor progression in the disease could reveal novel pathways of high clinical relevance. It can therefore be concluded that promoter hypermethylation of these genes, and loss of expression in ovarian cancers (GCTs) is relatively common and this may also be useful as a tumour marker for early diagnosis and subsequent disease monitoring. Hence, these epigenetic signatures could play a decisive role in designing treatment options for this category of ovarian cancer. These results may also have implications in better understanding the underlying epigenetic mechanisms in GCT development thus can provide prognostic indicators, and identify important gene targets for treatment of this type of cancer in females.
Methods
Study Population
The subjects were 25 patients affected with GCTs of ovarian origin without a positive family history. The study was approved by the Health Research Ethics Board of the Faculty of Natural Sciences. All of them were untreated at the time of study. Fifty normal individuals ranging in age from 20–60 years were studied simultaneously under similar experimental conditions. The tumor grading is as follows: 11 FIGO stage IA, 10 FIGO stage IB and 4 FIGO stage IC. All these diagnoses were reviewed by a gynaecologic pathologist and the tumor were assessed using standard criteria. An informed consent was taken from all the subjects prior the study. Fresh cancer tissue specimens were received from all the patient and these were cultured as per standard protocol. A part of the fresh tissue was used to isolate DNA and RNA for further analysis where as the cultured cells were used for the 5'-Aza-2'-deoxycytidine experiments. Microscopically, GCTs are composed of granulosa cells, theca cells, and fibroblasts in varying amounts and combinations. The term granulosa-theca cell tumor had been applied to all tumors in which both cell types were identified, regardless of the amounts present.
Cell Lines and DNA Isolation
Five human ovarian cancer cell lines (TOV21G, C13*, OAW28, OAW42, OV90 and KGN) from the American Type Culture Collection and European Collection of Cell Culture were maintained in RPMI 1640 supplemented with 10% fetal bovine serum (Hyclone, Logan, UT) and were grown at 37°C in 5% CO2 [31]. DNA was isolated from cultured cells using QIAamp DNA Mini kit (Qiagen Inc.) and quantified. LOH studies were performed using markers D1S199 and D1S234 specific for locus 1p36.11.
Methylation Status by Methylation-Specific PCR (MS-PCR)
DNA methylation patterns in CpG islands of tumor suppressor genes FHIT, FNACF and BRCA2 were determined by chemical modification with sodium bisulphite as described previously [32]. Briefly, 1 μg DNA l was denatured by NaOH (50 μl, final concentration, 0.2 M) for 10 min at 37°C. 1 μg of salmon sperm DNA (Sigma) was added as carrier before modification. Freshly prepared 30 μl of hydroquinone (10 mM, Sigma) and 520 μl of sodium bisulfite (3 M, pH 5.0, Sigma) were mixed and samples were incubated under mineral oil at 55°C for 16 hr. The DNA samples were desalted through Wizard columns (Promega, Madison, WI), desulfonated by NaOH (final concentration, 0.3 M) for 5 min at room temperature, followed by ethanol precipitation. DNA was resuspended in water and used immediately or stored at -20°C. 50 μl of bisulphite modified DNA was used for each MSP. Following primer pairs for FHIT gene, methylated CpG site, forward 5'-ttggggcgcgggtttgggtttttacgc-3' and reverse 5'-cgtaaacgacgccgaccccacta-3', unmethylated CpG site, forward 5'-ttggggtgtgggtttgggtttttatg-3', and reverse 5'-cataaacaacaccaaccccacta-3'; 189–262 bp relative to transcription start site, FANCF gene, methylated CpG site, forward 5'-tttttgcgtttgttggagaatcgggttttc-3' and reverse 5'-atacaccgcaaaccgccgacgaacaaaacg-3', unmethylated CpG site, forward 5'-tttttgtgtttgttggagaattgggttttt-3' and reverse 5'-atacaccacaaaccaccaacaaacaaaaca-3'; the primers corresponds to the position +280 to +432, BRCA2 gene, methylated CpG site, forward 5'-gacggttgggatgtttgataagg-3' and reverse 5'-aatctatcccctcacgcttctcc-3', unmethylated CpG site, forward 5'-agggtggtttgggatttttaagg-3' and reverse 5'-tcacacttctcccaacaacaacc-3'; these primers are 135 and 211 bp upstream of transcription start site and cyclin-D2 gene, methylated, forward 5'-tacgtgttagggtcgatcg-3' (-1427 to -1409) and reverse 5'-cgaaatatctacgctaaacg-3' (-1152 to -1171) and unmethylated, forward 5'-gttatgttatgtttgttgtatg-3' (-1616 to -1594) and reverse 5'-taaaatccaccaacacaatca-3' (-1394 to -1414). Each PCR reaction generated 74 bp products both with methylated and unmethylated primers for FHIT, 153 bp for FANCF both with methylated and unmethylated primers, 337 and 250 bp product with BRCA2 primers specific for methylated and unmethylated primers and 276 and 222 bp product for cyclin-D2 primers specific for methylated and unmethylated PCR reactions respectively. For the PCR reaction of 25 μl, 50 ng sodium bisulfite treated DNA was added to reaction buffer containing 0.2 mM dNTP, 16.6 mM (NH4)2SO4, 67 mM Tris pH 8.8, 10 mM β-mercaptoethanol, 1.5 mM MgCl2, 10 pmol of forward and reverse primers specific to the methylated and methylated DNA sequences and 1.25 units of AmpliTaq Gold (PE Biosystems, Foster City, CA, USA). The PCR reactions were cycled in a GeneAmp 9600 thermal cycler (Applied Biosystems) under the following conditions: preheat at 94°C for 3 min. followed by 40 cycles (94°C for 40 sec, 65°C for 40 sec, for FHIT, 4 cycles of 65°C and 36 cycles of 55°C for 60 seconds for FANCF and 62°C or 56°C for 40 seconds in methylated or unmethylated BRCA2 gene, 72°C for 45 sec and a final extension at 72°C for 7 min. The PCR conditions for cyclin-D2 are as follows: 1 cycle of 95°C for 5 min and 35 cycles at 95°C for 30 s, 55°C for 30 s, and 72°C for 45 s; and 1 cycle of 72°C for 5 min. In addition to these genes we also investigated the RUNX3 gene (Accession no AL023096) methylation status using the primers: forward, 5'-ataatagcggtcgttagggcgtcg-3', and reverse, 5'-gcttctactttcccgcttctcgcg-3' (64917–65031; 115 bp), for methylated DNA of RUNX3; and forward, 5'-ataatagtggttgttagggtgttg-3', and reverse, 5'-acttctactttcccacttctcaca-3' (64917–65031; 115 bp), for unmethylated DNA of RUNX3 with annealing temperature of 55°C for 20 seconds. For each PCR set, DNA isolated from normal peripheral lymphocytes of healthy individuals served as a negative methylation control. Human placental DNA was treated in vitro with SssI methyltransferase (NEB, Beverly, USA) to create completely methylated DNA at all CpG-rich regions and served as positive methylation control. Methylation-specific PCR products were analysed on 3% agarose gel electrophoresis with ethidium bromide staining. A positive control and a negative control were included in each amplification reaction.
FHIT, FANCF, Cyclin-D2 and RUNX3 Expression by RT-PCR
FHIT, FNACF and cyclin-D2 mRNA levels in cancer patients were compared to their expression in normal cells using semi-quantitative reverse transcriptase polymerase chain reaction. In brief, cDNA was synthesized using AMV reverse transcriptase (Promega, Mannheim, Germany) and amplified in duplex reactions in a total volume of 25 μl containing 150 μM of each dNTP, 1.5 mM MgCl2, 10 pmol of each primer pair and 1.0 units of Taq polymerase. After initial denaturation at 95°C (94°C) for 5 min, 30 cycles of 30 sec (45 sec) at 95°C (94°C), 30 sec (50 sec) at 58°C (55°C) and 45 sec (50 sec) at 72°C were performed followed by a final 10 min elongation step at 72°C. The values in the brackets correspond to FANCF. The following primers were used to amplify FHIT: forward 5'-gctcttgtgaataggaaacc-3' and reverse 5'-tcactggttgaagaatacagg-3' which yields 532 bp product spanning within exon 5 to exon 10, FANCF: forward 5'-ttcggaagtctttgctgcct-3' and reverse 5'-agtaataacacacgattgcc-3' which yields 413 bp product spanning from +733 to +1144. RT-PCR was performed for Cyclin D2 using the primers 5'-catggagctgctgtgccacg-3' (forward) and 5'-ccgacctacctccagcatcc-3' (reverse) with PCR conditions being: 1 cycle of 94°C for 3 min and 35 cycles at 94°C for 20 s, 55°C for 30 s, 72°C for 45 s followed by 72°C for 5 min. RT-PCR was also carried out for RUNX3 gene using the primers: forward 5'-aggcattgcgcagctcagcggagta-3' and reverse 5'-tctgctccgtgctgccctcgcactg-3' (152 bp). GAPDH1 was used as internal control. Each reaction was performed in triplicate. PCR products were electrophoresed on 2% agarose gels and quantified using densitometer (Molecular Dynamics). Fold increases in expression in cancer cells were calculated with respect to the levels of the transcripts in normal cells.
5-Aza-2'-deoxycytidine and n-butyrate treatment
Cell lines (TOV21G, C13*, OAW28, OAW42 and OV90), normal ovarian cells and tumour cells of ovarian GCTs were treated with demethylating agent 5-Aza-2' deoxycytidine (Sigma) for five days at a concentration of 2.5 μM, HDAC-inhibiting agent trichostatin (TSA) at a final concentration of 5 μM for the last 24 hours or a combination of both. Total RNA isolated from treated, untreated cell lines and cell of the cancerous tissue was reverse transcribed using random primers and the Pro-STAR first strand RT-PCR kit (Stratagene, La Jolla, CA). A semi-quantitative analysis of gene expression was performed in replicate experiments using 30 cycles of RT-PCR as described above.
Competing Interests
The authors declare that they have no competing interests.
Authors' Contributions
VSD for executing the MSP and RT-PCR experiments; completing manuscript; MS, for carrying out cell culturing; and SAH conceived and coordinated the study. All authors read and approved the final manuscript.
Figure 3 (a). Expression of FHIT, FANCF and Cyclin D2 genes before and after treatment with 5'-AZA-dC in different cell lines and ovarian tissue (both normal and cancerous) using RT-PCR. NT: no treatment; T: treatment with 5'-AZA-dC; T1–T3: tumour samples; NOV: normal ovarian cells. Co-amplified product of GAPDH served as an internal control. (b) Results obtained with another cell line KGN and tumours before and after treatment with 5'-AZA-dC.
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| 15574200 | PMC538268 | CC BY | 2021-01-04 16:36:34 | no | Mol Cancer. 2004 Dec 1; 3:33 | utf-8 | Mol Cancer | 2,004 | 10.1186/1476-4598-3-33 | oa_comm |
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Biomagn Res TechnolBiomagnetic Research and Technology1477-044XBioMed Central London 1477-044X-2-81557163010.1186/1477-044X-2-8ResearchCharacterisation of weak magnetic field effects in an aqueous glutamic acid solution by nonlinear dielectric spectroscopy and voltammetry Pazur Alexander [email protected] Department Biologie 1 Universität München-Bereich Botanik, Menzingerstr. 67, D-80638 München, Germany2004 30 11 2004 2 8 8 9 11 2004 30 11 2004 Copyright © 2004 Pazur; licensee BioMed Central Ltd.2004Pazur; 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
Previous reports indicate altered metabolism and enzyme kinetics for various organisms, as well as changes of neuronal functions and behaviour of higher animals, when they were exposed to specific combinations of weak static and alternating low frequency electromagnetic fields. Field strengths and frequencies, as well as properties of involved ions were related by a linear equation, known as the formula of ion cyclotron resonance (ICR, abbreviation mentioned first by Liboff). Under certain conditions already a aqueous solution of the amino acid and neurotransmitter glutamate shows this effect.
Methods
An aqueous solution of glutamate was exposed to a combination of a static magnetic field of 40 μT and a sinusoidal electromagnetic magnetic field (EMF) with variable frequency (2–7 Hz) and an amplitude of 50 nT. The electric conductivity and dielectric properties of the solution were investigated by voltammetric techniques in combination with non linear dielectric spectroscopy (NLDS), which allow the examination of the dielectric properties of macromolecules and molecular aggregates in water. The experiments target to elucidate the biological relevance of the observed EMF effect on molecular level.
Results
An ion cyclotron resonance (ICR) effect of glutamate previously reported by the Fesenko laboratory 1998 could be confirmed. Frequency resolution of the sample currents was possible by NLDS techniques. The spectrum peaks when the conditions for ion cyclotron resonance (ICR) of glutamate are matched. Furthermore, the NLDS spectra are different under ICR- and non-ICR conditions: NLDS measurements with rising control voltages from 100–1100 mV show different courses of the intensities of the low order harmonics, which could possibly indicate "intensity windows". Furthermore, the observed magnetic field effects are pH dependent with a narrow optimum around pH 2.85.
Conclusions
Data will be discussed in the context with recent published models for the interaction of weak EMF with biological matter including ICR. A medical and health relevant aspect of such sensitive effects might be given insofar, because electromagnetic conditions for it occur at many occasions in our electromagnetic all day environment, concerning ion involvement of different biochemical pathways.
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Background
Weak magnetic fields and extremely low frequency electromagnetic fields (EMF) are omnipresent in natural environmental and increasingly man-made factors. A possible influence on life processes was already mentioned in the late 19th century [1]. It is now recognized, that many organisms are capable of perceiving such fields, while less is known on the elementary perception. Three types of mechanisms are considered therefore, the orientation of ferromagnetic particles in tissues [2], singlet-triplet mixing states of macromolecules building radical pairs [3], and the ICR, whose persistent investigation began with the works of Liboff [4].
Ferromagnetism has been implicated in animal navigation (e.g. compass mechanism of migratory birds [5], and the magnetotaxis of certain bacteria [6]. The radical pair mechanism is independent of ferromagnetism and has putatively a higher magnetic sensitivity. It has been primarily studied in photosynthetic reaction centers and the respiratory chain [7], where triplet yields are modulated by electromagnetic interaction with fields as low as about 50 μT [8]. Already two decades ago effects were described by Blackman et al. [9], and later by [10-12], which require a combination of static and alternating magnetic fields. It turned out, that the magnetic field strength B of the static component and the frequency f of the alternating EMF relate to the "ion cyclotron resonance (ICR) formula":
whereas m is the mass and q the charge of ions involved. The explanation of the mechanism of this effect in an aqueous, more or less viscous environment seems to be difficult, nevertheless there are some efforts. Liboff [13] suggested that magnetic fields can interact in a resonant manner with endogenous AC electric fields in biological systems, instead of a direct interaction with external AC magnetic fields. Binhi [14] reviewed the mechanisms of magnetobiological effects, and tried to estimate the sensitivities and involved molecular topologies. Adair [15] questioned a model involving altered transition rates of excited ions by weak EMF, while others [16] consider the ionic environment, eg. properties of the water, with Ca2+ as the most investigated ion. An altered Ca2+-transport was found in human lymphocytes [4]. The motility of benthic diatoms is effected, if ICR conditions are matched for Ca2+ and K+ in the range of 8–64 Hz, and static field strengths comparable to geomagnetic fields [17]. The germination rate of Raphanus sativus was altered, when the ICR conditions for Ca2+, K+ and Mg2+ were applied to the seedlings [18]. ELF effects on macromolecules indicate an ICR effect possibly caused by additionally involved alternating electric fields [19]. It is noteworthy remarkable that ICR conditions can be matched by combinations of the local geomagnetic field and man-made electromagnetic fields, especially the frequency range of power lines (50 or 60 Hz). Liboff et al. [20] suggest to consider ICR effects for the evaluation of epidemiological childhood leukaemia studies. The assessment of elevated brain cancer risk has been evaluated by Aldrich et al. [21] on the assumption of interactions of the geomagnetic field and a 60 Hz field component from power lines.
NLDS was developed during the past decade in order to investigate dielectric properties of small particles in aqueous solutions, using relatively simple electrochemical equipment. In the simplest case, a sinusoidal alternating electric field is applied to the solution by 2 electrodes, using peak to peak voltages up to 1.5 V and frequencies of 1 to 1000 Hz. Particles with a dielectric constant different from that of their environment (generally water) distort the field. This induces alternating voltages over and currents through the solution, which are detected by 2 auxiliary electrodes in order to avoid polarisation effects. Phase shifts and distortions of the obtained signals, as compared to the input signal, contain information on damping and relaxation kinetics. Therefore, the signals are Fourier-transformed and evaluated as power spectra in the frequency domain [22-24]. Usually, the sample is compared to a reference, which lacks the solute, but otherwise is identical. Sample and reference can either be measured one by one in a single chamber device, or simultaneous with a "dual-chamber" setup, which also needs a two channel data acquisition, and allows a real-time differential-NLDS (DNLDS). The data are usually calculated using the decibel (dB) scale for the intensity (power) Pn:
Where U(n)sample is the signal output intensity of the nth harmonic from the sample measuring channel, and U(n)ref the corresponding value from the reference channel.
Zhadin et al. [25] reported the alteration of electric properties of an electrolyte under ICR conditions. They found an increasing ion current through an aqueous glutamic acid (Glu) solution in narrow frequency bands (resonance), which could be described by equation (1). These results are the starting point for the present work, which is aimed to further elucidate this conduction mechanism. The influence of the concentration of Glu has been investigated, and the time resolved electric current through the solution is analyzed using "non linear dielectric spectroscopy" (NLDS), which indicate microcolloidial properties of the solvent-solute system. The NLDS was amplified by two features: The option of simultaneous data acquisition in two cuvettes (DNLDS), and the frequency resolved voltammetry (FRV), whereby simultaneous a AC voltammetry is performed [26]. By recording NLDS spectra at varying electrode voltages from e.g. 100–1100 mV, additional information was obtained on redox potentials. The electrode current never increases proportionally with the applied voltage but remains constant in the range of the counter voltage to an existing redox potential given by the investigated electrode-electrolyte system. This was used to improve the method by recording differential spectra (DNLDS). The integral over the spectrum represents one data point of a simple (not frequency resolved) AC voltammetry, while the intensity course of corresponding spectral data points provide information about the dielectric state of the redox reaction, e.g. its capacitive, time-dependent properties.
Methods
Preparations
All preparations were performed with doubly de-ionized water. The solutions were degassed and stored under Argon, in order to avoid oxidation of the solute and increased electrode fouling during the subsequent measurements. An acidic solution of 2.24 mM Glu was adjusted to pH = 2.85 ± 0.03 with a stock solution of 5 mM HCl. Equilibration was assumed, when the pH varied less than ± 0.03 for at least one minute. All procedures were performed at 20°C. For yielding a reference signal, an aqueous solution of HCl was provided by diluting the HCl stock solution with water to pH = 2.85. All solutions were stored at 4°C under Argon.
Apparatus
The experimental arrangement for differential non-linear dielectric spectroscopy (DNLDS) is shown in Figure 1. It allows the simultaneous evaluation of a sample and a reference under same conditions. A double cuvette (K) is built up by two standard photometric plastic cuvettes (1 × 1 × 4.3 cm). Both contain electrode arrays (E1, E2) consisting each of 4 gold wires (Au 99.9%, Johnson Matthey, Karlsruhe) with a diameter of 0.25 mm, mounted parallel at a distance of 2 mm on a teflon frame. The required sample volume was 1 ml. These electrode carriers are mounted on a stable socket for electric connection and mechanical adjustment (not shown). The cuvettes are enclosed by a hermetically sealable plastic tank (T) with a copper bottom, which is filled at a height of 2 cm around the cuvettes with water for thermal coupling to an outer temperature controlled water bath. The setup is kept under Ar atmosphere throughout the experiment. Thermic control (20 ± 0.1 °C) of the cuvettes is provided by a water thermostat (Haake "G", Karlsruhe-Berlin, Germany) with a sequential home built temperature fine controller, ensuring highly stable working conditions for the electrodes. Once assembled, these components form a mechanically stable unit, with in- and outlets for gas and samples by small teflon hoses (not shown). The assembly is placed in the center of a solenoid (S), consisting of two cylindrical coils with a inner diameter of 16 cm and a height of 7 cm for applying the vertically orientated EMF (B). The coil for the static field component consisted of 300 turns of coated copper wire (diameter 0.5 mm), the other coil was winded above and had 50 turns.
Figure 1 Experimental facility. Schematic sketch of the arrangement for the differential NLDS (DNLDS) experiments (left, components not drawn to scale) and photograph of the opened permalloy shielding box with the assembled sample carrier (right): Two arrays of 4 gold electrodes (E1, E2, length 10 mm, distance 2 mm) each are located in two adjacent perspex cuvettes (C) of 1 × 1 × 4.3 cm, enabling simultaneous acquisition of two liquid samples (used volume 1 ml each) under the same environmental conditions. The cuvettes are enclosed by a tank (T) for providing an Argon protection gas atmosphere. This all is mounted on a socket housing water temperature control and magnetic field monitoring, and is centered inside a cylindrical solenoid (S) consisting of 2 coils with a inner diameter of 16 cm and a height of 7 cm for independent generating the static and the alternating magnetic fields of vertical direction (B). The input signal to the sample is applied by the electrodes labeled "in", the probe signals are taken by the electrodes labeled "out" and connected to preamplifiers with symmetric inputs. The complete arrangement is enclosed by a shielding box of 1 mm Permalloy, which is bonded inside with perspex.
For electric and magnetic shielding the complete setup resides in a grounded double-walled Permalloy box with a total wall thickness of 1 mm. A overall inhomogeneity ≤ 0.3 % of the generated fields was determined inside the box with a triaxial CXM539 magnetometer (CMT GmbH, Herrsching, Germany) over the cuvette locations. For coil calibration the relation of field strength to coil current could be ascertained directly in measurement series with the magnetometer for 0.1–100 μT, showing a overall deviation from linearity ≤ 0.2 % (DC and AC), so currents corresponding to even lower field strengths were obtained by extrapolation.
Signal processing was mostly done as previously described [27]. Figure 2 shows the schematic circuit diagram of the special NLDS measurement setup used here: The sinusoidal controlling voltage (100–1100 mV) for NLDS with a frequency of 2 Hz was applied to the two outer electrodes by a symmetric amplifier (output impedance 50 Ω). The inner two electrodes were connected to the input ports of a differential preamplifier. Because a simultaneous examination of two samples under same conditions is required, a second identical electrode array with preamplification must be available. The resulting signals were digitized by a computer controlled multi channel DA/AD-converter (Lab-PC+, National Instruments, Austin TX U.S.A.). This board also supplied the voltages for the NLDS and the control of the EMF. A function generator (Krohn-Hite Model 5200) generated the sine curve for the AC magnetic field with a frequency accuracy of 0.1 %. The two operational power amplifiers of a OPA 2541 chip drove the solenoids generating the constant as well as the variable magnetic field components, which were monitored by the coil currents and the magnetometer.
Figure 2 NLDS measurement setup (schematic). The voltage control signal is applied by a symmetric amplifier to the outer two of a plane 4 gold electrode array. The NLDS signal generated by the sample is clamped by the inner two electrodes. It will also be preamplified symmetrically, digitized by a fast computer controlled analog-digital converter and fourier analyzed by the data acquisition software. The static and dynamic magnetic field component is directed parallel to the electrode plane. The measuring station provides two such NLDS setups, enabling a simultaneous examination of two samples under same conditions.
For cleaning, the electrodes were first treated with chromosulfuric acid for 1 h at room temperature and intensively rinsed with de-ionized water. This procedure was repeated approximately once per week. An improved long-term electric stability was obtained by slight modifications of the treatments described by Woodward et al. [23] and Yardley et al. [28]: The electrodes were additionally washed with chloroform, sonicated for 20 min in a detergent solution (0.5 % Triton X-100 in water), treated with CaCl2 (0.5 M in water) in a ultrasonic bath (Bachhofer, Reutlingen), and finally rinsed with de-ionized water (<2 μS). This treatment resulted in amplitude deviations ≤ 5% over an experimental session of up to 2 h. If electrodes were not used for DC measurements, but for NLDS, they were additionally coated with a thin polymer film in order to improve noise reduction and stability [24].
Measurement techniques
The cuvettes could be charged with the test solutions, discharged and rinsed through the teflon hoses by a syringe. A sample volume of 1 ml was used. Device specific, systematic errors were routinely checked by exchanging the electrode arrays used for sample and reference measurements and testing several cuvettes of the same type. After loading they were flooded with Argon for about 10 min. in order to remove O2 from the solutions, avoid oxidation reactions and subsequent arising of reactive oxygen species (ROS) in the solute, then the hoses were sealed with rubber caps. After reaching a stable temperature of 20 ± 0.2°C, measurements were started. First 10 "dummy" scans were performed, in order to obtain a dynamic equilibration of the electrodes. Bdc = 40 μT was selected as static magnetic field component for the ICR condition, because it is of comparable intensity as the natural geomagnetic field of the earth. A new sample was used for every experiment, an "aging effect" of the test solutions was observed, similar to an earlier seen effect, which resulted in a decreasing reproducibility for experiments with magnetic field exposed lipid vesicles [27].
Three types of techniques for measuring the electric currents in the solutions were applied, always using the gold electrode array described above:
1) For the validation of the ICR parameters of the Glu-HCl solution, the experiment of Zhadin et al. [25] was repeated. The DC voltage of 80 mV was applied to the outer electrodes (+40 mV and -40 mV), and the current through the solution was calculated from the resulting voltage between the inner electrodes. The current calibration was earlier performed with 10 mM HCl and the Glu-HCl solution. By that way, used by many established voltammetric techniques [29], superimposing electrode transition potentials can be widely avoided, in contrary to a direct current measurement with a two electrode system. A constant magnetic field Bdc = 40 μT or 50 μT and a frequency sweep of the alternating magnetic field Bac = 50 nT (parallel to Bdc) from 2 to 7 Hz with 0.025 Hz/s and a resolution of 0.05 Hz were used.
2) For the investigation of the ICR transition with NLDS the same magnetic field setup is used like described under 1), the NLDS sine wave was applied on the electrodes (instead of the DC-voltage) and a constant magnetic field Bdc = 40 μT was used.
3) Finally the FRV setup allowed the frequency analysis of the electric signals with variable amplitudes using the DNLDS technique described above. Glu-HCl samples were exposed to constant ICR conditions (Bdc = 40 μT and Bac = 50 nT, 4.14 Hz fixed), for reference experiments only the static component (Bdc = 40 μT) was applied with Bdc switched off. The amplitude of the sinusoidal scanning voltage was increased in each experiment from 100–1100 mV in steps of 10 mV, record by record, the duration of each cycle was 4 s. The two data sets (from Glu-HCl and HCl sample) yielded by every single record were seperately Fourier transformed in order to get the spectra, these two spectra were divided by themselves (Glu-HCL spectrum by HCl spectrum) and the ratio spectrum was subsequently attached to a data file on a harddisc for later evaluation.
Results
Exploring Zhadin et al.'s experiment
Applying a constant magnetic field of Bdc = 40 μT at pH 2.85 and scanning the alternating magnetic field Bac from 2–7 Hz in steps of 0.05 Hz, a sharp peak was observed at 4.15 Hz. The peak current is about 20% larger than the mean ionic current of 7.4 nA, the peak width at half-height is 0.3 Hz (Figure 3). Equation (1) was validated by repeating the experiment ten times at an altered static magnetic field strenght of Bdc = 50 μT. The current peak shifted to 5.2 ± 0.05 Hz with a height of 9.08 ± 0.3 nA, which lies approximately 22% over the mean ionic background current. These data verify the results of Zhadin et al. [25], and the field-dependence is in agreement with Eqn. 1. The signal was observed over a concentration of 2–10 mM. The signal became too small at cGlu < 2 mM, and there was insufficient solubility cGlu >10 mM (at 20°C). Subsequently, the pH-dependence was determined under identical magnetic field and scanning conditions mentioned above. Resonance effects are only seen in a narrow pH range of Glu-HCl (pH 2.75 – 2.90), with an maximum at 2.85, and vanishes outside this range.
Figure 3 Current increase at ICR (DC). Current increase through the glutamic acid /HCl solution (2.24 mM, pH = 2.85) at and near ICR conditions. The static magnetic field strength is Bdc = 40 μT, the amplitude of the alternating field Bac is 50 nT, the frequency resolution Δf = 0.05 Hz. Course using a constant electrode voltage of 80 mV ("Zhadin's experiment").
After this verification of the experiment of Zhadin et al. [25], these electric measurements were accompanied by some UV-VIS light scattering investigations, which should give information about possible colloidal properties of the sample. Glu-HCl solutions were investigated at a wavelength of λ = 260 nm with the pH adjusted from pH 2.55 to 3.25, showing a significant scattering maximum around pH 2.8 (data not shown).
Further some DC voltage scans were performed with the gold electrode array for Glu at pH 2.85, and for dilute HCl adjusted to pH 2.85, applying only a static magnetic field Bdc = 40 μT (no Bac). A voltage range of 100–1000 mV was selected to allow a comparison with the voltammetric information out of the frequency resolved voltammetry (FRV). Again, maxima of conductivity were obtained, they lie at 250 ± 10 mV for Glu-HCl and 280 ± 10 mV for water/HCl pH 2.85 (data not shown).
NLDS spectroscopy
Next, the solutions were investigated by NLDS spectroscopy, in order to investigate in which way the frequency composition of the current spectra will change, when the predicted ICR condition for Glu-HCl is matched (Bdc = 40 μT and a Bac with f = 4.15 Hz). 15 experiments were performed and averaged. Figure 4 shows the power of the 2nd harmonic (referenced against dilute HCl, pH 2.85). The full dataset is shown in Figure 5 on an absolute current scale, for magnetic frequencies of 4.00–4.30 Hz in a 3d-representation. The 1st harmonic is split up into 2 closely spaced peaks around the ICR frequency. This is also well seen in Figure 4, an effect not seen in the "Zhadin's" DC experiments [25] without frequency resolution. Furthermore an increase of the 2–6 harmonics is seen in Figure 5 for 4.10 and 4.20 Hz magnetic frequency, closely flanking the ICR value. The average standard deviation of these experiments was 8.2 % of the average Power of all DNLDS spectra.
Figure 4 Current increase at ICR (AC). Course of the 2nd harmonics of NLDS spectra taken for every scanned frequency of Bac. Data were related to reference scans with Bdc = 40 μT, but without Bac. The grey bars indicate standard deviations. Other conditions like Figure 2.
Figure 5 NLDS spectra on ion cyclotron resonance (ICR) transition. 3D-representation of the NLDS resolved current through a glutamic acid / HCl solution (2.24 mM, pH 2.85) during transition of the ICR condition (static magnetic field Bdc = 40 μT, alternating field Bac = 50 nT, fBAC = 4.14 Hz) in steps of 0.05 Hz.
Kinetics
The following kinetic experiment should clarify, in which way the conductivity of the Glu solution is affected by repeated transitions through the ICR conditions. 12 experiments were performed, each with a new Glu-HCl sample.
100 DNLDS spectra were recorded with single 2 Hz sinus signals with 100 mV amplitude. Bdc = 40 μT was permanently applied in all experiments, while Bac with f = 4.15 Hz was applied only during measurement no. 20–39 and 60–79. Subsequently, the courses of the lowest 5 harmonics (for 2, 4, 6, 8 and 10 Hz) were normalized to ± 1, and all 12 experiments were averaged, Figure 6 therefore represents the kinetics averaged over a total of 60 datasets. Because of the standardization, data are scaled in arbitrary units (a.u.). The power difference between "on" (exposure) and "off" periods is 1.38 ± 0.34 dB, standard deviations are drawn as bars.
Figure 6 Current kinetics of switched ion cyclotron resonance (ICR) condition. Kinetics in arbitrary units (a.u.) of the ICR condition to a glutamic acid / HCl solution (2.24 mM, pH 2.85). The static magnetic field Bdc (40 μT) was applied permanently, the alternating field Bac (50 nT, 4.14 Hz) was applied as indicated by "on" and "off". One experiment consists of a set of 101 DNLDS spectra performed by a 2 Hz sinus signal with 100 mV Amplitude. The data first 5 harmonics (2, 4, 6, 8, 10 Hz) where normalized and then averaged. Data are calculated out of 12 independent experiments. The grey lines mark the standard deviations, the dotted straight line shows the linear regression of the negative drift, represented by the equation y = -0.0039t + 0.5015.
Changes of the signal intensity become obvious, when switching the alternating magnetic field on or off. Over the entire experiment there seems to be a constant drift which we take as an indication for irreversible processes. This drift is indicated by the dotted line, which results from a linear regression of the entire dataset (-0.0039t + 0.5015). The course seems to reach a new steady value after on/off switching of the alternating magnetic field with a time delay, which seems larger, when ICR is switched off. The average current change after the switching processes is -0.2 nA/s, the negative values result from comparison with a reference.
Differential NLDS experiments with variable control voltages (FRV)
Finally, the FVR method should show the intensity distributions of the harmonics of the DNLDS spectra and their dependence from the used amplitude of the electrode input voltage. Again Glu-at pH = 2.85 was investigated, using diluted HCl (pH 2.85) as the reference. The ratio of the resulting two NLDS power spectra was calculated according to equation (2), resulting in a logarithmic DNLDS spectrum. 101 such scans (4 s each) were performed for every single experiment, during which the amplitude of the applied course of 4 periods of a 2 Hz sine voltage increased from 100 to 1100 mV in 10 mV steps. Corresponding datapoints of the successive single DNLDS spectra generated one AC voltammogram each, for the respective frequency. Altogether, a set of 201 frequency resolved voltamogramms was obtained, because every spectrum contains 201 data values. Subsequently 20 such experiments were performed in which the solution was exposed to ICR conditions, alternating with 20 experiments, were only the static field was applied (Bdc = 40 μT), but not Bac. Each of the two groups of experiments were averaged separately. Then the two resulting datasets were subtracted (ICR experimental data minus data of the experiments with ICR condition switched off). This differential dataset had a total amplitude of 2.03 ± 0.38 dB, presenting just the contribution of Glu, because the voltammetric background from HCl was subtracted.
Subsequent data normalization should allow a better comparison of spectra recorded with different amplitudes and likewise of voltammograms at different frequencies. Therefore in Figures 7, 8 the full dataset is shown, again after standardization in a range from – 1 to 1. Figure 7 presents the data with standardization on the voltage axis for the voltammograms belonging to the individual frequencies. Figure 8 contains the same dataset, but with standardized spectra. The intensity maximum shifts with rising frequency from approx. 250 mV to 500 mV for frequencies <40 Hz, it then remains constant around 500–700 mV for higher frequencies. So most information will be contained in the low harmonic orders. Figure 9 shows the voltage dependent behaviour at the NLDS fundamental frequency (2 Hz) and three harmonics in the lower range (4, 8, and 12 Hz). Broad maxima are obvious, which seem to shift to higher voltages with increasing harmonic order by about 60 mV/Hz. The intensities increase to a local maximum at approx.25 Hz. At higher frequencies, the amplitude effects caused by the exposure to ICR conditions have a local maximum at 480 mV and merge into a continuum beyond 750 mV for all higher frequencies, according to the predominating capacitive damping of aqueous solutions with rising frequency.
Figure 7 DNLDS resolved voltammogram dataset (normalized to spectral axis):Normalizations of the DNLDS resolved voltammogram dataset (sinewave 2 Hz with amplitude rising from 100–1100 mV, details of gaining data see text) of a Glutamic acid / HCl solution (2.24 mM, pH 2.85) under ICR Conditions (Bdc = 40 μT, Bac = 50 nT, 4.14 Hz). Datapoints are colored resp. shaded according to the scale on the right border. Normalization of the spectra for each Amplitude shows a rising proportion of higher frequencies with a local (at about 500 mV) and a total maximum (at about 700–800 mV). By contrast, the proportions of the base frequency (2 Hz) and the lower harmonics decline.
Figure 8 DNLDS resolved voltammogram dataset (normalized to voltage axis):The same dataset and representation style like Figure 9, but with normalization of the single voltammograms for each frequency. For low frequencies (<5 Hz) Voltammograms have a maximum at about 250 mV, comparable to the pure DC volt scans. But with rising spectral harmonics voltammetric maxima occur at about 700 mV with overlaying intensity patterns of 4 and 16 Hz in distance. Worthy of remark are 62, 78 and 94 Hz, these all are four folds of the used base ICR resonance frequency 4.14 Hz.
Figure 9 Extracted voltage courses of the DNLDS resolved voltammogram dataset. Voltammograms for some harmonics of the DNLDS resolved voltammogram dataset (sine wave 2 Hz with variable amplitude 100–1100 mV, not normalized here, see text for details) of a glutamic acid / HCl solution (2.24 mM, pH 2.85) under ICR Conditions (Bdc = 40 μT, Bac = 50 nT, 4.14 Hz).
Discussion
All results suggest the existence of a sensitive magnetic field effect on the conductance of a aqueous Glu solution. The effect shows no linear dependency of magnetic field parameters, it is rather peaking in a narrow range of combinations of static magnetic field strengths and frequencies of additional alternating magnetic fields, described by Eqn. 1.
Several precautions were applied, in order to avoid artefacts as best as possible. So it has been shown, that the signal to noise ratio will be improved significantly by clamping the voltage drop inside the electrolyte and, if needed, by a subsequent calculation of the current by calibration functions, instead of a direct current measurement. These techniques are wide spread in voltammetry [26] and obligatory in NLDS [23]. Because the voltage clamping ideally should work without any electric current flow, the electrode surface transition potentials could more likely be excluded for causing the observed EMF effect ("electrode effects"). It should be least then apply, if a cell voltage is used bellow the electrochemical potentials of the electrode-electrolyte system, and independent from the other experimental setup.
Different explanations are recently discussed for the kind of EMF effect observed here, all of them suppose a non linear oscillator principle described by quantum mechanical terms, allowing energetic interactions with the environment far below the thermal equilibrium of life processes. This search for "wave functions fitting in a properly sized box" should consequently provide an explanation for the repeatedly observed effects of effectiveness windows, regarding specific field strengths and frequencies of the EMF, e.g. seen on green algae grown in a magnetic gradient [30]. Ion channels of biological membranes were proposed in a early work of Liboff [31] for a suitable environment supporting ICR. A model of Binhi et al. [14,32] is based on an interference mechanism of quantum states of ions within protein cavities. The quantum dynamic description of an ion is given for the case of ion-protein complexes that rotate in magnetic fields. The individual molecular rotation is taken into account. The spatial distances considered here are in the size of the molecules involved, cavities built by proteins, and their bond lengths.
A quantum electrodynamic description needing no additional supporting structures like protein molecules or lipid membranes was worked out by Giudice et al. [33], as an attempt to explain the experimental results of Zhadin et al. [25]. It is based on an underlying two-phase domain model of the solvent water, in which at room temperature ~40% of its volume is organized in spheres with a diameter of approximately 100 nm providing coherence for the included water molecules. These spheres should establish a stable frontier region with a thickness of ~4 nm, which allows a undisturbed ion movement, separated by an energy gap 0.26 eV against the surrounding, non coherent water phase. The circulation frequency of the ions in the frontier region should be given by equation (1) and be dependent on the external magnetic field strength. An additional superimposed alternating field Bac with the same frequency will modulate the radii of the orbits. As a consequence, the ion orbits fit no longer the frontier region and the ions escape into the surrounding water phase, where they increase the conductance. This model also tries to describe the results of [25] quantitatively, but takes therefore in account the electrode geometry of the original experiment.
Further attention should turned to the comparably long persistence time of the ICR state (see Figure 6) implying a comparable long lifetime. Considering the existence of supramolecular orders of liquid water, such long lifetimes (>10 s up to hours) have been predicted for these states sensitive to weak EMF at biologically relevant temperatures. Ponomarev et al. [34] propose linearly ordered chains and clusters like a liquid crystal phase in water which interact with EMF. The soliton theory was applied for description. Studies on the electromagnetic "memory effect" of water implicate even high sensitivity and long lifetimes [35], and are probably caused by the same mechanism as the effects observed here. An more hypothetical two phase model also providing boundary layers has been emphasized by Colic et al. [36]. The authors discuss the presence of micro-dispersed gas bubbles. But this possibly can be discarded more than likely in our experiments, because degassed solutions were used throughout.
Special attention deserve the obvious frequency dependent amplitude windows of the dielectric currents, which are observed in the NLDS experiments (FRV) with variable amplitudes. Two explanations for this effect would be possible. The additional electric field caused by the AC signal of the NLDS could modulate the charged particles inside a "quantum box", whatever will be the reason for its existence. An indication for such a mechanism could be the more or less ordered local maxima of conductivity in spectral as well as in the voltammetric domain of the data. But the frequency dependent conductivity band shifts of the FRV experiments (Figure 9) could either result from a "simple" interference with the frequency of the Bac field, which can be tuned on its part in discrete multiples, corresponding to possible "overtones" of the ICR (orbital) frequency of the ions. Interactions of the internal electric and external magnetic field could probably cause side band modulations. They are probably responsible for the seen splitting up of the ICR resonance peak (figure 4) when using a AC instead of a DC probe voltage.
For progressed investigation of the observed effects some additional properties of the electric charge environment of the Glu ion should be known. The isoelectric point of Glu is at pH 3.22, the pK of the α-COOH-group is 2.19, that of the β-COOH at 4.25, the small optimum for the EMF effect around pH 2.85 does not coincide with any of these points. The Debye-Hueckel radii for Glu are about 5 nm, they determine the free ion movement, and influence consequently the current. Moreover, they could be responsible for a proper fit of the spatial ion distribution to the environing structure whatever, which enables a resonant EMF effect.
Conclusion
The results strengthen the idea, that weak electromagnetic fields can cause an resonance effect on molecular or even supramolecular scale in electrolyte solutions [33,35], and thereby possibly, influence biological processes, which involve these electrolytes. In this work, the electric currents in a glutamic acid solution were investigated with frequency resolution after applying weak EMF. The resonance peaks and the overtone-analysis in response to weak static plus alternating EMF support the existence of the ICR phenomenon in aqueous solutions containing electrolytes. A analysis of the data is possible under the basic assumption of a far reaching principle of arrangement (realized e.g. by the solvent matrix), which allows quantum electrodynamic processes on the nano-physical scale or larger.
In general any kind of a suitable coherence mechanism should be essential for the observed effects in a dense medium like water, which had to support an energy gap against the thermal fluctuations of the environment, and enable a movement of charged particles which are only magnetically coupled to their outer environment. Not at least, the high sensitivity of the ICR to weak electromagnetic fields should be regarded. It makes the modulation of biological processes by the weak EMF of our everyday environment conceivable [37], possibly inducing likewise health risks and chances for new therapies, hardly minded till this day. Especially concerning the earlier [25] and the present study, glutamate is a neurotransmitter and is involved in a couple of other biological processes.
The geomagnetic field, with all its anomalies and regional differences [38], in combination with all the natural and civilizing EMF, overlap with a wide range of possible ICR of biologically relevant ions. But also technical applications basing on the ICR are imaginable, as a potential direction of future research. Its further investigation will be worthwhile, by new experiments, comparing field studies of health phenomena, and not at least a further clear up of its physical principle.
List of abbreviations
EMF: (low frequency) electromagnetic field
ICR: ion cyclotron resonance
NLDS: non linear dielectric spectroscopy
DNLDS: differential non linear dielectric spectroscopy.
FRV: frequency resolved voltammetry
Glu-HCl: A glutamate solution adjusted to pH 2.85 with hydrochloric acid (HCl).
Authors' contributions
The author itself carried out all experiments and drafted the manuscript.
Acknowledgements
The author thanks H. Scheer (München) for scientific care and frequent discussions for many years.
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| 15571630 | PMC538269 | CC BY | 2021-01-04 16:37:41 | no | Biomagn Res Technol. 2004 Nov 30; 2:8 | utf-8 | Biomagn Res Technol | 2,004 | 10.1186/1477-044X-2-8 | oa_comm |
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J CarcinogJournal of Carcinogenesis1477-3163BioMed Central London 1477-3163-3-151551130110.1186/1477-3163-3-15Short PaperComparative study of matrix metalloproteinase expression between African American and Caucasian Women Mason Jacquline A [email protected] Haile F [email protected] Kerrie [email protected] Marty [email protected] Agnes A [email protected] Department of Microbiology, College of Medicine Howard University, Washington, D.C. 20059, USA2 Department of Biology Howard University, Washington, D.C. 20059, USA3 Division of Pathology, Walter Reed Army Institute of Research, Washington, D.C. 20021, USA2004 29 10 2004 3 15 15 13 9 2004 29 10 2004 Copyright © 2004 Mason et al; licensee BioMed Central Ltd.2004Mason 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.
To date there are 26 human matrix metalloproteinases (MMPs) which are classified according to their substrate specificity and structural similarities. The four major subgroups of MMPs are gelatinases, interstitial collagenases, stromelysins, and membrane-type matrix metalloproteinases (MT-MMPs). This study investigates the expression of 26 MMPs, which have been shown to play a role in cancer metastasis. Breast tissues and cell lines derived from African American patients and Caucasian patients were assayed to demonstrate alterations in the transcription of genes primarily responsible for degrading the extracellular matrix (ECM). The expression levels of the extracellular matrix and adhesion molecules were analyzed using the gene array technology. Steady state levels of mRNAs were validated by RT-PCR analysis. Total RNA was isolated from tissue and cell lines and used in the RT-PCR assays. From this data, differential expression of MMPs between 6 breast cancer cell lines and 2 non-cancer breast cell lines was demonstrated. We have performed an in vitro comparison of MMP expression and established differences in 12 MMPs (3, 7, 8, 9, 11–15, 23B, 26, and 28) expression between African American and Caucasian breast cell lines. Thus, evidence indicates that altered expression of MMPs may play a role in the aggressive phenotype seen in African American women.
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Introduction
In 2003, it was estimated that approximately 1.3 million Americans would be diagnosed with invasive cancer. Of this group, racial/ethnic minorities account for a disproportionate number of these cancers [1,2]. Invasive breast cancer usually begins in either the lobules or the ducts of the breast. These tumors then metastasize via the breast associated and thoracic lymphatic tissue [3]. The incidence of breast cancer in Caucasian women (112 out of 100,000) is higher than in African American women (AA) (95 out of 100,000) after the age of 40, however, the mortality suffered by (AA)(37 out of 100,000) is higher than Caucasian women (CAU) (31 out of 100,000) at every age [4]. Thus a greater percentage of AA women die from breast cancer and resulting metastasis. In 2004, an estimated 215,990 new cases of invasive breast cancer is expected to occur among women in the United States [5]. Breast cancer is the most common cancer among AA women; however, the rate of newly diagnosed cases is about 13% lower than CAU women [6]. There is accumulating evidence that AA women have a higher frequency of more aggressive tumor types, which have been shown to lead to higher mortality rates. Studies show that compared Caucasian women (CAU), African American women (AA), regardless of age had proportionally more Grade III tumors and fewer Grade I and II tumors for all stages combined and for each individual stage group [7]. The grade of cancer has been shown to be a prognostic factor with higher-grade tumors being associated with reduced survival [7]. The most common cause of death in breast cancer patients is metastasis of breast cancer cells to bones, lungs, liver and brain and the progressive growth of the cancer at these sites [7,8]. Therefore, controlling breast cancer metastasis represents an effective method of preventing or slowing disease progression.
The extracellular matrix (ECM) is a complex structural entity surrounding and supporting organs and tissues of the body. The ECM plays a key role in cell-cell signaling, wound repair, cell adhesion and tissue function. Recent studies suggest that cell adhesion proteins located on breast cancer cells interact with the ECM [7]. This interaction induces increased production by the breast cancer cells of proteins that degrade the ECM. This degradation enables the tumor cells to invade the surrounding tissue and ultimately enter the circulatory system. Once they are in circulation, tumor cells travel to other organ sites where they progressively grow [7].
The matrix metalloproteinases (MMPs) are a family of structurally and functionally related endoproteinases that are involved in the degradation of the ECM. Currently, there are 26 identified human matrix metalloproteinases, which are classified according to their substrate specificity and structural similarities [8]. Abnormal expression of these proteins contributes to various pathological processes including rheumatoid arthritis and tumor growth, invasion and metastasis. The four main subgroups of MMPs are the interstitial collagenases, which catalyze degradation of fibrillar forms of collagen, the gelatinases which degrade gelatin and collagen that are abundant in basement membranes, the stromelysins, which degrade various substrates including proteoglycans, laminin and collagen I, II, and III and the membrane-type MMPs which have been shown to catalyze activation of progelatinase A, to degrade a variety of ECM substrates and to function as a fibrinolytic enzyme in the absence of plasmin [9].
MMP expression has bee shown to be elevated during development, pregnancy, and involution and has been shown to be related to tumor cell invasiveness [10]. This study investigates the expression levels of the 26 identified MMPs, which have been shown to play a role in the metastatic process using breast tissues and cell lines derived from AA and CAU women.
Materials and Methods
Cell Culture and Tissue RNA
All cell lines were purchased from American Type Culture Collection (Rockville, MD, USA). Cells were propagated in the recommended media and given new media every 2 to 3 days until 90% confluent (see table 1). Human Breast Tissue RNA was purchased from Ambion (Austin, TX).
RNA Extractions
RNA was extracted from the cell line using the RNAqueous (Ambion, Austin, TX). Cells were collected by low speed centrifugation and lysed by adding 200 μl of Lysis/Binding Solution. An equal volume of 64% ethanol was added to the lysate. The lysate/ethanol mixture was transferred to the RNAqueous Filter Cartridge and centrifuged for 1 minute at 13,400 rpm. The flow through was discarded and 700 μl of Wash Solution 1 was added to the RNAqueous Filter Cartridge and centrifuged for 1 minute. The column was washed twice with 500 μl of Wash Solution 2/3 and eluted with 110 μl Elution Solution. Isolated RNA was quantitated using the UltraSpec 2000 (Pharmacia Biotech). All RNA samples were stored at -70°C in RNA elution solution until further use.
Gene array
The Extracellular Matrix and Adhesion Molecule gene arrays were obtained from SuperArray (Frederick, MD). The array membranes were pre-hybridized with GEA hybridization solution and denatured salmon sperm DNA at 60°C for two hours. For each RNA sample, a labeling mix consisting of 4 μl 5X GEA labeling buffer, 2 μl biotin-16-dUTP, 1 μl RNase inhibitor, 1 μl reverse transcriptase, and 2 μl RNase-free water was prepared and an aliquot of 3 μg of RNA was added to each respective thin-walled PCR tube. The cDNA labels were created using a cycle of 3 minutes at 70°C, 2 minutes at 42°C, and an additional 90 minutes at 42°C. Two microliters of stop buffer was added and the mix denatured at 94°C for 5 minutes. The labeled cDNA was added to the membrane and allowed to hybridize overnight. The membranes were washed with 2X SSC/0.1% SDS and 0.1X SSC/0.5% SDS, blocked with blocking solution, and the probes were detected using AP-Strepavidin, specific buffers, CDP-Star and subsequent exposure to X-ray film for 30 seconds to 5 minutes. The autoradiograms were analyzed using ScanAlyzer and GEArray Analyzer (SuperArray, Frederick, MD).
Reverse Transcriptase Polymerase Chain Reaction (RT-PCR)
The RT-PCR reactions were performed in a P/E GeneAmp 9700 thermocycler (Perkin-Elmer Co., Norwalk CT), using the Access RT-PCR system (Promega, Madison, WI). The reaction mixes were prepared by combining 27. 5 μl of nuclease free water, 10 μl of AMV, 1 μl Tfl 5X reaction buffer, 2 μl dNTP mix, 50 pM of upstream primer, 50 pM of downstream primer in 1.5 μl volume each, 3 μl 25 mM MgSO4, 1.0 μl AMV reverse transcriptase, Tfl DNA polymerase and 1 μg of total RNA in a 0.5 ml thin walled Eppendorf tube on ice. The reaction mixes were then vortexed for 5 seconds and centrifuged. The PCR cycling profile was as follows: 48°C for 1 minute for reverse transcription of the RNA into cDNA, 94°C for 4 minutes to deactivate the reverse transcriptase, and 30 cycling sequences of denaturing at 94°C for 45 seconds, annealing at 55°C for 30 seconds, and extension at 72°C for 1 minute with a final extension at 72°C for 10 minutes. An aliquot of 20 μl of each RT-PCR reaction were run on 1.2% agarose gels, stained with ethidium bromide, photographed and subjected to densitometic measurements using the Chemi-Imager Tm 4000 (Alpha Innotech, Corporation, San Leandro, CA).
Results
Gene Array Analysis
Gene arrays were utilized to explore and compare the expression levels of extracellular matrix adhesion molecules in AA and CAU breast cancer cells. The array revealed elevated expression in 36% of the genes in AA samples when compared to their CAU counterparts. Of those elevated genes, 31% were from the cell membrane adhesion molecules group, 17% from the extracellular matrix proteins functional gene group, 37% from the proteases category, and 14% were protease inhibitors. Initial results of the gene array indicated a significant elevation of the proteases (data not shown). To further evaluate this, we proceeded with direct analysis of all known MMPs.
MMP RT-PCR Analysis
Comparison of the individual relative densities between AA and CAU women revealed elevated expression in 12 of the 26 MMPs (3, 7, 8, 9, 11, 12, 13, 14, 15, 23B, 26, and 28) (Figure 1 and Table 2). Elevated expression of MMP-3, 7, 8, 9, 11, 12, 13, 14, 23B, 26 and 28 in AA breast cancer cells was observed when the overall averages of the expression levels of all AA and CAU women cell lines were compared (Table 3).
Figure 1 RT-PCR expression of MMPs in African American and Caucasian breast cell lines and tissue.
Table 2 Matrix Metalloproteinase Expression Assessment by RT-PCR
African American Normal Caucasian
Cell Lines 2315 2320 2329 A1N4 10A MCF7 Hs578t 2336
MMPs
1 6 ± 2.7 12 ± .58 11 ± 1.15 5 5.7 6 ± 2.7 15 ± 2 11 ± 2.1
2 45.3 ± 4.2 32 ± .58 13.3 ± .58 6.7 11 11.7 ± 7.8 59 ± 1 33 ± 11
3 53 ± 11.5 47.3 ± 11 64 ± 7 9 14.3 26.5 18.3 40
7 73.3 ± 2.3 40.67 ± 2.1 17.6 ± 2.5 5.3 45.3 9 ± 1.7 38 ± 14.53 36.7 ± 4
8 14 ± 2.5 17 ± 2.5 16 ± 2.1 5 6 10 ± 1 15 ± 1.2 12 ± 1
9 66 ± 3 43 ± 5.2 18 ± 5.5 42.3 74.3 7.3 ± 1.5 28 ± 2.7 20.3 ± 5.13
10 7.3 ± .58 26 ± 1.7 18.7 ± 5.5 5.3 5 9.3 ± 1.15 66.3 ± 4 11.3 ± 1.5
11 11 ± 2 15.7 ± 2.9 15 ± 5 5 4 10 ± 4 13 ± 3.79 14 ± 0.58
12 10 ± 4 18 ± 8.9 16 ± 3.6 4 7 5 ± 1.15 5 ± 1 7 ± 2.1
13 10 ± 2 21.7 ± 4.2 7.7 ± 1.2 4 73.67 8.3 ± 2.1 18.7 ± 3.5 6.7 ± 1.15
14 12 ± 3 13.7 ± 1.5 16 ± 5 7 10 13 ± 8.5 11 ± 1.5 14 ± 3.1
15 70 ± 2.7 30 ± 0.58 57.7 ± 6.3 18 7.67 46.3 ± 4.5 81.3 ± 7.5 26.3 ± 6.8
16 8.7 ± 4 7.7 ± 1 15.7 ± 4 7.67 7.67 16 ± 2.9 16 ± 1 8 ± 2.7
17 N/D N/D N/D N/D N/D N/D N/D N/D
19v1 4 ± 1 7.3 ± .58 5 ± 1 5.33 4.33 5.3 ± 1.5 4.7 ± .58 11 ± 3.5
19v3 16 ± 6 13.3 ± 2.9 21.3 ± 8.7 15.3 10 18.3 ± 6.1 24 ± 4.2 25 ± 5
19v6 9.7 ± 4.6 9 ± .58 8 ± 5.2 7 6 12 ± 2.1 11 ± 2.1 9 ± 2.7
19v9 10 ± 1.0 10 ± 3.5 11 ± 2.0 7 7 10 ± 5 10 ± 2.0 10 ± 2.3
20 10.3 ± 3.2 45 ± .58 12.3 ± 2.1 11 9.3 23.7 ± 12.4 37.7 ± 6.0 11 ± 1
23A 5.7 ± 1.5 12 ± 2.0 8.7 ± 1.5 7 7 10 ± 2.8 5.7 ± .58 6.3 ± .58
23B 7 ± 1.1 32 ± 10.6 19.3 ± 1.5 3 7.3 10 ± 1.4 9.7 ± 1.5 12.7 ± 3.2
24 33 ± 1.2 19.7 ± 1.2 36 ± 1 18.67 30.2 33.7 ± 1 35 ± .58 37 ± 1.5
25 8 ± 1 12 ± 5.2 9 ± 1.0 5 6 13 ± 1.5 12.7 ± 1.0 7 ± 2.0
26 11 ± 1.0 11 ± 1.7 9 ± 2.5 11 8 10 ± 2 7 ± .58 9 ± 1.0
27 19 ± 7.2 19 ± 6.0 18 ± 8.0 6.7 11.67 15 ± 2.0 20 ± 13.2 19 ± 1.0
28 9 ± 5.6 14 ± 5.7 7 ± 2.0 15 18 7 ± .58 10 ± .58 9 ± 6.0
RT-PCR expression of MMPs in AA and CAU cells. Elevated expression in AA vs. CAU denoted in bold. Mean ± SD N/D: not detected
Table 3 Averaged Relative Density of MMP Expression For AAW CAU and Normal Cell Lines
AAW CAU Normal
MMPs
1 3.2 3.4 5.35
2 9.6 11.5 11.3
3 18.3 8.4 11.6
7 14.63 9.3 25.3
8 5.37 4.18 5.5
9 14.1 6.2 58.3
10 5.8 9.7 5.1
11 4.62 4.2 4.5
12 4.9 2 5.5
13 4.4 3.7 38.8
14 4.63 4.3 8.5
15 17.5 17.1 12.8
16 3.6 4.5 7.67
17 N/D N/D N/D
19v1 1.8 2.3 4.83
19v3 5.6 7.5 12.65
19v6 3.0 3.6 6.5
19v9 3.4 3.4 7
20 7.5 8.0 10.1
23A 2.9 2.4 7
23B 6.5 3.7 5.1
24 9.9 11.7 24.4
25 3.2 3.7 5.5
26 3.5 2.9 9.5
27 6.1 6 9.1
28 3.3 2.96 16.5
RT-PCR expression of MMPs in AA, CAU cancer cell lines and Normal cell lines.. Elevated expression in AA -vs- CAU denoted in bold. N/D: not detected
Discussion
Little is known as to why the incidence of breast cancer is lower yet mortality is higher in African American women. Many studies speculate that this is only a socio-economical problem [11]. However this investigation provides another possibility that may reveal molecular mechanisms that contribute to the increased mortality of AA women with breast cancer. The major threat to patients with breast cancer is tumor invasion and metastasis [12]. Tumor invasion is a complex process that requires interaction between the invasive cells and the ECM [13]. This process involves a cascade of events including angiogenesis, local invasion, and intravasation. One of these critical steps involves the proteolytic degradation of the ECM and basement membrane. This is partially done by the matrix metalloproteinases. One aspect related to cancer progression has been considered in numerous studies is the association of MMP expression with tumor grade and aggressiveness [14].
The GEArray Q Series Human Extracellular Matrix and Adhesion Molecules Gene Array were used to determine the expression profiles of various types of matrix and adhesion molecules. The array was divided into four components: cell adhesion molecules, extracellular matrix proteins, proteases and protease inhibitors. From analysis of the gene array, altered expression was observed in many of the proteases. These findings led to further study of the matrix metalloproteinases (data not shown).
Gene Array analysis of AA and CAU breast cancer cells indicates that there is altered expression of the genes in the Extracellular matrix and adhesion molecules, particularly the proteases. This group included 17 MMPs of which ten displayed elevated expression in AA women.
RT-PCR was performed to confirm the results of the gene array. We observed elevated expression of 12 MMPs in AA cell lines when compared to their CAU counterparts. These include one gelatinase (MMP-9), two interstitial collagenases (MMP-8, and 13), 3 stromelysins (MMP-3, 7, 11), two MT-MMPs (MMP-14 and -15) and 4 uncategorized MMPs (MMP-12, 23B, 26, and 28). There was no MMP-17 expression detected in any of the cell lines (Figure 1).
Studies have shown that normal mammary gland expression of MMPs is low except during times of development, pregnancy, and involution [10,15,16]. However, during pathologic states such as breast cancer, increased levels of MMPs have been reported in breast tumor cells as well as in the surrounding non-cancerous breast tissue [17].
Our results suggest that there is altered expression of MMPs in cell lines derived from AA and CAU women. It also demonstrates that there is greater expression of MMPs in AA women than in CAU women. This investigation indicates that altered expression of MMPs may play a role in the aggressive phenotype seen in AA women. This evidence suggests that the elevated expression levels of 12 MMPs may be a contributing factor in the higher mortality rates of AA breast cancer patients. This study is significant because it may reveal biomarkers of metastasis in AA women. To date, this is the first study to extremely investigate MMP expression in cell lines derived from African American patients.
Abbreviations used
MMP-Matrix metalloproteinases, MT-MMP-Membrane-type matrix metalloproteinases, AA-African American women, CAU-Caucasian, RT-PCR-Reverse transcriptase Polymerase Chain Reaction, ECM-Extracellular matrix
Author's Contributions
JAM performed the microarrays and RT-PCR experiments, was involved in tissue culture and prepared the first draft of the manuscript. HFY was responsible for primer design, and performed data analysis and densitometric readings of the gene arrays and RT-PCR. KL maintained all cells and tissues and assisted in the editing of this manuscript. MJ provided cell lines, training in microarray performance and editing. AAD conceived the study and participated in its design, coordination and funding, as well as preparation of the manuscript. All authors read and approved the final manuscript.
Table 1 Cell Lines and Tissue Samples
Human Breast Tissue Normal breast tissue (derived from CAU)
MCF-10A Mammary gland, fibrocystic disease (CAU)
A1N4 Mammary epithelial, chemically transformed (CAU)
CAUCASIAN (CAU)
HS578T Mammary gland; breast; carcinoma
MCF-7 Mammary gland; breast; epithelial; metastatic site: pleural effusion adenocarcinoma
CRL-2336 Mammary gland epithelial, primary ductal carcinoma
AFRICAN AMERICAN (AA)
CRL-2315 Breast, primary ductal carcinoma
CRL-2329 Carcinoma, ductal, primary; breast; mammary gland
CRL-2320 Carcinoma, ductal, breast; mammary gland; from metastatic site: lymph node
Acknowledgements
This work was supported by a grant from the US Army Research and Materiel Command under DAMD-17-01-1-0268 and DHHS / NCI U54 CA091431. The views expressed by the authors in no way represent the United States Army.
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| 15511301 | PMC538270 | CC BY | 2021-01-04 16:39:16 | no | J Carcinog. 2004 Oct 29; 3:15 | utf-8 | J Carcinog | 2,004 | 10.1186/1477-3163-3-15 | oa_comm |
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Proteome SciProteome Science1477-5956BioMed Central London 1477-5956-2-71555017010.1186/1477-5956-2-7ResearchValidation of a prefractionation method followed by two-dimensional electrophoresis – Applied to cerebrospinal fluid proteins from frontotemporal dementia patients Hansson Sara Folkesson [email protected] Maja [email protected] Kaj [email protected]ögren Magnus [email protected] Pia [email protected] Department of Clinical Neuroscience, Unit of Experimental Neuroscience, The Sahlgrenska Academy at Göteborg University, Sahlgrenska University Hospital/Mölndal, S-431 80 Mölndal, Sweden2 Department of Clinical Science, AstraZeneca R&D, Södertälje, Sweden3 Department of Experimental Medicine/Molecular Science, AstraZeneca R&D, Mölndal, Sweden2004 18 11 2004 2 7 7 2 2 2004 18 11 2004 Copyright © 2004 Hansson et al; licensee BioMed Central Ltd.2004Hansson 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 aim of this study was firstly, to improve and validate a cerebrospinal fluid (CSF) prefractionation method followed by two-dimensional electrophoresis (2-DE) and secondly, using this strategy to investigate differences between the CSF proteome of frontotemporal dementia (FTD) patients and controls. From each subject three ml of CSF was prefractionated using liquid phase isoelectric focusing prior to 2-DE.
Results
With respect to protein recovery and purification potential, ethanol precipitation of the prefractionated CSF sample was found superior, after testing several sample preparation methods.
The reproducibility of prefractionated CSF analyzed on 2-D gels was comparable to direct 2-DE analysis of CSF. The protein spots on the prefractionated 2-D gels had an increased intensity, indicating a higher protein concentration, compared to direct 2-D gels. Prefractionated 2-DE analysis of FTD and control CSF showed that 26 protein spots were changed at least two fold. Using mass spectrometry, 13 of these protein spots were identified, including retinol-binding protein, Zn-α-2-glycoprotein, proapolipoproteinA1, β-2-microglobulin, transthyretin, albumin and alloalbumin.
Conclusion
The results suggest that the prefractionated 2-DE method can be useful for enrichment of CSF proteins and may provide a new tool to investigate the pathology of neurodegenerative diseases. This study confirmed reduced levels of retinol-binding protein and revealed some new biomarker candidates for FTD.
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Background
Frontotemporal dementia (FTD) accounts for up to 20% of presenil dementia cases [1] and is, after Alzheimer's disease (AD), the second most common form of early onset dementia (at age < 65 years) [2]. The clinical picture in FTD is characterized mainly by changes in personality and social behavior, signs of disinhibition, lack of insight and changes in eating preferences [1]. Memory disturbances, which prevail in AD, may also be found in FTD but not usually to the same extent [3]. Post-mortem pathological examination reveals bilateral atrophy of the frontal and anterior temporal lobes in FTD and the ventricular system is sometimes widened frontally [4]. The histological findings provide a basis for the division of FTD into various subtypes. Neurofibrillary tangles, a prominent neuronal accumulation of hyperphosphorylated and filamentous forms of the microtubule associated protein tau, are found in FTD with Parkinsonism linked to chromosome 17, the hereditary variant of FTD, caused by mutations in the tau gene [5]. Another FTD variant, Pick's disease, is characterized by the presence of neuronal inclusion bodies called Pick bodies, containing filamentous tau and ubiquitin aggregates. The most common type of FTD is frontal lobe degeneration of non-Alzheimer type, which is clinically indistinguishable from Pick's disease, and histologically characterized by neuron loss and gliosis in the absence of distinctive histopathology, such as neurofibrillary tangles or other intracellular inclusions [4].
The diagnosis of FTD is often difficult and would be greatly enhanced by the use of disease specific neurochemical markers [6]. Several neuro-specific proteins in the cerebrospinal fluid (CSF) of FTD have been investigated [7,8] and elevation of cytoskeleton markers such as neurofilament light protein and tau have been found [7-10]. In order to expand the search for diagnostic biomarkers, which would also lead to a better understanding of the pathophysiological mechanisms of neurodegeneration, two-dimensional electrophoresis (2-DE) investigations of the CSF have previously been performed [11-15]. 2-DE can effectively separate several proteins and their isoforms simultaneously and is a useful tool for identifying quantitative and qualitative protein differences between the diseased and normal state. Previous proteomic studies by our group have shown for the first time that several proteins involved in FTD pathology are not effected in the CSF of AD patients and vise versa, thus establishing a likely difference in the pathophysiological mechanism between FTD and AD [11,12].
Some abundant proteins, for example albumin and immunoglobulins, limit the total amount of CSF proteins that can be loaded on the 2-D gel, resulting in difficulties detecting low abundant proteins of CSF. By using liquid phase isoelectric focusing (LP-IEF) as a prefractionation step prior to 2-DE we have previously shown that less abundant CSF proteins can be enriched, thus making them more easily detected and identified by mass spectrometry (MS) [16]. The advantage of this method is that a larger volume of CSF (3 ml) can be used as starting material and that proteins outside the selected pH interval of the 2-D gel can be excluded. For several years alternative prefractionation methods prior to 2-DE have been reported [17-22], each with its advantages and disadvantages.
The aim of the present study was to improve the prefractionation procedure for individual CSF samples and to determine its reproducibility. Moreover, the second aim of this study was to apply the method and further explore disease-influenced proteins in CSF from FTD patients compared to controls.
Results
Preparation of prefractionated CSF samples prior to 2-DE
Several strategies to reduce impurities, i.e. salt and ampholytes, of the prefractionated CSF samples were performed including, trichloroacetic acid (TCA)-acetone precipitation, ethanol precipitation, chloroform/methanol/water precipitation and, micro Bio-Spin desalting column. The protein pattern on the 2-D gels and protein recovery from the different sample preparation methods were compared to that of acetone precipitation, previously used prior to prefractionated 2-DE of CSF [16]. We found that ethanol precipitation gave the best result and allowed us to reduce the focusing time from 25 000 Vh to 20 000 Vh, as recommended by the Protean cell manufacturer. Evaluation of gels with ethanol precipitated sample showed that the same protein spots were present, or even a few more, with similar intensities as in the gels with the acetone treated CSF-samples (figure 1a and 1b). Furthermore, the optimal ethanol concentration was tested showing that a concentration of at least 70% ethanol was sufficient to generate a total protein recovery, determined by the RC DC protein assay (figure 1c). An ethanol concentration of 71.25% (3/4 95% ethanol and 1/4 sample) was used in the subsequent studies.
Figure 1 Comparison of different protein precipitation methods. a) 75% acetone precipitation and b) 70% ethanol precipitation of prefractionated SYPRO Ruby stained CSF proteins with pH 4.5–6.0, separated on IPG-strips, pH 4–7. The molecular weights (MW) are in kDa. c) The protein recovery as % of control (untreated CSF) after precipitation with indicated concentrations of ethanol and 75% acetone. The protein recovery was measured using the RC DC protein assay (Bio-Rad).
Both TCA-acetone and chloroform/methanol/water precipitation substantially reduced the number of spots and resulted in streaky 2-D gels (data not shown). The use of biospin columns gave well-focused 2-D gels but several protein spots were reduced or lost (data not shown).
Reproducibility of prefractionated and unfractionated CSF on 2-D gels
Four identical CSF samples were individually prefractionated by LP-IEF. Fractions 6–9 (having a pH of 4.5–6.0) were pooled and analyzed on pH 4–7 strips in four replicates. Coefficients of variation (CVs) were calculated for the protein spots quantities, determined by the PD-Quest software, and a selection of 20 spots had CVs ranging from 1–33% (15.4% ± 8.3%, mean ± SD) (Figure 2a). The spots were selected to represent a broad range of proteins present in each replicate, i.e. proteins of different molecular weights and isoelectric points (pIs), low, medium and high abundant proteins as well as different isoforms of the same protein. The reproducibility of direct 2-DE of CSF, using pH 4–7 gels, in four replicates was also determined and the CVs of a similar selection of 20 spots ranged from 1–35% (14.6% ± 7.9%, mean ± SD) (Figure 2b).
Figure 2 Reproducibility study of direct and prefractionated 2-DE of CSF on SYPRO Ruby stained 2-D gels. a) Represents a standard 2-D gel image of prefractionated (using LP-IEF) CSF fractions with pH 4.5–6.0, separated on a pH 4–7 IPG-strip. Numbers represents the CVs of encircled protein spots from four individually prefractionated, identical CSF samples separated by 2-DE. The mean CV of the 20 marked spots is 14.6% ± 7.9% (mean ± SD). b) Represents a standard 2-D gel image of directly analysed (after acetone precipitation) CSF proteins, separated on a pH 4–7 IPG-strip. Numbers represents the CVs of encircled protein spots from four replicate gels. The mean CV of the 20 marked spots is 15.4% ± 8.3% (mean ± SD). Molecular weights (MW) are in kDa.
Comparison of direct and prefractionated 2-DE
Comparison of direct and prefractionated 2-DE showed that the prefractionated gels contained more spots with a higher protein concentration (Figure 3). The spots in the pH 3–6 and pH 5–8 gels in particular were increased in both number and density after prefractionation.
Figure 3 Comparison of direct and prefractionated CSF on SYPRO Ruby stained 2-D gels. The upper figures represents standard images of direct 2-DE, and the lower figures represents prefractionated 2-DE. The pH interval of the IPG strips is denoted in the upper left corner of the gels and the pH range of the prefractionated CSF samples at the bottom of the gel images. Molecular weights (MW) are in kDa.
A proteomic study comparing prefractionated CSF from FTD patients and control subjects
The prefractionated 2-DE method was used to screen for changes in the CSF proteome of five FTD patients compared to that of five non-dementia controls in the pH intervals 3–6, 4–7, and 5–8 (Figure 4a,4b,4c).
Figure 4 Prefractionated CSF from FTD patients compared to non-demented controls. Protein densities increased (squares) or decreased (circles) at least two times in prefractionated FTD CSF, analyzed using SYPRO-Ruby stained 2-DE gels. The five FTD patients were 70.6 ± 5.6 (mean ± SD) year-of-age and the five non-demented controls were 59.2 ± 11.9 (mean ± SD) year-of-age. The numbers on the 2-D gel pattern correlate each identified protein to the data given in table 2. Molecular weights (MW) are in kDa. a) Fraction 2–5 with pH 1.5–4.5 was analysed using a pH 3–6 linear IPG-strip. b) Fraction 6–9 with pH 4.5–6.0 was analysed using pH 4–7 linear IPG-strip. c) Fraction 10–14 with pH 6.0–7.5 was analysed using pH 5–8 linear IPG-strip in the first dimension.
Comparing the protein densities of the gels, 10 spots were up regulated and 16 spots down regulated, at least two fold, in FTD patients compared to non-dementia controls. Increased proteins are marked with a square and decreased with a circle (Figure 4a,4b,4c). Thirteen of the protein spots, corresponding to 7 different proteins, were identified by mass spectrometry (MS) Table 1). In the FTD group the following protein spots were increased: One isoform of Zn-α-2-glycoprotein (ZAG), proapolipoproteinA1 (ProapoA1), β-2-microglobulin (β-2-m) and two isoforms of transthyretin (TTR), while a reduction was seen in four isoforms of serum albumin, two isoforms of alloalbumin and retinol binding protein (RBP), compared to controls (Table 1).
Table 1 CSF proteins increased or decreased, at least two fold in FTD vs. control
Spot no. Protein identity NCBI Acc. no. Theor. Mw (kDa) Theor.pI No. peptides matched Seq. cov. (%) Levels in FTD vs. control FTD spot norm. density (mean ± SD) Control spot norm. density (mean ± SD)
1 Zn-α-2-glycoprotein 141596 31.6 5.70 4 22 ↑ 42256 ± 7227 4189 ± 979
2 proapolipo- protein A1 178775 28.9 5.45 14 43 ↑ 11572 ± 10432 4575 ± 2309
3 retinol- binding protein 20141667 20.9 5.27 4 35 ↓ 3680 ± 1675 8131 ± 4857
4 serum albumin 113576 52.0 5.69 11 22 ↓ 1819 ± 1657 7272 ± 1812
5 serum albumin 113576 52.0 5.69 11 22 ↓ 2415 ± 964 7839 ± 1906
6 serum albumin 113576 66.0 5.69 12 20 ↓ 1003 ± 309 3098 ± 1428
7 serum albumin 113576 66.0 5.69 9 15 ↓ 1528 ± 587 4896 ± 2551
8 alloalbumin 178345 69.2 5.99 12 19 ↓ 1361 ± 530 7454 ± 1748
9 alloalbumin 178345 69.2 5.99 12 19 ↓ 2463 ± 806 8395 ± 1475
10 retinol-binding protein 20141667 20.9 5.27 4 35 ↓ 1744 ± 551 5871 ± 2040
11 transthyretin 339685 13.8 5.3 8 81 ↑ 45198 ± 26202 9149 ± 2685
12 transthyretin 339685 13.8 5.3 8 81 ↑ 303002 ± 72750 147732 ± 30928
13 β-2-microglobulin 4757826 12.9 5.77 5 46 ↑ 41938 ± 43510 12287 ± 3161
The proteins were identified by MALDI-MS. The spot numbers refer to those given in figure 4.
Discussion
In this study we present an improved method for increased detection of CSF proteins by a combination of LP-IEF and 2-DE, followed by SYPRO Ruby protein staining and protein identification by mass spectrometry, for investigation of protein differences in CSF of FTD patients compared to controls. To our knowledge no other prefractionation method combined with 2-DE has so far been developed and evaluated for CSF proteins.
The study showed that the reproducibility of prefractionated 2-D gels could be compared to that of direct 2-D gels, indicating that the extra prefractionation step did not introduce additional variation and could be reproduced from sample to sample. The protein detection and quantitative reproducibility of Coomassie Brilliant Blue [23], silver, [24,25] and SYPRO Ruby [26,27] staining of direct 2-DE gels has previously been described. In one SYPRO Ruby study [26] the reproducibility of the quantification of 20 proteins, selected to represent well matched proteins of different molecular weight and intensity, from four replicate gels, had CVs ranging from 3 to 33%. This is in agreement with our findings using a similar selection of 20 proteins, where the prefractionated 2-DE CVs ranged from 1–33% (mean 14.5%) and the direct 2-DE ranged from 1–35% (mean 15.4%). Mainly very faint spots have CVs in the higher range (Figure 2).
In addition to the high salt concentration of CSF (> 150 mmol/L), ampholytes are also introduced into the sample in the prefractionation step, LP-IEF. We previously reported that the focusing time in the first dimension of prefractionated 2-DE had to be increased [16] probably due to insufficient "clean up" of the sample by acetone precipitation. Therefore, different "clean up" procedures were tested. Precipitation using ethanol was found to be most effective, keeping the number and intensity of the protein spots constant and allowing us to reduce the focusing time in the first dimension. We found that TCA-acetone precipitation reduced the protein content of the sample in agreement with a study of directly analyzed CSF samples [13]. In contrast to the results of Yuan et al. [13] a substantial loss of protein spots using the Bio-Spin column was found. The reason might be that proteins are retained in the spin column to a higher degree in the presence of ampholytes (Servalytes), which are small charged peptides. Ethanol precipitation of plasma samples has previously been performed[28], showing that a concentration of 66.6% ethanol was sufficient to precipitate 99% of the proteins [28]. This is in agreement with our findings, that 100% of CSF proteins are precipitated at ethanol concentrations above 70%.
When comparing direct and prefractionated 2-DE it is evident that the prefractionated gels contain more spots, with higher protein quantities. Thus, the CSF proteins are enriched in the prefractionation step, simplifying their identification by MS, as shown in our previous study [16]. CSF analysis on the pH 3–6 and pH 5–8 gels in particular is improved by the prefractionation step, probably because the amount of CSF proteins in these pH ranges, without prefractionation, is rather low.
In order to widen our search for protein differences in the CSF of FTD patients the improved prefractionated 2-DE procedure was applied to CSF from five FTD patients and five control subjects. 26 protein spots were changed at least two fold and 13 of these protein spots, representing seven different proteins, were identified as ZAG, ProapoA1, β-2-m, TTR, RBP, serum albumin and alloalbumin. Our previous direct 2-DE study [12] of the FTD proteome showed that 7 proteins were significantly altered compared to controls, including granin like neuroendocrine precursor, apolipoprotein E, pigment epithelium derived factor, RBP, haptoglobulin and albumin.
A reduced level of RBP was consistent between our two studies, and in this case RBP was found reduced in both the pH 4–7 and the pH 5–8 gels. In contrast, CSF analysis of AD showed increased levels of one isoform of RBP [11], indicating a different role of RBP in the pathology of AD and FTD. RBP is synthesized by hepatic parenchymal cells, after binding to its ligand retinol, the complex is secreted into the circulation [29], where it further complexes with the plasma protein TTR. CSF RBP concentration has been shown to correlate to those of serum [30]. Serum RBP and retinol have been found to be reduced during acute infection and the decrease is proportional to the extent of the infection [31], suggesting that reduced RBP levels may result from an inflammation in the FTD brain.
Brain TTR is exclusively produced, secreted and regulated by the choroid plexus [32-34]. TTR makes up 25% of the total CSF protein content [32] and even higher concentrations exist during prenatal and early postnatal life, indicating an importance of the protein in CNS development [35]. In this study, the levels of two isoforms of TTR were increased in the CSF from FTD patients. To our knowledge, the TTR levels of FTD CSF have not previously been studied, but in AD the CSF levels were decreased in an immunological study, not differentiating between TTR isoforms [36]. In contrast, the direct 2-DE study of the AD proteome [11] showed an increased level of TTR, but of a more acidic isoform, compared to this study. This highlights the capacity of 2-DE to quantify specific isoforms.
One isoform of β-2-m was found increased in this study. Other studies have shown that CSF β-2-m is elevated in patients with various neurological diseases including AD [11], infectious meningoencephalitis [37], neurosarcoidosis [38] and AIDS dementia complex [39]. β-2-m constitutes the non-covalently bound light chain of major histocompatibility complex class I molecule (MHCI) [40]. The MHCI complex is expressed on the surface of all nucleated cells and the association of β-2-m to the MHCI transmembranal chain is an absolute requirement for the antigenic presentation function of the complex [40]. It has been proposed that conformational changes of the MHCI complex, associated with cell injury, can be responsible for increased shedding of β-2-m from the cell membranes with consequent expansion of the circulating β-2-m pool [41].
The function of ZAG is unknown but studies have shown that it is present in several body fluids, including CSF, sweat, saliva, seminal fluid, plasma, milk, amniotic fluid and urine, suggesting a fairly widespread exocrine function of the protein [42]. In this study increased levels of ZAG were found in FTD CSF and to our knowledge, ZAG has not previously been associated with dementia.
The level of ProapoA1 was also increased in this study. Our previous study of the FTD proteome [12] did not show any increase in ProapoA1. Nevertheless, our study of the AD proteome [11] detected reduced levels of 3 isoforms of ProapoA1.
The reason that several protein changes were inconsistent between this and our previous study of the FTD proteome may be explained by the fact that a smaller sample size and a different population of FTD patients, which is a rather heterogeneous disease, were used in this study. Due to the small sample size it must also be emphasized that the protein changes found in this study are preliminary. Moreover, direct and prefractionated 2-DE are still two different proteomic approaches and a somewhat different analytical window was not unexpected. Indeed, direct and prefractionated 2-D gels show different protein patterns, for example, the apolipoprotein E and apolipoprotein J isoforms seem to be missing in the prefractionated 2-D gels, which may be explained by the fact that lipoproteins tend to adhere to plastics [43] and could be lost during LP-IEF or additional sample transfer steps in the prefractionation procedure. However, the lipoprotein, ProapoA1 could still be detected in the prefractionated 2-D gels. The proteins most likely to be favored by a prefractionation step are low abundant hydrophilic proteins, which most likely are present in CSF. Nevertheless, this investigation of the FTD proteome failed to detect any very low abundant brain specific proteins.
As shown in the present study, the levels of specific isoforms are altered and these are unlikely to be detected using methods measuring the total concentration of a protein. Therefore, determination of posttranslational modifications is of importance for understanding the neuropathology in FTD, and 2-DE is a useful method for sensitive detection of different protein isoforms.
Conclusions
We have shown that the prefractionated 2-DE method is reproducible to the same extent as traditional 2-DE and can enrich CSF proteins in the gel. This approach may offer new perspectives on the pathology of neurodegenerative diseases. Prefractionated 2-DE analysis of FTD CSF proteins confirmed some of the proteins previously detected by direct 2-DE and revealed some new biomarker candidates. The protein changes should be further validated on a larger patient material, preferably also with complementary methods, in order to assess any of the proteins potential as biomarkers for FTD.
Methods
CSF samples
CSF samples were obtained from the Clinical Neurochemical Laboratory (Sahlgrenska University Hospital/ Mölndal). All CSF samples had a normal white-cell count, normal blood-brain barrier function and absence of intrathecal IgG and IgM production. The CSF samples for the proteomic study, described in table 2, were obtained from 5 FTD patients aged 70.6 ± 5.6 (mean ± SD) years and 5 non-dementia controls aged 59.2 ± 11.9 years (mean ± SD). FTD was diagnosed according to the Lund Manchester criteria [4]. The severity of dementia was evaluated using the Mini Mental State Examination (MMSE) [44]. The control group, "non-demented controls" consisted of subjects with minor psychiatric complaints or subjective memory complaints that could not be verified by clinical examination, CSF analysis or neuropsychological testing. All control individuals had MMSE scores of 29–30. Lumbar puncture was performed in the L4–L5 vertebral interspace. The first 12 mL of CSF was collected and gently mixed to avoid possible gradient effects. The CSF samples were then centrifuged at 2,000 g for 10 min to eliminate cells and other insoluble material, and stored at -80°C.
Table 2 CSF samples included in the prefractionated 2-DE study
Subject Agea) Sex Albumin ratiob) Tau (ng/L) Aβ (ng/L) MMSE
FTD 1 64 F 6.3 633 779 20
FTD 2 72 F 6.7 364 668 22
FTD 3 73 M 4.1 409 245 15
FTD 4 78 F 8.0 283 833 10
FTD 5 66 F 6.9 302 642 19
Control 1 63 M 6.7 368 599 29
Control 2 63 F 8.7 274 1070 30
Control 3 74 M 6.4 270 412 29
Control 4 54 F 5.5 210 1160 30
Control 5 42 M 2.8 318 1620 29
a) The age of the FTD group is 70.6 ± 5.6 years (mean ± SD) and the age of the control group is 59.2 ± 11.9 years (mean ± SD)
b) [CSF albumin(mg/L)] / [serum albumin(g/L)]
The study was approved by the Ethical Committee of Göteborg University. All participants or their relatives gave their informed consent to participation in the study, which was performed in accordance with the Declaration of Helsinki.
Purification and precipitation methods
To find a method for effective reduction of impurities and maximal protein recovery, 300 μL of prefractionated pooled CSF samples (fraction 6–9, pH 4.5–6.0) were purified using each of the following methods:
1. Ice cold acetone precipitation; acetone: sample (4:1, v/v) precipitated at -20°C for 2 hours.
2. Ice cold acetone-TCA precipitation; 2a) acetone: TCA: sample (4:10%:1, v/w/v) at -20°C for 45 min. 2b) acetone: TCA: sample (4:20%:1, v/w/v) at -20°C for 45 min. The protein pellet was washed 2 times with acetone after centrifugation.
3. Chloroform/methanol/water precipitation, chloroform: methanol: sample (4:8:3, v/v/v) at room temperature for 2 hours.
4. Ice cold ethanol precipitation; final concentrations of 60%, 70% and 80% ethanol was added to the sample and precipitated for 2 hours at -20°C.
5. Purification using micro Bio-Spin column (Bio-Rad, Hercules, CA, USA) with a MW cut off of 6 kDa. The purification procedure w two-dimensional electrophoresis (2-DE) as performed according to the manufacturer's instructions.
After precipitation all samples were centrifuged and the protein pellet analyzed on 2-D gels, described below.
The protein recovery of acetone and ethanol treated samples was measured using the RC DC protein assay (Bio-Rad) according to the manufacturer's instructions.
Prefractionation, sample preparation and 2-DE procedure
The CSF samples from individual patients were prefractionated using LP-IEF in the Rotofor cell (Bio-Rad). Three mL CSF sample was mixed with 9 mL millipore water, 1% ampholytes (Servalyte pH range 3–10, Serva Electrophoresis, GmbH, Germany), 20 mM dithiothreitol (DTT) and 1 × Complete antiprotease solution (Roche Diagnostics, Mannheim, Germany). The focusing was performed at 4°C and at 12 W constant power for 2.5 hours. Then the 20 Rotofor fractions were harvested and fraction 2–5 corresponding to pH 1.5–4.5, fraction 6–9 corresponding to pH 4.5–6.0 and fraction 10–14 corresponding to pH 6.0–7.5 were pooled and concentrated to 300 μL in a vacuum centrifuge prior to 2-DE.
In the FTD-study, the prefractionated pooled protein fractions were precipitated using 900 μL 95 % ice-cold ethanol (71.25% final conc. ethanol) for two hours at -20°C. The mixture was centrifuged at 10,000 × g for 10 min at 4°C. The protein pellets were air-dried and then resolved in a buffer containing 9 M urea, 35 mM tris, 42 mM DTT, 2% 3-((3-cholamidopropyl) dimethylammonio)-1-propanesulfonate (CHAPS), 0.66% sodium dodecyl sulfate (SDS), 2% IPG buffer and bromophenol blue.
The first dimension was carried out using immobilized pH gradient (IPG) strips (Bio-Rad), 7 cm, pH 3–6 for Rotofor fractions 2–5, pH 4–7 for Rotofor fractions 6–9 or pH 5–8 for Rotofor fractions 10–14. The IPG-strips were actively rehydrated in the CSF-protein sample for 12 h at 50 V followed by protein focusing for 20,000 Vh using the Protean IEF Cell (Bio-Rad). The IPG strips were placed in 5 ml equilibration solution (50 mM Tris-HCl pH 8.8, 6 M urea, 30% glycerol, 2% SDS, bromophenol blue) containing 1% DTT, and 2.5% iodoacetamide in the second equilibrium step for 2 × 15 min.
The second dimension was performed using the Nu-PAGE gel system (NOVEX, San Diego, CA, USA) with (2-(N-morpholino) ethane sulfonic acid (MES) buffer: 50 mM MES, 50 mM tris, 3.5 mM SDS, 1 mM EDTA), for 35 min at 200 V.
In the direct 2-DE procedure, 300 μL CSF proteins were precipitated using 900 μL ice-cold acetone and stored for two hours at -20°C. The mixture was then centrifuged at 10,000 × g for 10 min at 4°C. The 2-DE procedure was performed as described above for prefractionated 2-DE.
Visualisation and evaluation
The gels were stained using SYPRO Ruby Protein Stain (Molecular-Probes, Eugene, Oregon, USA) according to the manufacturer's instructions. Image acquisition and analysis were performed on a Fluor-S MultiImager (Bio-Rad). The protein spots were detected, quantified and matched with the PD-Quest 2-D gel analysis software, v.7.0 (Bio-Rad). The gels were normalized according to the total quantity in valid spots (the raw quantity of each spot in a member gel is divided by the total quantity of all the spots in that gel that have been included in the Master gel). Protein levels increased or decreased two fold were taken into account.
In-gel tryptic digestion and sample preparation
The protein digestion method has been previously described in detail [11]. Briefly, the gel pieces were digested with porcine trypsin (Promega Corporation, Madison, USA) and the peptides were extracted with formic acid (FA) and acetonitrile (ACN). The digested protein sample was dried under vacuum and then dissolved in 10 μL 0.2% triflouroacetic acid (TFA) (v/v). The samples were applied to the MS probe using the AnchorChip™ technology (Bruker daltonics, Bremen, Germany) as previously described [45]. Briefly α-cyano-4-hydroxy-cinnamic acid (CHCA) solution (100 g/L in 90% acetone, 0.005 % TFA) was spread out evenly on the sample plate surface creating the CHCA matrix layer. Then 2μL of the protein sample solution was applied to each anchor spot. After 2 min, the remaining liquid was removed by absorption using a paper tissue.
Mass spectrometry and database searching
Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis was performed using an upgraded Reflex II MALDI-TOF MS (Bruker-Franzen Analytik GmbH, Bremen, Germany) equipped with a two-stage electrostatic reflectron, a delayed extraction (time-lag-focusing) ion source, a high resolution reflector detector and a 2 GHz digitizer. The spectra were acquired in the reflection mode at an accelerating voltage of 20 kV. The mass spectra, acquired and analyzed using Bruker software, were initially calibrated by external calibration using a mixture of known peptides and later recalibrated using two auto digestion products of porcine trypsin as internal calibrants. The protein database search tool "MASCOT Peptide Mass Fingerprint" on the Matrix Science web site [46] was used to compare the monoisotopic m/z values of the tryptic fragments to those of known proteins in the NCBI database. A mass deviation of 100 ppm was tolerated and Homo sapiens was specified.
Statistical analysis
Coefficient of variation (CV) was calculated (standard deviation (SD)/ Mean × 100) of the normalized protein spot densities from four replicate 2-D gels.
In the proteomic study a 2-fold increase or decrease of normalized protein quantities was taken into account.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
SFH carried out all the 2D gel experiments, mass spectrometry analysis, participated in results evaluation and drafted the manuscript.
MP participated in the 2D gel experiments, results evaluation and participated to the manuscript writing.
KB contributed with material and critical reading of the manuscript.
MJ contributed with material and participated in the design of the study.
PD conceived the study, participated in its design and coordination, results analysis and supervised the manuscript writing.
Acknowledgements
The authors would like to thank Maria Lindbjer Andersson for excellent technical assistance.
This work was supported by grants from The Swedish Medical Research Council (grants# 12769, 13121), Stiftelsen för Gamla Tjänarinnor, Stockholm, Sweden; Lundgrens Vetenskapsfond, Gothenburg, Sweden; Stohnes stiftelse, Stockholm, Sweden; and Pfannenstills stiftelse, Malmö, Sweden.
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| 15550170 | PMC538271 | CC BY | 2021-01-04 16:37:14 | no | Proteome Sci. 2004 Nov 18; 2:7 | utf-8 | Proteome Sci | 2,004 | 10.1186/1477-5956-2-7 | oa_comm |
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BMC Endocr DisordBMC Endocrine Disorders1472-6823BioMed Central London 1472-6823-4-41555505910.1186/1472-6823-4-4Research ArticlePrevalence and determinants of diabetes mellitus among Iranian patients with chronic liver disease Alavian Seyed M [email protected] Behzad [email protected] Fariborz [email protected] Bagher [email protected] Department of Internal Medicine, Baghiatollah University of Medical Sciences, Tehran, Iran2 Tehran Hepatitis Center, Tehran, Iran3 Endocrinology and Metabolism Research Center (EMRC), Tehran University of Medical Sciences, Tehran, Iran2004 19 11 2004 4 4 4 14 7 2004 19 11 2004 Copyright © 2004 Alavian et al; licensee BioMed Central Ltd.2004Alavian 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
Alterations in carbohydrate metabolism are frequently observed in cirrhosis. We conducted this study to define the prevalence of diabetes mellitus (DM) and impaired glucose tolerance (IGT) in Iranian patients with chronic liver disease (CLD), and explore the factors associated with DM in these patients.
Methods
One hundred and eighty-five patients with CLD were enrolled into the study. Fasting plasma glucose and two-hour plasma glucose were measured in patients' sera. DM and IGT were diagnosed according to the latest American Diabetes Association criteria.
Results
The subjects included 42 inactive HBV carriers with a mean age of 42.2 ± 12.0 years, 102 patients with HBV or HCV chronic hepatitis with a mean age of 41.2 ± 10.9 years, and 41 cirrhotic patients with a mean age of 52.1 ± 11.4 years. DM and IGT were diagnosed in 40 (21.6%) and 21 (11.4%) patients, respectively. Univariate analysis showed that age (P = 0.000), CLD status (P = 0.000), history of hypertension (P = 0.007), family history of DM (P = 0.000), and body mass index (BMI) (P = 0.009) were associated with DM. Using Multivariate analysis, age (OR = 4.7, 95%CI: 1.8–12.2), family history of DM (OR = 6.6, 95%CI: 2.6–17.6), chronic hepatitis (OR = 11.6, 95%CI: 2.9–45.4), and cirrhosis (OR = 6.5, 95%CI: 2.4–17.4) remained as the factors independently associated with DM. When patients with cirrhosis and chronic hepatitis were analyzed separately, higher Child-Pugh's score in cirrhotic patients (OR = 9.6, 95%CI: 1.0–88.4) and older age (OR = 7.2, 95%CI: 1.0–49.1), higher fibrosis score (OR = 59.5, 95%CI: 2.9–1211.3/ OR = 11.9, 95%CI: 1.0–132.2), and higher BMI (OR = 30.3, 95%CI: 3.0–306.7) in patients with chronic hepatitis were found to be associated with higher prevalence of DM.
Conclusions
Our findings indicate that patients with cirrhosis and chronic hepatitis are at the increased risk of DM occurrence. Older age, severe liver disease, and obesity were associated with DM in these patients.
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Background
Alterations in carbohydrate metabolism are frequently observed in liver cirrhosis. Glucose metabolism impairment in cirrhotic patients, both in fasting state and in response to oral glucose or meals has been widely documented in the literature [1-5]. There is a wide variability in the prevalence of overt diabetes mellitus (DM) and impaired glucose tolerance (IGT) according to various reports. In fact, the laboratory methods and the criteria used to state glucose metabolism status are different among various studies, considering the fact that the criteria for diagnosing DM and IGT have been modified a couple of times during last decade. On the other hand, the majority of the papers focused on cirrhosis [2,3,6-10] although a number of studies evaluated patients with chronic hepatitis, as well [4,5,11,12]. Furthermore, in many studies investigating the correlation between liver disease and glucose tolerance, a number of potent factors for DM such as BMI have been overlooked.
In this study, for the first time in Iran, we reported the prevalence of DM and IGT in patients with chronic liver disease (CLD) using the latest generally accepted criteria, and investigated independent correlation of CLD with DM, considering other known possible DM risk factors. Moreover, we explored the factors that might be potentially associated with DM in patients with cirrhosis and chronic hepatitis.
Methods
From October 2002 to March 2003, all consecutive patients with chronic liver disease referred to "Tehran Hepatitis Center" were enrolled into the study. Tehran Hepatitis Center is a referral specialized clinic for liver diseases where many patients suffering from liver diseases and hepatitis from around Iran are referred to in order to receive consultation and clinical medical care. Pregnant females, patients under 20 years old, those who were on regular corticosteroid or hydrochlorothiazide therapy, known or suspected cases of hemochromatosis, or autoimmune disease were excluded. Having a clinical, biochemical (serum amylase and/or lipase elevation) or ultrasonographic evidence for chronic pancreatitis was also considered an exclusion criterion. No patient had a history of habitual drinking. All of the patients were Moslem and belonged to the white race.
A total number of 193 patients who met the criteria were eligible to enter the study. First group included 41 cirrhotic patients. Mean age was 52.1 ± 11.4 years ranging between 26–74 years. Diagnosis of cirrhosis was confirmed by histology in 17 (41.5%) patients. The occurrence of signs or biochemical evidences of liver decompensation, ultrasound features of portal hypertension and/or esophageal varices in gastroscopy were used for the clinical diagnosis in the remaining cases. No cirrhotic patients had evidence of hepatocellular carcinoma, screened by serum alpha-fetoprotein level test and abdominal ultrasonography. Severity of liver cirrhosis was graded according to Child-Pugh's classification. Second group included 102 patients with chronic hepatitis B, or C. Mean age was 41.2 ± 10.9 years ranging between 22–67 years. All patients had adequate documentation of elevated serum aminotransferases for more than 6 months, and viral hepatitis molecular assays by qualitative RT-PCR. In 91 (89.2%) patients liver biopsy reports were accessible. The histological staging and grading in liver biopsy was scored according to Knodell scoring. Third group included 42 inactive hepatitis B virus (HBV) carriers who were hepatitis B s antigen (HBsAg) positive, quantitative HBV DNA level less than 105 copies/ml (ROCHE Cobas Amplicore) and normal liver enzymes. The mean age in this group was 42.2 ± 12.0 years ranging between 23–84 years. Eight patients with nonalcoholic steatohepatitis (NASH) were initially enrolled but were not entered in analysis because of the low case number. Therefore analysis was finally performed for 185 patients.
Informed consent in writing was obtained from each patient involved and the study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki as approved by institutional review board. Initial data were collected by chart review and interview. Then, two venous blood samples were taken: First sample in the morning after a 12-hour overnight fast to measure fasting plasma glucose (FPG) level, triglycerides (TG) level, and cholesterol (Chol) level; and a second sample following 2 hours after eating 75 gr glucose to measure two-hour post-loaded glucose (2-hr PG). FPG and 2-hr PG measurement were repeated on two other samples in second session.
Patients receiving therapy with insulin or oral hypoglycemic medications were considered as diabetics. In other patients, FPG ≥ 126 mg/dl or 2-hr PG ≥ 200 mg/dl on more than one occasion was used as diagnostic for DM in agreement with the latest American Diabetes Association (ADA) criteria [13]. FPG between 110 mg/dl and 126 mg/dl was considered as impaired fasting glucose (IFG), and 2-hr PG between 140 mg/dl and 200 mg/dl was considered as IGT according to ADA definitions [13].
Body mass index (BMI) was calculated using the standard formula of weight in kilogram divided by square of height in meters (kg/m2).
Results were expressed as mean ± standard deviation (SD). Comparison between diabetic and non-diabetic groups was made using Students T test for continuous variables and the Chi-square or Fisher exact test for categorical variables. At univariate analysis the factors possibly associated with DM development were evaluated. A multivariate analysis based on a stepwise logistic regression model was used to assess the independent effect of all variables found significant at the univariate analysis. P-value less than 0.05 was considered significant. All statistical analyses were performed using SPSS for Windows software (version 10.0; SPSS Inc. Chicago, Illinois, USA).
Results
The sample population contained 150 (81.1%) males and 35 (18.9%) females. The overall mean age was 43.8 ± 12.0 years ranging between 22 and 84 years. In the total of 185 patients, DM was found in 40 (21.6%) patients, and IFG and/or IGT was found in 21 (11.3%) patients. Thirty patients were already aware of their problem whereas the diagnosis of DM was new in 10 patients.
By univariate analysis a number of variables including the factors mentioned by ADA as risk factors for type II diabetes [13] in addition to status and etiology of liver disease, interferon therapy, and history of or ongoing habitual smoking were compared between diabetic and non-diabetic patients (Table 1). We found that the prevalence of DM was significantly associated with older age (P = 0.000), CLD status (P = 0.000), history of hypertension (P = 0.007), family history of DM (P = 0.000), and higher BMI (0.009). Excluding the inactive carriers, we compared DM rate in two other groups. The prevalence of DM among cirrhotic cases (53.7%) was significantly higher than that of chronic hepatitis patients (13.7%), (P = 0.000).
Table 1 Clinical and epidemiological characteristics of CLD patients with and without DM
Diabetic Non-diabetic P-value
Number 40 148
Sex NS
Female 9 (22.5%) 26 (17.9%)
Male 31 (77.5%) 119 (82.1%)
Age 50.3 ± 11.4 40.2 ± 10.6 0.000
CLD status 0.000
Inactive carrier 4 (10.0%) 38 (26.2%)
Chronic hepatitis 14 (35.0%) 88 (60.7%)
Cirrhosis 22 (55.0%) 19 (13.1%)
CLD etiology NS
HBV 24 (60.0%) 85 (58.6%)
HCV 13 (32.5%) 58 (40.0%)
Cryptogenic 3 (7.5%) 2 (1.4%)
Family history of DM* 0.000
Yes 21 (52.5%) 29 (20.0%)
No 19 (47.5%) 116 (80%)
History of hypertension† 0.007
Yes 10 (25.0%) 13 (9.0%)
No 30 (75.0%) 132 (91.0%)
BMI 27.5 ± 4.6 24.7 ± 4.0 0.009
Interferon therapy NS
Yes 6 (15.0%) 49 (33.8%)
No 34 (85.0%) 96 (66.2%)
Smoking NS
Yes 13 (32.5%) 37 (25.5%)
No 27 (67.5%) 108 (74.5%)
TG 179.2 ± 74.1 142.8 ± 71.4 NS
Chol 192.4 ± 39.8 174.7 ± 44.4 NS
*Indicates presence of at least one first-degree relative affected by DM.
†Includes patients who require diet or antihypertensive agents.
At multivariate analysis age (P = 0.008, OR = 4.7, 95%CI: 1.8–12.2), family history of DM (P = 0.000, OR = 6.6, 95%CI: 2.6–17.6), chronic hepatitis (0.000, OR = 11.6, 95%CI: 2.9–45.4), and cirrhosis (P = 0.000, OR = 6.5, 95%CI: 2.4–17.4) showed up as the only factors independently associated with DM prevalence rate (table 2).
Table 2 Logistic regression analysis of factors associated with DM among patients with CLD
OR 95% CI P-value
Age
< 45 years 1.0
≥ 45 years 4.7 1.8 – 12.2 0.001
CLD status
Inactive carrier 1.0
Chronic hepatitis 11.6 2.9 – 45.4 0.000
Cirrhosis 6.5 2.4 – 17.4 0.000
Family history of diabetes
No 1.0
Yes 6.6 2.6 – 16.7 0.000
History of hypertension
No 1.0
Yes 2.3 0.7 – 8.1 NS
BMI
> 25 1.0
≥ 25 1.4 0.5 – 3.4 NS
Since both cirrhosis and chronic hepatitis were found as independent factors associated with DM occurrence, we analyzed these two groups separately. The results were summarized in table 3.
Table 3 Clinical and epidemiological characteristics of patients with and without DM separated to cirrhosis and chronic hepatitis groups
Patients with cirrhosis Patients with chronic hepatitis
Diabetic Non-diabetic P-value Diabetic Non-diabetic P-value
Number 22 19 14 88
Sex NS NS
Female 4 (18.2%) 3 (15.8%) 2 (14.3%) 12 (13.6%)
Male 18 (81.8%) 16 (84.2%) 12 (85.7%) 76 (86.4%)
Age 56.9 ± 10.6 46.6 ± 10.0 0.003 49.7 ± 7.8 39.9 ± 10.7 0.001
CLD etiology NS NS
HBV 11 (50.0%) 13 (68.4%) 10 (71.4%) 37 (42.0%)
HCV 8 (36.4%) 4 (21.1%) 4 (28.6%) 51 (58.0%)
Criptogenic 3 (13.6%) 2 (10.5%) - -
Family history of DM NS 0.01
Yes 9 (40.9%) 3 (15.8%) 8 (57.1%) 21 (23.9%)
No 13 (59.1%) 16 (84.2%) 6 (42.9%) 67 (76.1%)
History of hypertension NS NS
Yes 6 (27.3%) 1 (5.3%) 3 (21.4%) 8 (9.1%)
No 16 (72.7%) 18 (94.7%) 11 (78.6%) 80 (90.9%)
BMI 26.4 ± 3.4 26.4 ± 4.0 NS 27.7 ± 4.8 24.5 ± 3.1 0.02
Interferon therapy NS NS
Yes 2 (9.1%) 3 (15.8%) 4 (28.6%) 46 (52.3%)
No 20 (90.9%) 16 (84.2%) 10 (71.4%) 42 (47.7%)
Smoking NS NS
Yes 5 (22.7%) 5 (26.3%) 7 (50.0%) 25 (28.4%)
No 17 (77.3%) 14 (73.7%) 7 (50.0%) 63 (71.6%)
TG 145.5 ± 58.6 109.3 ± 75.2 NS 174.9 ± 82.5 140.6 ± 53.8 NS
Chol 141.6 ± 50.5 151.8± 32.5 NS 199.8 ± 42.8 163.9 ± 36.1 NS
Child-Pugh's score 0.04
Score A 13 (65.0%) 18 (94.7%) - - -
Score B 7 (35.0%) 1 (5.3%)
Histological staging* 0.003
0–1 - - - 1 (8.3%) 33 (41.8%)
2–3 3 (25.0%) 30 (38.0%)
4–6 8 (66.7%) 16 (20.3%)
Histological grading* NS
0–4 - - - 2 (16.7%) 36 (45.6%)
5–8 8 (66.7%) 34 (43.0%)
≥ 9 2 (16.7%) 9 (11.4%)
* According to Knodell score
Out of 41 cirrhotic patients, 22 (53.7%) patients were diabetic and 7 (17.1%) patients had IFG and/or IGT. Univariate analysis showed that only two factors were associated with DM rate. Mean of age in diabetic cases was significantly higher than that of non-diabetic ones (P = 0.003). Moreover, DM was significantly more prevalent in patients with Child-Pugh's score B than score A (P = 0.04). Since there was no score C in study sample, we could not evaluate this class of cirrhosis. After applying logistic model, only Child-Pugh's score kept its significance (P = 0.04, OR = 9.6, 95%CI: 1.0–88.4) as an independent predictive factor for DM in cirrhotic patients (table 4).
Table 4 Logistic regression analysis of factors associated with DM among cirrhotic patients and patients with chronic hepatitis.
OR 95% CI P-value
Cirrhosis
Age
< 45 years 1.0
≥ 45 years 2.1 0.4 – 10.5 NS
Child Pugh's Score
Score A 1.0
Score B 9.6 1.0 – 88.4 0.04
Chronic hepatitis
Age
> 45 years 1.0
≥ 45 years 7.2 1.0 – 49.1 0.04
Family history of diabetes
No 1.0
Yes 2.0 0.3 – 13.9 NS
BMI
> 25 1.0
≥ 25 30.3 3.0 – 306.7 0.004
Histological staging
0–1 1.0
2–3 59.5 2.9 – 1211.3 0.008
4–6 11.9 1.0 – 132.2 0.04
In chronic hepatitis group including 102 patients, DM was found in 14 (13.7%) patients and IFG and/or IGT was found in 11 (10.8%) patients. There was no significant difference between diabetic and non-diabetic cases in regard with sex, etiology of chronic hepatitis, hypertension, interferon therapy, smoking, and serum TG and Chol levels. On the other hand, diabetic patients had higher mean age compared with non-diabetic cases (P = 0.001). More cases of diabetic patients compared with non-diabetic ones had a family history of DM (P = 0.01). The mean BMI of patients with DM was higher than that of patients without DM (P = 0.02). Furthermore, liver biopsy showed significantly more fibrosis activity in diabetic patients compared with non-diabetic cases (P = 0.003), whereas no difference in diabetic vs. non-diabetic patients was seen with respect to histological grading. Multivariate analysis revealed that older age (P = 0.04, OR = 7.2, 95%CI: 1.0–49.1), higher BMI (P = 0.004, OR = 30.3, 95%CI: 3.0–306.7), and more severe fibrosis activity (stage 2–3: P = 008, OR = 59.5, 95%CI: 2.9–1211.3; stage 4–6: P = 0.04, OR = 11.9, 95%CI: 1.0–132.2) were the predictive variables for DM in patients with chronic hepatitis (table 4).
Discussion
There is a wide range in the prevalence of glucose metabolism alterations in cirrhotic patients in various studies. The frequency for overt DM has been reported from 10% to 50% and for IGT up to more than 70% [1-5,7,8,10,11]. Such variations may be mainly due to the criteria employed in the diagnosis of DM. In our study, we employed latest ADA criteria and DM was presented in 21.6% of patients with CLD (53.7% in cirrhosis, 13.7% in chronic hepatitis, and 9.5% in HBV inactive carrier). In Iran the estimated prevalence of overt DM in general population is in the range of about 7.5–10 percent [14-16], which is about 2 to 3 times less than what we found in this study, indicating that patients with CLD are a high-risk population for DM. In spite of this fact, we showed that 10 out of 40 (25%) diabetic cases found in this study were unaware of their endocrinal problem before being screened as part of this study. This finding highlights the importance of periodical screening of the patients with CLD especially in advanced stages.
Literature frequently demonstrated the higher prevalence of DM and IGT in cirrhosis than in chronic hepatitis [4,5,11]. However, about the higher DM rate in patients with chronic hepatitis compared with people without liver disease, there is not any widely general agreement. Some studies claimed that DM rate is not appreciably different when compared with general population or individuals without liver disease [11,17]. These studies believed that only cirrhosis but not chronic hepatitis were associated with DM [17]. In our study, multivariate analysis indicates independent association between chronic hepatitis and DM rate, despite the fact that we compared DM occurrence among three groups who all suffered from liver disease. Moreover, our data indicate that the frequency of DM increases significantly with the severity of the liver disease both in cirrhotic cases and in patients with chronic hepatitis. These findings suggest that liver fibrosis but not cirrhosis itself, is the event associated with glucose intolerance. A weak association between glucose intolerance and severity of liver disease in cirrhosis has already been reported by Muller et al [7], even though they used different criteria to evaluate liver damage severity. Another study applying Child-Pugh's classification showed similar results [11]. In this context it is worthy to note that although the underlying mechanism of glucose intolerance in cirrhosis has not been fully elucidated, it can be mainly explained by the insulin resistance [9], in addition to reduced glucose sensitivity in liver cirrhosis [18,19]. More interestingly, a recent study suggests that insulin resistance occurs already in the early stages of the chronic hepatitis course [12], and another study investigating chronic hepatitis patients with normal glucose tolerance revealed a strong relationship between insulin sensitivity and fibrosis score [20]. In contrast, there is another hypothesis that indicates CLD as a consequence of DM. This theory states that occurrence of insulin resistance initially facilitates lipolysis, and increases free fatty acid deposition in liver, which increases products of lipid peroxidation inducing oxidative stress. This results in cytokine-mediated hepatic inflammatory damage that induces collagen deposition and eventually fibrosis [21,22]. Although in this study we could not determine if the onset of DM was before or after of liver disease, our findings show that chronic hepatitis per se has an independent association with DM, and development of DM in chronic hepatitis patients was correlated to the severity of liver fibrosis.
No differences in DM rate were found when we studied patients according to CLD etiological categories both in cirrhosis and in chronic hepatitis group. A positive link between hepatitis C infection and development of DM has been suggested in some studies but not completely characterized. Mason et al surveying a large cohort of patients with viral chronic hepatitis showed a relatively strong association between DM and HCV infection [23]. They suggested that HCV infection may serve as an additional risk factor for the development of DM beyond that attributable to CLD alone [23]. In contrast, some studies provided evidence against a potential association between these two disorders [17,24]. A more recent study showed that the prevalence of DM among cirrhotic patients with hepatitis C was significantly higher than among those with cirrhosis due to cholestatic liver disease, but this rate difference was not significant when compared with cases with alcohol-induced cirrhosis [10]. In spite of the fact that they had not enrolled HBV infected cases in their sample population, it suggested that the mechanism of the DM in cirrhotic patients was related more closely to the underlying cause of liver cirrhosis [10]. Although no single etiological factor was found linked to DM occurrence in our study, we believe that our study was not designed to address this issue considering the fact that we had to exclude patients with NASH as another etiology of CLD because of small number of patients in this group. However, further prospective studies may shed more light on the relationship between DM and the underlying liver disease.
BMI did not differ between diabetic and non-diabetic cases among cirrhotic patients. This finding was expectable as reduction in the muscle mass is well described in liver cirrhosis, which can bias the comparison. On the other hand, among patients with chronic hepatitis BMI did remain as an independent predictive factor of DM. Some studies have indicated that obesity could be a potential risk factor for fibrosis in chronic hepatitis [25]. Our results suggest that increased BMI also has a role in the pathogenesis of DM in chronic hepatitis independent of liver fibrosis. Furthermore, Muller et al reported that basal free fatty acids and basal free glycerol plasma concentrations were increased in diabetic patients with liver cirrhosis when compared with those without diabetes [7]. In present study, TG and Chol levels in patients with chronic hepatitis and TG level in cirrhotic patients were higher in diabetic cases compared with non-diabetic ones although not statistically significant (table 3). These findings may have therapeutic implications in the management of patients with chronic hepatitis. It appears necessary to greatly encourage overweight patients to make a concerted effort to lose weight in order to both decrease the risk of DM development and to prevent the liver damage.
Although family history of DM is a well-known risk factor for DM, the correlation between DM and both cirrhosis and chronic hepatitis remained significant even when family history of DM was entered through logistic model. This finding, in concordance with similar studies [7,11], indicates that liver injury per se is associated with DM and a family history of DM is only an adjunctive factor. However, the possible reporting bias of family history of DM should be carefully considered as it is not unreasonable to think that patients with known DM may be more likely to know or think that they have a family history of DM. When cirrhosis and chronic hepatitis groups were analyzed separately, we observed that patients with a positive family history of DM did not show an increased frequency of DM, particularly in cirrhotic patients. Slightly less than 45% of cirrhotic cases with a negative family history of DM were diabetic. This finding does not support the speculation that cirrhotic patients only with a genetic predisposition for DM are prone to glucose intolerance as the manifestation of their liver disease.
Age is another definite risk factor for type II DM in the normal population [13], and it was expected also to be associated with DM in CLD. However, the odds ratio for both cirrhosis and chronic hepatitis was higher than that for age, implying a stronger association of the former factors. Although in the part of our study investigating cirrhotic patients, age lost its significance when it was entered in multivariate analysis, the majority of studies demonstrated that IGT and also DM were more frequently seen at advanced age in cirrhotic patients [2,7,11].
While DM was associated with hypertension in univariate analysis (table 1), in multivariate analysis it lost its significance as a predictor of DM (table 2). These two variables may both be related to the metabolic syndrome and adjustment for other related variables removes this association. On the other hand, it is currently believed that cardiovascular disease is rare in cirrhosis and studies demonstrate that cirrhotic patients, even in the presence of overt DM, have a low prevalence of vascular disease including hypertension [26]. Our findings support this hypothesis showing no difference in the rate of hypertension between diabetic and non-diabetic patients.
A few patients have so far been reported to develop DM during interferon therapy [27], but the evidences are insufficient in this regard as yet. In our study, interferon therapy had no impact on the prevalence of DM, since 15% of subjects with DM and about 39% without had a recorded use of interferon, and the difference was not significant. This is in line with some previous studies clarifying the impact of long-term administration of interferon on glucose metabolism [28].
Conclusions
In summery, the present study indicates a high prevalence of DM in patients with CLD in Iran although the relatively small number of patients in each of the three subgroups particularly cirrhosis and inactive carriers was a major shortcoming of our study and may have potentially underestimated or overestimated the prevalence of DM in these subgroups of patients. Since a considerable number of diabetic patients were unaware of their problem, it is imperative that the patients be screened for glucose intolerance periodically. For more severe stages of liver disease the screening interval appears to be shorter because of higher probability for DM occurrence. Furthermore, our findings show that weight reduction for overweight patients with chronic hepatitis is of benefit in order to prevent the occurrence of DM.
List of abbreviations
DM: Diabetes Mellitus; IGT: Impaired Glucose Tolerance; CLD: Chronic Liver Disease; BMI: Body Mass Index; HBV: Hepatitis B virus; HBsAg: Hepatitis B s Antigen; NASH: Nonalcoholic Steatohepatitis; FPG: Fasting Plasma Glucose; TG: Triglycerides; Chol: Cholesterol; 2-hr PG: Two-hour Post-loaded Glucose; ADA: American Diabetes Association; IFG: Impaired Fasting Glucose.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
Dr. Alavian participated in the design of the study and coordination and drafted the manuscript. Dr. Hajarizadeh conceived of the study, participated in the design of the study, performed the statistical analysis and drafted the manuscript. Dr. Nematizadeh designed the questionnaire and helped in data collection. Dr. Larijani facilitated the study progress and participated in coordination. 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 of "Endocrinology and Metabolism Research Center of Tehran University of Medical Sciences (EMRC)", Tehran, Iran.
The authors wish to thank Dr. Myron D Goldberg for critical reviewing of the manuscript and his helpful comments.
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| 15555059 | PMC538272 | CC BY | 2021-01-04 16:27:54 | no | BMC Endocr Disord. 2004 Nov 19; 4:4 | utf-8 | BMC Endocr Disord | 2,004 | 10.1186/1472-6823-4-4 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-421557595910.1186/1477-7819-2-42EditorialOpen Access to essential health care information Stokes Christabel EL [email protected] Manoj [email protected] BioMed Central, 34-42 Cleveland Street, London, UK2 Regional Cancer Centre, Thiruvananthapuram, Kerala, India2004 2 12 2004 2 42 42 24 11 2004 2 12 2004 Copyright © 2004 Stokes and Pandey; licensee BioMed Central Ltd.2004Stokes and Pandey; 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.
Open Access publishing is a valuable resource for the synthesis and distribution of essential health care information. This article discusses the potential benefits of Open Access, specifically in terms of Low and Middle Income (LAMI) countries in which there is currently a lack of informed health care providers – mainly a consequence of poor availability to information. We propose that without copyright restrictions, Open Access facilitates distribution of the most relevant research and health care information. Furthermore, we suggest that the technology and infrastructure that has been put in place for Open Access could be used to publish download-able manuals, guides or basic handbooks created by healthcare providers in LAMI countries.
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'Essential healthcare information' is the basic information required by primary health care workers to perform their role within the community. This basic information would be most useful if it is informed by relevant research, produced locally, and made available in the local language. The potential benefits of Open Access in terms of access to the research literature in general, and to research from low- and middle-income (LAMI) countries in particular, have been well described elsewhere.
We would like to introduce a new dimension into this debate: Open Access has an untapped potential to enhance the synthesis and distribution of essential healthcare knowledge. Open Access, as opposed to free access, allows readers the right to use the article without restriction. Local publishers can therefore filter, reproduce and distribute the most relevant research and healthcare information from any and all Open Access journals. In essence, they can create journals focused on local issues based on content from a variety of journals. These "local journals" can be circulated in print – a medium that remains essential in countries with limited computer and Internet access. To our knowledge, this has yet to be done, although we are hoping someone will exploit this opportunity soon.
In the future, we imagine the technology and infrastructure that has been developed for Open Access could be used to publish download-able manuals, guides or basic handbooks created by healthcare providers in these countries. These free resources could then be accessed worldwide and, where necessary, reproduced within local communities in the optimal medium. In an "author-pays" Open Access model the charges would be standard and could be covered by a national government organization or charity.
Open Access will increase the availability of research and, in doing so, stimulate researchers in LAMI countries to develop their own research and practices. With research published in the Open Access medium it also becomes possible for producers of healthcare materials to optimize the use of research produced from their own and other countries. Thus, Open Access will optimize the distribution of local healthcare information, with potential benefits worldwide.
Competing Interests
CELS is an employee of BioMed Central, an Open Access publisher that is funded through article-processing charges levied on accepted manuscripts. CELS receives a fixed salary, which is unaffected by the amount of money received by BioMed Central from article-processing charges.
MP is the Editor-in-Chief of World Journal of Surgical Oncology, an Independent journal published by BioMed Central.
Acknowledgements
This article was originally published as a component of the November issue of the INASP (International Network for the Availability of Scientific Publications) Newsletter .
| 15575959 | PMC538273 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Dec 2; 2:42 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-42 | oa_comm |
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Thromb JThrombosis Journal1477-9560BioMed Central London 1477-9560-2-121557419810.1186/1477-9560-2-12ReviewBlood coagulation and the risk of atherothrombosis: a complex relationship Spronk Henri MH [email protected] der Voort Danielle [email protected] Cate Hugo [email protected] Department of Internal Medicine, University Maastricht, Maastricht, The Netherlands2004 1 12 2004 2 12 12 21 10 2004 1 12 2004 Copyright © 2004 Spronk et al; licensee BioMed Central Ltd.2004Spronk 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 principles of Virchov's triad appear to be operational in atherothrombosis or arterial thrombosis: local flow changes and particularly vacular wall damage are the main pathophysiological elements. Furthermore, alterations in arterial blood composition are also involved although the specific role and importance of blood coagulation is an ongoing matter of debate. In this review we provide support for the hypothesis that activated blood coagulation is an essential determinant of the risk of atherothrombotic complications. We distinguish two phases in atherosclerosis: In the first phase, atherosclerosis develops under influence of "classical" risk factors, i.e. both genetic and acquired forces. While fibrinogen/fibrin molecules participate in early plaque lesions, increased activity of systemic coagulation is of no major influence on the risk of arterial thrombosis, except in rare cases where a number of specific procoagulant forces collide. Despite the presence of tissue factor – factor VII complex it is unlikely that all fibrin in the atherosclerotic plaque is the direct result from local clotting activity. The dominant effect of coagulation in this phase is anticoagulant, i.e. thrombin enhances protein C activation through its binding to endothelial thrombomodulin.
The second phase is characterized by advancing atherosclerosis, with greater impact of inflammation as indicated by an elevated level of plasma C-reactive protein, the result of increased production influenced by interleukin-6. Inflammation overwhelms protective anticoagulant forces, which in itself may have become less efficient due to down regulation of thrombomodulin and endothelial cell protein C receptor (EPCR) expression. In this phase, the inflammatory drive leads to recurrent induction of tissue factor and assembly of catalytic complexes on aggregated cells and on microparticles, maintaining a certain level of thrombin production and fibrin formation. In advanced atherosclerosis systemic and vascular wall driven coagulation becomes more important and elevated levels of D-dimer fragments should be interpreted as markers of this hypercoagulability.
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Background
The blood coagulation system comprises three basic elements: platelet adhesion, activation and aggregation, fibrin formation, and fibrinolysis. These elements interact with each other and with the blood vessel wall and under physiological conditions blood flow to tissues is unimpaired by clotting [1]. Under pathophysiological conditions, blood coagulation gets activated along the principles outlined by Virchov, which indicate that thrombosis (the formation of an intraluminal blood clot) always occurs through the interaction of three components: an altered vessel wall, an impaired or changed pattern of blood flow and an altered blood composition. The principles of Virchov's triad appear to be operational in each different type of thrombosis [2,3].
In venous thrombosis of the lower limbs, stasis, local inflammation on activated vascular endothelial cells induced by adhering leukocytes and platelets and in some cases direct vascular damage, promotes local thrombus formation. In a first episode of venous thrombosis the pre-existing composition of the blood is particularly important where congenital and acquired hypercoagulable factors such as factor V Leiden mutation and oral contraceptives, respectively, act in concert to accelerate clotting [4].
In disseminated intravascular coagulation (DIC), widespread fibrin formation is the result of systemic inflammatory changes that induce cellular tissue factor dependent activated blood coagulation as well as local alterations in microcirculatory flow and enhanced activity and permeability of capillary endothelial cells [5]. Again, DIC follows Virchov's principles, i.e. interactions among all three elements occur which are all relevant determinants of outcome.
In arterial thrombosis, local flow changes and particularly vascular wall damage are the main pathophysiological elements. Alterations in composition of the arterial blood are also involved but the specific role and importance of blood coagulation is an ongoing matter of debate [6,7]. While numerous studies have shown increased activity of the blood coagulation system in patients at risk of arterial thrombotic complications, Tracy concludes on the basis of genetic studies that there is no "compelling argument supporting the importance of a preexisting hypercoagulable state as a major risk factor for atherothrombotic disease" [8]. In a recent debate, Reitsma points out that in the context of atherosclerosis a hypercoagulable state is of minor importance for the risk of thrombosis and high levels of coagulation factors such as factor VIII are risk indicators rather than causal factors [6]. On the other hand, in the same debate Grant argues on the basis of biochemical, clinical and philosophical arguments that hypercoagulability is indeed an issue of importance in arterial thrombosis, illustrated on the basis of several observations in patients with diabetes and insulin resistance [7].
In spite of the apparent controversies regarding this topic, observational studies focused on activity of coagulation and fibrinolysis in patients with arterial vascular disease continue to be published. As an example of a "clotting" marker, measurement of fibrin D-dimer fragments by one of many commercial assays, has been implicated as a risk indicator since more than 15 years, in a range of patient studies related to severity of atherosclerosis and/or risk of (recurrent) thrombotic complications [9-25]. In general, these studies indicate that D-dimer, similar to C-reactive protein (CRP), is a moderate but consistent and independent marker of risk of cardiovascular disease, both in population studies and in patients at risk [22,24,26]. Given the actual debate on the relevance of coagulation in arterial vascular disease it is timely to consider whether D-dimer should be regarded a risk marker (or bystander), or a marker of a causal process, i.e. hypercoagulability. More specifically, the question remains whether hypercoagulability, here defined as an increased potential to produce fibrin in plasma (indicated by elevated thrombin production, fibrin production or both), as compared to individuals of similar age and sex, should be seen as a cause or merely consequence of atherosclerosis and thrombosis.
Thrombogenicity and atherosclerosis
In the majority of patients atherothrombotic complications develop on the basis of atherosclerosis in one or more coronary, cerebrovascular or peripheral arteries [27]. Atherosclerosis, a multifactorial disease, is the consequence of many years of exposure to atherogenic influences that lead already at young age to early lesions, or so-called "fatty streaks". Under influence of age- and sex-related factors these early lesions advance and this process is accelerated by genetic determinants (such as related to lipid and glucose metabolism and blood pressure) and environmental influences, including smoking and diet [28,29]. Arterial thrombi form in the course of progression to complex lesions, where the combination of vascular remodeling, erosion of the vessel luminal surface or frank rupture of plaques triggers the blood coagulation system. Central to this process is chronic inflammation and proteolysis culminating in plaque damage and exposure to luminal blood flow [27,30]. In addition, angiogenesis related neovessels are prone to rupture resulting in increased intra-plaque hemorrhage [31].
Activation of blood coagulation occurs primary through interaction of platelets, vessel wall and plasma proteins (so-called primary haemostasis). When injury to the blood vessel wall causes disruption of its endothelial layer, the underlying extracellular matrix is exposed. In this matrix, both von Willebrand factor (vWF) and collagen are present and after exposure, they will bind to specific receptors, glycoproteins (GP), present on the platelets. Dependent on the flow within the vessel other glycoproteins are involved in the adhesion of the platelets to the vessel wall. During low shear stress, GP Ia-IIa, GP VI and GP IV are the primary receptors for collagen, and during high shear forces, the primary indirect receptor for collagen is GP Ib-IX-V in a vWF dependent interaction. After adhesion of the platelets, they become deformed due to cytoskeletal changes, thereby exposing activated integrins and secreting ADP, serotonin etc. One of the integrins, GP IIb-IIIa, binds vWF (in high shear areas) or fibrinogen (in low shear areas) to mediate platelet aggregation under shear conditions. Also other platelet receptors and lipid products i.e. arachidonic acid, contribute to platelet aggregation. In this review however we will focus on secondary haemostasis, in which the interaction between circulating factor VII(a) to tissue factor, exposed by the damaged vessel wall, leads to activation of the coagulation cascade.
Several studies have shown that tissue factor is a prominent component of plaque lesions where it is localized in the outer membranes of infiltrating macrophages/foam cells and smooth muscle cells as well as on apoptotic cells and cell bodies (Figure 1) [30]. Unstable plaques contain most potent tissue factor activity; in addition, tissue factor-rich microparticles are being shed from activated and apoptotic cells and may contribute to acute thrombotic occlusion, particularly in the downstream microcirculation [30,31]. Formation of the tissue factor-factor VII(a) complex drives the intrinsic pathway of coagulation to form thrombin and fibrin. Platelet adhesion and activation, and interactions with leukocytes, accelerate the process of thrombin formation providing catalytic surfaces, expressing tissue factor and yielding coagulation proteases such as factor XIa that amplify thrombin generation [32-35]. According to Virchov's postulate acute arterial thrombosis occurs due to interaction among a damaged atherosclerotic vessel wall, an altered blood flow due to changes in shear stress related to atherosclerosis and blood elements, i.e. cells and coagulation proteins [3]. Whether the state of activity of the blood coagulation system (in other words "high risk blood") is really altered prior to thrombosis is the principal issue of controversy.
Figure 1 The initiation of an atherosclerotic lesion is characterized by retention of LDL and subsequent oxidative modification (oxLDL) within the matrix of the vascular intima. Stimulation of the overlying endothelial cells by oxLDL recruits monocytes from the circulation to the vessel wall. Differentiation of monocytes into macrophages and scavenger receptor mediated uptake of oxLDL aggregates results in the formation of foam-cells. Upon stimulation vascular smooth muscle cells (VSMC) migrate and proliferate. Tissue factor is expressed on macrophages and VSMCs within the advanced lesion and is likely to be involved in the conversion of accumulated fibrinogen into fibrin, although fibrin polymerization can be facilitated by other enzymes than thrombin. Furthermore, VSMCs and macrophage derived apoptotic bodies exposing TF probably contribute in thrombin formation. Considering atherosclerosis as a chronic inflammation, the inflammatory drive leads to IL-6 induced TF expression of circulating monocytes and the formation of microparticle exposing TF in the circulation, maintaining a certain level of thrombin production and fibrin formation. Increased circulating D-dimer levels are thus the result of fibrin proteolysis in both circulation and the advanced atherosclerotic lesion.
The contribution of blood coagulation to atherosclerosis: the role of fibrin/fibrinogen and its split products
The involvement of coagulation in the pathological substrate of atherosclerosis is beyond dispute. For many years pathologists have noted the abundant presence of fibrin in advanced atherosclerosis and this finding has fueled part of the debate on the relevance of fibrin or fibrinogen for vessel wall lesions. Rokitansky and later Duguid proposed the encrustation theory as concept for the role of fibrin in atherosclerosis (reviewed in [36]). In this concept thrombosis was considered an etiological factor of importance in atherosclerosis, which was probably based on the presence of the end product of clotting, fibrin. Later work confirmed that fibrin is indeed an abundant protein in the arterial vessel wall, but not confined to atherosclerotic lesions. Schwartz and colleagues demonstrated that fibrin was also present (although at lower ratios of fibrin: fibrin/fibrinogen) in the non-sclerotic regions of the carotid artery where it did not co-localize with tissue factor in about 50% of the sections studied [37]. Thus, it seems unlikely that all fibrin in the vessel wall is the direct result from local clotting activation; alternatively, inflammatory influences that are characteristic of atherosclerosis [27] activate the coagulation system and also stimulate the transfer of fibrinogen and fibrin molecules to the intima where fibrin can be polymerized also by other enzymes than thrombin [38].
Autopsy data have indicated that fibrinogen accumulation in the vessel wall may be an early event in atherosclerosis, i.e. a small amount of fibrinogen in a thickened intima was demonstrated in a 4 year old boy [36]. The deposition of fibrinogen was apparently associated with the presence of LDL in the vessel wall and was related to age and intimal thickening. These authors suggested that intimal deposition of fibrin or fibrinogen preceded or facilitated LDL accumulation in the arterial vessel wall. Direct evidence for such a function of fibrin or fibrinogen, however, is still lacking. Fibrinogen knockout mice against an apoE-/- background did not have fewer arterial lesions ranging from early lesions to complex fibrous plaques, suggesting that fibrinogen is not an essential molecule for atherosclerosis [39]. However, a later study demonstrated that fibrinogen was an important mediator of atherogenesis in apo(a) transgenic mice where the accumulation of apo(a) in the vessel wall and average lesion area were markedly attenuated in the fibrinogen-/- x apo(a) crossbred animals [40].
The specific effect of fibrin and its split products in the vessel wall has also been studied. In general it appears that with increasing complexity of lesions there is an increase in the presence of intimal fibrinogen/fibrin and threads of fibrin, as well as an accumulation of various split products that may be involved in atherogenesis. The effect of fibrin and its split products on smooth muscle cells may be such that fibrin stimulates proliferation, while split products inhibit this process. Fibrin cleavage products may be detrimental for endothelial cell function, increasing permeability and promoting endothelial cell migration [36,41,42]. Degradation products also enhance chemotaxis of smooth muscle cells and monocytes. Extracellular accumulation of fibrin(ogen) on monocytes stimulates cholesterol transfer from platelets to monocytes/macrophages and each of these mechanisms may be relevant to the development of atherosclerosis. In addition, D-dimer fragments induce Il-6 production by monocytes in vitro [41,42].
The complex interactions of plasminogen, plasmin and its inhibitor, with regard to vessel wall function and remodeling, have recently been reviewed and its discussion is beyond the scope of this paper [43]. However, a few points need to be addressed. Plasmin, produced by activation of plasminogen, is the crucial enzyme in fibrin degradation and generation of split products. Deficiency of plasminogen in mice (Plg-/-) results in markedly discrepant effects on atherosclerosis. While Plg-/- mice with an apoE-/- background showed an accelerated development and progression of intimal lesions, Plg-/- mice were protected against atherosclerosis in association with transplantation (reviewed in [43]). The origin of such apparently conflicting effects may lie in a dominant effect of the absence of plasminogen on lipid metabolism, including markedly lower HDL levels in the knockout mice in the first experiment, while an effect on leukocyte transport and migration was the major effect in the transplant experiments. Hence, as Plow et al conclude, "it may be the importance of the cellular migration as a rate-determining step that establishes the influence of plasminogen in either atherosclerosis or restenosis".
Accumulating fibrin that polymerizes in the vessel wall triggers fibrinolysis. Fibrinolytic enzymes tissue plasminogen activating factor and urokinase plasminogen activating factor (tPA and uPA, respectively) are present in intima and are secreted by endothelial cells and likely play an important role in vascular remodeling [42-44]. However, their intrinsic capacity to generate plasmin cleaving fibrin may also contribute to increased local fibrinolysis. In addition, the main inhibitor of fibrinolysis plasminogen activator inhibitor-1 (PAI-1) is also more abundantly expressed in tissues of patients with atherosclerosis. Under influence of inflammation, vascular endothelium may produce increased amounts of PAI-1 that might inhibit fibrin cleavage. However, it is questionable to what extend endothelial cells contribute to PAI-1 production in patients with atherosclerosis, since at least in patients with the metabolic syndrome, adipocytes and hepatocytes are more prominent sites of PAI-1 synthesis in relation to plasma PAI-1 [45].
The net effect on fibrin cleavage and progression of atherosclerosis cannot be estimated. The above mentioned experiments with Plg-/- mice give important clues regarding the range of mechanisms that are influenced. Effective fibrinolysis may be important in limiting fibrin accumulation and atherosclerosis in the initial phases. However, upon stronger inflammatory stimulation the effect on cell trafficking into the vessel wall becomes more dominant and the outcome may reverse such that impaired fibrinolysis may limit atherosclerosis. The latter would imply that high levels of PAI-1 may even be protective against atherosclerosis under certain conditions. Consequently, high concentrations of D-dimers, reflecting active fibrinolysis, may indeed be regarded as a sign of progressive atherosclerosis under inflammatory conditions.
All of these issues may have therapeutic consequences since several drugs that are routinely used in patients with atherosclerosis including statins, angiotensin converting enzyme inhibitors as well as angiotensin receptor blockers appear to influence the balance of coagulation and fibrinolysis, which may influence atherosclerosis on the long term by altering vascular properties [42].
Clinical studies of increased intravascular fibrin as indicator of severity of atherosclerosis; studies in peripheral arterial disease
As mentioned above, a large number of clinical studies in different groups of patients with atherosclerotic disease have generally shown that increased levels of D-dimer fragments in plasma are associated with an increased risk of severe atherosclerosis and an increased risk of vascular complications. In population based studies the contributable risk of an increased D-dimer level is quite small but statistically significant. In specific cohorts of patients the risk association is more outspoken, but of course here selection bias may produce slightly stronger associations than may be found in "real life".
Before addressing specific study findings a few general observations deserve attention.
First, strong associations between age and sex on the one hand, and D-dimer levels on the other hand, are noted [9,11,42]. D-dimer levels increase with age, are higher in women and may be influenced by a number of additional factors that differ per study.
Second, D-dimer levels are oftentimes associated with markers of inflammation, i.e. CRP and Il-6 [19,22,24,25,42,46]. In addition, D-dimers often correlate with fibrinogen levels, which may be related to inflammation, but fibrinogen is also the substrate for fibrin, thus a more straightforward substrate-enzyme-cleavage product relation may also play a significant role.
Before addressing the mechanisms we will consider D-dimer as an independent entity, i.e. a marker of disease severity. The most striking associations with clinical disease come from patients with peripheral artery disease (PAD), a reflection of systemic and advanced atherosclerosis in the majority of individuals. The total risk of clinical complications or mortality reaches figures of up to 25% annually in patients with PAD (48). In patients with PAD, elevated D-dimer levels are independent predictors of complications and are associated with severity of atherosclerosis [12-14,16,25]. Functionally, patients with PAD and highest D-dimers had the worst walking distance [23] and venous occlusion resulted in impaired fibrinolytic response in patients with PAD versus those without PAD [47]. Significant and independent associations between D-dimer and clinically relevant endpoints were also found in several studies in patients with PAD [15,18,25,48], in line with observations in other groups of patients with atherosclerotic manifestations.
Elevated levels of D-dimers are usually considered as a marker of increased clotting activity. This assumption is one of the key elements of the controversy regarding cause and consequence of hypercoagulability. Indeed, Herren et al observed increased levels of D-dimer in patients with PAD, correlating with severity of disease. They also noted an association between hypercoagulability (higher F1+2 and TAT) and occurrence of myocardial ischemia during exercise testing [13], suggesting a link between enhanced clotting activity, D-dimer levels and PAD. A similar link between activated clotting and higher D-dimer levels was also noted by van der Bom and colleagues showing that the association between D-dimers and severity of PAD was most apparent in those with highest thrombin cleavage fragment F1+2 and thrombin-antithrombin (TAT) levels [14]. However, a number of other studies in patients with atherosclerosis failed to reveal significant correlations between D-dimers and markers of thrombin generation [21,26]. This leads to the question whether D-dimer generation reflects hypercoagulability in blood, increased fibrin production and fibrinolysis in the arterial intima as part of advanced atherosclerosis, or an increased state of inflammation due to proteolytic cleavage of fibrin by neutrophilic enzymes such as elastase?
In spite of the substantial observational data, application of D-dimer assays or other risk factor measurements such as for CRP have not gained acceptance in individual patients with PAD or other cardiovascular disease yet. Thus, secondary prevention of complications is not guided by any laboratory assay, but limited to general recommendations such as the advice to stop smoking and the prescription of a platelet inhibiting drug [49]. Three reasons explain this lack of implementation: one is the substantial overlap in D-dimer (or CRP) values between normals and patients in general; second, the low specificity and three the lack of understanding the cause of the D-dimer production and its interpretation. In the context of this paper we will focus on the third reason and discuss the mechanisms that lead to elevated fibrin cleavage products in plasma in patients with atherosclerosis. Theoretically, there are different options to explain increased D-dimer levels in plasma. If D-dimers indicate increased systemic clotting activity then a specific anticoagulant intervention may theoretically be the preferred intervention. Clinical studies with anticoagulants in patients randomized or stratified on the basis of D-dimer levels have, however, not been carried out yet.
If however, D-dimers are a reflection of severity of atherosclerosis such an intervention may be inappropriate and potentially harmful because of the avoidable risk of bleeding and calcification of the arterial vessel wall upon long-term administration (at least with vitamin K antagonists). Alternatively, if D-dimer levels merely reflect inflammation, than therapy should preferably consist of anti-inflammatory agents including higher doses of aspirin, statins or ACE inhibitors. Thus, the interpretation of elevated D-dimer levels is quite important in order to guide decisions about individual therapy.
Inflammation and fibrin formation in atherosclerosis
Atherosclerosis is a chronic inflammatory disease [27,28]. This widely accepted concept is based on a body of evidence from experimental and human observational studies. An indication of systemic inflammation is an elevated level of plasma CRP, the result of increased production influenced by Il-6. Several meta-analyses have established that CRP is an independent predictor of mortality in patients with atherosclerosis [50,51]; thus, a systemically activated inflammatory system is probably involved in its pathogenesis, i.e. progression and extension of atherosclerosis, as well as plaque rupture in advanced atherosclerosis.
Studies from the sepsis field including models of endotoxemia and sepsis in humans and primates, respectively, have shown that inflammatory stimulation leads to activation of blood coagulation [52,53]. Tissue factor synthesis is a rapid consequence of endotoxin infusion, which is a strong inflammatory stimulus, and this is followed by tissue factor expression on inflammatory cells and on microparticles, inducing thrombin and fibrin generation. Il-6 is a dominant cytokine in this process, but Il-1β and TNF-α are also involved. These experimental studies also exposed discordance in time of the coagulation activation steps, in which not a true cascade but a delayed and protracted course of activation occurred (1). If we consider atherosclerosis as a chronic (or recurrent) inflammatory condition, than the recurrent inflammatory drive leads to recurrent induction of tissue factor (with intermediate phases of hypo-responsiveness to stimulation) and assembly of catalytic complexes on aggregated cells and on microparticles, maintaining a certain level of thrombin production and fibrin formation [32,33]. The increased level of fibrinogen and fibrin monomers may enhance the uptake by the vessel wall of lipid-loaded particles and macrophages. In the vessel wall, further fibrin polymerization can occur due to local thrombin or other proteases activities.
In this concept, there is an increased generation of thrombin and fibrin in the blood circulation due to increased presence of inflammatory cytokines and proteins, but this does not necessarily lead to increased free thrombin in plasma. One should realize that coagulation enzymes that are generated associate with any available "scavenger", which can be an inhibitor such as antithrombin, but could also be a protease activated receptor (PAR) on platelets or endothelial cells [54-56]. Thus, a lack of rise of TAT at a moment when an elevated D-dimer level is noted cannot be interpreted as proof of a lack of increased thrombin production. Similarly, a lack of rise in F1+2 does not necessarily imply lack of thrombin production, because the F1+2 fragment can associate with cell membranes and little is known about the influence of microparticles. In addition, there are issues of sensitivity of commercial laboratory tests, i.e. the F1+2 assay is not a very sensitive tool in general, for monitoring activated coagulation. In fact, a D-dimer assay is the tool of choice for excluding (venous) thrombosis because of its superior sensitivity as compared to other tests for activated clotting [57]. We would propose that an increased production of thrombin in atherosclerosis is associated with an altered distribution of thrombin over the available binding sites leaving a greater procoagulant fraction that converts fibrinogen to fibrin. In advanced atherosclerosis a diminution in natural anticoagulant mechanisms including antithrombin (reduced expression of glycosaminoglycans at endothelium) and activated protein C (by down regulation of thrombomodulin) contributes to a higher level of procoagulant thrombin in the absence of increased TAT levels. Due to lack in sensitivity and maybe redistribution of F1+2 fragments binding to cell membranes the increased thrombin production is mostly undetectable by commercial F1+2 assays. Finally, discordances in peak levels of thrombin and fibrin production and cleavage may obscure any mechanistic associations.
The exact contribution of subendothelial fibrin formation and cleavage to D-dimer levels in blood cannot be estimated. While locally deposited tissue factor acts as a trigger of thrombin generation, it has not been shown that this is a source of ongoing subendothelial coagulation activity. The recent discovery of factor VII in plaque contents and the in vitro evidence for production of this Gla-protein by smooth muscle cells might form a basis for local thrombin production, but there is no indication yet that this might be quantitatively important as compared to hepatic production of factor VII [58].
The point to make is that high D-dimer levels in blood from patients with atherosclerosis should be primarily viewed as an indication of systemic hypercoagulability, a conclusion based on the above arguments and the experimental evidence indicating the intimate relationship between inflammation and coagulation [52].
Inherited or acquired hypercoagulability and atherosclerosis?
Early studies from Rosendaal et al, suggested that thrombophilic traits including the prothrombin 20210 gene variant would be a risk factor for myocardial infarction in specific individuals such as heavy smoking young women [59]. These data led to speculations about the importance of inherited thrombophilia in arterial disease in general, but this association was refuted in a subsequent large study in young individuals [60]. Indeed, the prevailing opinion is that most known thrombophilic traits with an associated risk of venous thrombosis, do not influence the risk of arterial thrombosis [60,61]. This notion may lead to the erroneous assumption that a state of increased coagulation activity, irrespective the cause, would be of no significant influence for the risk to develop arterial thrombosis. In spite of discarding Rosendaal's data as being the result of bias due to lack of power and patient selection we should perhaps accept the possibility that in this specific group of individuals with an unhealthy lifestyle, hypercoagulable influences may have accumulated: smoking related inflammation (and tissue factor expression in arterial walls; [62]) in conjunction with estrogenic stimulation leading to an disproportionably high risk of arterial thrombosis even in the absence of overt atherosclerosis. In the majority of the population of young individuals (< 40 yrs) such scenarios do not play a major role and the risk of early atherosclerosis is influenced predominantly by "classical" risk factors. This concept matches with the observation that D-dimers levels are also no independent risk indicator in relatively healthy and younger populations including that of the Physicians Health Study [63].
In people of advanced age this situation may change considerably and the weight of risk factors may change over time. A study that specifically addressed this point is the Bruneck community study [64]. In this population study carotid artery atherosclerosis was monitored with duplex ultrasound, risk factors were recorded, baseline blood samples collected and individuals were prospectively followed in time. Conventional and clotting (candidate) risk factors were then linked to markers of disease. This study suggested a two-stage model of disease in which conventional risk factors such as dyslipidemia and smoking, influence early stages of atherosclerosis, while other factors including those linked to coagulation, influenced later stages of disease (Figure 1).
In advanced atherosclerosis the influence of coagulation may indeed be more prominent than in early stages, but it should be realized that acquired rather than genetically determined forces are involved. In this regard, the similarities between arterial thrombosis and the risk of recurrent venous thrombosis was used by Reitsma to make the point that inflammation is a key player under such conditions, reducing the influence that genetic thrombophilic background might inflict. On the other hand the same argument could be used to illustrate that indeed inflammation plays a more prominent role in advanced atherosclerosis where it more strongly drives the risk of thrombosis.
Let us consider this situation from the scope of venous thrombosis; this is not far edged because a recent study suggested similarities in risk factors between patients with previous venous thrombosis and atherosclerosis [65]. Recent studies clearly show that the risk of recurrent venous thrombosis depends on the one hand on persistent thrombus [66] as an inflammatory focus of disease as well as on persistent coagulation, i. e. elevated D-dimers, on the other hand [67]. Genetic influences such as factor V Leiden play no role of importance in the risk of recurrent venous thrombosis. In analogy with residual venous thrombus on the damaged venous vessel wall, advanced atherosclerosis represents a comparably damaged and inflammatory/thrombotic arterial vessel wall. Accordingly, similar plasma risk factors appear to be involved in recurrent venous thrombosis and arterial thrombosis (inflammatory markers and D-dimers).
The "Bruneck" model of atherosclerosis and recommendations
The influence of blood coagulation on atherosclerosis follows a two stage model in which variants may occur under exceptional conditions. In general in the first phase, roughly covering the first four decades of life, atherosclerosis develops under influence of "classical" risk factors, including hypercholesterolemia and smoking, i.e. both genetic and acquired forces. While fibrinogen/fibrin molecules participate in early plaque lesions, increased activity of systemic coagulation is of no major influence on the risk of arterial thrombosis, except in rare cases where a number of specific procoagulant forces collide. The dominant effect of coagulation is anticoagulant, i.e. thrombin enhances protein C activation through its binding to endothelial thrombomodulin. Defects in the protein C mechanism may indeed precipitate arterial thrombosis, but only under highly thrombogenic conditions. Fibrinolysis limits fibrin accumulation in the intima and herewith progression of plaque lesions. At this stage elevated PAI-1 levels may diminish fibrinolysis and may stimulate plaque progression, which may explain that in a large Japanese study the PAI 4G/5G polymorphisms appeared to be a risk factor for myocardial infarction in women [68].
The second phase is characterized by advancing atherosclerosis, with greater impact of inflammation and increased infiltration of fibrin in the arterial vessel wall, enforcing pro-inflammatory effects. The extensive interactions between inflammation and coagulation enzymes and inhibitors (in much greater detail than discussed here) amplify the chain of events that determine the risk of atherothrombosis. Inflammation overwhelms protective anticoagulant forces, which in itself may have become less efficient due to down regulation of thrombomodulin (TM) and endothelial cell protein C receptor (EPCR) expression. In this phase, evidence of activated coagulation is measurable in peripheral blood reflecting both the extent of atherosclerotic burden and the systemic clotting tendency, which poses a direct risk of thrombotic complications. This point of view deviates from Tracy's viewpoint and provides a more constructive model for integrating coagulation in arterial disease.
We would also propose that D-dimer and other, novel assays such as for endogenous thrombin generation ("endogenous thrombin potential") [69] or for activated factor XII (suggested as a novel risk factor for cardiovascular disease [70]), be used to determine prospectively the risk of new atherothrombotic complications and to guide randomized intervention trials in patients stratified on the basis of such plasma markers.
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| 15574198 | PMC538274 | CC BY | 2021-01-04 16:36:20 | no | Thromb J. 2004 Dec 1; 2:12 | utf-8 | Thromb J | 2,004 | 10.1186/1477-9560-2-12 | oa_comm |
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Hum Resour HealthHuman Resources for Health1478-4491BioMed Central London 1478-4491-2-151556084110.1186/1478-4491-2-15ReviewPublic sector reform and demand for human resources for health (HRH) Lethbridge Jane [email protected] Independent health policy consultant, London, UK2004 23 11 2004 2 15 15 20 1 2004 23 11 2004 Copyright © 2004 Lethbridge; licensee BioMed Central Ltd.2004Lethbridge; 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.
This article considers some of the effects of health sector reform on human resources for health (HRH) in developing countries and countries in transition by examining the effect of fiscal reform and the introduction of decentralisation and market mechanisms to the health sector.
Fiscal reform results in pressure to measure the staff outputs of the health sector. Financial decentralisation often leads to hospitals becoming "corporatised" institutions, operating with business principles but remaining in the public sector. The introduction of market mechanisms often involves the formation of an internal market within the health sector and market testing of different functions with the private sector. This has immediate implications for the employment of health workers in the public sector, because the public sector may reduce its workforce if services are purchased from other sectors or may introduce more short-term and temporary employment contracts.
Decentralisation of budgets and administrative functions can affect the health sector, often in negative ways, by reducing resources available and confusing lines of accountability for health workers. Governance and regulation of health care, when delivered by both public and private providers, require new systems of regulation.
The increase in private sector provision has led health workers to move to the private sector. For those remaining in the public sector, there are often worsening working conditions, a lack of employment security and dismantling of collective bargaining agreements.
Human resource development is gradually being recognised as crucial to future reforms and the formulation of health policy. New information systems at local and regional level will be needed to collect data on human resources. New employment arrangements, strengthening organisational culture, training and continuing education will also be needed.
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Introduction
This paper considers health sector reform and its impact on human resources for health (HRH) in developing countries and countries in transition. Health sector reform has been defined as the "sustained purposeful change to improve the efficiency, equity and effectiveness of the health sector" [1]. Health sector reform involves many fundamental changes to the way in which public services are financed, organised and delivered in both developing and developed countries, and often operates as part of a wider programme of public sector reform. Fiscal reform, the introduction of market mechanisms and decentralisation are three key elements of health sector reform. This paper will show the impact of these elements on human resources for health and attempt to assess the changing demand for health workers. A series of recommendations will seek to address some of the issues that have emerged for HRH demand during the process of health sector reform.
Impact of health sector reform on human resources for health (HRH)
Fiscal reform
The introduction of new budget management systems, designed to maintain financial control throughout government, is one of the most important elements of fiscal reform. These incorporate new financial planning and control systems that emphasise what outputs a department or agency will be expected to deliver. Overall, there is a focus on the performance of public services [2]. Mechanisms for monitoring and enforcement of targets are designed for all government departments. In the health sector, over 50% of costs are labour costs, so that demonstrating effectiveness depends largely on attempting to measure the work of health staff. Measuring outputs in health care is often difficult because of having to capture both the quality of care and patient outcomes.
Fiscal reform introduces new ways of allocating resources in line with government objectives [3]. There may not be a precise match with individual sectoral objectives. Fiscal reform also tries to encourage improved use of resources, which may inform the reorganisation and management of central agencies and the downsizing of the civil service.
Health sector employees are often part of the civil service, and so civil service reform has an impact on the employment and deployment of health workers. Civil service downsizing results from policies to cut the costs of the public sector and transfer the delivery of services to the private or non-profit sectors. These changes lead to a reduction in the size of the public health care workforce. Compensation schemes may include retraining and lump-sum severances to ease the transition of workers into the private sector. This may be accompanied by wage policy reform to limit and contain wage expenditures, again with the potential to affect health workers [4].
Trying to improve the performance of the health sector, one of the objectives of health sector reform, has been a slow process because the savings from reducing the size of the workforce are often not enough to raise salaries for the remaining staff. Several countries, including Zambia, have set up a separate health service agency, operating as a semi-autonomous government agency, that employs staff directly. Some writers argue that agencies need to be well-managed on a limited budget rather than be seen as an escape from civil service restrictions [5]. In addition, "the importance of political and institutional context in which reforms have to be implemented has been undervalued" [5]. Many ministries have a poor record of human resource management and planning. New information systems can provide a more accurate picture of the current number, type and distribution of staff, but civil service systems rarely provide incentives to reduce staff budgets. There may also be attempts to strengthen linkages between government departments, which are relevant for the health sector.
The decentralisation of budget management is another element of fiscal reform. In the health sector, this has been reflected in the decentralisation of service provision to semi-autonomous hospitals, because hospitals often consume the largest part of the health sector budget [6]. Set up as institutions run on business principles, "corporatised" hospitals bring the results of fiscal reform to local level. With limited available resources, there may be pressure to generate income through the introduction of user fees such as for health services, as well as trying to achieve outputs and outcomes at the lowest cost. Delegation of financial authority can also provide managers with the scope to use existing resources differently or consider different ways of delivering services, such as by using a range of local providers [2].
The motivations for introducing corporatisation may also vary from wanting to increase efficiency and achieve cost saving and quality improvements to just wanting to "free up a public function from constraints of ... red tape" [6]. Cassels argues that decentralisation has provided "major contradictions for health care" between accountability, competing priorities and equity and tensions between small-scale participation and managerial effectiveness required at a large scale [5].
In Mexico, new systems of financial management affected public health institutions by restricting the maintenance and upgrading of equipment and imposing cuts in the wages of health workers. This has led to the deterioration of working conditions and the quality of care provided by the public health sector [7].
Financial management
Part of a programme of fiscal reform involves the development of new systems and structures of financial management, which have organisational implications [3]. The role and functions of the finance ministry in central government are strengthened and it develops a dominant role over other government departments. This affects government health ministries, because the priorities of the finance ministry are often different from those of the health ministry and priority setting and resource allocation issues become sources of conflict. The nature of the relationship between the finance and health ministries has been exposed in the development of poverty reduction strategy papers [8].
In health systems, new forms of financing for the health sector may involve moving from a tax-based system to an insurance system. This, in turn, introduces new forms of budgetary management and control between insurance funds and health service providers as well as new systems of payment collection. Financial management is often accompanied by new information technology systems, which have the potential to change the ways in which public services are monitored [2].
Market mechanisms
The introduction of market mechanisms is often driven by the goal of fiscal stability. Stronger systems of budget and management control are introduced that focus on results. They affect sectoral priorities and available human resources. Market mechanisms may be introduced by making health care institutions operate within an internal health care market and subjecting some health services to wider market testing.
As part of developing a managed market in the public sector, the health sector is often reorganised into two separate purchasing and provider functions. The purchasing entity, typically a national or regional health authority, buys services from provider units within the government sector and is also encouraged to buy services from a range of providers in the private and NGO sectors [9]. This has immediate implications for the employment of health workers in the public sector, because the public sector may reduce its workforce if services are purchased from other sectors. The private sector may start to expand. Health workers often move from the public to the private sector because of better prospects and higher pay. Market testing has often led to changes in the size of the public sector workforce, increasing short-term and temporary employment contracts, and changes in wage levels [10,11].
User fees
User charges have been introduced as a way of generating income for the health sector. User fees in many countries have affected access to services and equity [12,13]. In Nicaragua, the introduction of user fees and separate services for private, paying patients started as a national initiative but is now incorporated into local health systems. User fees have become the main source of decentralised revenue. At hospital level, 30% goes towards salary supplements [14]. In Honduras, the revenues collected from user fees have contributed only 2% to the Ministry of Health expenditures but the administrative costs are 67% of the revenues collected [15]. In most countries, however, the preparation of staff and supporting systems for implementing user charges has been minimal. The introduction of user fees places new pressures on health workers, especially when user fees contribute to the actual wages and salaries of health workers.
A recent World Bank report (2002) presents informal payments as a hindrance to health sector reform. Payments made to health workers are considered to draw resources away from the health care system because they are given to individuals rather than institutions. Such payments operate as a private, unregulated system and the practice is often illegal. Poor people often avoid using health care facilities because of the need to make informal payments [16]. Stronger management capacity is needed to support and coordinate public, private and NGO providers and provide accountability so that revenue from user fees goes directly for service improvements [17]. This would also depend on health workers' being paid an adequate salary and the introduction of transparent systems to support the collection of user fees within the health care sector.
Performance management
Public sector and health sector reform often introduce new approaches to managing staff. Perhaps the most important innovation is "thinking differently about staff", which effectively underpins other changes. The three most innovative dimensions are "flexible staffing and recruitment practices, recognising achievement and developing performance contracts" [2].
The element of fiscal reform that emphasises outputs and outcomes of government services informs the development of performance management. It aims to address management problems relating to poor employee performance management, wage and non-wage incentives, job classification systems and ineffective payroll and personnel systems. Performance management may also be introduced as a way of improving standards within public services and making services more responsive to citizens. Wider programmes of training and capacity building for staff can accompany this. Some developing countries have experimented with performance management systems, with limited success [9]. Often the new "corporatised" hospitals have only limited management autonomy, and governments lack the capacity to manage performance in the health system [9].
Decentralisation
The delegation and decentralisation of administrative and management processes often accompany budgetary reforms. In Nicaragua, decentralisation was used to introduce market reforms. Budget cuts, loss of resources from primary health care, user fees and privatisation were introduced at the same time [14]. In 1991 Local Integrated Health Care Systems (SILAIS) were introduced, which are made up of a hospital and a network of primary care units. Each SILAIS has a separate Board of Directors consisting of local officials, church officials, health sector representatives, community members and the SILAIS director. This group monitors services and approves the local health plan and budget, but accountability remains unclear. The Ministry of Health controls funding through "performance agreements, and controls 80% of the health budget including staff levels and composition". Only recently have Local Health Systems been given the power to sack staff [14].
In Uganda at the time of decentralisation, salaries for staff on the payroll were a central responsibility, although this has now been decentralised through a special conditional grant. In the past, professional staff were put on the national payroll and nursing aides were hired locally for work in rural health centres and health posts and paid for by the Ministry of Local Government. After the decentralisation reforms, nursing aides were supposed to be paid by local committees, but in practice this often did not happen and they were not paid for long periods [18].
Botswana and Tanzania have had long experience of decentralisation. As a result of health sector reform, health staff were transferred to local government contracts although senior staff remained employed by the Ministry of Health. This has led to confused loyalties and management responsibilities. In some districts the "personality factor" has meant that individuals working together have managed to overcome some of these problems, in spite of the systems introduced. Senior staff who have subsequently been transferred to local government complain that that there is "little relationship between promotions/disciplinary actions and performance". In both countries there is some scope for local decision-making in relation to personnel management, but there is still resistance to distributing staff according to local needs. More incentives and other measures are considered necessary if regional imbalances of staff are to be addressed [17].
Decentralisation may lead to a loss of resources for the health sector. In Uganda, after decentralisation, once central government stopped a block grant, primary heath care was not given the allocation at local level that had been expected by the Ministry of Finance. There were also considerable district variations in the allocation of health resources. Although some districts did increase their health allocation, in many cases decentralisation led to fewer resources for health. One of the reasons cited for the decline in allocation of resources to the health sector was that a large part of the health budget goes on salaries and wages, which do not show any dramatic change in the sector. Decentralisation in this context led to problems of financial management and corruption at local level, new problems of governance with a lack of accountability and concerns over quality of services [18].
Some changes have run contrary to the main aims of reform, such as increased centralisation of controls over pay. Much health sector reform was to strengthen and rationalise budgeting, financial control and staff classification, but in some cases control over health sector staffing has remained at national level [19]. Even when transfer of budgets has taken place, there is confusion between local government and health sector responsibilities.
Changes in provision
The use of the private sector as a health service provider has had implications for the recruitment and retention of staff in the public sector. Some services have been privatised and are now run by local, national or international private companies. Other services have been contracted out to both private and non-profit service providers. This has resulted in movement of health workers from public to private or non-profit sectors [17].
In the public institutions that remain, market conditions have been introduced and services are contracted out, which has resulted in a widespread decrease in job security in many countries. Health workers have moved from collective-bargaining arrangements to individual contracts. Decentralisation and privatisation have contributed to the breakdown of national collective bargaining. In Eastern and Central Europe, new organisations and professional associations and reorganised trade unions have led to a breakdown in labour relations expertise [20].
Changes of responsibility for managing health services, from national to local level and from public to private sectors, have led to some confused accountabilities for health workers [11]. Health workers have moved from being accountable to both a public service and to their profession, to being accountable to a commercial employer with performance-related pay and conditions. This often causes tension between professional standards and pressure from the commercial employer.
The process of health sector reform has had an impact on human resources for health through new systems of financial and performance management, decentralisation and the introduction of market mechanisms. This has led to changes in the demand for health workers and in some cases the types of skills and expertise required from health workers. At the same time, the capacity of the new management systems is unable to create conditions in which a new health workforce can be developed. This can be seen particularly clearly in the process of budget decentralisation, which leads to a focus on local decision-making but where the capacity of local institutions to recruit, train and manage local health service workers is limited. This has an influence on the quality of health care delivered.
Implications for HRH demand
Demand for human resources in health systems that have experienced health sector reform must be considered in terms of the numbers of health workers and the skills and expertise needed currently and what will be anticipated in the future. There is a growing awareness that human resource issues need to be prioritised more effectively within reforms in order to secure an adequate health care workforce to deliver services now and in the future.
Public sector culture
Although health sector reform has included elements of human resources strategies such as improved education and training, restructured salary scales and a closer link between performance and reward, it has also had a fundamental impact on organisational culture and public sector ethos, which in turn influence demand for human resources.
A study of four countries in Eastern and Southern Africa concluded that "human resource development, personnel management and staff motivation are critical issues" [17]. Tanzania, although it has invested in human resources development, found that low salaries, delayed promotion opportunities and poor working conditions led to dissatisfaction in the workforce. Staff performance has been found to be unsatisfactory. Although monetary and non-monetary allowances were supposed to compensate for low wages, they have led to poor teamwork and lack of continuity in health service operations. The regional health team was found to spend 40% of its time out of the region on training and meetings.
Burkina Faso introduced health sector reforms in 1991 but they have not been fully implemented. It has recently introduced civil service reform, which "aims at a more flexible management and better performances of personnel". In a country where services are centralised, with an imbalance in personnel and low staff motivation and poor standards of care, there is resistance to the new reform. There has been a decline in standards of service between 1986 and 1997 [21]. Poor financial and human resources policies and management are resulting in high cost and poor quality of care. A recent study concluded: "Human resources should become the central focus for reform" [21].
Matheson (2002) points out that "the least systemically orientated area of recent public management reforms has been human resource management.... There is a danger that the constitutional, legal, cultural and leadership factors, which together create what is important and distinctive about public services, are not reflected on, or are dismissed as the bureaucratic problem which must be 'reformed' " [22].
Demand for health workers
Some national health sector reforms reduced the numbers employed in the health sector, as in Chile and Latvia. Others, as in Mexico and Zambia, led to a rise in employment [11]. In almost all countries, health worker employment was restructured. Health sector reform has often aimed to restructure organisations to reduce costs and the power of the workforce. Pressure of work and hours worked have in many cases increased since health sector reform. There is also an increased workload due to lack of staff, pressure for results and staff reductions [10,11]. This affects the future demand for health workers.
Health services have usually been seen as "essential services" and so health workers had the legal status of public servants. They had to account to both employers and professional bodies subject to strict regulation and registration rules. The effect of privatisation has been to change the pay and terms of employment and the legal status of health workers [11]. The public health sector has changed from being a public service to one with a greater commercial focus. This may have an effect on recruitment. The Nicaraguan government has continued to cut the number of doctors, changing to an hourly rate of reimbursement rather than salaries, and ending the commitment of government to employ graduating medical students [14]. This process is effectively influencing the demand for doctors in the public sector.
The introduction of flexible contracts and fall in full-time permanent contracts has been a characteristic of most reforms, leading to a reduction in long-term employment security. Some of these changes in terms and conditions have led to health workers' taking on second jobs. This may be caused by the increase in part-time employment and low and erratically paid wages [20]. In Eastern and Central Europe, women have been most affected by the reduction in jobs in the heath sector. Their prospects for redeployment are often limited due to a lack of mobility [11]. The growth of part-time work in the public sector is a sign of the changing demand for health workers.
Low pay levels have led to staff leaving the public sector and moving to the private sector, NGOs and aid agencies [9]. Low pay also contributes to low administrative capacity, as well as poor organisational discipline. In an analysis of health worker motivation, health sector reform was found to influence health worker motivation through changing organisational structures and community-client roles [23]. Organisational factors influence worker motivation through management structures and processes, communication processes, organisational support structures and processes, and ways of providing feedback about organisational and individual performance.
These changes in organisational culture have often had a negative impact on workers' motivation. Important informal factors – for example, staff commitment – have "become the prime means of direction, motivation, coordination and control" [22]. When staff commitment deteriorates over time, health workers may migrate, not only from the public sector to the private sector, but internationally. This results in a shortage of skilled health workers within the public sector, precipitating a growing demand for skilled health workers.
The aim of introducing market mechanisms to the public sector has been to improve economic efficiency. New skills are needed to implement commissioning and contracting of services. For example, contracts can be a powerful form of regulation if drawn up and monitored effectively, but increased expertise is required to establish this form of regulation.
Process of reform
Understanding the process of reform is important for understanding how changes have taken place but also what the critical factors are for successful policy implementation in future. "The process of reform offers numerous opportunities to alter the political equations that impede change" [19]. This is also significant for understanding the potential role that health workers can play within reforms.
In Latin America, health sector reform has been characterised by various forms of privatisation, competition among providers, new insurance systems, management autonomy for hospitals and increased community participation. The goals were efficiency, accountability and improved quality of services. A recent study looked at how groups, such as unions, play different roles in relation to reforms with some opposing and others supporting reform. Some public sector health workers have played an important role in supporting change [24]. The importance of "principled agents" in public sector organisations has also been noted [25]. Public servants can be motivated by managerial and incentive schemes to lead and support change. Linking popular and unpopular reforms has often led to reformers' changing their attitudes to reform. Networks of reformers can also play a role in supporting reformers in environments hostile to change [19].
Communities also influence health worker motivation through their expectations of services. As health sector reform also aims to empower service users, this focus will have a significant impact on the individual health worker in future [23]. The evidence showing the extent to which users of health services have been empowered by health sector reform is limited in developing countries [13].
However, health workers do play an important role in the implementation of health sector reform policies. The lack of consideration of the value of human resources in health sector reform programmes has meant that this has often been ignored. Gilson et al. (2003) recommended that: "Technical analysts might consider working with middle managers and health workers to ensure adequate consideration is given to implementation realities in proposal development" [12].
Knowledge gaps
The process of health sector reform is not complete. More research is needed to monitor changes still taking place as well as the outcomes of the reforms. One of the major changes is the role that health workers play in both the public and private sectors. How health workers perceive their roles in the different sectors and what the implications are for motivation, particularly in the public sector, will need further exploration.
The role of the public sector is changing and this is reflected in public sector institutions and the public sector ethos. How this changed public sector can demonstrate a commitment to health workers as well as harnessing their own commitment, still needs to be explored.
A further area of research needs to improve the understanding of human resources in health sector appraisal studies "by incorporating functional, institutional and policy dimensions. Only then will human resources become in practice the most valuable resource within any national system" [26].
Conclusion
There is a growing awareness that human resources for health (HRH) must be addressed more effectively within public sector reform. Stein thinks that HRH strategies need to be a "primary objective for public organisations" [27].
Public sector reforms have sometimes been characterised as containing the paradox of aiming to reward performance and empower staff whilst at the same time implementing downsizing and redundancy – the "human costs of reform" [28]. Issues resulting from these changes include loss of institutional memory and the use of downsizing as a way of making financial savings rather than administrative reform. Changing rules and processes do not always lead to changes in organisational culture. "Multi-faced interventions sustained long enough to achieve change" will be needed to change public sector culture [13].
To address some of these issues, action at strategic, regional and local levels will be needed to strengthen skills, expertise and analysis of HRH and to strengthen the integration of HRH issues into health policy making and with relevant agencies [26]. New human resources systems are needed at regional and local level.
Additional data on existing employment, retention and deployment issues must be made available to decision makers so as to relate them to health equity issues [29]. An increased awareness of the importance of improved coordination of facility planning and human resource planning at national and local level is needed. A better understanding of the role of organisational culture and public sector ethos in health worker motivation is needed [23]. This might be achieved by developing case studies of health workers as "drivers of change" and more process research to look at emerging practice.
The working conditions of health workers need to be improved. This might be achieved through developing more flexible employment arrangements that are employee-focused. The public sector needs to be encouraged to establish a "living wage" and other forms of worker security so that terms and conditions of public sector workers are better than those of private sector workers [26]. Health workers need to have access to continuous professional development that includes skills for performance management, management of contracts and other new ways of operating in reformed systems [29].
The role of central government in setting standards for professional practice and legal requirements for registration needs to be strengthened so that human resources policies, registration and regulation are mutually supportive. Registration requirements that include experience in rural or remote areas would help to address uneven distribution of health workers.
Declaration of competing interests
The author(s) declare that they have no competing interests.
Acknowledgements
This paper was commissioned for Working Group 3 of the Joint Learning Initiative on Human Resources for Health, initiated by the Rockefeller Foundation. Working Group 3 is responsible for the study of the current situation of management of HRH demand and the development of evidence-based strategies/solutions for policy makers.
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| 15560841 | PMC538275 | CC BY | 2021-01-04 16:37:43 | no | Hum Resour Health. 2004 Nov 23; 2:15 | utf-8 | Hum Resour Health | 2,004 | 10.1186/1478-4491-2-15 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1571550423410.1186/1471-2105-5-157Methodology ArticleGapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model Neuwald Andrew F [email protected] Jun S [email protected] Cold Spring Harbor Laboratory, 1 Bungtown Road, P.O. Box 100, Cold Spring Harbor, NY 11724, USA2 Department of Statistics, Harvard University, 1 Oxford Street, Cambridge MA, 02138, USA2004 25 10 2004 5 157 157 19 5 2004 25 10 2004 Copyright © 2004 Neuwald and Liu; 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
Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences.
Results
Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Giα subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases.
Conclusion
While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of family-specific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors.
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Background
As the genome projects continue to generate sequence data, it is increasingly common to find protein superfamilies with thousands of members in the protein database. Given sufficient numbers of sequences, sensitive iterative search and alignment procedures, such as PSI-BLAST [1] and SAM [2], often reveal that protein families previously thought to be distinct are, in fact, distantly related. Protein structural analysis likewise reveals subtle evolutionary relationships between protein families sharing very little sequence similarity. Since our ability to make protein structure and function predictions depends in large part on alignment accuracy, it is thus important to develop alignment methods able to handle these increasingly large and diverse sets of distantly related sequences.
Certain protein families within these large superfamilies are often very highly conserved across distantly related organisms. Such proteins include, for example, certain metabolic enzymes, DNA replication and repair factors, certain structural proteins, such as actin, the motor protein dynein, and regulatory and signalling factors, such as protein kinases and Ras-like GTPases. While many of these proteins seem relatively well characterized, we still cannot account for the strong selective constraints preserving their observed high degree of sequence conservation across major taxonomic groups. Presumably these patterns of conservation contain implicit information regarding still unknown functional mechanisms. To access this information, we recently developed a statistically based approach, called contrast hierarchical alignment and interaction network (CHAIN) analysis [3], that identifies, categorizes, and statistically characterizes co-conserved patterns in multiple alignments. The power of this approach strongly depends on the quality of the alignment, which thus motivated the development of the theoretical concepts and strategies described here.
Aligning distantly related sequences presents unique algorithmic and statistical challenges because such proteins often only share a minimal structural core with sizable insertions occurring between, and even within, core elements. Classical dynamic programming-based multiple alignment procedures typically have considerable difficulty spanning across these insert regions because the log-odds scores associated with weakly conserved core elements are often too low to offset the substantial gap penalties that such insert regions incur. This problem is further exacerbated when core elements contain short insertions or deletions within them.
To address this problems, we previously devised motif (or block) based multiple alignment procedures [4-6] that can easily jump over non-homologous insert regions. This approach seems easier to justify than attempting to align regions for which there is no statistical evidence of relatedness. A block based alignment strategy thus seeks to detect islands of subtle sequence similarity within otherwise dissimilar sequences. Fortunately, even when the conserved motifs are very subtle, such a procedure can take advantage of large numbers of available sequences to detect weak, yet statistically significant similarities.
Altschul at the National Center for Biotechnology Information (NCBI) likewise sought to address this problem through generalized affine gap costs [7], but the utility of this approach is unclear, as the NCBI currently does not support any public programs based upon it. The programs MUSCLE [8,9] and MAFFT [10] also are designed to avoid alignment of non-homologous regions and in other respects are generally superior to more widely used multiple alignment programs, such as Clustalw [11] and T-coffee [12]. Because MUSCLE and MAFFT can handle large data sets, we explored the use of these programs for CHAIN analysis (Neuwald, unpublished). Somewhat surprisingly, these failed to achieve the degree of accuracy needed to detect subtle, co-conserved patterns, such as those recently identified and structurally confirmed within P loop GTPases [3]. We found that, although these programs align regions globally conserved in the sequences well, for several large test sets they fail to accurately align regions conserved only within more closely related subsets. This is, of course, a major drawback to their general application for CHAIN analysis. By contrast, PSI-BLAST [1], which seems less likely to produce high quality global alignments given its simple alignment procedure nevertheless in many cases does a better job of aligning database sequences relative to the query. Thus PSI-BLAST (albeit with some modifications to improve alignment accuracy [3]) has turned out to be more generally useful than these other methods for CHAIN analysis, which like PSI-BLAST is query centric. Note, however, that a systematic comparison of various methods within the context of CHAIN analysis has not yet been done.
More relevant to our purpose here, another drawback to the use of MUSCLE, MAFFT, and similar programs for CHAIN analysis is that these will align randomly generated sequences – a characteristic incompatible with the statistical basis of CHAIN analysis. MUSCLE and MAFFT perform well on small sets of relatively diverse representative sequences, such as the BALIBASE benchmark sets [13], because they incorporate heuristics that unfortunately also can compromise statistical rigor and, as a result, confuse random noise with biologically valid homology. Statistically the best alignment for random sequences is the 'null alignment', that is the procedure should leave such sequences unaligned – a property of PSI-BLAST that played a key role in choosing it for CHAIN analysis.
To maintain statistical rigor in our formulations here, we will 'let the data speak' by modelling only those characteristics of the sequences that can be justified by the input data. Such an approach cannot be applied, however, to small benchmark alignment sets, because these lack sufficient sequences – less than the number of amino acids whose parameters are being estimated. Thus, while a rigorous statistical approach has severe limitations when applied to small datasets, it works very well when applied to large, diverse sets of distantly related sequences, as demonstrated, for example, by some of our earlier analyses [14-16].
Two other theoretical issues, which are important to the multiple alignment problem, are devising an objective measure of alignment quality and an efficient strategy for finding the best alignments based on this measure. Our previous methods [4-6] addressed these issues using a Bayesian statistical approach for modelling an arbitrary number of multiply aligned ungapped blocks, each of arbitrary length, in conjunction with a Gibbs sampling procedure for exploring the 'space' of all such alignments. Gibbs sampling is a Markov chain Monte Carlo (MCMC) method that iteratively realigns the sequences with probability proportional to how much the model is thereby improved. Theoretically, beginning from an arbitrary starting alignment, this process will ultimately sample alignments according to the posterior distribution defined by our Bayesian model. Exploring the alignment space in this way is more efficient than taking a greedy approach (one that always chooses the transition to the best alignment) because an element of chance allows the sampler to maneuver around locally optimal traps.
Within this MCMC sampler we implemented specific operations on the alignments, including those allowing for realignment of a sequence against the alignment model, shortening or lengthening of blocks, and creation of recombinant alignments. Such operations function like catalysts to help the sampler avoid or more quickly escape from local optima. Here we expand on the number of these operations and modify our Bayesian model to allow for short insertions or deletions within blocks. In theory, such an approach could be used to sample representative multiple alignments from the posterior distribution, which is relevant to CHAIN analysis because this could be used to adjust position-specific amino acid frequencies for alignment uncertainty. Doing so for the model and operations described here, however, is non-trivial and thus is a topic for a future publication built upon this one. Our primary objective here is merely to obtain the optimal alignment. Thus we also introduce various annealing-like strategies for luring the sampler toward optimum alignments. These include simulated annealing, which is applied within sampling routines, and other intervention strategies. Our primary motivation for developing and implementing these concepts and strategies is to improve CHAIN analysis, as is illustrated here for G-protein α subunits, which belong to the P loop GTPase class [17], prolyl endopeptidases, which belong to the α,β-hydrolase fold class [18,19], and transitional endoplasmic reticulum (p97) ATPases [20], which belongs to the AAA family [21-23] within the AAA+ class [14,24,25].
Problem definition
The fundamental problem addressed here is to identify the essential features – the common structural core – characteristic of a large set of distantly related proteins. Given an input sequence set, we build a Bayesian statistical model with adjustable parameters to reflect the relationships among the proteins. We also design a stochastic search algorithm, with an MCMC sampler as its backbone, to explore possible alignments and corresponding model parameters in order to find alignment models that best 'explain' the input data. The model parameters specify, for example, the number and lengths of the motifs, their locations within each sequence, the residue frequencies observed at each position in each motif, and other properties (described below). We may thus envision our sampler as searching through a discrete space where each point, corresponding to a particular alignment, has a probability associated with it. The probability function appears fairly smooth inasmuch as nearby points (similar alignments) have roughly comparable probabilities. As the sampler traverses from one point to another, it favors moves toward the better alignments, that is, toward that part of the alignment space with greater posterior probability. Since it is computationally prohibitive for the sampler to consider many transitions at one time, a key design issue is the selection of allowed transitions between points.
Results and discussion
The block-motif model
We first define the alignment model in precise mathematical terms, which provides a scoring scheme that allows us to judge which alignment is better than another. Here, for the sake of conciseness and readability, we will keep the discussion on a conceptual level whenever possible. Interested readers can consult our earlier publications for further details [4,6].
As illustrated in Fig. 1, this previously described block-based motif model assumes that the aligned core of each protein sequence consists of m co-linear ungapped motifs, of width w1,... , wm, respectively. Each motif is modelled by a position specific frequency matrix Θi, whereas residues outside the motif blocks follow a common frequency distribution. Independent prior Dirichelet distributions are employed for these frequency parameters.
Since both m and the wi's are unknown, we assume that they are uniformly distributed in a certain range a priori (see [6] for details). We also employ a "fragmentation model," which allows non-informative aligned columns to be ignored by the motif model. Although we use no explicit gap penalties between motifs, our prior imposes a large penalty on alignments with large m. Let S denote the sequence data and let A denote the motif alignments (which also includes m and the wi's). Then the posterior alignment distribution is:
P(A | S) ∝ P(A) ∫ P(S | A, Θ)P(Θ)dΘ.
Based on this distribution, our algorithm (as implemented in the PROBE program [5]) attempts to maximize P(A | S), the so-called "maximum a posteriori (MAP)" score.
Hidden Markov models for gapped motifs
A major drawback of the previous block-based alignment approach is that it disallows insertions or deletions within motif blocks. Here we describe hidden Markov model (HMM) [26,27] structures for insertions and deletions, which will be used by our current algorithm via the operation GAPALIGN (see below). The general architecture for these HMMs is given in Fig. 2, and detailed descriptions, including the definition for our scoring function g(A, Λ), are given in Methods.
For an intuitive notion of how within-motif penalties influence the total MAP score, consider a gap-opening penalty of say 20 bits (i.e., p = 1/220) and an extension penalty of 2.5 bits. Then, for example, the overall MAP would need to improve by 25 bits in order to justify a 'surgical operation' on a sequence involving an insertion of three dummy residue (i.e., to 'correct' a deletion in a sequence) or a deletion of three residues (i.e., to 'correct' an insertion in a sequence). The statistical problem is thus that of finding the right penalty so that the sampler only adds insertions or deletions when the data provides sufficient justification. In a Bayesian context, this justification is based on the posterior inference of the overall number of insertions and deletions from what it finds in the aligned sequences.
Markov chain Monte Carlo methods
The Bayesian analysis described in Methods provides us the posterior distribution of the alignment up to a normalizing constant. Although this distribution defines the answer to our problem, namely inferring the optimal alignment, it is difficult to make sense out of it because of the huge size of the alignment space. Fortunately, recent progress in using MCMC methods for statistical analysis has made it possible to study this function.
MCMC methods, of which the Gibbs sampler is a special case, refer to a set of techniques developed by physicists since the 1950s to simulate variables from a given probability distribution up to a normalizing constant. The central idea of these techniques is to evolve a Markov chain, each step of which perturbs the current state (alignment) slightly, with the equilibrium distribution of the chain being the target distribution. A MCMC scheme is usually constructed in two steps: (i) propose a new state according to a certain reversible transition rule, and (ii) accept or reject the proposal according to the probability ratio between the proposed and the current states [28].
The broad utility and general applicability of these techniques are exemplified and popularized by recent developments in statistics: if one can sample from g(A, Λ) one obtains a set of "typical" alignments according to the posterior distribution, which provides information regarding the most likely alignment(s) supported by the data and its variability. In practice, however, one may wish to find the optimum of this function and explore only around this optimum considering the difficulty of summarizing a set of distinct alignments in a meaningful way. MCMC is also an important ingredient of an optimization technique termed "simulated annealing" [29], of which we will develop a variation. A good MCMC scheme should have the following property: (a) its transition rules should collectively allow the sampler to access every point in the space; (b) these transitions should also allow for global changes, such as, for example, recombination between two alignments; and (c) the acceptance rate of these proposals should be reasonable (10~50%). The sections below will focus on designing such transitions for multiple alignment.
An algebraic system for touring the alignment space
The elementary mathematical operations of addition and subtraction define a means of transitioning between points in the discrete space of natural numbers. "Global" operations, such as multiplication and integer division, allow transitions between more distant points in this space. Likewise, we define both elementary and global operations on multiple alignments as a means of transitioning between points in alignment space. In this case a set of unaligned sequences (termed the null alignment) serves the same role as the natural number zero. Formal mathematical descriptions of the alignment and of certain simple operations are provided in our earlier papers [4,6]. Since the new operations described here involve various combinations of these simple operations, it is straightforward to derive these new operations from the previously published descriptions.
There are two issues to consider in the design of multiple alignment operations. First, the reversibility of MCMC algorithms requires that every operation have an "inverse" so that the sampler can readily transit in either direction. Second, to help find the optimal alignment according to our Bayesian model, which is our main objective, annealing techniques and less restrictive acceptance rules should be considered for certain complex operations. By doing so the target alignment distribution has to be distorted to some degree, though the global optimum of the distribution remains the same.
All alignments described here are collinear multiple alignments (CMAs), which are defined to contain zero or more motif blocks arranged collinearly in each sequence. Partial or complete deletion of any motif from a particular sequence is modelled by aligning that motif against null residues ('-'), which the sampler may insert anywhere in the sequence. Sequences may also contain more than one repeat of the entire protein domain, each of which is modelled by the full set of motifs. (The identification of repeat domains will be described elsewhere; Spouge and Neuwald, unpublished.) For clarity, we describe operations deterministically, though it should be kept in mind that our sampler applies these stochastically.
Elementary operations
The HideInsert operation (inverse ShowInsert) is applied to 'surgically' remove a region of the sequence that appears to correspond to a typically short insertion within a conserved motif. This operation thus changes the real sequence into an idealized sequence that, presumably, more closely resembles the canonical characteristics of the protein class. As a result, the sampler needs to maintain both a real and an idealized version of each protein's sequence and to store the operational derivation used to obtain the ideal sequence from the real. Algorithmically it is convenient to deal with insert regions in this way because otherwise the sampler would need to look up the locations of insertions and deletion within each sequence when applying other operations. The FillDeletion operation (inverse UnfillDeletion) likewise converts a sequence that contains a deletion of either part of or all of a motif into an idealized sequence in which the deletion has been filled in with null or 'dummy' ('-') residues. Note that HideInsert and FillDeletion merely define data structure interconversions that allow basic operations, which were initially defined for ungapped motifs, to be efficiently applied to gapped motifs.
The Align operation assigns motif positions within a sequence and thereby adds that sequence to the alignment, UnAlign removes the sequence from the alignment. Note that these operations disallow gaps within motif blocks.
The AddColumn and DeleteColumn operations add and remove aligned columns, respectively. Note that these operations may add or remove columns internal to a motif as well as at the edges. Moreover, AddColumn may also insert a column an arbitrary number of residues beyond the current edge of a motif. This is important for motif 'fragmentation' [4,30], a procedure that allows certain nonconserved positions inside of a motif to be ignored by the alignment statistical model.
Compound operations
Elementary operations can be combined in a coordinated manner in various ways to produce compound operations that better facilitate escape from local traps. For example, GapAlign (inverse UngapAlign) combines the row operation Align with the sequence operations HideInsert and FillDeletion in order to add a sequence to an alignment with insertions and deletions. The GapAlign operation is performed using dynamic programming to obtain a gapped alignment of a sequence against a statistical model of the current alignment. The trace back procedure determines how to apply the HideInsert and FillDeletion operations to the true sequence and how the Align operation is then applied to the resultant idealized sequence.
We define several compound operations on a motif block: AddBlock, ShiftRight, and TrimRight (with inverses: DeleteBlock, ShiftLeft, and TrimLeft, respectively). Another compound operation, MoveColumn, which transfers a column from one position to another within a block, is its own inverse. Conceptually, AddBlock and DeleteBlock simply iteratively apply the AddColumn and DeleteColumn operations, respectively. Because our motif alignments are collinear, the position of an added block within each idealized sequence must be specified in a manner consistent with this collinear arrangement and, in order to add a new block in this way, the sampler may need to insert null residues at certain positions within some of the idealized sequences. This is an example of operational flexibility. Similar operational flexibility is required for the ShiftRight and ShiftLeft operations, which remove one or more columns from one end and append it to the other end of a motif. TrimRight and TrimLeft allow poorly conserved residues to be trimmed from a motif block based on their relative entropy. These operations thus provide a means to manually edit motif-based alignments as discussed below.
Three compound operations involving two motif blocks are: TransferColumn, Splitblock and FuseBlocks. TransferColumn deletes a column from one block and adds it to another block. Splitblock splits a single block into two leaving two contiguous motif blocks in each of the idealized sequences. During future realignment operations the sampler typically induces these abutted blocks to drift apart. Splitblock's inverse operation, FuseBlocks, merges two blocks into one, which typically requires forced realignment of motif positions in each sequence in order to join the blocks together. All such forced realignments are followed by additional optimization via sampling prior to deciding whether to reject or accept this new configuration. We thus typically have to violate the MCMC's acceptance-rejection rule to enable such a move, which distorts the target distribution. The awkwardness of this procedure may be advantageous, however, inasmuch as it forces the sampler out of local traps in alignment space. Fig. 3 illustrates the effect of applying compound operations during Gibbs sampling.
Recombinational operations
As an aid to locating the optimum alignment, we define recombination operations that combine the best features of two distinct, fairly well refined alignments. These operations require that the sampler first generate a population of fairly well refined alignments starting from distinct, randomly selected points in alignment space. All of these input alignments must, of course, contain the same set of sequences.
The Recombine operation must be applied to two alignments that are fairly similar because the sampler needs to locate at least one crossover point between them. A crossover point is a set of positions, one position in each aligned sequence, such that the same set of blocks in the first alignment lie to the left of each of those points, while the same set of blocks in the second alignment lie to the right of each point. Because this requirement often proves difficult to satisfy for every sequence, we define the Recombine operation flexibly by allowing a certain number of sequences to violate this rule. In this case, violating sequences are removed prior to recombination and sampled back in afterwards (using the GAPALIGN operation).
The Intersect operation takes as input two distinct alignments and produces a new alignment containing only those aligned columns common to corresponding motifs in both input alignments. More precisely, we first find the common blocks shared by the two alignments, where a common block is defined as two aligned motif blocks (one in each alignment) that overlap within corresponding sequences. To allow for some flexibility, these are defined as blocks for which at least some minimum fraction (say 50%) of the sequences are consistently aligned in both input alignments. (Inconsistently aligned sequences are removed from the alignment prior to performing this operation.) Then, for each pair of common blocks, we find the sub-block shared by both blocks. Next, we create a new alignment containing only these Intersecting sub-blocks. Finally, sequences that were inconsistently aligned between the two starting alignments are sampled back into the resulting alignment. The Intersect operation allows the sampler to be reinitialized starting with a consensus alignment that aligns only those regions with high likelihood scores and eliminates those regions about which the sampler is less certain. Subsequent sampling will then extend these sub-blocks, add new blocks, and explore more extensively the alignment space.
Parameter settings for operations
There are no absolute rules on how to choose parameter settings for these algebraic operations, such as, for example, the maximum increase in motif length allowed during the MoveColumn operation or the number of disordered blocks to tolerate for the Recombine operation. We find, in fact, that it often matters little which settings are used and the slight degree to which it does matter depends on the particular protein class being analyzed. As a result, any biologically reasonable parameter settings work well. For example, since weakly conserved motifs are never a hundred residues long, motif blocks typically should be limited to no more than, say, fifty residues in length. Nevertheless our algorithm tolerates unreasonable parameter settings, because then it either simply rejects the corresponding alignment space transitions (though with some degradation in performance) and/or learns to avoid applying useless operations through its memory module, as described below.
High level sampling strategies
Having specified various operations on the alignment space, we now need to specify when and how often to apply them, as well as how to escape from local traps and thus to most rapidly converge on an optimum or nearly optimum alignment.
Providing the sampler with a memory
Since some of the alignment operations are computationally expensive, it would be helpful to avoid applying them over and over again when this proves to be unfruitful. For example, if the sampler has already converged on the correct number of motifs, applying the AddBlock operation may be a waste of time. On the other hand, we don't want to eliminate any operation entirely, as at some point it may be useful. To do this we define both short-term and long-term sampling memories. The short-term memory allows a rapid response to sudden changes while the long-term memory adds stability so that the sampler does not over respond to short term trends. Details are given in Methods.
Simulated annealing with a thermostat
Let the target alignment distribution be denoted generically as π (X). As the sampler converges on near optimum alignments, typically it has difficulty 'dropping' into the global optimum of π (X) because the chance of selecting the highest probability alignment is still very small due to the sheer number of near optimum alignments. This is true for the same reason that the most likely outcome of obtaining exactly 5,000 heads and 5,000 tails in 10,000 flips of a fair coin is extremely unlikely.
A standard way around this problem is to take power of π (X) to some exponent, renormalizing it and using the "powered-up" distribution, denoted as πT(X) ∝ π1/T (X) with the "temperature" parameter varying from a very large value to near-zero, for sampling. This procedure is a key component of simulated annealing [29], which has the same effect on sampling as lowering the temperature has on annealing of single stranded DNA into double stranded DNA in solution. By 'cooling' the system (i.e., letting T → 0), we raise the probability of high-density points and lower the probability of low-density points, so as to allow the best alignment to win out over alignments that are nearly as good. If the temperature is lower too abruptly, however, the sampler may get trapped in a sub-optimum alignment, so that the annealing strategy needs to be devised carefully.
We have built a 'thermostat' into the sampler that keeps track of variations in the (T = 1) probability densities of the sampled alignments. If the variance of log π (X) in a given number K of consecutive iterations at a given temperature is below a certain threshold (so that the posterior probabilities barely change), the sampler may be stuck in a (presumably local) optimum, and the thermostat raises the temperature a bit. On the other hand, if the log π (X) are varying wildly and, in particular, if they are greatly diverging from the best (i.e., highest probability) alignment found thus far, then the sampler may be wandering away from near optimum alignments and the thermostat lowers the temperature. This approach thus attempts to keep the sampler just above its 'glass transition temperature' [31], designated Tg. Details are given in Methods.
Since there are no absolute criteria for determining whether the sampler has actually found the optimum alignment, it is necessary to devise heuristics for terminating the computation. We retain the same criterion used in earlier Gibbs samplers, such that if the alignment fails to improve after a specified number of sampling cycles, then the program stops and returns the best alignment found. Since picking the right number of cycles depends heavily on the number and nature of the input sequences (as well as the user's patience), the user can modify this parameter. As an alternative strategy, two or more programs may also be run in parallel until they both converge on the same alignment.
Progressive refinement strategy
When painting a picture, it is helpful to first draw a rough sketch so that details will end up in the right place relative to each other. Similarly the sampler uses the following progressive refinement strategy to avoid being too "shortsighted."
There are five stages to this strategy. In the first stage, the sampler applies the Align operation, which aligns the sequences against contiguous ungapped blocks; it also applies compound ungapped motif operations. The initial numbers of block motifs and columns in each block are sampled from binomial distributions with means between roughly 5~10 blocks and 10~30 columns each, respectively. In the second stage, which is introduced after the sampler begins to converge on a local optimum under the ungapped block-motif model, elementary and compound column operations are introduced, which allow these ungapped blocks to 'fragment', thereby permitting nonconserved columns to be ignored by the alignment model (mathematical details are found in [6]). Recombination operations are also applied during and after this stage. In the third stage, the GapAlign operation based on a simple gapped sampling procedure [14] with very conservative gap penalties is introduced, which allows the sampler to add short gaps within motif blocks and to delete part or all of a block. In the fourth stage, the number of blocks is fixed (although other operations are retained) and recombination and simulated annealing procedures are used to help guide the sampler into a (hopefully) global optimum. These first four stages are implemented in the program GISMO (see below). A fifth stage, which is implemented in the program GARMA (see below), recombines a set of alignments independently found by GISMO and optimizes the recombinants using a GapAlign procedure based on the HMM model described above. (Here we apply another annealing strategy, termed prior annealing, where early on low HMM gap penalty priors are used to introduce gaps more liberally, and later high HMM gap penalty priors are used to eliminate less convincing gaps.) GapAlign sampling is performed by Viterbi alignment of the sequence against the HMM where the HMM emission and transition probably parameters are sampled from the posterior distribution. Afterwards the resultant alignment is either rejected or accepted based on our new scoring function g(A, Λ).
Manual application of alignment operations
Despite attempts to codify and fully automate optimization of a multiple sequence alignment, the algorithm may still create an alignment model that lacks certain properties observed to be biologically important for a particular class of proteins. Take the situation, for example, where a motif, which occurs as a single block in most of the proteins, is split in two by a sizable insertion in other proteins and where the sampler, due to the a priori parameter settings chosen before the analysis, fails to split this motif into two blocks. In this case, a biologically more meaningful alignment may be achieved by manually intervening to split this ungapped region (followed, ideally, by additional optimization via MCMC sampling perhaps using adjusted prior probabilities). To accommodate such tweaking, we thus allow manual application of various operations. We find that splitting and trimming of aligned blocks are particularly helpful in this regard. Such manually modified alignments then may be reintroduced into a population of similar alignments for recombination and selection via our genetic algorithm [5] followed by further optimization.
Implementation and examples
The theoretical concepts and strategies just described were implemented in the programs GISMO (Gibbs-like sampling with multiple operations), GARMA (genetic algorithm for recombinant multiple alignment) and GAMBIT (gapped alignment with MCMC-based indel tempering). GARMA recombines the output alignments provided by GISMO and then applies simulated annealing strategies on the recombinants. GAMBIT performs on a single alignment the same optimization procedures that GARMA performs on recombinants. Manual application of alignment operations may be performed using another program, TweakAln. These programs along with sample alignments are available from the authors. Multiple alignment of thousands of sequences in this way may take substantial time (e.g., overnight on a 10-processor Linux cluster), but this is not critical because, once performed for a particular protein class, such an alignment can be updated readily by seeding the sampler with a previously optimized alignment. Here we apply these programs to several large protein classes within the context of CHAIN analysis, which is our primary reason for generating such alignments.
Application to CHAIN analysis
CHAIN analysis both decomposes into distinct categories and quantifies the sequence constraints associated with conserved patterns in a multiple alignment. This yields evolutionary clues regarding the underlying structural mechanisms presumably preserving these patterns. Aspects of these mechanisms can be inferred by comparing category-specific selective constraints with known structures of members of the protein class being investigated, as illustrated in three recent publications [3,32,33].
'Contrast hierarchical alignments', such as are shown in Figs 4,5,6, are the primary output from CHAIN analysis. In constructing such an alignment, three sets of related sequences are multiply aligned: (i) a 'displayed set', (ii) a 'foreground set', which is a superset of the displayed set, and (iii) a 'background set'. The displayed set corresponds to the aligned sequences of interest within the foreground set (i.e., only the alignment for these sequences is actually shown). The foreground set corresponds to the sequences whose selective constraints are being measured. These are not shown explicitly, but rather are merely represented by conserved patterns and residue frequencies shown below the displayed alignment (as in Fig. 4A). The original CHAIN analysis procedure uses a modified version of the PSI-BLAST algorithm to align these sequences. Here these PSI-BLAST alignments are compared with motif-based foreground alignments created using GISMO, GARMA, GAMBIT, and TweakAln.
CHAIN analysis measures selective constraints in terms of the difficulty of randomly drawing the amino acids observed at a particular position in the foreground alignment from the distribution at that position in the background alignment. In the examples here, unless specified otherwise, the overall frequency of amino acids generally observed in proteins serves as an implicit background set at each position. Foreground positions with compositions closely resembling the background presumably are subject to little or no selective constraints, while positions with compositions strikingly different from (i.e., that contrast with) the background are subject to strong constraints. In Figs 4,5,6 these constraints are displayed in the histograms above the alignments.
Gα and P loop GTPases
We first examine in this way G protein α subunits. G proteins [17] are heterotrimers, consisting of an α, a β and a γ subunit, that mediate transduction of extracellular signals to the cellular interior. As do many members of the P loop GTPase class, the Gα subunit functions as a binary switch that is turned on by binding GTP in response to the signal and thereby relays this information to downstream components of the pathway. This switch is turned off by hydrolysis of GTP to GDP, an event mediated by GTPase activating proteins (GAPs).
Gα subunits are unique among such GTPase switches inasmuch as their GAP domain is contained within the Gα polypeptide chain itself rather than existing as a distinct protein. This unique arrangement presents particular difficulties for CHAIN analysis because, during subsequent iterations, the PSI-BLAST algorithm tends to slightly overextend the alignment beyond Gα's region of homology to other P loop GTPases and into the C-terminal region of the GAP domain. As a result, the foreground patterns for the Walker A motif are mistakenly aligned against the C-terminal end of the GAP domain (Fig. 4B). By contrast, the Gibbs sampler avoids this misalignment problem because it can readily jump over the internal GAP domain (Fig. 4A). This thus illustrates how our motif-based approach avoids a serious problem encountered by PSI-BLAST.
α,β-hydrolase fold enzymes
Similar misalignment problems may be encountered between motif regions even when the aligned proteins lack large inserts. This is seen, for example, when aligning α,β-hydrolase fold proteins [18,19], which correspond to a large class of enzymes possessing a catalytic triad (typically consisting of a serine, an aspartate and a histidine) at their active sites. These three residues are involved in an electron transfer mechanism and thus are generally very highly conserved, despite the often very weak pairwise similarity between many members of this class. CHAIN analyses of prolyl oligopeptidases reveals that our motif-based alignment assigns very strong selective constraints to all three of these catalytic residues, the aspartate and histidine of which are shown in Fig. 5A. This is as expected, because conservation of one member of the catalytic triad is highly correlated with conservation of the other two, as the α,β-hydrolase electron transfer mechanism requires all three residues. In contrast, the PSI-BLAST alignment assigns a strong selective constraint to the catalytic serine (not shown in Fig. 5) but much weaker constraints to these other two catalytic residues (Fig. 5B). This is because the PSI-BLAST algorithm finds it much easier to correctly align the catalytic serine but, due to weak sequence similarity, often either misaligns or fails to extend the alignment into the C-terminal region of this domain. (The fraction of sequences that fail to align with this region is indicated near the bottom of Fig. 5B). Thus our motif-based approach again provides a better measure of the selective constraints acting on these residues.
P97 an AAA+ ATPase
Improved identification of a short insertion within a motif by our approach is illustrated through CHAIN analysis of p97, a transitional endoplasmic reticulum AAA+ ATPase (recently reviewed in [20]). AAA+ ATPases are a large and diverse class of chaperone and chaperone-like proteins [14,24,25]. They are characterized by the presence of one or more AAA+ modules, each of which consists of an α,β-fold domain, which it shares with other P loop NTPases, followed by a helical bundle domain. P97 contains two AAA+ modules, designated D1 and D2; our analysis was performed on the D1 module, whose structure is known [34]. These AAA+ modules often associate to form homohexameric complexes such that a prominently conserved arginine (R362A in Fig. 6 and 7) and a conserved acidic residue (D333 in Figs 6 and 7) in one module are positioned near a Walker B conserved acidic residue (E305 in Fig. 7) and a bound ATP-Mg2+ in an adjacent AAA+ module.
When our motif-based approach was applied (with prior annealing) to AAA+ ATPases (Fig. 6A), it introduced within the Box VII motif of the p97 D1 module a two-residue insertion (most often a phe-gly; F360-G361 in Figs 6 and 7) immediately before a prominently conserved arginine (R362). By contrast, the PSI-BLAST alignment tends to misalign this region and, consequently, obscures both the two-residue insertion and the prominence of the conserved arginine (as indicated by the histogram height over this position; see Fig. 6B). The phenylalanine within this insert forms a CH-π interaction with an alanine (A409 in Figs 6 and 7) within the adjacent AAA+ module's three-helix bundle domain. Notably, an arginine often occurs at this alanine position in related AAA+ modules and is believed to sense bound ATP in the adjacent AAA+ module. (The region containing this arginine thus is termed the 'sensor II region'.) PSI-BLAST again does a poorer job aligning this sensor II arginine against A409 of p97 compared with our motif-based method. The improved motif-based alignment thus better reveals how the p97 AAA+ D1 module presumably utilizes an alternative configuration for sensing and responding to bound nucleotide relative to typical AAA+ modules (Fig. 7). In particular, two highly conserved p97 family-specific features – namely the phe-gly insertion, which is highly conserved in eukaryotes though replaced by a pro-gly in eubacteria and archaea, along with a third well conserved arginine directly preceding this insert (R359 in Figs 6 and 7) – are likely to perform an important role associated with p97's unique cellular function.
Conclusions
With a view to improving alignments for CHAIN analysis, we have enhanced our earlier motif-based methods by developing (i) a HMM for insertions and deletions within motifs, (ii) an expanded algebraic system of operations on multiple alignments and (iii) various annealing and sampling strategies that facilitate rapid convergence on optimum or near optimum alignments. Furthermore, our approach, due to its rigorous statistical basis, fills a gap left by current multiple alignment methods inasmuch as it aligns only those characteristics of the input sequences that may be justified statistically. Thus it is useful for statistical analysis of conserved patterns in multiple alignments. Our statistical model likewise provides objective criteria for evaluating curated alignments, thereby guiding manual application of various operations. In the future, our MCMC sampling methods could be used to estimate alignment uncertainties, which will be useful for estimating background amino acid frequencies for CHAIN analysis. These approaches also serve as a starting point for further enhancements that integrate MCMC sampling, HMM and PSI-BLAST methods, which, based on our earlier analyses [16], seem likely to improve both alignment accuracy and search sensitivity.
When this motif-based approach was applied to CHAIN analysis of families belonging to large and diverse protein classes, we found numerous examples, three of which are described here, where this does a better job of revealing subtle, biologically important sequence features than does PSI-BLAST. This is in large part due to the ability of our statistical model and sampling strategies to find weakly conserved islands of homology within a sea of essentially nonconserved regions. While this motif based approach will not become the default method for CHAIN analysis – especially considering that PSI-BLAST alignments also may be optimized using these approaches – it, nevertheless, often more accurately aligns very distantly related sequences and thus can provide a better measure of selective constraints in this situation.
Methods
HMM architecture
We model gaps within motif blocks through the HMM shown in Fig. 2. The corresponding probability matrix for transitions between HMM states internal to the ith motif is:
where 1 ≤ i ≤ m and 1 ≤ x <wi and where M, I, and D denote match, insertion and deletion states, respectively. The probability matrix for transitions between motifs is:
where 1 <i <m and where these transitions each emit a string of zero or more residues. Note that the contribution to the log-posterior probability of the lengths of these strings and of their emission probabilities (as well as those of M and I states) are specified by our ungapped statistical model [6], upon which this HMM is based and thus are unspecified by the HMM. Note also that the treatment we provide here easily can be generalized to cases where transitions I → D and D → I are allowed or where gap penalties are motif-specific.
Statistical inference of indel penalties
For a given alignment A, let f(A) be its log-posterior probability as in [6]. If we allow insertions and deletions within motifs, then each motif i within each sequence Sk is associated with a "path" through the HMM indicating its alignment against motif model Θi. Let the collection of these paths be Λ. Next, we denote the total number of transitions of type M → M, M → I, ..., by
Nmm, Nmi, Nmd, Nim, Nii, Ndm, Ndd.
It then follows that the likelihood of the gap parameters is
with independent prior distributions
(αo, βo, 1 - αo - βo) ~ Dirichlet(ao, bo, nm - ao - bo),
αe ~ Beta(ae, ni - ae), and βe ~ Beta(be, nd - be),
where ao, bo, nm, ae, ni, be, nd are prior pseudo counts given by the user. The corresponding maximum likelihood estimates (MLEs) are
The joint posterior distribution for the alignment and gap parameters is
g(A, Λ, α, β) ∝ P(S | A, Λ) × P(A) Λ h(Λ | α, β) P(α, β),
where P(S | A, Λ) × P(A) is computed the same way as in the original block-motif model [6], and
P(α, β) = Dirichlet(ao, bo, nm - ao - bo) × Beta(ae, ni - ae) × Beta(be, nd - be).
Given the alignment Λ, we have the conditional posterior distribution
Sampling on this distribution can be performed by drawing the following random variables:
Parameter collapsing
For computational efficiency, we integrate out the α and β to get
This gives rise to a new posterior g(A, Λ) with h(Λ) replacing h(Λ | α, β) P(α, β) in our previous formula [6] and frees us from having to fix or update the gap parameters. This also allows us to determine the optimum posterior gap penalties based on the sequence data.
Prior specifications
Suppose that we expect to see one insertion in every K1 residues and one deletion in every K2 residues. Then we set
and set no to reflect the strength of this conviction. We suggest using priors reflecting conservative gapping where, for example, K1 = K2 = 1000 and no = nM N, where is the total number of match positions in all of the motifs and N is the total number of aligned sequences.
For gap extension prior probabilities, if one expects to see an average insertion length of L1, and deletion length of L2, then we let
We set the prior pseudo counts n1 to be equal to the total number of expected insertions within motifs nM / K1. Likewise, n2 is set equal to the expected number of deletions nM / K2. In order to have different gap parameters for each motif, one need only keep specific counts of insertions and deletions for each motif, as the formula h(Λ) then applies to each motif individually, and we only need to multiply these h( ) functions together when computing the total 'penalty'.
The sampler's memory
For long-term memory we monitor among the sampler's previous iterations the number of times No (where typically, No = 25) that a type "o" operation has been applied and the number of times no that it was "successful" (i.e., resulted in an increase of the posterior probability). The same is done for short-term memory except that in this case we monitor the number of short-term successes mo over Mo previous applications (where typically Mo = 5). At the next iteration, we then assign a probability of applying this operation, where ws ≥ 0 and wl ≥ 0 are the weights given to the short and long-term memories, respectively, and where wp ≥ 0 specifies the minimum frequency at which this operation is applied. Typically, we set ws = wl = 1 and 0.2 ≤ wp ≤ 0.66, so that operations that previously proved to be unfruitful will only be performed about one-tenth to one-third as often as those that always yield improvements in the alignment.
The sampler's thermostat
We define an intuitive sampling temperature T' = 300/T and, thus, πT'(X) ∝ π300/T (X). On this 'pseudo-degrees-Kelvin' scale sampling from the true distribution π (X) (i.e., 300°) corresponds to sampling at 'room temperature'. After a period of sampling at room temperature until 'convergence', which is defined by the sampler's failure to improve the MAP after a specified number of iterations, simulated annealing is initiated. During this stage, whenever the probability densities of the sampled alignments averaged over say 20 iterations fluctuate by more than some maximal value, say Δlog(p) ≥ 50 nats, the temperature is lowered by 1–5°. If, on the other hand, the probability densities of the sampled alignments fluctuate on average less than some minimal value, say Δlog(p) ≤ 5 nats, the temperature is raised by say 1°. (The precise parameters used are not critical and may depend somewhat on the input sequence set.) This period of thermostatic sampling is again applied until convergence.
Authors' contributions
AFN developed the algorithmic strategies and early ad hoc approaches conceptually similar to the statistically rigorous procedures described in Methods, which were designed by JSL. AFN implemented the procedures and performed the sequence analyses. Both authors wrote and approved the final manuscript.
Acknowledgements
This work was supported by NIH grant LM06747 to A.F.N. and by NSF grants DMS0204674 and DMS0244638 to J.S.L.
Figures and Tables
Figure 1 The structure of a sequence containing m ungapped motifs denoted ak,1 to ak,m.
Figure 2 General architecture for our multiple motif HMM. States and transition probabilities between states are defined in Methods. Bold transition arrows emit residue strings.
Figure 3 An example of a series of compound operations applied to a motif based alignment. This series splits and fuses blocks in order to escape from kinetic traps in alignment space. See text for details.
Figure 4 CHAIN analysis of P loop GTPase-specific constraints acting on the Giα subunit. The displayed sequences are representatives of the Giα family of Gα proteins from distinct phyla. The foreground sequence set, which also includes P loop GTPases outside of the Giα family, are represented by the conserved residue patterns below the alignments. The number specified after the word 'pattern' gives the actual number of aligned sequences. (A) Motif-based contrast hierarchical alignment. Phyla are indicated in the leftmost column. Note that a purged foreground set [5] was used for the motif-based alignment in (A) and is thus smaller than the full set used by PSI-BLAST in (B). PSI-BLAST compensates for sequence redundancy by down weighting sequences [35] rather than by purging. The corresponding residue frequencies are given in integer tenths below the conserved patterns. For example, a '7' in integer tenths indicates that the corresponding residue directly above it occurs in 70–80% of the sequences. Deletion frequencies are similarly given in integer tenths (black; range 10–100%) or hundredths (gray; range 1–9%) as indicated. Histograms above the alignments display the strengths of the selective constraints acting at each position; aligned residues subject to the strongest constraints are highlighted for emphasis. For a complete description of CHAIN analysis alignments see [3]. (B) PSI-BLAST generated contrast hierarchical alignment. Sequence identifiers are indicated in the leftmost column. Note that, unlike the motif-based alignment in (A), PSI-BLAST misaligns the foreground set's Walker A region (represented by the patterns below the displayed alignment). In order to accentuate this alignment error, the background alignment in (B) consists of the foreground alignment in (A).
Figure 5 CHAIN analysis of α,β-hydrolase fold constraints acting on prolyl oligopeptidases. See legend to Fig. 4 for descriptions. (A) Motif-based contrast hierarchical alignment. The bars directly below the displayed sequences indicate motifs with the narrow line indicating a deletion relative to that motif and wide bars indicating catalytic residues. (B) PSI-BLAST generated contrast hierarchical alignment. The histogram heights for the catalytic aspartate and histidine in this alignment are shorter in this figure because, unlike the motif-based alignment in (A), the PSI-BLAST algorithm assigns relatively stronger constraints to the nucleophilic catalytic residue (not shown) that is much easier to align correctly.
Figure 6 CHAIN analysis of the D1 AAA+ module of p97 ATPases. See legends to Figs 4 and 5 for descriptions and text for discussion. (A) Motif-based contrast hierarchical alignment. The bars directly below the aligned sequences indicate motif regions; wide bars indicate residues shown in Fig. 7 and discussed in the text. (B) PSI-BLAST generated contrast hierarchical alignment.
Figure 7 Structural location of the two-residue insert in Box VII of p97. The structure of the first domain (D1) of p97 from rat [34] is shown. The corresponding alignment is shown in Fig. 6.
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| 15504234 | PMC538276 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Oct 25; 5:157 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-157 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-391556084510.1186/1742-4690-1-39ResearchHuman T lymphotropic virus type-1 p30II alters cellular gene expression to selectively enhance signaling pathways that activate T lymphocytes Michael Bindhu [email protected] Amrithraj M [email protected] Hajime [email protected] Lei [email protected] Gerold [email protected] Kathleen [email protected] Michael D [email protected] Center for Retrovirus Research and Department of Veterinary Biosciences, The Ohio State University, Columbus, Ohio 43210, USA2 Department of Statistics, College of Mathematical and Physical Sciences, The Ohio State University, Columbus, Ohio 43210, USA3 Department of Microbiology and Immunology, State University of New York Upstate Medical University, Syracuse, New York 13210, USA4 Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University, Columbus, Ohio 43210, USA5 Comprehensive Cancer Center, The Arthur G. James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, Ohio 43210, USA6 Department of Safety Assessment, Merck &Co., Inc. WP45-224, West Point PA 19486, USA2004 23 11 2004 1 39 39 19 8 2004 23 11 2004 Copyright © 2004 Michael et al; licensee BioMed Central Ltd.2004Michael 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
Human T-lymphotropic virus type-1 (HTLV-1) is a deltaretrovirus that causes adult T-cell leukemia/lymphoma and is implicated in a variety of lymphocyte-mediated disorders. HTLV-1 contains both regulatory and accessory genes in four pX open reading frames. pX ORF-II encodes two proteins, p13II and p30II, which are incompletely defined in the virus life cycle or HTLV-1 pathogenesis. Proviral clones of the virus with pX ORF-II mutations diminish the ability of the virus to maintain viral loads in vivo. Exogenous expression of p30II differentially modulates CREB and Tax-responsive element-mediated transcription through its interaction with CREB-binding protein/p300 and represses tax/rex RNA nuclear export.
Results
Herein, we further characterized the role of p30II in regulation of cellular gene expression, using stable p30II expression system employing lentiviral vectors to test cellular gene expression with Affymetrix U133A arrays, representing ~33,000 human genes. Reporter assays in Jurkat T cells and RT-PCR in Jurkat and primary CD4+ T-lymphocytes were used to confirm selected gene expression patterns. Our data reveals alterations of interrelated pathways of cell proliferation, T-cell signaling, apoptosis and cell cycle in p30II expressing Jurkat T cells. In all categories, p30II appeared to be an overall repressor of cellular gene expression, while selectively increasing the expression of certain key regulatory genes.
Conclusions
We are the first to demonstrate that p30II, while repressing the expression of many genes, selectively activates key gene pathways involved in T-cell signaling/activation. Collectively, our data suggests that this complex retrovirus, associated with lymphoproliferative diseases, relies upon accessory gene products to modify cellular environment to promote clonal expansion of the virus genome and thus maintain proviral loads in vivo.
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Background
Human T-lymphotropic virus type 1 (HTLV-1), the first characterized human retrovirus, causes adult T cell leukemia/lymphoma (ATL) and is associated with several lymphocyte-mediated disorders such as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) [1]. Mature CD4+ T lymphocytes are the primary targets of HTLV-1 infection [2]. Although the mechanism by which the virus causes oncogenic transformation of host T lymphocytes is incompletely understood, altered gene expression has been associated with the initiation or progression of ATL [3]. This complex retrovirus encodes structural and enzymatic gene products, as well as regulatory and accessory proteins from open reading frames (ORF) in the pX region between env and the 3' long terminal repeat (LTR) of the provirus [4]. The well characterized Rex and Tax proteins are encoded in the ORF III and IV respectively. Rex is a nucleolus-localizing phosphoprotein, involved in nuclear export of unspliced or singly spliced viral RNA [5]. Tax is a nuclear and cytoplasmic localizing phosphoprotein that interacts with cellular transcription factors and activates transcription from the viral promoter, Tax-responsive element (TRE) and enhancer elements of various cellular genes associated with host cell proliferation [6]. Emerging evidence has documented the role of pX ORF I and II gene products in the replication of HTLV-1 [7,8]. There are four proteins expressed from these ORFs – p12I, p27I, p13II, and p30II. pX ORFs I and II mRNAs are present in infected cell lines and freshly isolated cells from HTLV-1-infected subjects [9], as well as in ATL and HAM/TSP patients [10]. Antibodies [11,12] and cytotoxic T cells [13] that recognize recombinant proteins or peptides of the pX ORF I and II proteins are present in HTLV-1 infected patients and asymptomatic carriers.
Using molecular clones of HTLV-1 with selective mutations of ORF I and II, we have tested the requirement of p12I and p13II/p30II in the establishment of infection and maintenance of viral loads in a rabbit model of infection [14-16]. ORF II protein p30II contains a highly conserved bipartite nuclear localization signal (NLS) and localizes within the nucleus of cells [17-19]. In addition, p30II contains serine- and threonine-rich regions with distant homology to transcription factors Oct-1 and -2, Pit-1, and POU-M1 [20]. Previous studies from our laboratory have demonstrated that p30II also co-localizes with p300 in the nucleus and physically interacts with CREB binding protein (CBP)/p300 and differentially modulates cAMP responsive element (CRE) and TRE mediated transcription [18,21]. Recent reports also indicate a post-transcriptional role of HTLV-1 p30II and HTLV-2 p28II (homologous protein encoded in the HTLV-2 pX ORF II region), in modulating the export of tax/rex RNA from the nucleus [22,23]. Therefore, p30II appears to be a multi-functional protein with transcriptional and post-transcriptional roles in regulating viral gene expression. Based on these reports, we hypothesized that p30II functions as a regulator of cellular and viral gene expression to promote HTLV-1 replication.
Gene arrays have primarily been employed to study the changes in gene expression profile of HTLV-1-immortalized and transformed cell lines or in cells from ATL patients and attempts to test the influence of individual HTLV-1 viral proteins on cellular gene expression have been limited to Tax [3,24-27]. Herein we used the Affymetrix U133A human gene chip to confirm the role of p30II as a regulator of gene expression and identified several novel and important alterations in gene expression profiles, unique to cell cycle regulation, apoptosis and T cell signaling/activation. In addition, using semi-quantitative RT-PCR, we have confirmed the expression of multiple genes modulated by p30II in Jurkat T cells and primary CD4+ T lymphocytes. We then tested the influence of p30II in T cell signaling using reporter assays representing critical T lymphocyte transcription factors. This is the first report that demonstrates the role of p30II as an activator of key transcription factors involved in T cell signaling/activation. Together, our data suggests that HTLV-1, a complex retrovirus associated with lymphoproliferative disorders, uses accessory genes to promote lymphocyte activation to enhance clonal expansion of infected cells and maintain proviral loads in vivo.
Results
p30II and Analysis of Cellular Gene Expression in Jurkat T Lymphocytes
Stable expression of HTLV-1 p30II in Jurkat T lymphocytes was established using recombinant lentiviruses (Fig. 1). At 10 days post-transduction, GFP expression was greater than 95% in Jurkat T lymphocytes transduced with recombinant lentivirus expressing GFP alone (controls) or p30II and GFP (samples) (Fig. 2). RT-PCR was used to confirm the expression of p30II mRNA in the sample cells and absence of p30II mRNA expression in control cells (Fig. 2). p30II protein expression was also confirmed by western immunoblot assay (data not shown) using methods as previously reported [28]. Differential gene expression and comparative analysis was done to identify probes with at least 1.5 fold difference in expression between control and p30II and verified for cluster formation [29]. Quality control criteria evaluations included comparison of the ratios of 3' signal to 5' signal of two housekeeping genes, beta-actin and GAPDH, which were between 0 and 3. Additional hybridization controls were used in each array and included BioB, BioC, BioD, and Cre. These controls were all present and in a linear relationship of intensity. Quantitative RNA levels were determined by comparing the average differences representing the perfectly matched minus the mismatched for each gene-specific probe set before analysis with data mining software to identify probes with at least 1.5 fold differences [29,30].
Figure 1 Schematic illustration of lentiviral vectors expressing both p30HA and GFP (sample vector) as bicistronic messages and GFP alone (control vector) from elongation factor 1 alpha promoter. Abbreviations: LTR – Long Terminal Repeats; RRE – Rev Response Element; EF1 α – Elongation Factor 1 alpha promoter; IRES – Internal Ribosome Entry Site; WPRE – Woodchuck Hepatitis Post-transcriptional Regulatory Element.
Figure 2 Triplicate p30II samples express GFP and p30II while triplicate controls express only GFP. (A) Flow cytometric analysis illustrating the expression of GFP in Jurkat T cells 10 days post spin-infection with lentiviral vectors. Both sample (expressing p30II and GFP) and control (GFP alone) group contains relatively high and similar levels of GFP. (B) RT-PCR demonstrating the expression of p30II in Jurkat T cells 10 days post spin-infection with lentiviral vectors. Jurkat T cells spin-infected with sample vector express p30II while the control vector spin-infected cells do not express p30II. RT-PCR was performed with triplicate samples and controls. GAPDH was used as a control for the integrity of the message. (C) Representative western blot showing p30II expression from cell lysate (p30II migrates at ~28 kD). M = Mock vector infected cell lysate, p30 lv = p30 lentivirus vector infected cell lystate, MW = biotin molecular weight markers.
We then categorized genes deregulated by HTLV-1 p30II into those upregulated or downregulated in expression. We further grouped genes deregulated by p30II based on their functions, such as apoptosis, cell cycle, cell adhesion, transcription/translation factors and T cell activation or cell signaling. In all the categories, p30II was an overall repressor of cellular gene expression, while selectively increasing the expression of certain key regulatory genes (Table 1, see Additional file 1). The total number of genes of known biological or molecular function that were decreased in expression was 318 compared to 126 genes that were increased in expression.
p30II Modulates Multiple Cellular Gene Networks
Based on changes in gene expression in p30II expressing cells (Table 1), p30II would be predicted to modulate apoptosis. These include Bcl-2 related/interacting genes such as anti-apoptotic Bcl-2-related protein A1, anti-apoptotic MCL1, cell-death regulator Harakiri, apoptotic protector BNIP1 (downregulated) and pro-apoptotic BIK (upregulated). In addition, p30II expression correlated with downregulation of genes associated with Fas mediated apoptosis pathway such as tumor suppressing subtransferable candidate 3 and TNF receptor superfamily member 25. p30II expression was also associated with decreased expression of caspases (2 and 4) and increased expression of genes associated with the DNA fragmentation pathway (CIDE-B and CIDE-3). In addition, p30II expression correlated with decreased expression of many other apoptosis related genes including CD28, Lck, cyclin B1, Cullin 5, Adenosine A2a receptor, TAF4B and NCK-associated protein 1.
Multiple genes involved in cell cycle regulation were altered in p30II expressing Jurkat T lymphocytes. These include checkpoint suppressor 1, cytosolic branched-chain amino acid transaminase 1, histone deacetylase 6, cyclin B1, WEE1 kinase, CDC14A, Lck, JAK2, GAS7, BZAP45, Cullin, Rab6 GTPase activating protein (downregulated) and TERF1, AKAP8, DDX11, MSH2 and JUN-D (upregulated). Another gene down regulated by p30II expression was MDM2, which is over expressed in certain types of leukemia [31] and capable of enhancing the tumorigenic potential of cells by inhibiting p300/PCAF mediated p53 acetylation [32].
p30II expression was associated with altered expression of several genes involved in cell-to-cell adhesion. These include decrease in integrin (integrin β8) immunoglobulin (MADCAM1), a counter-receptor for P-selectin (SELPLG), cadherin (desmocollin 3), protocadherin (PC-LKC) liprin (PPF1BP1), CD84/Ly-9, CD58, CD43/sialophorin and glycosyl-phosphatidyl-inositol phospholipase D1. Expression of p30II correlated with increase in integrin receptor α1 subunit and KIT ligand.
A number of genes encoding transcriptional control factors or regulators of transcription were repressed in p30II expressing Jurkat T lymphocytes. These included decreased expression of TATA-binding protein associated factor 4 (TAF4), two co-repressors (Enolase-1 and Chromosome 19 ORF2 protein), a novel specific coactivator for mammalian TEFs, namely TONDU [33], homeo box genes (mesenchyme homeo box 1, homeobox A1), T-box genes (T-box 21) and proteins containing helix-loop-helix domain, which are known to be critical in cell growth/differentiation and tumorigenesis (neuronal PAS domain protein 2, Myc-associated factor protein, inhibitor of DNA binding-3). Additionally, p30II expression correlated with down regulation of zinc finger proteins (zinc finger protein 36), a group of transcription regulators proposed to be candidates in malignant disorders [34] and coiled coil proteins (JEM-1). p30II was also associated with downregulation of many genes with positive transcriptional effects (including SEC14-like 2, Nurr 1, CITED2/MRG1, LXR alpha and SMARCA2). Reduced expression of HDAC6, a histone deacetylase and nuclear receptor coactivator 3 (CBP interacting protein) with histone acetyltransferase and pCAF/CBP recruiting abilities [35] are particularly interesting, since p30II contains multiple highly conserved lysines, which could play a role in acetylation [18,21]. Expression of p30II was also associated with decrease in GAS 7, which has sequence homology to Oct and POU family of transcription factors [36] and decreased expression of translation initiation factor 2 (IF2) and eukaryotic translation elongation factor 1δ (EEF1D). In contrast, p30II expression in Jurkat T lymphocytes was associated with an increase in expression of eukaryotic translation elongation factor 1α (EEF1A2), a putative oncogene [37], and enhanced expression of HTLV enhancer factor, Jun-D, TAF1C, Kruppel-type zinc finger, PQBP1, AF4 and SOX4.
p30II Expression Alters Patterns of T-Cell Signaling Gene Networks
Genes involved in T-cell signaling were differentially affected by p30II expression. Expression of p30II was associated with decreased expression of CD28, a co-stimulatory molecule with a distinct role in T lymphocyte activation [38] and reduced gene expression of CD46 and Lck tyrosine kinase, a member of the Src family of tyrosine kinases activated by T cell surface receptors [39]. In contrast, cells expressing p30II had enhanced Vav-2 and CD72 gene expression. Additionally, p30II expression correlated with decrease in the level of CHP, an endogenous calcineurin inhibitor, which would be predicted to promote NFAT expression by p30II (see below). Moreover, p30II expression was associated with increased expression of Jun-D and c-Fos, suggesting activation of AP-1 mediated transcription. p30II expression was associated with decreased expression of protein kinase D (PKD), which negatively modulates JNK signaling pathway [40], mediates cross-talk between different signaling systems, and is critical in processes as diverse as cell proliferation and apoptosis [41]. Interestingly, in Jurkat T lymphocytes expressing p30II, there were no detectable levels of I kappa B kinase gamma (IKKγ), which is important for NF-κB signaling in response to both T cell activation signals and Tax [42]. p30II expression was associated with increased Hematopoetic Progenitor Kinase-1 (HPK-1), a known NF-κB activator [43]. p30II expression was also associated with decreased Ras GRP2, a guanyl nucleotide exchange factor that increases Ras-GTP, suggesting a decrease in the level of activated Ras (Ras-GTP). Seminquantitative RT-PCR analysis in Jurkat T lymphocytes and primary CD4+ T lymphocytes correlated directly with the gene array and confirmed the altered expression of each of three selected genes involved in these T cell activation/signaling pathway (Fig. 3A through 3D).
Figure 3 Semiquantitative RT-PCR of CHP, JUN-D and NFATc in controls and p30II expressing Jurkat T lymphocytes (A and B) and primary CD4+ T lymphocyte (C and D) samples. PCR products were separated by electrophoresis (A and C), normalized to GAPDH and quantified by densitometry (B and D). In panel B, dark grey bars indicate indicate p30II expressing cells and light grey bars indicate control (empty vector) cells. In panel D, dark grey bars indicate indicate p30II expressing cells and white bars indicate control (empty vector) cells. Data points are mean of triplicates. CHP was downregulated while JUN-D and NFATc was upregulated by p30II. Fold decrease/increase in activity in the presence of p30II are indicated above each bar.
p30II Influences NFAT, NF-κB and AP-1-mediated Transcription in Co-Stimulated Jurkat T lymphocytes
Using luciferase reporter assays, we directly tested the ability of p30II to influence NFAT, NF-κB and AP-1 driven transcription, all key transcription factors in T cell activation. Although p30II expression overall resulted in a repressive pattern of gene expression, our data indicated that the viral protein selectively alters the cellular environment to promote NFAT, NF-kB and AP-1 mediated transcription in Jurkat T cells undergoing co-stimulation. We transiently co-transfected NF-κB, AP-1, or NFAT luciferase reporter plasmids and a p30II expression plasmid into Jurkat T lymphocytes, and then stimulated the cells with well established co-stimulators of T cells including PMA or ionomycin or both, anti-CD3 or anti-CD28 or both. p30II increased the NFAT driven luciferase reporter gene activity from 2.2 to 10.7 fold depending on co-stimulatory treatment (Fig. 4A), indicating that p30II effectively enhanced NFAT driven transcription, when stimulated with ionomycin or anti-CD3. NF-κB driven luciferase reporter gene activity was increased from 3.1 to 11.4 fold, depending on co-stimulation (Fig. 4B). However, p30II only modestly increased AP-1-driven luciferase reporter gene activity from 1.2 to 5.2 fold in the presence of co-stimulator treatments (Fig. 4C). Collectively, these data indicate that p30II selectively promotes NFAT, NF-kB and AP-1 mediated transcription in Jurkat T lymphocytes undergoing co-stimulation and thus would be predicted to favor cell survival or influence cell activation.
Figure 4 p30II activates NFAT, AP-1 and NF-κB transcriptional activity in Jurkat T lymphocytes. Black bars indicate control and grey bars indicate p30II. Data points are mean of triplicate experiments. Fold increase in activity in the presence of p30II is indicated above each bar. p30II increased the NFAT-luc activity from 2.2 to 10.7 fold depending on co-stimulatory treatment e.g., PMA, ionomycin, CD3, CD28 etc. (A), p30II increased NF-κB-luc activity from 3.1 to 11.4 fold (B) and modestly increased the AP-1 driven luciferase reporter gene activity from 1 to 5 fold in the presence of co-stimulator treatments (C).
Discussion
Our study represents a comprehensive analysis of gene expression patterns influenced by a retrovirus accessory protein in T lymphocytes. Overall, this study confirmed that p30II is a regulator of cellular genes, either directly or indirectly, and also identified several potential new functional roles for p30II. Our approach included methods to strengthen the reliability of our data by (a) use of triplicate samples and appropriate controls (b) use of multiple software for data analysis (c) minimization of nonspecific hybridization and background signals by using Affymetrix chip [44] (d) use of a well-characterized T lymphocyte system (Jurkat) and (e) verification of microarray data by semiquantitative RT-PCR in Jurkat T lymphocytes and primary CD4+ T lymphocytes (f) validation of microarray data by reporter assays, all of which were consistent with our micro array findings. Some of these findings are consistent with previous studies using gene arrays to test HTLV-1-transformed cell lines. For example, HTLV-1 infected cell lines contain low levels of caspase-4 and high levels of JUN [3] and cyclin B1 levels are low in HTLV-1 leukemic T cells [45]. Our study represents a comprehensive analysis of gene expression patterns influenced by a retrovirus accessory protein in T lymphocytes. An important caveat our approach of using gene arrays is that this method, while useful to indicate if an individual gene is increased or decreased in expression and therefore predicted to influence a cell signaling pathway, does not reveal the composite of transcription regulation in vivo. This may explain, in part, why our reporter gene data, which is more dependent upon the availability of transcription factors in total, may not directly, correlate to an individual gene expression result.
Others have used gene array approaches to study HTLV-1-related changes in gene expression. Harhaj et al [24] studied the gene expression in HTLV-1 mediated oncogenesis using human cDNA array analysis of normal and HTLV-1 immortalized T cells and found that the expression of a large number of genes involved in apoptosis were deregulated in HTLV-1 immortalized T cells. Subsequently, the same type of cDNA arrays were employed by De La Fuente et al [25] to study upregulation of a number of transcription factors in HTLV-1-infected cells, including zinc fingers, paired domains, and basic helix-loop-helix (bHLH) proteins. Gene expression profiles of fresh peripheral blood mononuclear cells (PBMC) from acute and chronic ATL patients were used to identify the genes associated with progression of ATL including a T cell differentiated antigen (MAL), a lymphoid specific member of the G-protein-coupled receptor family (EBI-1/CCR7) and a novel human homolog to a subunit (MNLL) of the bovine ubiquinone oxidoreductase complex [26]. Using NIH OncoChip cDNA arrays containing 2304 cancer related cDNA elements, Ng et al, 2001 [27] compared normal and Tax-expressing Jurkat T lymphocytes and identified Tax induced changes in gene expression, associated with apoptosis, cell cycle, DNA repair, signaling factors, immune modulators, cytokines, growth factors, and adhesion molecules. Recently, Affymetrix, GeneChip microarrays containing oligonucleotide hybridization probes representative of ~7000 genes were used to compare the expression profiles of normal activated peripheral blood lymphocytes to HTLV-I-immortalized and transformed cell lines [3]. In this study, by employing a gene chip representing ~33000 genes, we tested the role of p30II on cellular gene expression profile of a larger number of genes. Gene expression data from cells in which exogenously expressed proteins, which may also be tagged for identification, may not represent what would occur during the natural infection. However, these patterns provide important clues for functional alterations which may occur during the viral protein expression. The "natural" or in vivo amount of expression of regulatory and accessory gene products encoded from the HTLV-1 pX gene region has not been clearly defined. Recent studies using RT-PCR analysis of cell lines suggests that pX ORF 1 and 2 mRNA is expressed at significantly lower amounts compared to tax/rex mRNA, full length genomic, or singly spliced envelope mRNA [46].
Expression from the IL-2 promoter requires binding of several transcription factors, including NFAT, AP-1 and NF-κB. NFAT is vital to proliferation of peripheral lymphocytes for HTLV-1 infection [47] while AP-1 is linked to the dysregulated phenotypes of HTLV-1 infected T cells [48] and malignant transformation [49]. Activation of AP-1 occurs through Tax-dependent and independent mechanisms in HTLV-1-infected T cells in vitro and in leukemia cells in vivo [48]. NF-κB is highly activated in many hematopoietic malignancies, HTLV-1 infected T cell lines and in primary ATL cells, even when Tax expression levels are low [49] and due to its anti-apoptotic activity, it is considered to be a key survival factor for several types of cancer. Ours is the first report demonstrating the ability of an HTLV-1 accessory protein to have broad modulating activities on the transcriptional activity of NF-κB, NFAT and AP-1. Further studies will be required to confirm the mechanisms of p30II in T cell activation and to test the comparative role of p30II expression in context to other regulators of transcription such as Tax.
We have previously reported that another HTLV-1 accessory protein p12I stimulates NFAT mediated transcription, when stimulated with PMA, indicating that p12I acts synergistically with Ras/MAPK pathway to promote NFAT activation and thus may facilitate host cell activation and establishment of persistent HTLV-1 infection [50]. Our data indicates that p30II enhanced NFAT driven transcription significantly when stimulated with ionomycin or CD3, and therefore likely uses a different mechanism than p12I. To modulate NFAT driven transcription and subsequent T cell activation/signaling, it is possible that these two accessory proteins act synergistically. AP-1 is able to interact with transcriptional coactivator CBP/p300, as well as viral CREs and mediate HTLV-1 gene expression [48,51,52]. Intriguingly, we have previously reported that p30II interacts with CBP/p300 at the KIX domain of CBP, influences CRE and TRE mediated transcription [18] and disrupts CREB-Tax-p300 complexes on TRE probes [21]. NF-κB and NFAT [53] are also known to interact with the transcriptional coactivator CBP/p300. Therefore, it is possible that p30II modestly activates the transcriptional activity of NFAT, NF-κB and AP-1, at least in part, by its interaction with CBP/p300. In parallel, the HIV-1 accessory protein Vpr causes a modest increase in NF-κB, NFAT and AP-1 mediated transcription in a cell-cycle dependent fashion by causing G2 arrest [54]. Similar to HIV-1 Vpr, our gene array findings indicate that HTLV-1 p30II expression was associated with decrease in cyclin B1 and WEE1 kinase levels, suggesting that p30II expression likely cause G2 arrest and may thus modulate transcriptional activity of NFAT, NF-κB and AP-1, in a cell-cycle dependent manner. An important caveat of our data is the use of Jurkat T cells, which while representing human T cells, are IL-2 independent and transformed. Thus, differences in responsive genes expected from non-transformed T cells for the transcription factors screened in our study may be due to our cell line model.
HTLV-1 mediated interference with normal T-cell apoptosis is thought to be a mechanism of tumorigenicity [2], but specific mechanisms by which HTLV-1 infection or any particular HTLV-1 gene products influence on T-cell survival are not fully understood. Similar to the effect of HTLV-1 Tax on apoptosis related genes [24,27], we found that p30II also deregulates multiple genes resulting in possible pro-apoptotic and anti-apoptotic effects. Since apoptosis is a well-known mechanism of cellular defense against viral infection, a possible role of p30II in lymphocyte apoptosis might correlate with the requirement of p30II in maintaining proviral loads in vivo [15]. Previous studies indicate that several members of the cell cycle machinery have altered expression in HTLV-1 infected cells [3]. Several recent studies have reviewed the aberrations in cell cycle caused by HTLV-1 Tax [6,55].
p30II appears to regulate viral gene expression and modulate immune response. We have previously reported that, p30II activated HTLV-1 LTR at lower concentrations and repressed at higher concentrations [18]. Interestingly, p30II expression was associated with downregulation of lck (p56), which suppresses the HTLV-1 promoter [56] and upregulate HTLV enhancer factor, which is known to bind to LTR at a region involved in regulation of gene expression by the ets family of transcription factors [57]. Additionally, p30II expression was associated with altered expression of cellular genes involved in immune modulation such as CD46, CD43, CD58, IFNγ and CD72.
Conclusions
Overall, this study supports our earlier reports on the repressive role of HTLV-1 p30II in gene expression [18,21,23] and sheds light on potential mechanisms by which p30II functions in HTLV-1 replication or leukemogenesis. Our data confirmed that p30II while a negative regulator of cellular genes, also influences T cell signaling, apoptosis and the cell cycle. Many of the effects of p30II appear to overlap or counteract the influence of other HTLV-1 regulatory proteins like Tax or other accessory proteins such as p12I. It is possible that these proteins act coordinately or synergistically. We postulate that, by modulating the expression of various HTLV-1 proteins, the virus employs selective use of these viral proteins during different stages of the infection. However, since information on the expression profile of HTLV-1 proteins during stages of the infection is limited, additional studies are required to explore this possibility. Such future studies might provide new directions in the development of therapeutic interventions against HTLV-1 disorders, which are associated with immune-mediated mechanisms.
Methods
Lentiviral vectors and other plasmids
The plasmid pWPT-IRES-GFP was generated by cloning the internal ribosome entry site (IRES) sequence from pHR'CMV/Tax1/eGFP [58] (Gerald Feuer, SUNY, Syracuse) into pWPT-GFP plasmid (Didier Trono, University of Geneva). Subsequently, the plasmid pWPT-p30IIHA-IRES-GFP was created by cloning the p30II sequence from ACH [59] with the downstream influenza hemagglutinin (HA1) tag (Fig. 1). Sanger sequencing confirmed both the plasmids to have the correct sequence and were in frame. GFP and p30IIHA expression were confirmed by fluorescence activated cell sorting (FACS) analysis (Beckman Coulter, Miami, FL) and western blot respectively. GFP expression from each of the plasmids was confirmed by flow cytometry (Beckman Coulter) and the p30IIHA expression from pWPT-p30IIHA-IRES-GFP plasmid was confirmed by western blot using mouse monoclonal anti-hemagglutinin antibody (1:1000) (Covance, Princeton, NJ) as described previously [18,21]. The plasmid pME-p30IIHA was created by cloning p30II sequence from ACH with HA1 tag, into pME-18S (G. Franchini, NIH). Other plasmids used include previously reported pRSV-βGal [18] and AP-1, NF-κB and NFAT-luciferase reporter plasmids [50].
Recombinant lentivirus production and infection of Jurkat T lymphocytes and primary CD4+ T lymphocytes
Recombinant lentiviruses were produced by transfecting pHCMV-G, pCMVΔR8.2 and pWPT-p30IIHA-IRES-GFP (sample) or pWPT-IRES-GFP (control) as described previously [60]. Briefly, 293T cells (5 × 106) were seeded in a 10-cm dish and transfected the following day with 2 μg of pHCMV-G, 10 μg of pCMVΔR8.2 and 10 μg of pWPT-p30IIHA-IRES-GFP or pWPT-IRES-GFP using the calcium phosphate method. Supernatant from 10 to 20 dishes was collected at 24, 48 and 72 h post transfection, cleared of cellular debris by centrifugation at 1000 rpm for 10 min at room temperature and then filtered through a 0.2 μm filter. The resulting supernatant was then centrifuged at 6,500 g for 16 h at 4°C. The viral pellet was suspended in cDMEM (DMEM containing 10% FBS and 10% streptomycin and penicillin) overnight at 4°C and the concentrated virus was aliquoted and stored at -80°C. To determine the virus titer, serial dilutions of the virus stock were used to spin infect 293T cells and 48 h post infection, eGFP expression and p30II expression was measured by flow cytometry and RT-PCT respectively. Briefly, on the day before infection, 293T cells (1 × 105) were seeded in a 6-well plate. The medium was removed the following day and the cells were then incubated with the diluted virus containing 8 μg/ml polybrene (Sigma, St. Louis, MO). Cells were then spin-infected by centrifugation at 2700 rpm for 1 h at 30°C, supplied with fresh medium and cultured for 48 h. Then cells were treated with trypsin (Invitrogen, Carlsbad, CA), pelleted and resuspended in D-PBS (Invitrogen) for fluorescence activity cell sorting (FACS) analysis on an ELITE ESP flow cytometer (Beckman Coulter). One × 106 cells were used to perform western blot to detect the expression of p30II HA. Jurkat T lymphocytes (clone E6.1, American Type Culture Collection) were transduced with recombinant virus at multiplicity of infection of 4 in the presence of 8 μg/ml polybrene (Sigma) and spin-infected at 2700 rpm for 1 h at 25°C. Primary CD4+ T cells were extracted using dynabead CD4 positive isolation kit (Dynal Biotech, Lake Success, NY) according to manufacturer's instructions. Primary CD4+ T cells were stimulated with Phytohemagglutinin (PHA) for 48 h, transduced with recombinant virus at multiplicity of infection of 20 in the presence of 8 μg/ml polybrene (Sigma) and spin-infected at 2700 rpm for 1 h at 25°C. At 10 days post-transduction, GFP expression of controls and samples were verified to be above 90% by FACS analysis and the presence of p30II mRNA expression in samples (and absence in controls) was verified by RT-PCR (Fig. 2).
Western Immunoblot assay
Cells were lysed in buffer containing phosphate-buffered saline, 1% Nonidet P-40, 0.5% sodium deoxycholate, and 0.1% sodium dodecyl sulfate (SDS). Cell lysates were prepared by centrifugation at 14,000 rpm (Beckman) for 20 min at 4°C. Protein concentrations were determined by BCA assay (micro-BCA Protein Assay®, Pierce, IL). Equal amounts of proteins were mixed with Laemmli buffer (62.5 mM Tris [pH 6.8], 2% SDS, 10% glycerol, 0.2% bromophenol blue, 100 mM dithiothreitol). After boiling for 5 min, samples were electrophoresed through 12% polyacrylamide gels. The fractionated proteins were transferred to nitrocellulose membranes (Amersham Pharmacia Biotechnology) at 100 V for 1 h at 4°C. Membranes were blocked with 5% non-fat dry milk in PBS with 0.1% Tween for 16 hours, then incubated with mouse anti-HA monoclonal Ab (1:1,000) (clone 16B-12) (Covance Research Products, Princeton, NJ), for overnight at 4°C, and developed by using horseradish peroxidase-labeled secondary Ab (1:1,000) and enhanced chemiluminescence reagent (Cell Signaling Technology, Beverly, MA).
Probe preparation and microarray analysis
According to the instructions of manufacturers, total cellular RNA was isolated from transduced Jurkat T lymphocytes using RNAqueous (Ambion, Austin, TX). To test the concentration and purity of the RNA samples, absorbance at 260 nm and 280 nm were measured and the 260/280 ratio was calculated using a spectrophotometer (Genequant, Amersham Pharmacia, Piscataway, NJ). The 260/280 ratio of all the RNA samples were between the range of 1.9–2.1. The probe preparation for GeneChip was performed according to the Affymetrix GeneChip Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA). Briefly, cDNA was synthesized using genechip T7-Oligo (dT) promoter primer kit (Affymetrix) and superscript double stranded cDNA synthesis kit (Invitrogen), according to the manufacturers instructions. cDNA cleanup was done using Genechip Sample Cleanup module (Affymetrix). In vitro transcription was performed on the cDNA to produce biotin-labeled cRNA with ENZO RNA Transcript labeling kit (Affymetrix), according to the manufacturer's instructions. Complimentary RNA (cRNA) cleanup was performed using Genechip Sample Cleanup module (Affymetrix). The quality of total RNA and biotin-labeled cRNA of all the samples and controls were checked by calculating the ratio of absorbance at 260 nm and 280 nm (between 1.9 to 2.1) using a spectrophotometer (Genequant) and agarose gel electrophoresis. The labeled cRNA was fragmented to 50–200 nucleotides, and hybridized to U133A arrays (Affymetrix) using GeneChip® Hybridization Oven (Affymetrix). Arrays were washed and stained using GeneChip® Fluidics Station 400 (Affymetrix) and scanned by GeneArray Scanner (Affymetrix).
Quality control criteria evaluations done as part of the basic analysis include (1) The ratios of 3' signal to 5' signal of two housekeeping genes, beta-actin and GAPDH were between 0 and 3. (2) The hybridization controls BioB, BioC, BioD, and Cre were all present and in a linear relationship of intensity. (3) The scale factors between arrays did not vary by 3 fold. (4) The background intensity was not significantly higher than expected. (5) The percent of gene present was monitored and found to be not less than the standard 30%. To determine the quantitative RNA level, the average differences representing the perfectly matched minus the mismatched for each gene-specific probe set was calculated. Differential gene expression and comparative analysis was done using Data Mining Tool® (Microarray suite 5) to identify probes with at least 1.5 fold difference in expression between control and p30II and verified for cluster formation by dCHIP software [29]. The biological and molecular functional grouping of these probes was done using Gene Ontology Mining Tool (Affymetrix) [30].
RT-PCR
One μg of RNA was converted to cDNA (Reverse Transcription system, Promega, Madison, WI) as described by the manufacturer. cDNA from 100 ng of total RNA was amplified with AmpliTaq DNA polymerase (Perkin Elmer, Boston, MA), PCR products were separated by agarose gel electrophoresis, normalized to GAPDH and quantified using alpha imager spot densitometry (Alpha Innotech, San Leandro, CA). DNA contamination was tested by performing a control with no reverse transcriptase. The PCR primers for p30II were as follows: TAG CAA ACC GTC AAG CAC AG (forward) and CGA ACA TAG TCC CCC AGA GA (reverse). The PCR primers for CHP were as follows: CCC ACA GTC AAA TCA CTC GCC (forward) and ATG GTC CTG TCT GCG ATG CTG (reverse). The PCR primers for JUN-D were as follows: CTC TCA GTG CTT CTT ACT ATT AAG CAG (forward) and TTA TCT AGG AAT TGT CAA AGA GAA GATT (reverse). The PCR primers for NFATc were as follows: TTG GGA GAG ACA TGT CCC AGA TT (forward) and TCA TTT CCC CAA AGC TCA AAC A (reverse). The results were expressed as a graph. Statistical analysis was performed using Student's t test, P < 0.05.
Transient transfection and reporter gene assay
Analysis of AP-1, NF-κB, and NFAT transcriptional activity in pME- and pME-p30II-transfected Jurkat T lymphocytes was performed as described previously [50]. Briefly, transient transfection of Jurkat T lymphocytes was done by electroporating 107 cells in cRPMI (RPMI 1640 containing 10% fetal bovine serum (FBS) and 10% streptomycin and penicillin) at 350 V and 975 μF using Bio-Rad Gene Pulser II (Bio-Rad, Laboratories, Hercules, CA) with 30 μg of pME-p30 or pME empty plasmid, 10 μg of reporter plasmid (NFAT-Luc, AP-1 Luc or NF-κB Luc), and 1 μg of pRSV-Gal plasmid or 1 ug pWPT-IRES-GFP plasmid. The transfected cells were seeded in six-well plates at a density of 5 × 105/ml and were either left untreated or stimulated with 20 ng/ml of phorbol myristate acetate (PMA) (Sigma) or with 2 μM ionomycin (Sigma), or both at 6 h post-transfection, followed by incubation for 18 h prior to lysis for analysis of luciferase activity. Stimulations with anti-CD3 and/or anti-CD28 antibodies (each at 3 μg/ml) (BD Pharmingen, San Diego, CA) were carried out 18 h post-transfection. Following 8 h of stimulation, to measure luciferase activity, the cells were lysed with Cell Culture Lysis Reagent (Promega), and the cell lysates were tested for luciferase activity according to the manufacturer's protocol. Transfection efficiency was normalized by staining with 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside (X-Gal) (Sigma) and counting β-Gal expressing cells. Transfection efficiency was also normalized by counting GFP positive cells under the fluorescence microscope. Results were expressed as mean of optimized luciferase activity (luciferase activity/percentage cells stained positive for β-Gal expression) in arbitrary light units (ALU) with standard error (SE) from a minimum of triplicate experiments. Statistical analysis was performed using Student's t test, P < 0.05.
List of Abbreviations
Arbitrary light units, ALU
Phorbol myristate acetate, PMA
Fetal bovine serum, FBS
Human T-lymphotropic virus type 1, HTLV-1
Adult T cell leukemia/lymphoma, ATL
Competing Interests
The author(s) declare that they have no competing interests.
Authors Contributions
Bindhu Michael, Amrithraj M. Nair, Hajime Hiraragi, Lei Shen, Gerold Feuer, Kathleen Boris-Lawrie and Michael D. Lairmore have all met the definition of author as outlined by the Retrovirology journal. Each has made substantive intellectual contributions to a published study. Bindhu Michael, Amrithraj Nair, Hajime Hiraragi Gerold Feuer, Kathleen Boris-Lawrie and Michael D. Lairmore have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. Lei Shen performed drafting the article or revising it critically for important intellectual content in particular sections related to biostatistical analysis. Each author has given final approval of the version to be published. Each author have participated sufficiently in the work to take public responsibility for appropriate portions of the content.
Supplementary Material
Additional File 1
Listing of genes modulated by HTLV-1 p30II.
Click here for file
Acknowledgments
We thank M. Kotur and R. Meister for technical assistance in FACS, A. Bakaletz, S. Fernandez, Y. Liu-Stratton and Y. Luo for technical support, data analysis, and valuable suggestions in design of the micro array experiment. We also thank L. Silverman, S. J. Kim, P. Green and L. Mathes for critical review of the manuscript, and G. Franchini, G. Crabtree and D. Trono for sharing valuable reagents. This work was supported by National Institutes of Health grants CA100730 and RR14324 awarded to M. Lairmore and CA-70529 from the National Cancer Institute, awarded through The Ohio State University Comprehensive Cancer Center.
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| 15560845 | PMC538277 | CC BY | 2021-01-04 16:36:37 | no | Retrovirology. 2004 Nov 23; 1:39 | utf-8 | Retrovirology | 2,004 | 10.1186/1742-4690-1-39 | oa_comm |
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RetrovirologyRetrovirology1742-4690BioMed Central London 1742-4690-1-391556084510.1186/1742-4690-1-39ResearchHuman T lymphotropic virus type-1 p30II alters cellular gene expression to selectively enhance signaling pathways that activate T lymphocytes Michael Bindhu [email protected] Amrithraj M [email protected] Hajime [email protected] Lei [email protected] Gerold [email protected] Kathleen [email protected] Michael D [email protected] Center for Retrovirus Research and Department of Veterinary Biosciences, The Ohio State University, Columbus, Ohio 43210, USA2 Department of Statistics, College of Mathematical and Physical Sciences, The Ohio State University, Columbus, Ohio 43210, USA3 Department of Microbiology and Immunology, State University of New York Upstate Medical University, Syracuse, New York 13210, USA4 Department of Molecular Virology, Immunology and Medical Genetics, The Ohio State University, Columbus, Ohio 43210, USA5 Comprehensive Cancer Center, The Arthur G. James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, Ohio 43210, USA6 Department of Safety Assessment, Merck &Co., Inc. WP45-224, West Point PA 19486, USA2004 23 11 2004 1 39 39 19 8 2004 23 11 2004 Copyright © 2004 Michael et al; licensee BioMed Central Ltd.2004Michael 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
Human T-lymphotropic virus type-1 (HTLV-1) is a deltaretrovirus that causes adult T-cell leukemia/lymphoma and is implicated in a variety of lymphocyte-mediated disorders. HTLV-1 contains both regulatory and accessory genes in four pX open reading frames. pX ORF-II encodes two proteins, p13II and p30II, which are incompletely defined in the virus life cycle or HTLV-1 pathogenesis. Proviral clones of the virus with pX ORF-II mutations diminish the ability of the virus to maintain viral loads in vivo. Exogenous expression of p30II differentially modulates CREB and Tax-responsive element-mediated transcription through its interaction with CREB-binding protein/p300 and represses tax/rex RNA nuclear export.
Results
Herein, we further characterized the role of p30II in regulation of cellular gene expression, using stable p30II expression system employing lentiviral vectors to test cellular gene expression with Affymetrix U133A arrays, representing ~33,000 human genes. Reporter assays in Jurkat T cells and RT-PCR in Jurkat and primary CD4+ T-lymphocytes were used to confirm selected gene expression patterns. Our data reveals alterations of interrelated pathways of cell proliferation, T-cell signaling, apoptosis and cell cycle in p30II expressing Jurkat T cells. In all categories, p30II appeared to be an overall repressor of cellular gene expression, while selectively increasing the expression of certain key regulatory genes.
Conclusions
We are the first to demonstrate that p30II, while repressing the expression of many genes, selectively activates key gene pathways involved in T-cell signaling/activation. Collectively, our data suggests that this complex retrovirus, associated with lymphoproliferative diseases, relies upon accessory gene products to modify cellular environment to promote clonal expansion of the virus genome and thus maintain proviral loads in vivo.
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Background
Human T-lymphotropic virus type 1 (HTLV-1), the first characterized human retrovirus, causes adult T cell leukemia/lymphoma (ATL) and is associated with several lymphocyte-mediated disorders such as HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) [1]. Mature CD4+ T lymphocytes are the primary targets of HTLV-1 infection [2]. Although the mechanism by which the virus causes oncogenic transformation of host T lymphocytes is incompletely understood, altered gene expression has been associated with the initiation or progression of ATL [3]. This complex retrovirus encodes structural and enzymatic gene products, as well as regulatory and accessory proteins from open reading frames (ORF) in the pX region between env and the 3' long terminal repeat (LTR) of the provirus [4]. The well characterized Rex and Tax proteins are encoded in the ORF III and IV respectively. Rex is a nucleolus-localizing phosphoprotein, involved in nuclear export of unspliced or singly spliced viral RNA [5]. Tax is a nuclear and cytoplasmic localizing phosphoprotein that interacts with cellular transcription factors and activates transcription from the viral promoter, Tax-responsive element (TRE) and enhancer elements of various cellular genes associated with host cell proliferation [6]. Emerging evidence has documented the role of pX ORF I and II gene products in the replication of HTLV-1 [7,8]. There are four proteins expressed from these ORFs – p12I, p27I, p13II, and p30II. pX ORFs I and II mRNAs are present in infected cell lines and freshly isolated cells from HTLV-1-infected subjects [9], as well as in ATL and HAM/TSP patients [10]. Antibodies [11,12] and cytotoxic T cells [13] that recognize recombinant proteins or peptides of the pX ORF I and II proteins are present in HTLV-1 infected patients and asymptomatic carriers.
Using molecular clones of HTLV-1 with selective mutations of ORF I and II, we have tested the requirement of p12I and p13II/p30II in the establishment of infection and maintenance of viral loads in a rabbit model of infection [14-16]. ORF II protein p30II contains a highly conserved bipartite nuclear localization signal (NLS) and localizes within the nucleus of cells [17-19]. In addition, p30II contains serine- and threonine-rich regions with distant homology to transcription factors Oct-1 and -2, Pit-1, and POU-M1 [20]. Previous studies from our laboratory have demonstrated that p30II also co-localizes with p300 in the nucleus and physically interacts with CREB binding protein (CBP)/p300 and differentially modulates cAMP responsive element (CRE) and TRE mediated transcription [18,21]. Recent reports also indicate a post-transcriptional role of HTLV-1 p30II and HTLV-2 p28II (homologous protein encoded in the HTLV-2 pX ORF II region), in modulating the export of tax/rex RNA from the nucleus [22,23]. Therefore, p30II appears to be a multi-functional protein with transcriptional and post-transcriptional roles in regulating viral gene expression. Based on these reports, we hypothesized that p30II functions as a regulator of cellular and viral gene expression to promote HTLV-1 replication.
Gene arrays have primarily been employed to study the changes in gene expression profile of HTLV-1-immortalized and transformed cell lines or in cells from ATL patients and attempts to test the influence of individual HTLV-1 viral proteins on cellular gene expression have been limited to Tax [3,24-27]. Herein we used the Affymetrix U133A human gene chip to confirm the role of p30II as a regulator of gene expression and identified several novel and important alterations in gene expression profiles, unique to cell cycle regulation, apoptosis and T cell signaling/activation. In addition, using semi-quantitative RT-PCR, we have confirmed the expression of multiple genes modulated by p30II in Jurkat T cells and primary CD4+ T lymphocytes. We then tested the influence of p30II in T cell signaling using reporter assays representing critical T lymphocyte transcription factors. This is the first report that demonstrates the role of p30II as an activator of key transcription factors involved in T cell signaling/activation. Together, our data suggests that HTLV-1, a complex retrovirus associated with lymphoproliferative disorders, uses accessory genes to promote lymphocyte activation to enhance clonal expansion of infected cells and maintain proviral loads in vivo.
Results
p30II and Analysis of Cellular Gene Expression in Jurkat T Lymphocytes
Stable expression of HTLV-1 p30II in Jurkat T lymphocytes was established using recombinant lentiviruses (Fig. 1). At 10 days post-transduction, GFP expression was greater than 95% in Jurkat T lymphocytes transduced with recombinant lentivirus expressing GFP alone (controls) or p30II and GFP (samples) (Fig. 2). RT-PCR was used to confirm the expression of p30II mRNA in the sample cells and absence of p30II mRNA expression in control cells (Fig. 2). p30II protein expression was also confirmed by western immunoblot assay (data not shown) using methods as previously reported [28]. Differential gene expression and comparative analysis was done to identify probes with at least 1.5 fold difference in expression between control and p30II and verified for cluster formation [29]. Quality control criteria evaluations included comparison of the ratios of 3' signal to 5' signal of two housekeeping genes, beta-actin and GAPDH, which were between 0 and 3. Additional hybridization controls were used in each array and included BioB, BioC, BioD, and Cre. These controls were all present and in a linear relationship of intensity. Quantitative RNA levels were determined by comparing the average differences representing the perfectly matched minus the mismatched for each gene-specific probe set before analysis with data mining software to identify probes with at least 1.5 fold differences [29,30].
Figure 1 Schematic illustration of lentiviral vectors expressing both p30HA and GFP (sample vector) as bicistronic messages and GFP alone (control vector) from elongation factor 1 alpha promoter. Abbreviations: LTR – Long Terminal Repeats; RRE – Rev Response Element; EF1 α – Elongation Factor 1 alpha promoter; IRES – Internal Ribosome Entry Site; WPRE – Woodchuck Hepatitis Post-transcriptional Regulatory Element.
Figure 2 Triplicate p30II samples express GFP and p30II while triplicate controls express only GFP. (A) Flow cytometric analysis illustrating the expression of GFP in Jurkat T cells 10 days post spin-infection with lentiviral vectors. Both sample (expressing p30II and GFP) and control (GFP alone) group contains relatively high and similar levels of GFP. (B) RT-PCR demonstrating the expression of p30II in Jurkat T cells 10 days post spin-infection with lentiviral vectors. Jurkat T cells spin-infected with sample vector express p30II while the control vector spin-infected cells do not express p30II. RT-PCR was performed with triplicate samples and controls. GAPDH was used as a control for the integrity of the message. (C) Representative western blot showing p30II expression from cell lysate (p30II migrates at ~28 kD). M = Mock vector infected cell lysate, p30 lv = p30 lentivirus vector infected cell lystate, MW = biotin molecular weight markers.
We then categorized genes deregulated by HTLV-1 p30II into those upregulated or downregulated in expression. We further grouped genes deregulated by p30II based on their functions, such as apoptosis, cell cycle, cell adhesion, transcription/translation factors and T cell activation or cell signaling. In all the categories, p30II was an overall repressor of cellular gene expression, while selectively increasing the expression of certain key regulatory genes (Table 1, see Additional file 1). The total number of genes of known biological or molecular function that were decreased in expression was 318 compared to 126 genes that were increased in expression.
p30II Modulates Multiple Cellular Gene Networks
Based on changes in gene expression in p30II expressing cells (Table 1), p30II would be predicted to modulate apoptosis. These include Bcl-2 related/interacting genes such as anti-apoptotic Bcl-2-related protein A1, anti-apoptotic MCL1, cell-death regulator Harakiri, apoptotic protector BNIP1 (downregulated) and pro-apoptotic BIK (upregulated). In addition, p30II expression correlated with downregulation of genes associated with Fas mediated apoptosis pathway such as tumor suppressing subtransferable candidate 3 and TNF receptor superfamily member 25. p30II expression was also associated with decreased expression of caspases (2 and 4) and increased expression of genes associated with the DNA fragmentation pathway (CIDE-B and CIDE-3). In addition, p30II expression correlated with decreased expression of many other apoptosis related genes including CD28, Lck, cyclin B1, Cullin 5, Adenosine A2a receptor, TAF4B and NCK-associated protein 1.
Multiple genes involved in cell cycle regulation were altered in p30II expressing Jurkat T lymphocytes. These include checkpoint suppressor 1, cytosolic branched-chain amino acid transaminase 1, histone deacetylase 6, cyclin B1, WEE1 kinase, CDC14A, Lck, JAK2, GAS7, BZAP45, Cullin, Rab6 GTPase activating protein (downregulated) and TERF1, AKAP8, DDX11, MSH2 and JUN-D (upregulated). Another gene down regulated by p30II expression was MDM2, which is over expressed in certain types of leukemia [31] and capable of enhancing the tumorigenic potential of cells by inhibiting p300/PCAF mediated p53 acetylation [32].
p30II expression was associated with altered expression of several genes involved in cell-to-cell adhesion. These include decrease in integrin (integrin β8) immunoglobulin (MADCAM1), a counter-receptor for P-selectin (SELPLG), cadherin (desmocollin 3), protocadherin (PC-LKC) liprin (PPF1BP1), CD84/Ly-9, CD58, CD43/sialophorin and glycosyl-phosphatidyl-inositol phospholipase D1. Expression of p30II correlated with increase in integrin receptor α1 subunit and KIT ligand.
A number of genes encoding transcriptional control factors or regulators of transcription were repressed in p30II expressing Jurkat T lymphocytes. These included decreased expression of TATA-binding protein associated factor 4 (TAF4), two co-repressors (Enolase-1 and Chromosome 19 ORF2 protein), a novel specific coactivator for mammalian TEFs, namely TONDU [33], homeo box genes (mesenchyme homeo box 1, homeobox A1), T-box genes (T-box 21) and proteins containing helix-loop-helix domain, which are known to be critical in cell growth/differentiation and tumorigenesis (neuronal PAS domain protein 2, Myc-associated factor protein, inhibitor of DNA binding-3). Additionally, p30II expression correlated with down regulation of zinc finger proteins (zinc finger protein 36), a group of transcription regulators proposed to be candidates in malignant disorders [34] and coiled coil proteins (JEM-1). p30II was also associated with downregulation of many genes with positive transcriptional effects (including SEC14-like 2, Nurr 1, CITED2/MRG1, LXR alpha and SMARCA2). Reduced expression of HDAC6, a histone deacetylase and nuclear receptor coactivator 3 (CBP interacting protein) with histone acetyltransferase and pCAF/CBP recruiting abilities [35] are particularly interesting, since p30II contains multiple highly conserved lysines, which could play a role in acetylation [18,21]. Expression of p30II was also associated with decrease in GAS 7, which has sequence homology to Oct and POU family of transcription factors [36] and decreased expression of translation initiation factor 2 (IF2) and eukaryotic translation elongation factor 1δ (EEF1D). In contrast, p30II expression in Jurkat T lymphocytes was associated with an increase in expression of eukaryotic translation elongation factor 1α (EEF1A2), a putative oncogene [37], and enhanced expression of HTLV enhancer factor, Jun-D, TAF1C, Kruppel-type zinc finger, PQBP1, AF4 and SOX4.
p30II Expression Alters Patterns of T-Cell Signaling Gene Networks
Genes involved in T-cell signaling were differentially affected by p30II expression. Expression of p30II was associated with decreased expression of CD28, a co-stimulatory molecule with a distinct role in T lymphocyte activation [38] and reduced gene expression of CD46 and Lck tyrosine kinase, a member of the Src family of tyrosine kinases activated by T cell surface receptors [39]. In contrast, cells expressing p30II had enhanced Vav-2 and CD72 gene expression. Additionally, p30II expression correlated with decrease in the level of CHP, an endogenous calcineurin inhibitor, which would be predicted to promote NFAT expression by p30II (see below). Moreover, p30II expression was associated with increased expression of Jun-D and c-Fos, suggesting activation of AP-1 mediated transcription. p30II expression was associated with decreased expression of protein kinase D (PKD), which negatively modulates JNK signaling pathway [40], mediates cross-talk between different signaling systems, and is critical in processes as diverse as cell proliferation and apoptosis [41]. Interestingly, in Jurkat T lymphocytes expressing p30II, there were no detectable levels of I kappa B kinase gamma (IKKγ), which is important for NF-κB signaling in response to both T cell activation signals and Tax [42]. p30II expression was associated with increased Hematopoetic Progenitor Kinase-1 (HPK-1), a known NF-κB activator [43]. p30II expression was also associated with decreased Ras GRP2, a guanyl nucleotide exchange factor that increases Ras-GTP, suggesting a decrease in the level of activated Ras (Ras-GTP). Seminquantitative RT-PCR analysis in Jurkat T lymphocytes and primary CD4+ T lymphocytes correlated directly with the gene array and confirmed the altered expression of each of three selected genes involved in these T cell activation/signaling pathway (Fig. 3A through 3D).
Figure 3 Semiquantitative RT-PCR of CHP, JUN-D and NFATc in controls and p30II expressing Jurkat T lymphocytes (A and B) and primary CD4+ T lymphocyte (C and D) samples. PCR products were separated by electrophoresis (A and C), normalized to GAPDH and quantified by densitometry (B and D). In panel B, dark grey bars indicate indicate p30II expressing cells and light grey bars indicate control (empty vector) cells. In panel D, dark grey bars indicate indicate p30II expressing cells and white bars indicate control (empty vector) cells. Data points are mean of triplicates. CHP was downregulated while JUN-D and NFATc was upregulated by p30II. Fold decrease/increase in activity in the presence of p30II are indicated above each bar.
p30II Influences NFAT, NF-κB and AP-1-mediated Transcription in Co-Stimulated Jurkat T lymphocytes
Using luciferase reporter assays, we directly tested the ability of p30II to influence NFAT, NF-κB and AP-1 driven transcription, all key transcription factors in T cell activation. Although p30II expression overall resulted in a repressive pattern of gene expression, our data indicated that the viral protein selectively alters the cellular environment to promote NFAT, NF-kB and AP-1 mediated transcription in Jurkat T cells undergoing co-stimulation. We transiently co-transfected NF-κB, AP-1, or NFAT luciferase reporter plasmids and a p30II expression plasmid into Jurkat T lymphocytes, and then stimulated the cells with well established co-stimulators of T cells including PMA or ionomycin or both, anti-CD3 or anti-CD28 or both. p30II increased the NFAT driven luciferase reporter gene activity from 2.2 to 10.7 fold depending on co-stimulatory treatment (Fig. 4A), indicating that p30II effectively enhanced NFAT driven transcription, when stimulated with ionomycin or anti-CD3. NF-κB driven luciferase reporter gene activity was increased from 3.1 to 11.4 fold, depending on co-stimulation (Fig. 4B). However, p30II only modestly increased AP-1-driven luciferase reporter gene activity from 1.2 to 5.2 fold in the presence of co-stimulator treatments (Fig. 4C). Collectively, these data indicate that p30II selectively promotes NFAT, NF-kB and AP-1 mediated transcription in Jurkat T lymphocytes undergoing co-stimulation and thus would be predicted to favor cell survival or influence cell activation.
Figure 4 p30II activates NFAT, AP-1 and NF-κB transcriptional activity in Jurkat T lymphocytes. Black bars indicate control and grey bars indicate p30II. Data points are mean of triplicate experiments. Fold increase in activity in the presence of p30II is indicated above each bar. p30II increased the NFAT-luc activity from 2.2 to 10.7 fold depending on co-stimulatory treatment e.g., PMA, ionomycin, CD3, CD28 etc. (A), p30II increased NF-κB-luc activity from 3.1 to 11.4 fold (B) and modestly increased the AP-1 driven luciferase reporter gene activity from 1 to 5 fold in the presence of co-stimulator treatments (C).
Discussion
Our study represents a comprehensive analysis of gene expression patterns influenced by a retrovirus accessory protein in T lymphocytes. Overall, this study confirmed that p30II is a regulator of cellular genes, either directly or indirectly, and also identified several potential new functional roles for p30II. Our approach included methods to strengthen the reliability of our data by (a) use of triplicate samples and appropriate controls (b) use of multiple software for data analysis (c) minimization of nonspecific hybridization and background signals by using Affymetrix chip [44] (d) use of a well-characterized T lymphocyte system (Jurkat) and (e) verification of microarray data by semiquantitative RT-PCR in Jurkat T lymphocytes and primary CD4+ T lymphocytes (f) validation of microarray data by reporter assays, all of which were consistent with our micro array findings. Some of these findings are consistent with previous studies using gene arrays to test HTLV-1-transformed cell lines. For example, HTLV-1 infected cell lines contain low levels of caspase-4 and high levels of JUN [3] and cyclin B1 levels are low in HTLV-1 leukemic T cells [45]. Our study represents a comprehensive analysis of gene expression patterns influenced by a retrovirus accessory protein in T lymphocytes. An important caveat our approach of using gene arrays is that this method, while useful to indicate if an individual gene is increased or decreased in expression and therefore predicted to influence a cell signaling pathway, does not reveal the composite of transcription regulation in vivo. This may explain, in part, why our reporter gene data, which is more dependent upon the availability of transcription factors in total, may not directly, correlate to an individual gene expression result.
Others have used gene array approaches to study HTLV-1-related changes in gene expression. Harhaj et al [24] studied the gene expression in HTLV-1 mediated oncogenesis using human cDNA array analysis of normal and HTLV-1 immortalized T cells and found that the expression of a large number of genes involved in apoptosis were deregulated in HTLV-1 immortalized T cells. Subsequently, the same type of cDNA arrays were employed by De La Fuente et al [25] to study upregulation of a number of transcription factors in HTLV-1-infected cells, including zinc fingers, paired domains, and basic helix-loop-helix (bHLH) proteins. Gene expression profiles of fresh peripheral blood mononuclear cells (PBMC) from acute and chronic ATL patients were used to identify the genes associated with progression of ATL including a T cell differentiated antigen (MAL), a lymphoid specific member of the G-protein-coupled receptor family (EBI-1/CCR7) and a novel human homolog to a subunit (MNLL) of the bovine ubiquinone oxidoreductase complex [26]. Using NIH OncoChip cDNA arrays containing 2304 cancer related cDNA elements, Ng et al, 2001 [27] compared normal and Tax-expressing Jurkat T lymphocytes and identified Tax induced changes in gene expression, associated with apoptosis, cell cycle, DNA repair, signaling factors, immune modulators, cytokines, growth factors, and adhesion molecules. Recently, Affymetrix, GeneChip microarrays containing oligonucleotide hybridization probes representative of ~7000 genes were used to compare the expression profiles of normal activated peripheral blood lymphocytes to HTLV-I-immortalized and transformed cell lines [3]. In this study, by employing a gene chip representing ~33000 genes, we tested the role of p30II on cellular gene expression profile of a larger number of genes. Gene expression data from cells in which exogenously expressed proteins, which may also be tagged for identification, may not represent what would occur during the natural infection. However, these patterns provide important clues for functional alterations which may occur during the viral protein expression. The "natural" or in vivo amount of expression of regulatory and accessory gene products encoded from the HTLV-1 pX gene region has not been clearly defined. Recent studies using RT-PCR analysis of cell lines suggests that pX ORF 1 and 2 mRNA is expressed at significantly lower amounts compared to tax/rex mRNA, full length genomic, or singly spliced envelope mRNA [46].
Expression from the IL-2 promoter requires binding of several transcription factors, including NFAT, AP-1 and NF-κB. NFAT is vital to proliferation of peripheral lymphocytes for HTLV-1 infection [47] while AP-1 is linked to the dysregulated phenotypes of HTLV-1 infected T cells [48] and malignant transformation [49]. Activation of AP-1 occurs through Tax-dependent and independent mechanisms in HTLV-1-infected T cells in vitro and in leukemia cells in vivo [48]. NF-κB is highly activated in many hematopoietic malignancies, HTLV-1 infected T cell lines and in primary ATL cells, even when Tax expression levels are low [49] and due to its anti-apoptotic activity, it is considered to be a key survival factor for several types of cancer. Ours is the first report demonstrating the ability of an HTLV-1 accessory protein to have broad modulating activities on the transcriptional activity of NF-κB, NFAT and AP-1. Further studies will be required to confirm the mechanisms of p30II in T cell activation and to test the comparative role of p30II expression in context to other regulators of transcription such as Tax.
We have previously reported that another HTLV-1 accessory protein p12I stimulates NFAT mediated transcription, when stimulated with PMA, indicating that p12I acts synergistically with Ras/MAPK pathway to promote NFAT activation and thus may facilitate host cell activation and establishment of persistent HTLV-1 infection [50]. Our data indicates that p30II enhanced NFAT driven transcription significantly when stimulated with ionomycin or CD3, and therefore likely uses a different mechanism than p12I. To modulate NFAT driven transcription and subsequent T cell activation/signaling, it is possible that these two accessory proteins act synergistically. AP-1 is able to interact with transcriptional coactivator CBP/p300, as well as viral CREs and mediate HTLV-1 gene expression [48,51,52]. Intriguingly, we have previously reported that p30II interacts with CBP/p300 at the KIX domain of CBP, influences CRE and TRE mediated transcription [18] and disrupts CREB-Tax-p300 complexes on TRE probes [21]. NF-κB and NFAT [53] are also known to interact with the transcriptional coactivator CBP/p300. Therefore, it is possible that p30II modestly activates the transcriptional activity of NFAT, NF-κB and AP-1, at least in part, by its interaction with CBP/p300. In parallel, the HIV-1 accessory protein Vpr causes a modest increase in NF-κB, NFAT and AP-1 mediated transcription in a cell-cycle dependent fashion by causing G2 arrest [54]. Similar to HIV-1 Vpr, our gene array findings indicate that HTLV-1 p30II expression was associated with decrease in cyclin B1 and WEE1 kinase levels, suggesting that p30II expression likely cause G2 arrest and may thus modulate transcriptional activity of NFAT, NF-κB and AP-1, in a cell-cycle dependent manner. An important caveat of our data is the use of Jurkat T cells, which while representing human T cells, are IL-2 independent and transformed. Thus, differences in responsive genes expected from non-transformed T cells for the transcription factors screened in our study may be due to our cell line model.
HTLV-1 mediated interference with normal T-cell apoptosis is thought to be a mechanism of tumorigenicity [2], but specific mechanisms by which HTLV-1 infection or any particular HTLV-1 gene products influence on T-cell survival are not fully understood. Similar to the effect of HTLV-1 Tax on apoptosis related genes [24,27], we found that p30II also deregulates multiple genes resulting in possible pro-apoptotic and anti-apoptotic effects. Since apoptosis is a well-known mechanism of cellular defense against viral infection, a possible role of p30II in lymphocyte apoptosis might correlate with the requirement of p30II in maintaining proviral loads in vivo [15]. Previous studies indicate that several members of the cell cycle machinery have altered expression in HTLV-1 infected cells [3]. Several recent studies have reviewed the aberrations in cell cycle caused by HTLV-1 Tax [6,55].
p30II appears to regulate viral gene expression and modulate immune response. We have previously reported that, p30II activated HTLV-1 LTR at lower concentrations and repressed at higher concentrations [18]. Interestingly, p30II expression was associated with downregulation of lck (p56), which suppresses the HTLV-1 promoter [56] and upregulate HTLV enhancer factor, which is known to bind to LTR at a region involved in regulation of gene expression by the ets family of transcription factors [57]. Additionally, p30II expression was associated with altered expression of cellular genes involved in immune modulation such as CD46, CD43, CD58, IFNγ and CD72.
Conclusions
Overall, this study supports our earlier reports on the repressive role of HTLV-1 p30II in gene expression [18,21,23] and sheds light on potential mechanisms by which p30II functions in HTLV-1 replication or leukemogenesis. Our data confirmed that p30II while a negative regulator of cellular genes, also influences T cell signaling, apoptosis and the cell cycle. Many of the effects of p30II appear to overlap or counteract the influence of other HTLV-1 regulatory proteins like Tax or other accessory proteins such as p12I. It is possible that these proteins act coordinately or synergistically. We postulate that, by modulating the expression of various HTLV-1 proteins, the virus employs selective use of these viral proteins during different stages of the infection. However, since information on the expression profile of HTLV-1 proteins during stages of the infection is limited, additional studies are required to explore this possibility. Such future studies might provide new directions in the development of therapeutic interventions against HTLV-1 disorders, which are associated with immune-mediated mechanisms.
Methods
Lentiviral vectors and other plasmids
The plasmid pWPT-IRES-GFP was generated by cloning the internal ribosome entry site (IRES) sequence from pHR'CMV/Tax1/eGFP [58] (Gerald Feuer, SUNY, Syracuse) into pWPT-GFP plasmid (Didier Trono, University of Geneva). Subsequently, the plasmid pWPT-p30IIHA-IRES-GFP was created by cloning the p30II sequence from ACH [59] with the downstream influenza hemagglutinin (HA1) tag (Fig. 1). Sanger sequencing confirmed both the plasmids to have the correct sequence and were in frame. GFP and p30IIHA expression were confirmed by fluorescence activated cell sorting (FACS) analysis (Beckman Coulter, Miami, FL) and western blot respectively. GFP expression from each of the plasmids was confirmed by flow cytometry (Beckman Coulter) and the p30IIHA expression from pWPT-p30IIHA-IRES-GFP plasmid was confirmed by western blot using mouse monoclonal anti-hemagglutinin antibody (1:1000) (Covance, Princeton, NJ) as described previously [18,21]. The plasmid pME-p30IIHA was created by cloning p30II sequence from ACH with HA1 tag, into pME-18S (G. Franchini, NIH). Other plasmids used include previously reported pRSV-βGal [18] and AP-1, NF-κB and NFAT-luciferase reporter plasmids [50].
Recombinant lentivirus production and infection of Jurkat T lymphocytes and primary CD4+ T lymphocytes
Recombinant lentiviruses were produced by transfecting pHCMV-G, pCMVΔR8.2 and pWPT-p30IIHA-IRES-GFP (sample) or pWPT-IRES-GFP (control) as described previously [60]. Briefly, 293T cells (5 × 106) were seeded in a 10-cm dish and transfected the following day with 2 μg of pHCMV-G, 10 μg of pCMVΔR8.2 and 10 μg of pWPT-p30IIHA-IRES-GFP or pWPT-IRES-GFP using the calcium phosphate method. Supernatant from 10 to 20 dishes was collected at 24, 48 and 72 h post transfection, cleared of cellular debris by centrifugation at 1000 rpm for 10 min at room temperature and then filtered through a 0.2 μm filter. The resulting supernatant was then centrifuged at 6,500 g for 16 h at 4°C. The viral pellet was suspended in cDMEM (DMEM containing 10% FBS and 10% streptomycin and penicillin) overnight at 4°C and the concentrated virus was aliquoted and stored at -80°C. To determine the virus titer, serial dilutions of the virus stock were used to spin infect 293T cells and 48 h post infection, eGFP expression and p30II expression was measured by flow cytometry and RT-PCT respectively. Briefly, on the day before infection, 293T cells (1 × 105) were seeded in a 6-well plate. The medium was removed the following day and the cells were then incubated with the diluted virus containing 8 μg/ml polybrene (Sigma, St. Louis, MO). Cells were then spin-infected by centrifugation at 2700 rpm for 1 h at 30°C, supplied with fresh medium and cultured for 48 h. Then cells were treated with trypsin (Invitrogen, Carlsbad, CA), pelleted and resuspended in D-PBS (Invitrogen) for fluorescence activity cell sorting (FACS) analysis on an ELITE ESP flow cytometer (Beckman Coulter). One × 106 cells were used to perform western blot to detect the expression of p30II HA. Jurkat T lymphocytes (clone E6.1, American Type Culture Collection) were transduced with recombinant virus at multiplicity of infection of 4 in the presence of 8 μg/ml polybrene (Sigma) and spin-infected at 2700 rpm for 1 h at 25°C. Primary CD4+ T cells were extracted using dynabead CD4 positive isolation kit (Dynal Biotech, Lake Success, NY) according to manufacturer's instructions. Primary CD4+ T cells were stimulated with Phytohemagglutinin (PHA) for 48 h, transduced with recombinant virus at multiplicity of infection of 20 in the presence of 8 μg/ml polybrene (Sigma) and spin-infected at 2700 rpm for 1 h at 25°C. At 10 days post-transduction, GFP expression of controls and samples were verified to be above 90% by FACS analysis and the presence of p30II mRNA expression in samples (and absence in controls) was verified by RT-PCR (Fig. 2).
Western Immunoblot assay
Cells were lysed in buffer containing phosphate-buffered saline, 1% Nonidet P-40, 0.5% sodium deoxycholate, and 0.1% sodium dodecyl sulfate (SDS). Cell lysates were prepared by centrifugation at 14,000 rpm (Beckman) for 20 min at 4°C. Protein concentrations were determined by BCA assay (micro-BCA Protein Assay®, Pierce, IL). Equal amounts of proteins were mixed with Laemmli buffer (62.5 mM Tris [pH 6.8], 2% SDS, 10% glycerol, 0.2% bromophenol blue, 100 mM dithiothreitol). After boiling for 5 min, samples were electrophoresed through 12% polyacrylamide gels. The fractionated proteins were transferred to nitrocellulose membranes (Amersham Pharmacia Biotechnology) at 100 V for 1 h at 4°C. Membranes were blocked with 5% non-fat dry milk in PBS with 0.1% Tween for 16 hours, then incubated with mouse anti-HA monoclonal Ab (1:1,000) (clone 16B-12) (Covance Research Products, Princeton, NJ), for overnight at 4°C, and developed by using horseradish peroxidase-labeled secondary Ab (1:1,000) and enhanced chemiluminescence reagent (Cell Signaling Technology, Beverly, MA).
Probe preparation and microarray analysis
According to the instructions of manufacturers, total cellular RNA was isolated from transduced Jurkat T lymphocytes using RNAqueous (Ambion, Austin, TX). To test the concentration and purity of the RNA samples, absorbance at 260 nm and 280 nm were measured and the 260/280 ratio was calculated using a spectrophotometer (Genequant, Amersham Pharmacia, Piscataway, NJ). The 260/280 ratio of all the RNA samples were between the range of 1.9–2.1. The probe preparation for GeneChip was performed according to the Affymetrix GeneChip Expression Analysis Technical Manual (Affymetrix, Santa Clara, CA). Briefly, cDNA was synthesized using genechip T7-Oligo (dT) promoter primer kit (Affymetrix) and superscript double stranded cDNA synthesis kit (Invitrogen), according to the manufacturers instructions. cDNA cleanup was done using Genechip Sample Cleanup module (Affymetrix). In vitro transcription was performed on the cDNA to produce biotin-labeled cRNA with ENZO RNA Transcript labeling kit (Affymetrix), according to the manufacturer's instructions. Complimentary RNA (cRNA) cleanup was performed using Genechip Sample Cleanup module (Affymetrix). The quality of total RNA and biotin-labeled cRNA of all the samples and controls were checked by calculating the ratio of absorbance at 260 nm and 280 nm (between 1.9 to 2.1) using a spectrophotometer (Genequant) and agarose gel electrophoresis. The labeled cRNA was fragmented to 50–200 nucleotides, and hybridized to U133A arrays (Affymetrix) using GeneChip® Hybridization Oven (Affymetrix). Arrays were washed and stained using GeneChip® Fluidics Station 400 (Affymetrix) and scanned by GeneArray Scanner (Affymetrix).
Quality control criteria evaluations done as part of the basic analysis include (1) The ratios of 3' signal to 5' signal of two housekeeping genes, beta-actin and GAPDH were between 0 and 3. (2) The hybridization controls BioB, BioC, BioD, and Cre were all present and in a linear relationship of intensity. (3) The scale factors between arrays did not vary by 3 fold. (4) The background intensity was not significantly higher than expected. (5) The percent of gene present was monitored and found to be not less than the standard 30%. To determine the quantitative RNA level, the average differences representing the perfectly matched minus the mismatched for each gene-specific probe set was calculated. Differential gene expression and comparative analysis was done using Data Mining Tool® (Microarray suite 5) to identify probes with at least 1.5 fold difference in expression between control and p30II and verified for cluster formation by dCHIP software [29]. The biological and molecular functional grouping of these probes was done using Gene Ontology Mining Tool (Affymetrix) [30].
RT-PCR
One μg of RNA was converted to cDNA (Reverse Transcription system, Promega, Madison, WI) as described by the manufacturer. cDNA from 100 ng of total RNA was amplified with AmpliTaq DNA polymerase (Perkin Elmer, Boston, MA), PCR products were separated by agarose gel electrophoresis, normalized to GAPDH and quantified using alpha imager spot densitometry (Alpha Innotech, San Leandro, CA). DNA contamination was tested by performing a control with no reverse transcriptase. The PCR primers for p30II were as follows: TAG CAA ACC GTC AAG CAC AG (forward) and CGA ACA TAG TCC CCC AGA GA (reverse). The PCR primers for CHP were as follows: CCC ACA GTC AAA TCA CTC GCC (forward) and ATG GTC CTG TCT GCG ATG CTG (reverse). The PCR primers for JUN-D were as follows: CTC TCA GTG CTT CTT ACT ATT AAG CAG (forward) and TTA TCT AGG AAT TGT CAA AGA GAA GATT (reverse). The PCR primers for NFATc were as follows: TTG GGA GAG ACA TGT CCC AGA TT (forward) and TCA TTT CCC CAA AGC TCA AAC A (reverse). The results were expressed as a graph. Statistical analysis was performed using Student's t test, P < 0.05.
Transient transfection and reporter gene assay
Analysis of AP-1, NF-κB, and NFAT transcriptional activity in pME- and pME-p30II-transfected Jurkat T lymphocytes was performed as described previously [50]. Briefly, transient transfection of Jurkat T lymphocytes was done by electroporating 107 cells in cRPMI (RPMI 1640 containing 10% fetal bovine serum (FBS) and 10% streptomycin and penicillin) at 350 V and 975 μF using Bio-Rad Gene Pulser II (Bio-Rad, Laboratories, Hercules, CA) with 30 μg of pME-p30 or pME empty plasmid, 10 μg of reporter plasmid (NFAT-Luc, AP-1 Luc or NF-κB Luc), and 1 μg of pRSV-Gal plasmid or 1 ug pWPT-IRES-GFP plasmid. The transfected cells were seeded in six-well plates at a density of 5 × 105/ml and were either left untreated or stimulated with 20 ng/ml of phorbol myristate acetate (PMA) (Sigma) or with 2 μM ionomycin (Sigma), or both at 6 h post-transfection, followed by incubation for 18 h prior to lysis for analysis of luciferase activity. Stimulations with anti-CD3 and/or anti-CD28 antibodies (each at 3 μg/ml) (BD Pharmingen, San Diego, CA) were carried out 18 h post-transfection. Following 8 h of stimulation, to measure luciferase activity, the cells were lysed with Cell Culture Lysis Reagent (Promega), and the cell lysates were tested for luciferase activity according to the manufacturer's protocol. Transfection efficiency was normalized by staining with 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside (X-Gal) (Sigma) and counting β-Gal expressing cells. Transfection efficiency was also normalized by counting GFP positive cells under the fluorescence microscope. Results were expressed as mean of optimized luciferase activity (luciferase activity/percentage cells stained positive for β-Gal expression) in arbitrary light units (ALU) with standard error (SE) from a minimum of triplicate experiments. Statistical analysis was performed using Student's t test, P < 0.05.
List of Abbreviations
Arbitrary light units, ALU
Phorbol myristate acetate, PMA
Fetal bovine serum, FBS
Human T-lymphotropic virus type 1, HTLV-1
Adult T cell leukemia/lymphoma, ATL
Competing Interests
The author(s) declare that they have no competing interests.
Authors Contributions
Bindhu Michael, Amrithraj M. Nair, Hajime Hiraragi, Lei Shen, Gerold Feuer, Kathleen Boris-Lawrie and Michael D. Lairmore have all met the definition of author as outlined by the Retrovirology journal. Each has made substantive intellectual contributions to a published study. Bindhu Michael, Amrithraj Nair, Hajime Hiraragi Gerold Feuer, Kathleen Boris-Lawrie and Michael D. Lairmore have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. Lei Shen performed drafting the article or revising it critically for important intellectual content in particular sections related to biostatistical analysis. Each author has given final approval of the version to be published. Each author have participated sufficiently in the work to take public responsibility for appropriate portions of the content.
Supplementary Material
Additional File 1
Listing of genes modulated by HTLV-1 p30II.
Click here for file
Acknowledgments
We thank M. Kotur and R. Meister for technical assistance in FACS, A. Bakaletz, S. Fernandez, Y. Liu-Stratton and Y. Luo for technical support, data analysis, and valuable suggestions in design of the micro array experiment. We also thank L. Silverman, S. J. Kim, P. Green and L. Mathes for critical review of the manuscript, and G. Franchini, G. Crabtree and D. Trono for sharing valuable reagents. This work was supported by National Institutes of Health grants CA100730 and RR14324 awarded to M. Lairmore and CA-70529 from the National Cancer Institute, awarded through The Ohio State University Comprehensive Cancer Center.
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| 15581431 | PMC538278 | CC BY | 2021-01-04 16:02:45 | no | BMC Bioinformatics. 2004 Dec 7; 5:189 | latin-1 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-189 | oa_comm |
==== Front
Nutr Metab (Lond)Nutrition & Metabolism1743-7075BioMed Central London 1743-7075-1-131553325010.1186/1743-7075-1-13ResearchComparison of energy-restricted very low-carbohydrate and low-fat diets on weight loss and body composition in overweight men and women Volek JS [email protected] MJ [email protected]ómez AL [email protected] DA [email protected] MR [email protected] G [email protected] B [email protected] R [email protected] DN [email protected] WJ [email protected] Human Performance Laboratory, Department of Kinesiology, University of Connecticut, 2095 Hillside Road, Unit-1110, Storrs, CT 06269-1110, USA2004 8 11 2004 1 13 13 27 7 2004 8 11 2004 Copyright © 2004 Volek et al; licensee BioMed Central Ltd.2004Volek 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.
Objective
To compare the effects of isocaloric, energy-restricted very low-carbohydrate ketogenic (VLCK) and low-fat (LF) diets on weight loss, body composition, trunk fat mass, and resting energy expenditure (REE) in overweight/obese men and women.
Design
Randomized, balanced, two diet period clinical intervention study. Subjects were prescribed two energy-restricted (-500 kcal/day) diets: a VLCK diet with a goal to decrease carbohydrate levels below 10% of energy and induce ketosis and a LF diet with a goal similar to national recommendations (%carbohydrate:fat:protein = ~60:25:15%).
Subjects
15 healthy, overweight/obese men (mean ± s.e.m.: age 33.2 ± 2.9 y, body mass 109.1 ± 4.6 kg, body mass index 34.1 ± 1.1 kg/m2) and 13 premenopausal women (age 34.0 ± 2.4 y, body mass 76.3 ± 3.6 kg, body mass index 29.6 ± 1.1 kg/m2).
Measurements
Weight loss, body composition, trunk fat (by dual-energy X-ray absorptiometry), and resting energy expenditure (REE) were determined at baseline and after each diet intervention. Data were analyzed for between group differences considering the first diet phase only and within group differences considering the response to both diets within each person.
Results
Actual nutrient intakes from food records during the VLCK (%carbohydrate:fat:protein = ~9:63:28%) and the LF (~58:22:20%) were significantly different. Dietary energy was restricted, but was slightly higher during the VLCK (1855 kcal/day) compared to the LF (1562 kcal/day) diet for men. Both between and within group comparisons revealed a distinct advantage of a VLCK over a LF diet for weight loss, total fat loss, and trunk fat loss for men (despite significantly greater energy intake). The majority of women also responded more favorably to the VLCK diet, especially in terms of trunk fat loss. The greater reduction in trunk fat was not merely due to the greater total fat loss, because the ratio of trunk fat/total fat was also significantly reduced during the VLCK diet in men and women. Absolute REE (kcal/day) was decreased with both diets as expected, but REE expressed relative to body mass (kcal/kg), was better maintained on the VLCK diet for men only. Individual responses clearly show the majority of men and women experience greater weight and fat loss on a VLCK than a LF diet.
Conclusion
This study shows a clear benefit of a VLCK over LF diet for short-term body weight and fat loss, especially in men. A preferential loss of fat in the trunk region with a VLCK diet is novel and potentially clinically significant but requires further validation. These data provide additional support for the concept of metabolic advantage with diets representing extremes in macronutrient distribution.
weight lossAtkins diethormonesabdominal fatregional body compositionlow-carbohydrate diet
==== Body
Introduction
Recent reports showing a greater weight loss with a free-living very low-carbohydrate ketogenic (VLCK) than a low-fat diet after 3 and 6 months [1-5] has generated interest in mechanisms that may account for these responses. Earlier work that involved comparison of isocaloric formula VLCK and low-fat (LF) diets [6], indicated that weight loss was greater with a VLCK, suggesting a metabolic advantage (i.e., a greater weight loss with one diet over another with different macronutrient distribution but the same energy content) [7,8].
Although several studies have shown that VLCK diets result in greater reductions in body mass, it remains unclear how these diets affect the composition of weight loss and the distribution of fat loss. Some early reports show that VLCK diets result in preferential loss of fat and preservation of lean body mass [9-12], suggestive of a nutrient partitioning effect. In accordance with this notion, we recently reported that a free-living 6-week VLCK diet prescribed to be isoenergetic resulted in significant decreases in fat mass and increases in lean body mass in normal-weight men [13]. However, other studies have not shown a preferential loss of fat on a VLCK diet [14]. No studies have examined the effects of a VLCK diet on the distribution of fat loss. Since accumulation of fat in the abdominal area is associated with insulin resistance, diabetes, dyslipidemias and atherosclerosis [15], demonstration of the effects of a VLCK diet on regional fat distribution is important.
Volek and Westman [16] have reviewed the potential favorable effects of VLCK diets while other reviews that have focused on the potential adverse effects of VLCK diets caution to avoid or limit their use [17-19]. Given the varying opinions in respect to VLCK diets, we thought it was important to provide additional information related to the effects of a VLCK diet on weight loss, body composition, and regional fat distribution. We previously reported that a VLCK diet has favorable effects on biomarkers for cardiovascular disease [20-22]. The primary purpose of this investigation was to compare the effects of isocaloric, energy-restricted (-500 kcal/day from estimated needs to maintain weight) VLCK and LF diets on weight loss, body composition, trunk fat, and REE in overweight men and women.
Methods
Subjects
A total of twenty-eight healthy volunteers (15 men and 13 women) were recruited by flyers and word-of-mouth. Subjects were between 20 and 55 y, nonsmokers, and greater than 25 percent body fat determined via dual-energy X-ray absorptiometry (DEXA). Subjects went through a thorough screening procedure to ensure they would be committed to completing the study. Exclusion criteria included a body mass >145 kg (because of technical difficulties in performing DEXA), post-menopausal women, overt diabetes, cardiovascular, respiratory, gastrointestinal, thyroid or any other metabolic disease, weight change ± 2 kg over the last month, adherence to special diets, use of nutritional supplements (except a daily multi-vitamin/mineral), and use of medications to control blood lipids or glucose. The majority of subjects were sedentary and were instructed not to start an exercise program during the study. Those who were active were instructed to maintain the same level of physical activity throughout the study. Baseline characteristics of men and women stratified by diet order are shown in Table 1 (see additional file 1). The study was conducted in accordance with the guidelines of the Institutional Review Board at the University of Connecticut.
Experimental Approach
Our primary research question was to compare VLCK and LF diets on weight loss, fat loss, and trunk fat loss. We addressed this in several ways. First, subjects were initially randomly assigned to either a LF or VLCK weight loss diet. Weight loss, body composition (fat mass and lean body mass), trunk fat, and resting energy expenditure (REE) were assessed before and after each diet (Phase I). Because there is often a great deal of variation in response to diet, we decided that a direct comparison of responses to a VLCK and LF diet should be made in the same person. To achieve this aim, we asked subjects to switch to the opposite diet after completion of the first diet period (Phase II), after which the same measurements were assessed (i.e., each subject consumed a VLCK and LF diet). This experimental approach allowed us to compare these two diets in two ways: a between group comparison of subjects who either consumed a VLCK or LF diet during Phase I, and a within group comparison of subjects who consumed both a VLCK and LF diet. The within group comparison was further analyzed to determine if the order of diets had any effect on the responses. Subjects kept detailed food diaries during three 1 wk periods (21 days total) of each diet. Men consumed each diet for 50 days whereas women consumed the diets for approximately 30 days in order to control for possible effects of menstrual phase on some of the dependent variables measured in this study [23,24]. All testing for women was performed between days 2–4 of the follicular phase as self-reported by the women.
Diet Interventions
Both experimental diets were designed to be hypoenergetic (-500 kcal/day). Energy levels were assigned to the nearest 200 kcal increment based on REE obtained using indirect calorimetry at the start of the study and appropriate activity factors. Standard diabetic exchange lists were used to ensure a constant energy and macronutrient balance of protein (~20% energy), fat (~25% energy), and carbohydrate (~55% of energy) during the LF diet. The LF diet was also designed to contain <10% saturated fat and <300 mg cholesterol (i.e., a Step-I diet). Foods encouraged during the LF diet included whole grains (breads, cereals, and pastas), fruit/fruit juices, vegetables, vegetable oils, and low-fat dairy and meat products. We developed customized diabetic exchange lists for the VLCK diet period in order to ensure a constant energy and balance of protein (~30% energy), fat (~60% energy), and carbohydrate (~10% of energy) throughout the day. There were no restrictions on the type of fat from saturated and unsaturated sources or cholesterol levels. Foods commonly consumed on the VLCK diet were beef (e.g., hamburger, steak), poultry (e.g., chicken, turkey), fish, oils, various nuts/seeds and peanut butter, moderate amounts of vegetables, salads with low-carbohydrate dressing, moderate amounts of cheese, eggs, protein powder, and water or low-carbohydrate diet drinks. Low-carbohydrate bars and shakes (Atkins Nutritionals, Inc., Hauppauge, NY) were provided to subjects during the VLC diet. A daily multi-vitamin/mineral complex that provided micronutrients at levels ≤ 100% of the RDA was given to subjects during both experimental diets.
All subjects received extensive initial instruction and follow-up by registered dietitians on how to translate foods/meals into diabetic exchanges. Subjects were also provided with a packet outlining specific lists of appropriate foods, recipes, and sample meal plans that were compatible with their individual preferences for both experimental diets. Subjects received thorough instructions for completing detailed weighed food records during three 7-day periods (21 days total) for each diet. Food measuring utensils and scales were provided to subjects to ensure accurate reporting of food/beverage amounts consumed. Food diaries were analyzed for energy and macro/micronutrient content (NUTRITIONIST PRO™, Version 1.3, First Databank Inc, The Hearst Corporation, San Bruno, CA). The program had no missing values for the nutrients reported. The database was extensively modified by our group to include new foods and recipes.
To ensure that carbohydrates were restricted throughout the VLCK diet, subjects tested their urine daily using reagent strips (Bayer Corporation, Elkhart, IN) at the same time of day and recorded the result on log sheets. The test is specific for acetoacetic acid, which produces a relative color change when it reacts with nitroprusside. We have found this to be a very sensitive indicator of carbohydrate restriction and compliance to a VLCK diet in our prior studies [13,21,22,25]. Subjects were required to report to the laboratory each week to monitor weight, dietary compliance, and check the level of ketones (during the VLCK diet only). Subjects received follow-up counseling and dietetic education in necessary.
Body Mass and Body Composition
Body mass and body composition were measured in the morning after a 12 h overnight fast. Body mass was recorded to the nearest 100 g on a digital scale (OHAUS Corp., Florham Park, NJ) with subjects either nude or wearing only underwear. Whole body and regional body composition were assessed using a fan-beam DEXA (Prodigy™, Lunar Corporation, Madison, WI). Regional analysis of the trunk was assessed according to anatomical landmarks by the same technician using computer algorithms (enCORE version 6.00.270). Coefficients of variation for lean body mass, fat mass, and bone mineral content on repeat scans with repositioning on a group of men and women in our laboratory were 0.4, 1.4, and 0.6%, respectively.
Resting Energy Expenditure
Resting energy expenditure measurements were made by indirect calorimetry (MedGraphics CPX/D, Medical Graphics Corporation, St. Paul, MN) after an overnight fast (>12 h) with subjects resting supine in comfortable thermoneutral conditions. The metabolic cart was calibrated with a standard gas mixture each morning. Subjects were instructed to relax quietly in a dimly lit room without sleeping for 30 min and oxygen consumption (VO2) and VCO2 were averaged during the last 20 min for determination of REE [26]. We assessed reliability on two subjects who were tested two times per day for six consecutive days. The coefficient of variation for REE (kJ/day) was 2.95% for duplicate measures on the same day and 6.20% between days.
Statistical Analysis
Changes in body weight, body composition, and REE between diets were assessed using independent t-tests for between group comparisons (i.e., Phase I responses) and dependent t-tests were used to assess within group comparisons. All statistical analyses were performed with Statistica 5.5 for windows (StatSoft Inc, Tulsa, OK). Significance was set at P ≤ 0.05.
Results
Dietary nutrient intakes (Table 2)
There were no differences in dietary nutrient intakes between groups at baseline. Subjects complied very well with the given instructions for both diet interventions according to analysis of diets records. During the diet interventions, all dietary nutrients were significantly different between the VLCK and LF diets with the exception of total dietary energy (women only) and alcohol (see additional file 1 Table 2). Dietary energy was higher during the VLCK than the LF diet in men. We achieved our goals for each diet with <25% of total energy coming from fat on the LF diet and <10% of total energy coming from carbohydrate on the VLCK diet. All subjects were in ketosis throughout the VLCK diet as indicated by color changes on the urinary reagent strips (data not shown), indicating compliance in terms of carbohydrate restriction.
Between group comparison of subjects who either consumed a VLCK or LF diet
The reductions in body mass, total fat mass, and trunk fat mass were significantly greater after the VLCK than the LF diet for men, but not for women (Fig 1). The greater reduction in trunk fat was not merely due to the greater total fat loss in men, because the ratio of trunk fat/total fat was also significantly reduced during the VLCK diet in men (VLCK 57.9 ± 1.8 to 57.1 ± 1.7%; LF 60.2 ± 1.3 to 61.4 ± 1.1%). Although the ratio of trunk fat/total fat in women was reduced more on the VLCK diet (51.9 ± 2.4 to 51.2 ± 2.3%) compared to the LF diet (44.2 ± 2.2 to 44.5 ± 2.3%), this was not significant. There were no significant differences in REE expressed in absolute terms between the VLCK diet (men 2005 ± 283 to 1865 ± 96; women 1177 ± 43 to 1161 ± 101 kcal/day) and the LF diet (men 2352 ± 316 to 2119 to 137; women 1319 ± 92 to 1224 ± 100 kcal/day). Expressed relative to body mass, REE was maintained in men consuming the VLCK diet (19.6 ± 0.7 to 19.8 ± 0.7 kcal/kg) but decreased on the LF diet (20.4 ± 1.0 to 19.0 ± 0.8 kcal/kg). As expected, the respiratory exchange ratio decreased on the VLCK compared to the LF diet further indicating compliance to the VLCK diet.
Figure 1 Mean decreases in body mass, total fat mass, trunk fat mass, and lean body mass in men who consumed a very low-carbohydrate ketogenic (VLCK) diet (n = 8) or a low-fat (LF) diet and in women who consumed a VLCK (n = 7) and LF (n = 6) diet. *P < 0.05 from LF change in men (independent t-test).
Within group comparison of subjects who consumed both a VLCK and LF diet
Dependent t-tests were used to assess the difference between changes on the VLCK and LF diets. Again, the VLCK diet resulted in significantly greater reductions in body mass, total fat mass, and trunk fat mass for men. For these variables, the reductions were also significantly greater in women, in contrast to the results from between group comparisons (Fig 2). Individual data showing the comparison between diets for each person is shown for body mass (Fig 3), total fat mass (Fig 4), and trunk fat mass (Fig 5). In men, a majority benefited more from the VLCKD in terms of weight loss (11/15 subjects), total fat loss (11/15 subjects), and trunk fat loss (12/15 subjects). In women, a majority also benefited more from the VLCK diet in terms of weight loss (8/13 subjects), total fat loss (10/13 subjects), and trunk fat loss (12/13 subjects). It is noteworthy that 5 men showed more than a 10 pound difference in weight loss when the diets were compared. There was a preferential loss of fat in the trunk region as evidenced by significantly greater reduction in the ratio of trunk fat to total body fat after the VLCKD in both men and women. There were no significant differences in REE responses between diets.
Figure 2 Mean decreases in body mass, total fat mass, trunk fat mass, and lean body mass in men (n = 15) and women (n = 13) who consumed both a very low-carbohydrate ketogenic (VLCK) and a low-fat (LF) diet in a randomized and balanced fashion. *P < 0.05 from LF change (dependent t-test).
Figure 3 Individual differences between weight loss on a very low-carbohydrate ketogenic (VLCK) diet minus weight loss on a low-fat (LF) diet for each person. Positive numbers reflect greater weight loss on the VLCK, whereas negative numbers indicate greater weight loss on the LF diet. Red circles = order of diets VLCK then LF. Blue diamonds = order of diets LF then VLCK.
Figure 4 Individual differences between total fat loss on a very low-carbohydrate ketogenic (VLCK) diet minus total fat loss on a low-fat (LF) diet for each person. Positive numbers reflect greater weight loss on the VLCK, whereas negative numbers indicate greater weight loss on the LF diet. Red circles = order of diets VLCK then LF. Blue diamonds = order of diets LF then VLCK.
Figure 5 Individual differences between trunk fat loss on a very low-carbohydrate ketogenic (VLCK) diet minus trunk fat loss on a low-fat (LF) diet for each person. Positive numbers reflect greater weight loss on the VLCK, whereas negative numbers indicate greater weight loss on the LF diet. Red circles = order of diets VLCK then LF. Blue diamonds = order of diets LF then VLCK.
The results presented thus far indicate that VLCK diets result in superior weight loss and fat loss in men, and to a lesser extent in women, compared to a low-fat diet. To determine if this finding was influenced by the order the diets were implemented, we compared the responses to both diets between those who consumed the VLCK diet first to those who consumed the LF diet first. The individual responses to both diets over time are shown for body mass (Fig 6), total fat mass (Fig 7), and trunk fat mass (Fig 8). Statistically comparing the responses to a VLCK and LF diet within subjects, the only variable that was significantly affected by the order of the diet was body mass. In other words, the advantage of the VLCK over the LF diet was more dramatic for those who started the VLCK first. The individual responses reveal that three men and four women who did VLCK first, actually regained body mass and fat mass after the switch to the LF diet, whereas no subjects regained weight or fat mass after switching to the VLCK diet.
Figure 6 Individual changes in body mass in men (upper panels) and women (lower panels) who started on a very low-carbohydrate ketogenic (VLCK) and switched to a low-fat (LF) diet (left panels) and vice versa (right panels). Mean response is shown in red.
Figure 7 Individual changes in total fat mass in men (upper panels) and women (lower panels) who started on a very low-carbohydrate ketogenic (VLCK) and switched to a low-fat (LF) diet (left panels) and vice versa (right panels). Mean response is shown in red.
Figure 8 Individual changes in trunk fat mass in men (upper panels) and women (lower panels) who started on a very low-carbohydrate ketogenic (VLCK) and switched to a low-fat (LF) diet (left panels) and vice versa (right panels). Mean response is shown in red.
Discussion
We previously reported superior responses with a VLCK over a LF diet in a number of cardiovascular risk factors in these subjects [25,27]. The results of this study demonstrate that short-term VLCK diets also outperform LF diets in terms of weight loss and fat loss. These effects occurred despite apparently similar energy deficits between diets and in the case of men, significantly greater energy intake. Greater weight loss with a VLCK over a LF diet is consistent with the findings from other studies, and provides further support for the concept of metabolic advantage [7,8]. Since food was not provided this conclusion cannot be made with certainty, but we find it highly unlikely that any potential error in quantifying energy intake would account for the dramatic differences in weight and fat loss between diets. We can say with confidence that we studied subjects that were restricting carbohydrates to very low levels as verified by dietary food records, urine ketones, and low resting respiratory exchange ratios obtained with indirect calorimetry.
The basic principle on which weight loss diets are based is to reduce dietary energy intake below energy expenditure. Whether the relative composition of macronutrients can influence the magnitude or composition of weight loss achieved on an energy-restricted diet has been a point of contention. Several comparisons of isocaloric VLCK and LF diets, like the current report, show greater weight loss on a VLCK diet [6,16] supporting the long held notion of a metabolic advantage [28]. Given such evidence, it is difficult to understand the alternate position claiming a calorie must be a calorie in order to satisfy the first law of thermodynamics [29]. Although the origin of the difference in weight loss between VLCK and LF diets remains controversial, such a response clearly does not violate any thermodynamic laws [7]. Not all studies have shown greater weight loss with a VLCK diet [30] and the specific conditions that are required to elicit a metabolic advantage remain unknown.
One argument is that the greater weight loss on ad libitum VLCK diets is a result of spontaneously reducing energy intake [31], and this has been reported previously [32]. A reduction in energy intake on a VLCK diet has a logical physiologic basis and could account for a portion of the greater weight observed in studies that involved free-living ab libitum VLCK diets. Ketone levels increase several-fold on a VLCK diet, and β-hydroxybutyrate (the major circulating ketone body) has been shown to directly inhibit appetite [33]. Also, the low glycemic nature of a VLCKD may prevent transient dips in blood glucose, which can occur with higher carbohydrate diets. Thus, avoidance of hypoglycemic episodes may reduce appetite [34]. In this study we did not report a significantly lower energy intake on the VLCK compared to the LF diet. In fact, a higher energy intake was observed on the VLCK diet in men. In this case, it is often claimed that inaccurate reporting of dietary intake or errors in nutrient databases (e.g., overestimation of calories from certain cuts of meats) account for the greater weight reducing effects of VLCK diets. On the other hand, LF diets are frequently encouraged because of their high bulk and over-reporting seems as likely on a LF as a VLCK diet. In the absence of a clear reason why error in these studies should always go in one direction – LF rarely do better than VLCK – one has to take the data at face value. Also, the large difference in weight loss between men on the VLCK and LF diets in the present study suggests that at least some impact of macronutrient composition is being seen.
Metabolic advantage may occur on a VLCK diet due to the demand on protein turnover for gluconeogenesis [35], greater thermogenic effect of protein and loss of energy as heat [36,37], and/or excretion of energy in the form of ketones via urine, feces, and/or sweat. Although we did not see a difference in REE, the metabolic advantage on a VLCK diet may be below the sensitivity of our measurements. Further, since REE was obtained in a postabsorptive state, this does not rule out a potential benefit derived from the acute postprandial thermic effect of protein ingestion. In terms of REE, there was a slight advantage for men on the VLCK diet when expressed relative to body mass, which could benefit long-term weight maintenance but this needs to be validated in studies of longer duration.
Although the issue of whether VLCK diets result in greater weight loss compared to LF diets has obvious significance, a primary purpose of this study and an equally important question relates to the composition of weight loss. In a meta-analysis, Garrow and Summerbell [38] predict from regression analysis that for a weight loss of 10 kg by dieting alone, the expected loss from fat mass is 71%. The few studies that have assessed body composition suggest that VLCK diets may result in preferential loss of fat mass. Benoit et al. [10] showed that a 10 day VLCK diet (4.2 MJ/day) resulted in a weight loss of -6.6 kg in obese men, 97% of which was fat mass. Young et al. [9] compared the effects of three isoenergetic (7.5 MJ/day), isoprotein (115 g/day) diets containing varying carbohydrate contents (30, 60, and 104 g/day) on weight loss and body composition in obese men. After 9 weeks, weight loss was 16.2, 12.8, and 11.9 kg and fat accounted for 95%, 84%, and 75% of the weight lost, respectively. Willi et al. [11] showed that an 8 week VLCK diet (2.7–3.0 MJ/day) resulted in a weight loss of -15.4 kg and an increase in lean body mass of +1.4 kg in obese adolescents. An 8-week VLCK diet in overweight women resulted in a decrease in body mass of -5 kg, 80% of which was fat mass [12]. Our laboratory recently reported that a 6 week VLCK diet resulted in significant decrease in body mass (-2.2 kg), entirely accounted for by a decrease in fat mass (-3.3 kg) and concomitant increases in lean body mass (+1.1 kg) in normal-weight men [13]. The body composition results from the present study are in closer agreement with predictions from the meta-analysis [38].
A novel and potentially clinically significant finding was a preferential loss of fat in the trunk region with a VLCK diet, which was approximately three-fold greater during the VLCK than the LF diet. Upper body fat carries a greater health risk than fat stored in other regions of the body and thus an effective weight loss approach should consider the regional distribution of fat loss. Proportionally, trunk fat mass comprised less of the total fat mass after the VLCK but not the LF diet. The mechanisms regulating composition of weight loss and distribution of fat loss during VLCK diets remain unclear, but could be mediated in part by changes in hormones such as insulin, leptin, or cortisol that could differentially impact nutrient partitioning.
In summary, this study showed greater weight loss and fat loss preferentially from the trunk region in subjects on a closely monitored free-living VLCK diet compared to a LF diet. These diets were prescribed to be energy restricted and isocaloric. The superiority of the VLCK diet over the LF diet was most dramatic for men, but when individual responses were examined, a group of women clearly showed metabolic advantage as well. Indeed, 12/13 women experienced greater fat loss in the trunk region during the VLCK diet compared to the low-fat diet. Such a response is consistent with a metabolic advantage of VLCK diets. The ultimate proof for such a theory will depend on the findings from carefully controlled feeding and metabolic studies that encompass physiological measurements to isolate plausible mechanisms.
Supplementary Material
Additional File 1
Table 1. Baseline characteristics of men and women based on their starting diet. Table 2. Daily intakes of dietary energy and nutrients at baseline and during both diets.
Click here for file
Acknowledgments
This study was supported by a grant from The Dr. Robert C. Atkins Foundation, New York, NY.
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| 15533250 | PMC538279 | CC BY | 2021-01-04 16:37:45 | no | Nutr Metab (Lond). 2004 Nov 8; 1:13 | utf-8 | Nutr Metab (Lond) | 2,004 | 10.1186/1743-7075-1-13 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-901556939110.1186/1471-2164-5-90Research ArticleCharacterization of the chicken inward rectifier K+ channel IRK1/Kir2.1 gene Mutai Hideki [email protected] Lawrence C [email protected] Emily [email protected] Nami [email protected] John Carl [email protected] Department of Pathology and Laboratory Medicine, Division of Neuropathology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA2 Present address: Department of Otolaryngology, Program of Neuroscience, Harvard Medical School and Massachusetts Eye and Ear Infirmary, Boston, MA, USA3 Present address: Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA2004 29 11 2004 5 90 90 27 8 2004 29 11 2004 Copyright © 2004 Mutai et al; licensee BioMed Central Ltd.2004Mutai 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
Inward rectifier potassium channels (IRK) contribute to the normal function of skeletal and cardiac muscle cells. The chick inward rectifier K+ channel cIRK1/Kir2.1 is expressed in skeletal muscle, heart, brain, but not in liver; a distribution similar but not identical to that of mouse Kir2.1. We set out to explore regulatory domains of the cIRK1 promoter that enhance or inhibit expression of the gene in different cell types.
Results
We cloned and characterized the 5'-flanking region of cIRK1. cIRK1 contains two exons with splice sites in the 5'-untranslated region, a structure similar to mouse and human orthologs. cIRK1 has multiple transcription initiation sites, a feature also seen in mouse. However, while the chicken and mouse promoter regions share many regulatory motifs, cIRK1 possesses a GC-richer promoter and a putative TATA box, which appears to positively regulate gene expression. We report here the identification of several candidate cell/tissue specific cIRK1 regulatory domains by comparing promoter activities in expressing (Qm7) and non-expressing (DF1) cells using in vitro transcription assays.
Conclusion
While multiple transcription initiation sites and the combinatorial function of several domains in activating cIRK1 expression are similar to those seen in mKir2.1, the cIRK1 promoter differs by the presence of a putative TATA box. In addition, several domains that regulate the gene's expression differentially in muscle (Qm7) and fibroblast cells (DF1) were identified. These results provide fundamental data to analyze cIRK1 transcriptional mechanisms. The control elements identified here may provide clues to the tissue-specific expression of this K+ channel.
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Background
The inward rectifier potassium channel IRK1/Kir2.1 helps controls cell excitability through setting the resting membrane potential [1]. Its dominant role of inward rectification for the normal function of skeletal and cardiac muscles is shown by the complete loss of inward rectifying current and K+-induced dilations in arterial myocytes from Kir2.1 knockout mice [2] and periodic paralysis, and by cardiac arrhythmias in Anderson's syndrome caused by point mutation of human Kir2.1 [3].
Kir2.1 expression is detected in excitable cells in brain, heart, and skeletal muscle in both mouse and chick [4-8]. In addition, chicken IRK1 (cIRK1) is expressed in the cochlea [8], a feature not observed in mammals [9,10]. In this report, we first analyzed cIRK1 genomic DNA to identify transcriptional initiation sites and distinct motifs that are important for the expression of this potassium channel gene. Using in vitro promoter assays with fragments of the cloned cIRK1 locus, we also identified several candidate control domains that may participate in regulating the channel's exquisite tissue-specific transcription.
Results
Structure of the chick IRK1 genomic locus
We began this study by isolating cIRK1 genomic clones from a chicken genomic DNA phage library. A series of overlapping clones were isolated by screening the library using full length cIRK1 cDNA as a specific probe. Approximately 6.5 kilobase pairs (kb) of the cIRK1 5'-flanking region were sequenced (Genbank AF375660), including exon 1 and a portion of the intron. cIRK1 contains two exons: exon 1 includes only upstream non-coding sequence (5'-untranslated region, 5'UTR) while exon 2 includes 5'UTR (216 bp), the full open reading frame (1,284 bp), and the 3'UTR (520 bp) (Figure 1A). The single intron is estimated to be approximately 4.9 kb in length. A comparison of the cDNA and genomic sequences shows the splice site to be located between positions 103 and 104 of cIRK1 cDNA (GenBank U20216). Sequence data also showed that the intron had consensus donor (GU) and acceptor (AG) sequences (Fig. 1B). This genomic structure of the cIRK1 locus resembles that of mKir2.1 [11], which possesses two exons separated by a 5.5 kb intron. In a previous report [8], an approximately 5.5 kb cIRK1 transcript was detected, in addition to one 2.5 kb in length, in brain, cerebellum, heart, skeletal muscle, and cochlea. Since we have identified polyadenylation signals at bp positions 1,645–1,650 and 1,865–1,870 of the cDNA, we conclude that cIRK1 has 2 exons and no additional exon in the 3'UTR. This is also supported by the fact that multiple attempts to extend the cDNA by library screening or 3'RACE were not successful.
Figure 1 Genomic structure of chick IRK1 and determination of transcription initiation sites (A) Restriction map of chick IRK1/Kir2.1 genomic locus. The four overlapping genomic clones are shown above the restriction map. Solid boxes indicate exons. E; EcoRI site, X; XbaI site. The probe used for northern blots is underlined. (B) Genomic sequence surrounding splice sites. Exon 1 ends at +103 and exon 2 begins at +104 (based on cIRK1 cDNA, U20216), as indicated. Intronic sequences are in italics; bold letters indicate donor site GT and acceptor site AG. (C) Primer extension analysis. Approximate size, in bases, is indicated on the left. Products of about 80 bases were present in brain, but not in DF1 fibroblasts or in controls (tRNA). (D) Determination of transcription initiation sites using 5'RACE. Shown are the results of the secondary PCR using the 5'-nested primer and a primer designed at 64-41 of cIRK1 cDNA (N-R2), and controls using the 5'-nested primer only (N only) or the reverse primer only (R2 only).
Identification of transcription initiation sites
Before studying the motifs and elements of the promoter region, we sought to determine the transcription initiation sites of cIRK1 using primer extension and 5'RACE. When the specific reverse complementary sequence to the cIRK1 5'UTR at positions 61–41 was utilized as a primer for reverse transcription, a product of approximately 80 bases was generated from brain mRNA (Fig. 1C). Specificity of cDNA synthesis was determined by the absence of the band from yeast tRNA and DF1 cells – a chicken embryonic fibroblast cell line which does not express detectable levels of cIRK1 mRNA (Fig. 3). To confirm the precise transcriptional initiation sites, oligo-capped RNA based 5'RACE [12] was carried out using brain total RNA as template. This method results in RNA of which only transcripts with intact 5'-ends are capped by an RNA-oligo. We then reverse-transcribed this RNA using a specific primer R1 designed at positions 146–122 of cIRK1 cDNA. PCR was first performed with the supplied (GeneRacer) 5'-primer and the reverse primer R1. Subsequent nested PCR, using the supplied 5'-nested primer and a second reverse primer R2 designed at positions 64–41 of cIRK1 cDNA, produced multiple fragments. Those of approximately 110 bp were determined to be specific products of the primer set (N-R2) because they were not detected when the PCR was carried out using either the 5'-nested primer alone (N only) or the R2 primer alone (R2 only, Fig. 1D). The PCR fragments were cloned and multiple clones were sequenced. All of them contained 30 bp of 5'-capped oligo plus several lengths of cIRK1 transcript (80, 81, 82, and 100 bp), which occurred with similar frequencies. Therefore, the actual transcribed 5'-flanking regions were 16, 17, 18, or 36 nucleotides longer than the 5'-end of the clone originally isolated from a chick cochlea cDNA library [8]. The confirmed initiation sites are shown in bold in Fig. 2. The 3'-most initiation site, located 16 nucleotides upstream of the 5'-end of the previously reported cIRK1 cDNA, was numbered +1. Our identification of multiple transcription initiation sites indicates that exon 1 of cIRK1 can be 119, 120, 121, or 139 bases in length.
Figure 2 Motifs in 5'-flanking region of cIRK1 DNA sequence of the 5'-flanking region (from -417 to +33) is shown. Highlighted are the positions of two E boxes (asterisks), a NF-κB site (heavy underline), a putative TATA box (plus signs), four Sp1 sites (dashed double underlines), and an NF-Y binding site (dotted underline). Transcription initiation sites are shown in bold, with the most downstream site numbered +1. The 5'-ends of the fragments used in the promoter assays are shown with slashes and the construct names (see also Figure 4).
Figure 3 IRK1 is expressed in Qm7 cells, but not in DF1 cells. Northern blot analyses were performed using 5 μg of poly A(+) RNA from each cultured cell type. Two bands whose sizes (5.5 kb and 2.5 kb) are similar to those previously reported in chick tissues [8] were detected in Qm7 cells but not in DF1 cells. g3pdh is included as a loading control.
Comparison of the 5'-flanking regions of cIRK1 and mKir2.1
We then analyzed the motifs contained in the cIRK1 5'-flanking region, based on the presumption that there might be significant divergence between chicken and mammal IRK1/Kir2.1 promoter regions that could account for their different gene expression patterns. The 5'-flanking region of cIRK1, shown in Fig. 2, shares no evident homology with the comparable region of the mKir2.1 promoter [11]. The corresponding region of the putative human Kir2.1 promoter shows high similarity to the mKir2.1 promoter (62.6% over1,017 bp), however, the hKir2.1 promoter also failed to show significant homology with the cIRK1 5'-flanking region. While both the cIRK1 5'-flanking region and the mKir2.1 promoter have high GC contents, the chick promoter is substantially more GC-rich (71.0% in chick and 62.8% in mouse from position -390 to -1) and contains 38 CpG dinucleotides as compared to 17 CpGs in mouse. The cIRK1 promoter region has four consensus Sp1 binding elements (at positions -216, -194, -182, and -70; Fig. 2), while the mKir2.1 promoter contains three Sp1 sites [11]. Consensus binding sites for several other factors were also identified. E boxes (CANNTG) were present at -316 and at -46, and an inverted CCAAT (NF-Y element) [13] was found at -98, while the mKir2.1 promoter region contains 3 E boxes and one NF-Y element. A striking difference from mKir2.1 is that the cIRK1 promoter was found to contain a putative TATA box (TATTAA), absent in mKir2.1, at -56. Overall, while GC-rich domains and motifs are present in both the chick and mouse promoter regions, the extent and number of the motifs are different between the cIRK1 and mKir2.1 promoters. In addition, a TATA box-like sequence is present only in the chick promoter.
In vitro promoter analysis and cell-specific regulatory regions
We next sought to determine which of these avian-specific upstream elements, including the putative TATA-box, might play a role in cIRK1 transcription. First, we tested whether the 5'-flanking region up to approximately 2 kb contained sufficient elements to activate cIRK1 transcription, and then we investigated the minimal promoter region by constructing a series of 5'-flanking region deletion fragments, including a portion of exon 1, inserted into plasmids upstream of the luciferase reporter (Fig. 4A). Promoter activities were measured following transient transfection into DF1 or quail myoblast Qm7 cells, which do not and do express endogenous avian IRK1, respectively (Fig. 3). We hypothesized that identification of tissue specific regulatory regions of cIRK1 could be achieved by a comparison of reporter gene expression data obtained from these two cell lines. Figure 4B shows that cIRK1 transcription was activated in many of the deletion constructs in both DF1 and Qm7 cells, indicating that both contain sufficient factors to activate cIRK1 expression, whether or not they express the gene endogenously. However, promoter activities were differentially regulated in the two cell types when the relatively long 5'-flanking regions were included in the constructs upstream of the reporter luciferase gene (Fig. 4B). First, in Qm7 cells, deletion of a domain from position -2142 to -976 resulted in an approximately 60% loss of promoter activity (3B-2256 versus 3B-1089). In addition, deletion of the segment from -975 to -727 resulted in a recovery of activity (3B-840). This indicates that the region between -975 and -727 can function to repress transcription, but that this repression can be overcome by upstream elements. These effects were generally not seen in DF1 cells, except that 3B-840 exhibited somewhat higher activity than did 3B-1229. The region between -1115 and -976 showed a weak inhibitory effect on reporter expression in DF1 cells, while it had a positive regulatory function in Qm7 cells. Finally, a dramatic loss (59%) of promoter activity was observed upon deletion of the domain from -417 to -286 in Qm7 cells (3B-531 versus 3B-399). While this effect was substantial in Qm7 cells, it was absent in DF1 cells. These data indicate that the element(s) in this region activate IRK1 gene expression only in the myoblast cell line Qm7.
Figure 4 Identification of domains that affect cIRK1 expression (A) Constructs used in in vitro promoter assays. The putative TATA box, an NF-Y binding site, and an E box are shown in black, hatched and lined boxes, respectively. Numbers in parentheses indicate the length in bp of the promoter region/exon 1 included in each test plasmid. The transcription initiation site is marked with an asterisk. Luc, luciferase reporter gene. (B) In vitro transcription analysis. Plasmid constructs were transiently transfected into DF1 (left) or Qm7 cells (right). Results are shown as fold increases compared with the basal luciferase activity from cells transfected with control pGL-3B vector. Standard errors are shown as bars. *; p value < 0.05, **; p < 0.005.
The relative promoter activities exhibited by 3B-399 and shorter constructs were similar in the two cell lines. Activities decreased significantly between 3B-399 and 3B-226, but not between 3B-226 and 3B-206. Candidate active regulatory elements in this region included three Sp1 consensus sites arranged in tandem at -216, -194, and -182. The construct 3B-206, which lacks the NF-Y element activated transcription as well or slightly better than 3B-226. Point mutation of this element in the mKir2.1 minimal promoter resulted in a significant increase of promoter activity, suggesting a significant role for the NF-Y element in mouse compared with chicken [11]. Specific mutation of the putative TATA box led to a 60.3 % loss of activity in DF1 cells, and an 80.6 % loss in Qm7 cells, but not to a complete loss of promoter activity. The construct 3B-109 showed no significant activity compared with the mock vector, indicating that the 5'-flanking region up to -92 upstream is necessary for promoter activity. Taken together, two distal regions, -1115 to -727 and -417 to -286, were found to be candidate cell/tissue specific regulatory domains in the cIRK1 promoter, while the 5' flanking region proximal to -285 is predicted to activate ubiquitously. The GC rich regions, including Sp1 motifs, and the putative TATA box regulate the promoter positively.
Discussion
This is the first report describing a promoter analysis of an avian potassium channel. Our data show that cIRK1 and mKir2.1 have similar genomic organizations comprised of a single intron dividing the 5'UTR. In addition, in the 5'-flanking region, the cIRK1 promoter appears to contain a unique putative TATA box. Our results demonstrating positive regulatory activity of the domain that includes the TATTAA sequence strongly suggest that this element does indeed recruit and bind TATA-binding protein [14], thereby contributing significantly to gene expression. No TATA boxes are found in mammalian K+ channel promoters such as those for Kir2.1 [9], Kir3.1 [15], Kir3.4 [16], Kv1.4 [17], Kv1.5 [18] and Kv3.1 [19,20]. Further studies may determine whether chick orthologous K+ channel genes share their genomic organizations with those of mammals.
It should also be emphasized that elements other than the putative TATA box also participate in, although to a lesser extent, transcription initiation of cIRK1. Just upstream of the transcription initiation site lie several binding sites for transcriptional regulation, including Sp1 sites and an E box; these are also present in mKir2.1 and have been shown to activate that gene's expression [11]. While the significant role shown for the NF-Y site in mKir2.1 [9] was not apparent in the case of cIRK1 promoter activation, Sp1 and E box-binding proteins may work cooperatively in promoter activation, as this mechanism has been reported in the study of telomerase reverse transcriptase gene regulation [21]. Overall, the difference in transcriptional regulation of IRK1/Kir2.1 between chicken and mouse is that regulation of cIRK1 expression depends not only on the Sp1 sites and E box, but also on the putative TATA box. It is known that IRK1/Kir2.1 is expressed in the sensory epithelium of the chick cochlea [8], but that it is not expressed in the mature cochlea of the mouse [9] or rat [10]. Whether the putative TATA box leads to the expression of cIRK1 in the cochlea awaits further analysis.
While the results of our in vitro transcription analyses indicate that cIRK1 transcription can be activated both in expressing Qm7 and in non-expressing DF1 cell lines, and therefore likely in a variety of tissues, they also showed the existence of several cell specific regulatory domains. Examination of transcriptional regulation by the region -417 to -286 indicated that element(s) in this region increase promoter activity only in the myoblast cell line Qm7. Candidate elements within this region that might have cell-specific activity are an E box (CAGGTG) at -316 and an activating component of transcription activator NF-κB (GGGRNNYYCC; where R is A or G, Y is C or T, and N is any base) at -296 [22,23]. Various mouse helix-loop helix transcription factors such as atonal homolog Math1 [24] and MyoD [25] are reported to associate with E boxes and are required for tissue-specific cell type determination. The chicken Math1 ortholog Cath1 is expressed in the developing brain regions that express cIRK1 [26]. However, our co-expression of Math1 with the cIRK1 promoter construct 3B-531 did not enhance promoter activity (data not shown).
We also identified a region between -1115 and -727 with weak repressor activity that exhibited different properties in the two cell lines. Potential binding motifs present in this region are: a site for interferon regulatory factor-1 at -1030, an AP-4 site at -978, a thyroid hormone beta receptor binding element (TRE) at -962, another E box at -954, a binding site for the potent repressor CTF/NF-I at -859 [29], and a Drosophila homeotic gene Antennapedia binding motif [30] at -786. Among these, TRE has been shown to regulate a fast-activating K+ conductance in inner hair cells [27], and an E box has been shown to function as a repressive element when bound by Mad1/Max in differentiated HL60 cells [28].
Our observation of the transcriptional activation of cIRK1 in DF1 cells which do not express the gene endogenously clearly suggests the existence of additional mechanisms to repress cIRK1 in DF1 cells. Since domains acting to strongly silence cIRK1 expression in DF1 cells were missing in the 5'-flanking region characterized in this study, additional distant control regions may lie further upstream, downstream, or in the intronic sequences. Although at this point clearly speculative, the existence of many CpG dinucleotides in cIRK1 suggests the possibility of gene repression through DNA methylation of CpG dinucleotides leading to heterochromatin condensation of the adjacent genomic locus [31]. Tissue-specific expression of cIRK1 might consequently be explained by a combination of regulation via the specific motifs studied here, regulation via additional domains outside this region, and the potential modification of the cIRK1 genomic locus through DNA methylation.
Conclusions
We have identified multiple transcription initiation sites and several candidate regulatory elements in the chicken potassium channel gene cIRK1. These results provide fundamental data to further analyze cIRK1 transcriptional mechanisms. While the use of multiple transcription initiation sites and the combinatorial participation of multiple domains in activating cIRK1 expression are similar to those seen for mouse Kir2.1, the cIRK1 promoter is distinct in that it exhibits a higher GC-content than does the mKir2.1 promoter, and by the presence of a functional putative TATA box that is not observed in the mKir2.1 promoter. Transcriptional control domains identified here form the foundation of an in-depth analysis of tissue-specific expression of this K+ channel as well as the species-specific expression of cIRK1 in the chicken cochlea.
Methods
cIRK1 genomic cloning and upstream sequence analysis
Chick IRK1 genomic clones were isolated by screening a chick genomic library in the lambda FIX II vector (Stratagene) using cIRK1 cDNA (GenBank U20216) as a screening probe. Briefly, the probe was random primer-labeled with 32P-α-dCTP and hybridized with phage plaques blotted onto HybondN nylon membranes (Amersham Biosciences) overnight at 65°C in 6x SSC, 250 μg/ml salmon sperm DNA, 5x Denhardt's solution, and 0.1% SDS. The membranes were washed in 1x SSC and 0.1% SDS, and exposed to x-ray film. Four positive clones were further analyzed following digestion with XbaI and/or EcoRI, and subcloning into pBlueScript-KS. The promoter region (AF375660) was sequenced from both ends. Comparisons between the putative human Kir2.1 promoter region (AC005242), the mouse Kir2.1 promoter region (AF072673), and the coding regions of mKir2.1 cDNA (AF021136) and hKir2.1 cDNA (U24055) were performed using the Wilbur-Lipman DNA alignment method. Transcription factor binding elements were predicted based on the TRANSFAC algorithm [32] and the transcription element search system .
Primer extension, 5'RACE, and northern blot analysis
Total RNA was extracted from tissues and cells using TRIZOL reagent (Invitrogen) and treated with DNaseI. Three micrograms of total RNA (brain, DF1, or yeast tRNA) were reverse transcribed with 32P-end-labeled primer in 10 mM DTT, 50 μg/ml actinomycin D and 0.5 mM dNTP using Superscript II (Invitrogen) at 47°C for 60 min. The primer was designed at 61–41 of cIRK1 cDNA (U20216) [5'-TGT TAA GAT CCG CGG GGA CAC-3']. Reaction mixtures were fractionated on 7% polyacrylamide (PAA)/7 M Urea gels. The 3'-most transcription initiation site was numbered +1.
RNA ligase-mediated rapid amplification of 5' cDNA ends (5'RACE) was carried out using the GeneRacer kit (Invitrogen). In brief, 3 μg of total RNA were dephosphorylated, decapped and ligated with a 44-base RNA-oligo, according to manufacturer protocols. Next, the RNA was reverse transcribed using R1 primer designed at 146-122 of cIRK1 cDNA [5'-GCA GAG TTA GCT TAA CAA GTA ACC G-3'] at 42°C for 1 hr. The PCR was performed in a reaction mix containing 1x PCR buffer, 200 nM each of 5'-forward primer [5'-CGA CTG GAG CAC GAG GAC ACT GA-3'] and R1, 100 μM dNTPs, 5 μCi 32P-α-dCTP, 5% DMSO, and 1.25 U of AmpliTaq (Perkin-Elmer). Reaction conditions were; 94°C for 3 min, 5 cycles at 94°C for 1 min, 57°C for 5 min, 72°C for 2 min, and then 30 cycles at 94°C for 1 min, 57°C for 1 min, 72°C for 1 min, followed by 72°C for 5 min. Nested PCR was performed using the same conditions, with first round PCR products as template, and using the 5'-nested forward primer [5'-GGA CAC TGA CAT GGA CTG AAG GAG TA-3'] and R2 primer designed at 64-41 of cIRK1 cDNA [5'-GGG TGT TAA GAT CCG CGG GGA CAC-3']. Reaction products were eluted from the 7% non-denaturing PAA gel, re-amplified, then ligated into the pCR4-TOPO-TA cloning vector (Invitrogen) and sequenced.
Northern blotting was performed using poly A(+) RNA isolated using the FastTrak 2.0 kit (Invitrogen). Five micrograms of poly A(+) RNA were applied to each lane, fractionated on a 0.8% agarose gel, and transferred to a HybondN membrane. The membrane was hybridized with a random-primed 32P-labeled cIRK1 cDNA fragment (522 bp; 1,384–1,915) overnight at 42°C, washed, and exposed to x-ray film. A quail glyceraldehyde-3-phosphate dehydrogenase (g3pdh) gene fragment (696–968 bp, 97.4% identical to chick) was used as an internal control. Message sizes were estimated by comparisons with RNA molecular weight markers (Invitrogen) run in the adjacent lane.
In vitro transcription assay
Primers were designed to amplify each promoter region of interest. Constructs 3B-2256 (4201–6456 bp of cIRK1 5'-flanking region, AF375660), 3B-1229 (5228–6456 bp), 3B-531 (5926–6456 bp), and 3B-226 (6231–6456 bp) were generated by PCR, cloned into pBS-KS, and then subcloned into the multiple cloning site (between KpnI and XhoI) of pGL-3B (Promega). Constructs 3B-1089 (5368–6456 bp) and 3B-840 (5617–6456 bp) were obtained from a PCR fragment (1229 bp) with the 5'-region digested by SpeI and PvuII or XbaI, respectively. 3B-399 (6058–6456 bp) was derived from 3B-531 by removing the 5'-region by SacI and SmaI digestion, and 3B-206 was generated by exonuclease III digestion of 3B-226 followed by recircularization. 3B-109 (6348–6456 bp) was generated by SpeI and NheI digestion of 3B-206. Construction of 3B-206 with a mutated putative TATA box (3B-206dT) was accomplished by replacing the putative TATA box (TATTAA) with a StuI site (AGGCCT) by amplifying the original construct using primer sets designed to be complementary to sequences just outside the elements of interest, yet including StuI sites at their 5'-ends.
The chick fibroblast cell line DF1 was purchased from ATCC and cultured at 5% CO2 and 39°C in Dulbecco's minimum essential medium (DMEM) with 1.5 g/L sodium bicarbonate, 10% fetal calf serum, and 100 U/ml penicillin/streptomycin. The quail myoblast cell line Qm7 [25] was cultured in medium 199 with 10% tryptose phosphate, 10% fetal calf serum and 100 U/ml penicillin/streptomycin in 5% CO2 at 37°C. The ability of Qm7 cells to differentiate into myotube-like morphology was confirmed periodically as previously described [33]. One microgram of each construct with 0.1 μg of control vector pRL-TK were mixed in 50 μl of calcium phosphate buffer (140 mM NaCl, 5 mM KCl, 750 μM Na2HPO4, 6 mM dextrose, 25 mM HEPES at pH = 7.15, and 120 mM of CaCl2), and were incubated with 2 × 104 cells in 24-well dishes for 8 hrs in 500 μl of DMEM. The transfection mixes were replaced with fresh growth media and incubation was continued for 48 hrs. In vitro promoter activities were determined using the Dual-Luciferase Reporter Assay System (Promega). Briefly, transfected cells in each well were lysed in 100 μl of 1x Passive Lysis Buffer for 15 min at room temperature. Lysates were centrifuged briefly, and 20 μl of the supernatant was mixed with 100 μl of Luciferase Assay Reagent II, followed by 100 μl of SG reagent. Luciferase activities reflecting cIRK1 promoter activities and control thymidine kinase promoter activities were measured for 10 seconds after premeasurement periods of 2 seconds. All experiments were performed in duplicate and repeated at least 3 times (n= 3–6). The data were analyzed using a t-test assuming equal variance between two samples. Results are shown as fold increases ± standard error when compared with the basal luciferase activities from cells transfected with mock vector pGL-3B.
Authors' contributions
HM obtained the sequence of cIRK1 genomic DNA and did all the database searches to identify motifs and control regions in the 5'-flanking region of cIRK1. He also conducted 5'RACE, Northern blot, cell culture, construction of deletion mutant, in vitro transcription analysis, and drafted the manuscript. LCK mapped much of the genomic structure and determined some of the sequence. EL and NK participated in maintenance of the cell lines and in vitro transcription analysis. JCO participated in design and coordination of the study and finalized the manuscript.
Acknowledgments
We thank Dr. Charles Emerson for the gift of Qm7 cells. We also thank Dr. James Davis and Dr. Stefan Heller for reading the manuscript and providing helpful suggestions. This work was supported by a grant from the National Institute on Deafness and other Communication Disorders to J.C.O. (DC02755).
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| 15569391 | PMC538280 | CC BY | 2021-01-04 16:32:43 | no | BMC Genomics. 2004 Nov 29; 5:90 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-90 | oa_comm |
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-911557920710.1186/1471-2164-5-91Research ArticleLower rate of genomic variation identified in the trans-membrane domain of monoamine sub-class of Human G-Protein Coupled Receptors: The Human GPCR-DB Database Wahlestedt Claes [email protected] Anthony J [email protected] Salim [email protected] Center for Genomics and Bioinformatics, Karolinska Institutet, Berzelius väg 35, 17177 Stockholm, Sweden2 Department of Genetics, University of Leicester, University Road, Leicester, LE1 7RH, UK2004 4 12 2004 5 91 91 28 7 2004 4 12 2004 Copyright © 2004 Wahlestedt et al; licensee BioMed Central Ltd.2004Wahlestedt 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 have surveyed, compiled and annotated nucleotide variations in 338 human 7-transmembrane receptors (G-protein coupled receptors). In a sample of 32 chromosomes from a Nordic population, we attempted to determine the allele frequencies of 80 non-synonymous SNPs, and found 20 novel polymorphic markers. GPCR receptors of physiological and clinical importance were prioritized for statistical analysis. Natural variation and rare mutation information were merged and presented online in the Human GPCR-DB database .
Results
The average number of SNPs per 1000 bases of exonic sequence was found to be twice the average number of SNPs per Kilobase of intronic regions (2.2 versus 1.0). Of the 338 genes, 111 were single exon genes, that is, were intronless. The average number of exonic-SNPs per single-exon gene was 3.5 (n = 395) while that for multi-exon genes was 0.8 (n = 1176). The average number of variations within the different protein domain (N-terminus, internal- and external-loops, trans-membrane region, C-terminus) indicates a lower rate of variation in the trans-membrane region of Monoamine GPCRs, as compared to Chemokine- and Peptide-receptor sub-classes of GPCRs.
Conclusions
Single-exon GPCRs on average have approximately three times the number of SNPs as compared to GPCRs with introns. Among various functional classes of GPCRs, Monoamine GPRCs have lower number of natural variations within the trans-membrane domain indicating evolutionary selection against non-synonymous changes within the membrane-localizing domain of this sub-class of GPCRs.
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Background
The 7TM (7 trans-membrane domain proteins) genes, also known as the hetero-trimeric GTP-binding protein (G protein)-coupled receptors (GPCRs), are members of a large family of genes with an estimated 700 members in the human genome [1]. These receptors are plasma membrane-bound and have evolved to respond to a large number of extracellular and chemical signals. Upon interaction with their ligands, GPCRs act through the G proteins in signaling pathways that influence physiological functions. All GPCRs, in spite of great diversity in sequence composition, share a common protein structure. An N-terminal extracellular domain of variable length is followed by seven hydrophobic transmembrane-helices, connected by three intracellular (IL) and three extracellular (EL) loops, which then terminates in a C-terminal intracellular domain [2]. The functional and structural role of the different domains has been elucidated by systematic point mutations and crystal structure analysis for many of the human GPCR proteins. Several studies have collectively analyzed the occurrence, and importance of coding GPCR SNPs [3-5] and also the relevance and importance of mutations within these genes for the pharmaceutical industry [6]. The functional significance of thousands of point mutations has been described by a large number of investigations as evident at NCBI's PubMed Central. Mutation databases dedicated to GPCR mutations are currently available online like GPCR DB [7] and tinyGRAP [8]. Although an extensive collection of mutations is available at these sources, the distribution of these mutations and variations within the gene or peptide, along with common SNPs is not easily accessible or evident. The SNP databases in public domain (for example: NCBI's dbSNP) have highlighted all non-synonymous SNPs (nsSNPs). Also HGVBase has further classified the location of the amino acid within the encoded proteins to more accurately predict the detrimental effects of a change in peptide sequence. From a pharmacogenetics viewpoint, the information about natural variations within GPCR transcripts and peptides, with allele frequency and validation data and disease association, is an important, yet currently unavailable, public resource. A database with functional promoter SNP, allele frequency, peptide variation information, population and haplotype information presented in a graphically accessible format would facilitate pharmacogenomics research related to GPCR proteins. HUMAN GPCR-DB aims to provide such a public resource. Also the rate of false positive SNPs determined experimentally in GPCR genes is reported to be relatively high [5]. For typical case-control association studies, prevailing designs favor highly polymorphic loci as against loci where the frequency of the minor allele is below 10%. Therefore more nsSNPs need to be validated and frequencies determined across ethnically diverse populations. We have designed genotyping assays and attempted to validate a number of GPCR nsSNPs for which no validation information was available on public databases, and deposited the validation information at HUMAN GPCR-DB. We have also collected published literature SNPs and added to our online database.
Several recent studies have focused on the subset of nsSNPs that most likely influence phenotype [9-13]. Comparatively, fewer attempts have been made on predicting and validating functional promoter SNPs [14]. As a part of a parallel work, we have developed a streamlined bioinformatics and wet-lab analysis methods to identify putative functional promoter SNPs with up to 70% probability of influencing gene expression. By applying this analysis package to all of the 338 genes in our database, we have highlighted, putative functional promoter SNPs. HUMAN GPCR-DB also attempts to merge SNP and other variations from published articles from PubMed and online SNP databases to facilitate direct identification of the functional significance of a natural variation.
Results
A total of 427 non-redundant Human GPCR peptides were obtained from Swissprot of which 338 had Swissprot ID and the remaining had only TrEMBL identifications. Also, 89 of the 427 GPCR were classified as olfactory receptors. While all of the 427 entries were included in the HUMAN GPCR-DB database, only non-olfactory (i.e. 338) were considered for further analysis of gene structure, alternative transcripts, SNPs, and protein variations. Although the TrEMBL entries have also been included, the data displayed for these entries would be more accurately presented in future updates of the database. The statistical calculations for the genomic SNPs are based on the 338 non-olfactory entries, while the data for nsSNPs encoding peptide variations is based on 222 entries for which there are documented evidence for a 7-TM domain structure.
Transcript information for each gene was used for calculating average exonic and intronic SNPs. For genes with multiple transcript variants, the longest transcript was selected. Average number of SNPs per exon (n = 1511) of the 338 genes was one, while the average number of SNPs per intron (n = 1174) was nine. However, the average number of SNPs per 1000 bases of exonic sequence was twice the average number of SNPs per kilobase of intronic regions (2.2 versus 1.0). Of the 338 genes, 111 were single exon genes, that is, were intronless. The average number of exonic-SNPs per single-exon gene was 3.5 (n = 395) while that for multi-exon genes was 0.8 (n = 1176). This observation is in agreement with earlier observation based on a smaller number of GPCRs, where, compared to intronless GPCRs, exons in genes with introns on average had fewer SNPs [15].
Of the 1511 SNPs from the exons of 338 GPCR transcripts, 816 SNPs were coding SNPs, among which 392 were nsSNP; 211 of which had no validation information in either of the source databases, i.e. NCBI/dbSNP and HGVBase. The number of validated and non-validated SNPs is shown in Table 1. By excluding genes with 'probable', 'putative', 'precursor' and other ambiguous terms as a part of their description (as designated by Ensembl), we reduced the number of SNPs from 211 to 123 non-validated, nsSNPs which were considered as our list of prime candidates for the wet-lab validation process (Table 2). As a part of our first stage validation process, we designed assays for 80 of the 123 SNPs in GPCR genes of interest based on our better understanding of their role in human disease and physiology. Finally, of the 80 assayed SNPs, 20 were found to be polymorphic in our Nordic population sample consisting of DNA from 16 un-related healthy individuals. Of the 20 polymorphic markers 12 had a minor allele frequency higher than 10%.
Table 1 Distribution of validated and non-validated SNPs.
Synonymous SNPs Non-synonymous SNPs Total SNPs
Validated SNPs 212 181 393
Non-validated SNPs 212 211 423
Total 424 392 816
The number of synonymous and non-synonymous SNPs and those with validation information, as deposited at NCBI/dbSNP, during 2003–4.
Table 2 Number of GPCRs in validated and non-validated categories.
Nr. of GPCR genes Total nsSNPS Validated nsSNPs Non-validated nsSNPs This study
338 (genes classified as GPCRs) 392 181 211 (123 were considered for validation). 80 of 123 assayed. 20 were polymorphic
222 (bearing evidence for 7-TM domains) 283 112 (Added 120 from [16]) 171
101 (classified in 3 sub-groups – Monoamine, Peptide and Chemokine). 182 53 (Added 83 from [16]) = 136 (used in Table 3).
The initial 338 GPCRs were selected as per annotation by Ensembl. Support for structural evidence of 222 GPCRs was obtained from SWISSPROT. Classification of GPCRs was obtained from GPCRDB , Ensembl , International Union of Pharmacology .
Table 2 shows the number of GPCRs categorized by different criteria, and SNPs categorized in the two groups of validate and non-validated nsSNPs. Of the 338 genes, 222 had a documented GPCR structure as described by SWISSPROT/TrEMBL database. The total number of nsSNPs in this subset of 222 proteins was 283, of which 112 had validation information (leaving 171 with no validation information). To these we added 120 SNPs from Pubmed reports [14]. The identity of the 120 variations from published reports was verified to be SNPs and not rare mutations. Therefore a significant proportion of 'disease causing', rare variations were eliminated since they were reported from rare family based disease cases. These 120 SNPs are represented in the HUMAN GPCR-DB as 'rs-missing' since dbSNP records for many of these were not found. As future updates from dbSNP assign rs-IDs to the new SNPs, our database would update the records likewise.
According to the International Union of Pharmacology , 101 GPCRs from 338 were categorized either as 'peptide receptors' (n = 47) or 'chemokine receptors' (n = 54) or 'monoamine receptors' (n = 36). The distribution of nsSNPs across the 5 structural and functional domains (N-terminus, external loops, trans-membrane, internal loops and C-terminus) of these 101 GPCRs was calculated (Table 3). These 101 GPCRs have in total 182 nsSNPs, of which 53 SNPs have validation information in the major public databases. To these 53 SNPs we added 86 SNPs from published PubMed sources [14], bringing the total number of validated SNPs, used for this analysis, to 136 SNP. The 20 SNPs validated in this study were not included for this analysis since we wanted to analyze publicly available data only, at this time. The distribution of nsSNP numbers was compared between individual groups (monoamines-receptors only, or chemokine-receptors only or peptide-receptors only) and in various combinations with other two groups (monoamines plus chemokines or peptides plus monoamine, etc). None of the groups of receptors deviated from the mean of the three groups together, in any significant way. The N-terminus and external-loop SNPs were then combined in one group, and C-terminus and internal-loop SNPs in another group, and compared with nsSNPs in TM region. The nsSNP distribution in the three domains approached significance (Pearson's p-value 0.06) in the Monoamine sub-group of GPCRs. We then calculated the average number of nsSNP per 1000 bases of each of the 5 domains. The average number of nsSNPs in the TM region of Monoamine receptors (two SNPs per kilobase) was half of the average for each of the other groups (four or five nsSNPs per kilobase). This difference was not observed for any of the other four (N-term, e- and i-loops and C-term) structural domains of the peptides (Table 3).
Table 3 SNP distribution in peptide domains.
Peptide domain Genes N-term e-loop TM i-loop c-term p-value N-term + e-loop TM C-term + i-loop p-value
Monoamine + Peptide + Chemokines 101 20 (3.7) 17 (3.6) 43 (3.0) 29 (3.5) 27 (4.5) 37 (3.7) 43 (3.0) 56 (4.0)
Monoamine Only 36 7 (5.7) 6 (3.9) 11 (1.9) 16 (3.3) 18 (3.9) 0.26 13 (4.7) 11 (1.9) 34 (3.5) 0.06
Peptide Only 47 10 (2.9) 8 (3.9) 25 (4.2) 11 (4.3) 15 (5.7) 0.89 18 (3.3) 25 (4.2) 26 (5.0) 0.79
Chemokine Only 18 3 (4.0) 3 (2.8) 7 (2.5) 2 (2.5) 4 (4.5) 0.87 6 (3.3) 7 (2.5) 6 (3.5) 0.72
Chemokine + Monoamine 54 10 (5.1) 9 (3.4) 18 (2.1) 18 (3.2) 12 (4.1) 0.93 19 (4.1) 18 (2.1) 30 (3.5) 0.78
Peptide + Monoamine 83 17 (3.7) 14 (3.9) 36 (3.2) 27 (3.6) 23 (4.9) 0.94 31 (3.8) 36 (3.2) 50 (4.2) 0.96
Chemokine + Peptide 65 13 (3.1) 11 (3.5) 32 (3.7) 13 (3.8) 19 (5.4) 0.87 24 (3.3) 32 (3.7) 32 (4.6) 0.71
Total number of nsSNP in various domains of 3 subgroups of GPCR proteins.
Abbreviations: N-term : N terminus; C-term : C terminus; i-loop : internal loops; e-loop : external loops; TM: trans-membrane. The numbers in the brackets are the average number of SNPs per 1000 base pairs of a specific domain. The Fisher's Exact p-values were calculated using a 5 × 2 contingency table at . Contingency tables of 2 × 3 were constructed at . P-values of below or close to 0.05 are considered significant.
We compiled together the functional properties of peptide variations from published records along with the knowledge of the location and the two alleles of a nsSNP in our database. Searching PubMed records, we found 38 nsSNPs located in the precise position, and substituting the same amino acid, as those studied for functional analysis shown in Table 4 [See additional file 1].
Database interface and layout
The HUMAN GPCR-DB is currently online . This database allows for 3 alternative queries, either Ensembl gene ID, or Swissprot/TrEMBL ID or part of the gene name or description. Resulting hits are displayed along with total number of coding SNPs and protein variations. These links in turn display a graphic representation of the SNP locations within exons (or peptide) and the query gene. The exons are drawn in proportion to the largest exon, while the introns are of fixed length. The SNP list provides allele information, flanking sequences (for assay development and strand verification, etc) and links for validation information and source databases. The promoter information is drawn in a similar manner, with mouse conserved regions indicated with green bars underneath and SNPs within conserved regions marked with a symbol 'M' in the SNP full-list. SNPs predicted to influence protein binding according to our prediction model are marked 'T' in the SNP full-list. The link for protein variations displays a window with SNP, marked in red arrows, and mutation distribution across 3 regions of the peptide; N-terminus, C-terminus and trans-membrane and loop regions. Association with diseases and the corresponding PubMed ID are displayed as a popup menu following the link under 'disease' column.
Discussion
We have constructed a database, which combines mutation information with validated SNP information from publicly available sources. We have then attempted to validate and determine the frequencies of 80 of the 123 non-synonymous SNPs for which no validation information was available publicly. Proportion of true polymorphic loci was 20%, in agreement with reported expectations from several studies [5].
The statistical approach for the analysis of distribution of natural variations in GPCRs, presented here is borrowed from two recent studies [15,16]. While in the first of these studies [15] 64 GPCR genes were sequenced in 82 individuals of divergent ethnic backgrounds and resulting frequency distribution of nsSNPs were compared with non-GPCR genes, the later study [16] analyzed differences in distribution of published and publicly available nsSNP in 62 GPCR genes, across the 5 peptide domains. For our current report we analyzed 222 GPCR genes with over 200 nsSNPs (283 snSNPs available on public databases, and 120 nsSNP from PubMed records) [16]. Transcripts lacking introns had on average higher density of SNPs (2-fold) than those with introns, in agreement with an earlier published report [15]. The distribution of the nsSNP in the 5 different peptide domains (N-term, e-loops, trans-membrane, i-loops and C-term) was found not to be different between any of the ligand specific sub-groups of GPCR proteins, namely peptide receptors, monoamine receptors and chemokine receptors. We reasoned that the evolutionary constraints on outer- and inner-loops along with the N-term and C-term regions would be related to the function of these regions while the constraints on the trans-membrane regions might be related to their structure. We, therefore compared the distribution of validated nsSNP in the 3 major peptide domains (e-loops + N-term = region 1; trans-membrane = region 2; i-loops + C-term = region 3). We observed a difference in nsSNP distribution across the three regions, which approached significance (Pearson's p-value = 0.06). There was a two-fold decrease in frequency of occurrence of nsSNP in the TM region of monoamine receptors as compared to the other sub-groups of receptors. This indicates that there might perhaps be a functional selection against variations, acting on TM domains of monoamine receptors, which is less selective on the TM domains of peptide receptors, and chemokine receptor GPCRs. A recent study reported differences between the distribution of 'disease causing' and 'non-disease causing' variations in different sub-groups of GPCR family members [16]. Our study excluded rare mutations, which were known to be associated with disease, and therefore a similar comparison was not possible.
Although HUMAN GPCR-DB database does obtain the bulk of the information and data from Ensembl and dbSNP, it is not merely a subset of these major databases. While Ensembl provides sequence and genetic variation information, it provides SNP validation information obtained from public sources, which may include, as shown in several published studies, up to 50% false positives. NCBI's dbSNP provides validation-, submitter- and method-information, yet rates of false positives have proven to be high. These databases harbor information of genetic variations for all coding and non-coding regions of the human genome. The HUMAN GPCR-DB, in addition to providing this set of information, provides in-house validation and assay-information for non-validated nsSNPs. HUMAN GPCR-DB provides natural variation, mutation, promoter- and peptide-variation information along with gene structure and peptide 7-TM structure information and SNP validation information of a focused group of clinically important genes.
Transcriptional regulatory regions on the 5'-FR of human genes encode short sequences which serve as targets for binding of transcription factors (TFs). Eukaryotic TFs tolerate considerable sequence variation in their target sites and recent works in bioinformatics [17-19] have developed reliable methods to model the DNA binding specificity of individual TFs [20]. Currently the most successful approach to overcome this information gap is based on the assumption that gene sequences conserved between species (here Human and Mouse) would most likely mediate biological function [21-25]. Our recent study (our manuscript, 2004) describes a method for the detection and validation of functionally important SNPs in the 5'-flanking regions of human. The rate of successful detection of SNPs influencing TFBS using our method is approximately 70%. This prediction algorithm has been used to highlight SNPs in 5' flanking regions of the GPCR in the HUMAN GPCR-DB genes to facilitate selection and study of functionally important promoter SNPs. The knowledge of functional promoter SNPs would help us study disease related GPCR in more details.
The functional domains of human GPCRs have over the passed decade been dissected by systematically mutating the peptide sequence [8]. A collection of mutations and their disease significance and influence on the function of the protein together with common variations within the human population would facilitate our understanding of the variations and disease association. HUMAN GPCR-DB attempts to merge SNP and mutation information along with disease information in easily accessible and user-friendly manner. Although direct links to disease databases would be included in the next release, the existing information about the source publication can be helpful in obtaining the relevant details about the mutations.
Current and future updates
Of the 123 nsSNPs without validation information, we have currently validated 80 SNPs. Future updates would include information about the remaining nsSNPs, and any additional which are reported by public databases. We have also collected 120 published SNPs, which have as yet not been deposited at public databases, or are in the process of being deposited. We would have a complete update of SNP data for every new release of NCBI and HGVBase SNP tables. New GPCR identification and characterization, or changes in existing GPCR genes or proteins information would be updated once a year from Ensembl and SwissProt Databases. PubMed references would be updated monthly or as often as necessary to complete the mutation coverage of the 222 GPCR proteins. Haplotype information and genetic association studies for available SNPs along with published records on functional promoter SNPs would be added. A valuable addition would be to indicate variation frequencies in ethnically diverse populations. We are currently adding such information about the allele frequencies and populations in the database and would be provided in future updates.
Conclusions
Single-exon GPCRs on average have approximately three times the number of SNPs as compared to GPCRs with introns. Among various functional classes of GPCRs, Monoamine GPRCs have lower number of natural variations within the trans-membrane domain indicating evolutionary selection against non-synonymous changes within membrane localizing domain. The HUMAN GPCR-DB compiles SNPs and mutations in one database. Using a recently developed method for identification of functionally important SNPs in the 5'-flanking regions of human, with approximately 70% success rate, the database highlights such SNPs to facilitate selection and study of functionally important promoter SNPs.
Methods
The list of Human GPCR genes was compiled by collecting gene names from several different sources and subsequently the list was updated by removing duplicates and entries with incomplete information like peptide fragments, partial sequences and hypothetical proteins. The Ensembl MART genome server database was queried for GPCR family members. The Gene Ontology server Amigo was queried with the search term 'GO:0004930', which describes the GPCR group of genes. The list of Swiss-Prot and TrEMBL entries were fetched from GPCRDB [7], ensemble, International Union of Pharmacology . All the lists were merged and redundancies removed and Ensembl gene numbers (ENSG) were obtained for all of the genes from Ensembl genome server. The final list consisted of total of 427 genes with unique ENSG numbers. Of these 427 genes, 89 genes belonged to the sub-family of olfactory genes. Also of the 427 genes, 222 had demonstrable or convincing evidence for GPCR domain structure and sequence information in Ensembl and SwissProt databases.
The gene and transcript map information were obtained from Ensembl databases 'homo_sapien_core_25_34d' and 'ensemble_mart_25_1', released in September 2004. The tables for gene mapping, SNP mapping and Human-Mouse alignment were obtained from ensemble, dbSNP, HGVBASE and UCSC and installed locally. For the chromosomal and genomic location of SNPs and validation information, NCBI's dbSNP tables 'snp', 'snpcontigloc', and 'snpcontiglocusid' and Ensembl's tables 'ContigHit' and 'locus' were used. Information about the location of the SNP within protein domains was obtained from Swissprot using bioperl modules for accessing protein features. HGVBase release version 14 was used for obtaining HGVBASE SNP identities and validation and frequency information. The flanking sequence information was obtained from both Ensembl's RefSNP table and by downloading sequence flat files from dSNP (ds_flat_chr'1–22. X, Y'.fa);
For mapping Transcription Factor Binding Sites, the TFBS perl programming system [26] was used. This program applies position weight matrices (PWM) to DNA sequences to generate mathematical probability for the binding of a TF, based on the earlier described thermodynamics of binding energy [27-29]. Recent reviews and articles describe methods related to PWM and the bioinformatics of regulatory site prediction [18,30]. For determining human-mouse conserved regions, global best alignments files were downloaded from UCSC. SNPs in 5' flanking sequences were analyzed according to the differences in the absolute bind score derived from the matrices for each TF. A total of 78 factors from vertebrate class were used, hosted at the TFBS database, JASPAR [31]. MySQL™ version 4.0 with ActiveState™ Komodo version 2.3 as perl programming IDE and bioperl modules version 1.2 were used for database development. The web server technology used was Apache™ 2.0 with PHP 4.0, as supplied by NuSphere™ version 3.0. Non-synonymous SNPs were identified from all the SNPs, after the construction of the GPCR SNP database based on data acquired directly from dbSNP and HGVBASE. Validation of SNPs was carried out by DynaMetrix Inc., UK, using the DASH platform [32]. The allele frequency validation was performed on DNA samples from 16 anonymous individuals of Nordic descent, with the Institutional Review Board Approval KI 02-544.
Abbreviations
7TM: 7 transmembrane; GPCR:G-protein Coupled Receptors; SNP: single Nucleotide Polymorphism; nsSNP : non-synonymous SNPs; cSNP : coding SNP; rSNP : regulatory SNPs; N-term : N terminus; C-term : C terminus; e-loop : external loops; i-loop : internal loops.
Authors' contributions
SM-T did all the coding, analysis, manuscript preparation and reviewer correspondence. AJB contributed genotyping information and validated a number of SNPs. CW provided running costs and assistance with writing the manuscript. All authors read and approved the final manuscript.
Supplementary Material
Additional File 1
Table 4 A list of natural non-synonymous variations and mutations with references to articles describing the phenotype associated with the variation.
Click here for file
Acknowledgements
Thanks to Hang Mao Lee for assistance with GPCR classification and sequence collection. We are grateful to Fang Wang for assisting with disease association and frequency determination work. Pfizer Inc. and Swedish Science Foundation supported this work.
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| 15579207 | PMC538281 | CC BY | 2021-01-04 16:32:42 | no | BMC Genomics. 2004 Dec 4; 5:91 | utf-8 | BMC Genomics | 2,004 | 10.1186/1471-2164-5-91 | oa_comm |
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Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-231556657510.1186/1465-9921-5-23ResearchInhibition of c-Jun NH2-terminal kinase or extracellular signal-regulated kinase improves lung injury Lee Hui Su [email protected] Hee Jae [email protected] Chang Sook [email protected] Young Hae [email protected] Jihee Lee [email protected] Department of Physiology, Division of Cell Biology, Ewha Medical Research Institute, Ewha Womans University College of Medicine, 911-1 Mok-6-dong, Yangcheon-ku, Seoul 158-056, Korea2 Department of Microbiology, Division of Cell Biology, Ewha Medical Research Institute, Ewha Womans University College of Medicine, 911-1 Mok-6-dong, Yangcheon-ku, Seoul 158-056, Korea2004 27 11 2004 5 1 23 23 27 8 2004 27 11 2004 Copyright © 2004 Lee et al; licensee BioMed Central Ltd.2004Lee 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 in vitro studies have determined that the activation of mitogen-activated protein (MAP) kinases is crucial to the activation of transcription factors and regulation of the production of proinflammatory mediators, the roles of c-Jun NH2-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) in acute lung injury have not been elucidated.
Methods
Saline or lipopolysaccharide (LPS, 6 mg/kg of body weight) was administered intratracheally with a 1-hour pretreatment with SP600125 (a JNK inhibitor; 30 mg/kg, IO), or PD98059 (an MEK/ERK inhibitor; 30 mg/kg, IO). Rats were sacrificed 4 hours after LPS treatment.
Results
SP600125 or PD98059 inhibited LPS-induced phosphorylation of JNK and ERK, total protein and LDH activity in BAL fluid, and neutrophil influx into the lungs. In addition, these MAP kinase inhibitors substantially reduced LPS-induced production of inflammatory mediators, such as CINC, MMP-9, and nitric oxide. Inhibition of JNK correlated with suppression of NF-κB activation through downregulation of phosphorylation and degradation of IκB-α, while ERK inhibition only slightly influenced the NF-κB pathway.
Conclusion
JNK and ERK play pivotal roles in LPS-induced acute lung injury. Therefore, inhibition of JNK or ERK activity has potential as an effective therapeutic strategy in interventions of inflammatory cascade-associated lung injury.
JNKERKLPSacute lung injuryNF-κB
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Background
Lipopolysaccharide (LPS) causes acute lung injury associated with the activation of macrophages, an increase in alveolar-capillary permeability, neutrophil influx into the lungs, and parenchymal injury [1]. This pulmonary response contributes to the pathogenesis of various acute inflammatory respiratory diseases. Mitogen-activated protein (MAP) kinases are crucial in intracellular signal transduction, mediating cell responses to a variety of inflammatory stimuli, such as LPS, tumor necrosis factor (TNF) and interleukin (IL)-1. Recently, various in vitro studies have shown that pharmacological inhibitors of MAP kinases strongly affect the production of inflammatory mediators [2,3]. Through the use of specific inhibitors, the potential role of these kinases in inflammatory lung diseases is beginning to be studied. Treatment with p38 MAP Kinase inhibitors has been proposed as a selective intervention to reduce LPS-induced lung inflammation due to decreases in neutrophil recruitment to the air spaces [4,5]. However, the functions of c-Jun NH2-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) in LPS-induced lung injury remain unclear.
Cytokine-induced neutrophil chemoattractant (CINC) has been shown, in rodent models of lung injury, to play an important role in neutrophil migration into the lung [6]. Matrix metalloproteinases (MMPs), including MMP-9, allow activated neutrophils to permeate subsequent extracellular matrix (ECM) barriers after adhesion, and also for transendothelial cell migration, since these proteolytic enzymes digest most of the ECM components in the basement membranes and tissue stroma [7]. Another inflammatory mediator, nitric oxide (NO), has been linked to a number of physiologic processes, including leukocyte-dependent inflammatory processes and oxidant-mediated tissue injury [8,9]. Like CINC and MMP-9, overproduction of NO, which is dependent on the activity of inducible NO synthase, has been reported to contribute to endothelial or parenchymal injury, as well as to induce an increase in microvascular permeability, resulting in lung injury [10,11]. These inflammatory mediators are produced in response to LPS, TNF and IL-1 [6,11] and are regulated at the transcription level by nuclear factor-kappa B (NF-κB) [6,12].
NF-κB activation is regulated by phosphorylation of the inhibitor protein, IκB-α, which dissociates from NF-κB in the cytoplasm. The active NF-κB can then translocate to the nucleus, where it binds to the NF-κB motif of a gene promoter and functions as a transcriptional regulator. In vivo activation of NF-κB, but not other transcription factors, has also been demonstrated in alveolar macrophages from patients with acute respiratory distress syndrome (ARDS) [13]. Our previous study indicated that NF-κB activation is an important mechanism underlying both LPS-induced NO production, and also MMP-9 activity and resulting neutrophil recruitment [14]. Therefore, the activation of NF-κB binding to various gene promoter regions appears to be a key molecular event in the initiation of LPS-induced pulmonary disease.
Once activated, MAP kinases appear to be capable of further signal transduction through kinase phosphorylation, as well as modulating phosphorylation of transcription factors [15-17]. Activator protein (AP)-1, another transcription factor mediating acute inflammation, is activated through MAP kinase signaling cascades in response to various factors, such as LPS, cytokines, and various stresses and in turn regulates genes encoding inflammatory cytokines, such as TNF-α, IL-1, IL-6, and IL-8 [18]. Davis [19] reported that activated JNK is capable of binding the NH2-terminal activation domain of c-Jun, activating AP-1 by phosphorylating its component c-Jun. AP-1 can then translocate into the nucleus to promote transcription of downstream genes. However, action of MAP kinases on the upstream of NF-κB activation remains controversial [20-22]. Here, using a selective JNK inhibitor, SP600125, and the downstream MEK inhibitor of ERK, PD98059, we focused on the roles of JNK and ERK in LPS-induced acute lung injury and production of CINC, MMP-9, and NO. In addition, we investigated the regulatory effects of these MAP kinases on the NF-κB activation pathway during acute lung injury.
Methods
Experimental Animals
Specific pathogen-free male Sprague-Dawley rats (280–300 g) were purchased from Daehan Biolink Co. (Eumsung-Gun, Chungbuk, Korea). The Animal Care Committee of the Ewha Medical Research Institute approved the experimental protocol. The rats were cared for and handled according to the National Institute of Health (NIH) Guide for the Care and Use of Laboratory Animals.
Experimental Protocols
Six groups of specific pathogen-free male Sprague-Dawley rats (280–300 g) were used: (1) controls received an intratracheal (IT) instillation of 0.5 ml of LPS-free saline (0.9 % NaCl); (2) an LPS-treated group received an IT instillation of 6 mg/kg body weight of LPS (Escherichia coli lipopolysaccharide, 055:B5, Sigma Chemical Co., St. Louis, MO) in 0.5 ml LPS-free saline; (3) an LPS-SP600125 group was injected with SP600125 (Calbiochem, La Jolla, CA) 1 hour before the IT instillation of 6 mg/kg body weight of LPS in 0.5 ml of LPS-free saline. (4) a saline-SP600125 group was injected with SP600125 1 hour before IT instillation of 0.5 ml of LPS-free saline (0.9 % NaCl); (5) an LPS-PD98059 group was injected with PD98059 (BIOMOL Research Laboratories, Plymouth, PA) 1 hour before IT instillation of 6 mg/kg body weight of LPS in 0.5 ml of LPS-free saline. (6) a saline-PD98059 group was injected with PD98059 1 hour before IT instillation of 0.5 ml of LPS-free saline (0.9 % NaCl). SP600125 or PD98059 was injected intraorally via a size 8 French feeding tube at a dose of 30 mg/kg body weight [5,23]. For IT instillation, rats were treated with enflurane anesthesia. The trachea was then exposed after a 1 cm midline cervical incision, and LPS or saline was injected intratracheally through a 24-gauge catheter. LPS or saline administration was immediately followed by 3 insufflations of 1 ml of air through the catheter and by rotating the animals to attempt to homogeneously distribute LPS or saline in the lungs. After a few minutes, the rats recovered from the anesthesia and were immediately placed in a chamber. Animals were sacrificed 4 hours after LPS treatment, and the following parameters were monitored: (1) phosphorylation of JNK, ERK, and p38 MAP kinase in lung tissue; (2) cell differential count, and measurement of protein content and lactate dehydrogenase (LDH) activity in bronchoalveolar lavage (BAL) fluid; (3) cytokine-induced neutrophil chemoattractant (CINC) expression, matrix metalloproteinase (MMP)-9 activity or expression and nitrite production in lung tissue, BAL fluid or the supernatants of alveolar macrophage cultures; (4) DNA binding activity of nuclear factor-kappa B (NF-κB) in lung tissue and alveolar macrophages; (5) serine phosphorylation and degradation of IκB-α in lung tissue. In addition, phosphorylation of JNK and ERK was also determined at 2, 4, 14 or 24 hours after LPS treatment to determine the kinetics of the kinase activation in lung tissue.
Isolation of BAL cells, Lung Tissue, and Cell Counts
Four hours after LPS treatment, the rats were sacrificed, and BAL was then performed through a tracheal cannula with aliquots of 8 ml each using ice-cold Ca2+/Mg2+-free phosphate-buffered medium (145 mM NaCl, 5 mM KCl, 1.9 mM NaH2PO4, 9.35 mM Na2HPO4, and 5.5 mM dextrose; pH 7.4) for a total of 80 ml for each rat. The bronchoalveolar lavagate was centrifuged at 500 × g for 5 min at 4°C and cell pellets washed and resuspended in phosphate-buffered medium. Cell counts and differentials were determined using an electronic coulter counter with a cell sizing analyzer (Coulter Model ZBI with a channelizer 256; Coulter Electronics, Bedfordshire, UK), as described by Lane and Mehta [24]. Red blood cells, lymphocytes, neutrophils, and alveolar macrophages were distinguished by their characteristic cell volumes [25]. The recovered cells were 98% viable, as determined by trypan blue dye exclusion. Following lavage, lung tissue was removed, immediately frozen in liquid nitrogen, and stored at -70°C.
Measurement of Total Protein and lactate dehydrogenase (LDH) Activity
To assess the permeability of the bronchoalveolar-capillary barrier, total protein was measured according to the method of Hartree [26], using bovine serum albumin as the standard. Total protein and LDH activity were measured in the first aliquot of the acellular BAL fluid. LDH activity, a cytosolic enzyme used as a marker for cytotoxicity, was measured at 490 nm using an LDH determination kit according to the manufacturer's instructions (Roche Molecular Biochemicals, Mannheim, Germany). LDH activity was expressed as U/L, using an LDH standard.
Western Blot Analysis
Lung tissue homogenate samples (55 μg or 100 μg protein/lane for JNK, ERK, p38 MAP kinase, IκB-α and CINC) or aliquots of acellular BAL fluid (70 μl/lane for CINC and MMP-9) were separated on a 10% or 20% SDS-polyacrylamide gel. Separated proteins were electrophoretically transferred onto nitrocellulose paper and blocked for 1 hour at room temperature with Tris-buffered SAL containing 3% BSA. The membranes were then incubated with an anti-rabbit phospho-JNK/JNK antibody, anti-rabbit phospho-ERK/ERK, anti-rabbit phospho-p38 MAP kinase/p38 MAP kinase, antiserum against rat CINC, anti-human MMP-9 monoclonal antibody or anti-rabbit phospho-IκBα (Ser32)/IκBα at room temperature for 1 hour. Antibody labeling of protein bands was detected with enhanced chemiluminescence (ECL) reagents according to the supplier's protocol.
Zymographic Analysis of MMP-9
The gelatinolytic activities in BAL fluid, or the supernatants of alveolar macrophage cultures, were determined using zymography with gelatin copolymerized with acrylamide in the gel according to previously published methods [14]. To obtain the supernatants of alveolar macrophage cultures, lavage cells were resuspended in RPMI-1640 medium (Mediatech, Washington, DC), containing 2 mM glutamine, 100 units/ml mycostatin without fetal bovine serum (FBS). Aliquots of 1 ml, containing 106 alveolar macrophages, were added to 24-well plates (Costar, Cambridge, MA) and incubated at 37°C in a humidified atmosphere of 5% CO2 for 2 hours. The nonadherent cells were then removed, and adherent cells were counted and further incubated in 1 ml RPMI medium. After a 24 hour incubation, the supernatant was collected and filtered.
Aliquots of BAL fluid and the culture supernatants, normalized for equal volume (8 μl) or amount of protein (8 μg), were electrophoresed on a 10% SDS-PAGE gel with 0.1% gelatin as a substrate without boiling under non-reducing conditions. After removing SDS with 2.5% Triton X-100 for 2 hours, gels were incubated for 20 hours at 37°C in 50 mM Tris-Cl (pH 7.4) containing 10 mM CaCl2 and 0.02% NaN3. The gels were then stained for 1 hour in 7.5% acetic acid/10% propanol-2 containing 0.5% Coomassie Brilliant Blue G250 and destained in same solution without dye. Positions of gelatinolytic activity are unstained on a darkly stained background. The clear bands on the zymograms were photographed on the negative (Polaroid's 665 film) and the signals were quantified by densitometric scanning using an UltroScan XL laser densitometer (LKB, Model 2222-020) to determine the intensity of MMP-9 activity as arbitrary densitometric units. To confirm MMP-9 activity, aliquots of BAL fluid were analyzed by Western blotting with anti-human MMP-9 monoclonal antibody, which was raised against MMP-9 secreted by human HT1080 fibrosarcoma cells [27] and cross-reacts with rat MMP-9 [28].
Nitrite Assay in BAL fluid and Alveolar Macrophage Culture
NO levels in the first aliquot of the acellular BAL fluid, and the supernatants of alveolar macrophage cultures, were measured using a nitrite assay. Direct measurement of NO is difficult due to the very short half-life [29]. However, the stable oxidation end product of NO production, nitrite, can be readily measured in biological fluids and has been used in vitro and in vivo as an indicator of NO production [30]. Briefly, lavage cells were resuspended in RPMI-1640 medium (Mediatech, Washington, DC), containing 2 mM glutamine, 100 units/ml mycostatin, and 10% FBS. Aliquots of 1 ml, containing 106 alveolar macrophages were added to 24-well plates (Costar, Cambridge, MA) and incubated at 37°C in a humidified atmosphere of 5% CO2 for 2 hours. The non-adherent cells were then removed by vigorous washing with two 1 ml of RPMI medium. After incubating the cells for 24 hours, the supernatant was collected and filtered.
Nitrite was assayed after adding 100 μl Greiss reagent (1% sulfanilamide and 0.1% naphthylethylenediamide in 5% phosphoric acid) to 50 μl samples of BAL fluid and cell culture. Optical density at 550 nm (OD550) was measured using a microplate reader. Nitrite concentrations were calculated by comparison with OD550 of standard solutions of sodium nitrite prepared in cell culture medium. Data were presented as μM of nitrite.
Nuclear Extracts
Nuclear extracts were prepared by a modified method of Sun et al. [31]. Lavage cells were resuspended in Dulbecco's modified Eagle's medium (DMEM; Mediatech, Washington, DC), supplemented with 5% FBS (HyClone, Logan, UT), 2 mM glutamine, and 1,000 units/ml penicillin-streptomycin. DMEM medium (5 ml), containing 5 × 106 alveolar macrophages, was added to 6-well plates and incubated at 37°C, in a humidified atmosphere of 5% CO2 for 2 hours. The nonadherent cells were then removed with two 1 ml aliquots of DMEM. At the end of the incubation, adherent cells (> 95% alveolar macrophages) were harvested and then resuspended in hypotonic buffer A (100 mM HEPES, pH 7.9, 10 mM KCl, 0.1 M ethylenediaminetetraacetic acid [EDTA], 0.5 mM dithiothreitol [DTT], 1% Nonidet P-40, and 0.5 mM phenylmethylsulfonyl fluoride [PMSF]) for 10 min on ice, then vortexed for 10 s. Nuclei were pelleted by centrifugation at 12,000 rpm for 30 s. Nuclear extracts were also prepared from lung tissue by the modified method of Deryckere and Gannon [32]. Aliquots of frozen tissue were mixed with liquid nitrogen and ground to powder using a mortar and pestle. The ground tissue was placed in a Dounce tissue homogenizer (Kontes Co., Vineland, NJ) in the presence of 4 ml of buffer A to lyse the cells. The supernatant containing intact nuclei was incubated on ice for 5 min, and centrifuged for 10 min at 5,000 rpm. Nuclear pellets obtained from alveolar macrophages or lung tissue were resuspended in buffer C (20 mM HEPES, pH 7.9, 20% glycerol, 0.42 M NaCl, 1 mM EDTA, and 0.5 mM PMSF) for 30 min on ice. The supernatants containing nuclear proteins were collected by centrifugation at 10,000 rpm for 2 min, and stored at -70°C.
Electrophoretic Mobility Shift Assay (EMSA)
Binding reaction mixtures (10 μl), containing 5 μg (4 μl) nuclear extract protein, 2 μg poly (dI-dC)•poly (dI-dC) (Sigma Co., St. Louis. MO), and 40,000 cpm 32P-labeled probe in binding buffer (4 mM HEPES, pH 7.9, 1 mM MgCl2, 0.5 mM DTT, 2% glycerol, and 20 mM NaCl), were incubated for 30 min at room temperature. The protein-DNA complexes were separated on 5% non-denaturing polyacrylamide gels in 1 × TBE buffer, and autoradiographed. Autoradiographic signals for activated NF-κB were quantitated by densitometric scanning using an UltroScan XL laser densitometer (LKB, Model 2222-020, Bromma, Sweden) to determine the intensity of each band.
The oligonucleotide used as a probe for EMSA was a double-stranded DNA fragment, containing the NF-κB consensus sequence (5'-CCTGTGCTCCGGGAATTTCCCTGGCC-3'), labeled with [α-32P]-dATP (Amersham, Buckinghamshire, UK), using DNA polymerase Klenow fragment (Life Technologies, Gaithersburg, MD). Cold competition was performed by adding 100 ng unlabeled double-stranded probe to the reaction mixture.
Statistical Analysis
Values were expressed as means ± standard errors. Data were compared among the groups by one-way ANOVA followed by a Tukey's post hoc test. A P value of < 0.05 was considered to be statistically significant.
Results
Phosphorylation of JNK and ERK in Lung Tissue
To determine JNK and ERK activation in the lung tissue from LPS treated animals, Western blot analysis with a phospho-specific JNK antibody or ERK antibody was employed. Figures 1A and 1B showed time courses of LPS-induced phosphorylation, or activation, of JNK1/2 and ERK1/2. Phosphorylation of these MAP kinases substantially increased beginning 4 hours after LPS treatment, and progressively further increased (JNK activation) or were maintained (ERK activation) for up to 24 hours after LPS treatment. SP600125 pretreatment partially inhibited LPS-induced phosphorylation of JNK1/2 in lung tissue at 4 hours after LPS treatment (Figure 2A), but this inhibitor had little effect on the activation of ERK1/2 (2C) and p38 MAP kinse (2E). PD98059 pretreatment specifically inhibited the activation of ERK1/2 (Figure 2D), but neither the activation of JNK1/2 (2B) nor p38 MAP kinase (2F). Both JNK and ERK activation were barely detectable in the animals treated with saline or saline-kinase inhibitors.
Figure 1 Time course of phosphorylation of JNK (A) and ERK (B), in lung tissue from rats treated with saline (0 time) or LPS (2–24 h). Western blots with anti-phospho-JNK/JNK antibody or phospho-ERK/ERK antibody were employed in order to monitor JNK or ERK phosphorylation. Relative values for levels of phosphorylated JNK1/2 or ERK1/2 normalized to JNK1/2 or ERK1/2 are indicated below the gel. Results are representative results from 5 rats in each group.
Figure 2 Phosphorylation of JNK (A, B), ERK (C, D)and p38 MAP kinase (E, F) in lung tissue 4 hours after saline or LPS treatment. The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125 (IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO);LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Western blots with anti-phospho-JNK/JNK antibody, phospho-ERK/ERK antibody or phospho-p38 MAP kinase/p38 MAP kinase were employed in order to monitor JNK, ERK or p38 MAP kinase phosphorylation. Relative values for levels of phosphorylated JNK1/2, ERK1/2 or p38 MAP kinse normalized to JNK1/2, ERK1/2 or p38 MAP kinase are indicated below the gel. Results are representative results from 5 rats in each group.
Total Protein and LDH Activity in BAL Fluid and Neutrophil Influx into Lungs
BAL protein contents (Figure 3A) and LDH activity (Figure 3B) in LPS-treated animals were significantly increased (p < 0.05). BAL protein increased 2.9-fold, and LDH activity increased 4.7-fold. This indicates that IT LPS treatment of rats induced acute lung injury. However, SP600125 or PD98059 pretreatment significantly inhibited LPS-induced changes in protein contents, by 63 and 74%, respectively, and BAL LDH activity by 71 and 86%, respectively (P < 0.05). There were no significant differences in these parameters between saline-SP600125, saline-PD98059, and saline control animals (p < 0.05).
Figure 3 Levels of total protein (A), activity of LDH (B) and neutrophil numbers (C) in bronchoalveolar lavage fluid. The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125 (IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO);LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Values represent means ± SEM of results from 5 rats in each group. * Significant differences between saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
BAL cells were differentially analyzed, in order to evaluate the effects of these kinase inhibitors on LPS-induced neutrophil influx. As shown in Figure 3C, neutrophil counts of the total lung lavage cells in LPS-treated animals significantly increased by a factor of 26, compared to values in saline-treated animals, indicating a significant increase in neutrophil influx into the alveolar spaces (p < 0.05). SP600125 or PD98059 significantly suppressed BAL neutrophil counts by 53 or 46 %, respectively (vs LPS animals, p < 0.05). The BAL neutrophil counts in saline-kinase inhibitor animals were not significantly different from those of the saline control animals (p < 0.05).
CINC, MMP-9 and NO Production in Lungs or Alveolar Macrophages
CINC, MMP-9 and NO were chosen in our experiments as representative inflammatory mediators, because of their important roles in neutrophil influx and lung damage, and also because their gene regulation is dependent on NF-κB. Figure 4 illustrates representative Western blots of lung tissue and BAL fluid for CINC. CINC protein expression was undetectable in the samples of saline control animals, but was markedly increased by LPS treatment for 4 hours. By densitometric analysis, CINC protein in lung tissue (Figure 4A and 4Clane 2) and BAL fluid (Figure 4B and 4Dlane 2) from LPS animals was approximately 7- and 2.5-fold higher than in saline control animals, respectively. SP600125 or PD98059 significantly decreased the level of LPS-induced CINC expression, by 50 and 62%, respectively, in lung tissue (Figure 4A and 4Clane 3, p < 0.05) and, by 76 and 97%, respectively, in BAL fluid (Figure 4B and 4Dlane 3, p < 0.05). These kinase inhibitors alone had little effect on CINC levels in the lung tissue and lavage fluid.
Figure 4 CINC expression in lung tissue (A, C) and bronchoalveolar lavage fluid (B, D). The groups represent rats treated as follows: The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125(IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125(IO); LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Western blots with anti-CINC antibodies were performed on the samples of lung tissue and BAL fluid. Densitometry of CINC bands is expressed in arbitrary densitometric units. Values are represented as means ± SEM of results from 5 rats in each group. * Significant differences between saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
BAL fluid (Figure 5A and 5D), and the supernatants from alveolar macrophage cultures (Figure 5B and 5E), were analyzed for evidence of MMP-9 activity, using gelatin zymography. The BAL fluid from the saline control animals showed undetectable gelatinolytic bands. LPS treatment induced a distinct increase in the amount of gelatinolytic activity and the most prominent band was found to be a 92 kD species in the BAL fluid, corresponding to a molecular weight identical to MMP-9 [25,26]. This was confirmed to be MMP-9 by Western blot analysis with the antiMMP-9 monoclonal antibody (Figure 5C and 5Flane 2). In the supernatants from alveolar macrophage cultures of saline control animals, MMP-9 activity was also barely detectable, but was also markedly increased in the sample from LPS animals. SP600125 pretreatment significantly inhibited LPS-induced MMP-9 activity by 54% in BAL fluid, and by 30% in the supernatants from alveolar macrophage cultures (Figure 5A and 5Blane 3, p < 0.05). Similarly, PD98059 pretreatment significantly inhibited LPS-induced MMP-9 activity by approximately 67% in BAL fluid and the supernatants from alveolar macrophage cultures (Figure 5D and 5Elane 3, p < 0.05). MMP-9 activity was completely undetectable in the samples from saline-kinase inhibitor animals. The inhibitory effect of these kinase inhibitors on MMP-9 expression in BAL fluid was also observed (Figure 5C and 5Flane 3).
Figure 5 Gelatinolytic activities in bronchoalveolar lavage fluid (A, D) and the supernatants of alveolar macrophages in culture (B, E). MMP-9 expression in bronchoalveolar lavage fluid (C, F). The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125(IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO); LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Alveolar macrophages (106/m1 of RPMI medium) were incubated for 24 hours. BAL fluid and culture supernatants were analyzed by sensitive zymography, followed by scanning densitometry. 92 kD and 66 kD gelatinolytic bands correspond to MMP-9 and MMP-2, respectively. Densitometry of 92 kD bands is expressed in arbitrary densitometric units. Western blots of BAL fluid with anti-MMP-9 antibodywere employed to monitor MMP-9. Values are represented as means ± SEM of results from 5 rats in each group. * Significant differences between saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
NO levels in BAL fluid and alveolar macrophages in culture were determined by measurement of nitrite in their supernatants. Figure 6A illustrates that in vivo exposure to LPS for 4 hours resulted in a 6.2-fold increase in NO level in BAL fluid, compared with the control animals. This increase was significantly inhibited by SP600125 or PD98059 (69% and 81% inhibition, respectively, p < 0.05, vs LPS animals). In LPS treated animals, nitrite production from alveolar macrophages cultured for 24 hours was increased 3.2-fold (Figure 6B). SP600125 or PD 98059 significantly suppressed LPS-induced NO production by alveolar macrophages by 89 and 58%, respectively (p < 0.05). NO level was only slightly changed in the samples from saline-kinase inhibitor animals.
Figure 6 NO production in BAL fluid (A) and alveolar macrophages in culture (B). The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125 (IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO); LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Alveolar macrophages (106/m1 of RPMI medium) were incubated for 24 hours. BAL fluid and culture supernatants were analyzed using nitrite assays. Values are represented as means ± SEM of results from 5 rats in each group. * Significant differences compared with saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
NF-κB Activation in Lung Tissue and Alveolar Macrophages
Figure 7 shows NF-κB activation in lung tissue and alveolar macrophages, identified 4 hours after IT instillation of saline or LPS. In LPS-treated animals, the DNA-binding activities of NF-κB in lung tissue were markedly enhanced (Figure 7A and 7Clane 2). This enhancement was significantly depressed (67% inhibition, p < 0.05, vs LPS animals) by a 1-hour pretreatment with SP600125 (Figure 7Alane 3), whereas pretreatment with PD98059 did not inhibit LPS-induced activation of NF-κB (7C lane 3). Significant activation of NF-κB was also shown in alveolar macrophages from LPS-treated animals, compared to that seen in the saline control animals (Figure 7B and 7Dlane 2, p < 0.05). SP600125 resulted in significant decreases (45%, p < 0.05) in LPS-induced NF-κB activation in alveolar macrophages (Figure 7Blane 3). However, PD98059 did not inhibit LPS-induced NF-κB activation in alveolar macrophages (Figure 7Dlane 3). Saline or kinase inhibitors alone had little effect on NF-κB activation in lung tissue and alveolar macrophages.
Figure 7 EMSA illustrating DNA-binding activity of NF-κB to the NF-κB motif in lung tissue (A, C), and alveolar macrophages (B, D). The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125 (IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO); LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Nuclear extracts were prepared in lung tissue and alveolar macrophages (5 × 106 alveolar macrophages). Addition of 100 ng of unlabeled cold competitor to the LPS samples successfully competed for NF-κB binding, and eliminated the specific band. Densitometry of NF-κB bands on EMSA is expressed in arbitrary densitometric units. Values are represented as means ± SEM of results from 5 rats in each group. * Significant differences compared with saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
The addition of the cold competitor eliminated the specific bands in the samples from LPS-treated animals, indicating that the band on the autoradiogram was specific for NF-κB binding (Figure 7A,7B,7C and 7Dlane 5).
Phosphorylation and Degradation of IκB-α in Lung Tissue
In order to investigate a possible mechanism underlying the actions of these kinase inhibitors on LPS induction of pathways leading to NF-κB activation, serine phosphorylation and degradation of IκB-α, in lung tissue from LPS and LPS-kinase inhibitor animals were analyzed by Western blotting with anti-phospho-IκB-α (serine 32), and anti-IκB-α Ab. As shown in Figure 8, LPS treatment resulted in the induction of serine phosphorylation of IκB-α, and a substantial reduction in IκB-α protein content in lung tissue (8A and 8C lane 2), whereas these events were significantly inhibited by SP600125 (8A and 8C lane 3). PD98059, however, caused no significant changes in the LPS-induced phosphorylation and degradation of IκB-α (Figure 8B and 8Dlane 3). These kinase inhibitors, alone, had little effect on the phosphorylation and degradation of IκB-α.
Figure 8 Phosphorylation (A, B) and degradation (C, D) of IκB-α in lung tissue. The groups represent rats treated as follows: Saline, saline (IT); LPS, LPS (IT); LPS-SP600125, LPS (IT) and a pretreatment with SP600125 (IO), Saline-SP600125, saline (IT) and a pretreatment with SP600125 (IO); LPS-PD98059, LPS (IT) and a pretreatment with PD98059 (IO), Saline-PD98059, saline (IT) and a pretreatment with PD98059 (IO). Animals were sacrificed 4 hours after LPS treatment. Western blots with anti-serine phospho-IκBα (Ser32)/IκBα antibody were employed to monitor phosphorylated IκB-α and IκB-α. Densitometry of phospho-IκB-α/IκB-α bands is expressed in arbitrary densitometric units. Values represent means ± SEM of results from 5 rats in each group. * Significant differences compared with saline, p < 0.05, and + significant difference compared with LPS group, p < 0.05.
Discussion
In the present study, we determined: (1) the in vivo relation between activation of JNK or ERK and LPS-induced acute lung injury; (2) the inhibition of JNK or ERK resulted in reductions of LPS-induced increases in lung injury parameters, such as total protein content and LDH activity in BAL fluid, neutrophil influx into the lungs, and proinflammatory gene products, such as CINC, MMP-9 and NO; and (3) the activation of JNK is involved in the LPS signaling pathway leading to NF-κB activation through phosphorylation of IκB-α and sequential degradation of IκB-α, whereas activation of ERK signaling is not associated with these NF-κB pathways.
JNK and ERK are known to play important roles as upstream regulators of the induced expression of inflammatory mediators in response to cytokines, stress, and cytoskeletal reorganization [19,33]. However, these results were exclusively obtained during in vitro experiments. Therefore, it is important to clarify the functions of these MAP kinases under pathological conditions. Furthermore, up to date, there have been no studies addressing the issues of the significance of JNK and ERK signaling in LPS-induced acute lung injury. Data from the present study clearly indicate that JNK and ERK were both activated after in vivo LPS exposure in models of acute lung injury. The degree of JNK and ERK activation from the baseline seems to be similar between these kinases, and to be generally higher than that of p38 MAP kinase (data not shown). The time course of JNK activation (progressive increase up to 24 hours after LPS treatment) appears to occur in parallel with those of the biochemical lung injury variables and neutrophil influx into the lungs after intratracheal treatment with LPS [14]. Consistent with our in vivo data, Ishii et al. [34] have reported that both JNK and ERK were simultaneously activated in the lung during ischemia and reperfusion. To explore a new therapeutic strategy for preventing the occurrence of LPS-induced lung inflammation and injury, we attempted to inhibit JNK or ERK activity by pretreatment with a selective JNK inhibitor, SP600125, or a specific inhibitor of MEK/ERK, PD98059, 1 hour before LPS treatment. Inhibition of either JNK or ERK activity resulted in reduction of LPS-induced increases in lung injury parameters, as well as neutrophil influx into the lungs, indicating that aggravating signals related to both JNK and ERK are associated with LPS-induced acute lung injury.
Since the increases in production or activity of CINC, MMP-9 and NO in BAL fluid were correlated with neutrophil influx and lung injury [6,8,9,11], the inhibitory effects of JNK or ERK on production of these inflammatory mediators are functionally important, and can thus be adopted as therapeutic interventions. Recently, it has been demonstrated that JNK inhibition by SP600125, in both ischemia and reperfusion periods, almost completely suppressed TNFα release into BAL fluid [34] and IL-1-induced MMP expression in synoviocytes and in joint arthritis [23]. These in vivo data with the specific JNK inhibitor are clearly consistent with an aggravating role of JNK in inflammation and tissue destruction. However, JNK1-/- mice have increased susceptibility to hyperoxia-induced lung injury [35]. Through in vitro studies of primary monocytes and macrophage cell lines, ERK has been linked to production of MMP-9, and to IL-8 release in response to LPS [36] or Helicobacter pylori [16]. Consistent with our data with ERK, Zhang et al. [37] reported that inhibition of ERK by PD98059 in vivo suppressed hyperoxia-induced cell death in lung tissues. ERK, however, also plays a protective role in myocardial ischemia/reperfusion injury [38], and in myoglobinuric acute renal injury [39]. Taken together, the primary role of JNK or ERK may play either a protective or injurious role under different experimental conditions.
The activation of NF-κB has been associated with lung injury in LPS- and silica-treated rats [6,40,41], and patients with ARDS [42] and asthma [43]. Data from our previous study indicate that in vivo activation of NF-κB in LPS-treated rats preceded the transcription of genes for proinflammatory mediators or lung inflammation and injury, i.e. the increases in neutrophil numbers, total protein, and LDH activity in BAL fluid [9]. These results suggest that NF-κB is an important intracellular target for the early detection and prevention of lung injury. Since the NF-κB element is believed to be the main regulator of CINC, MMP-9 and iNOS expression, and MAP kinase pathways have been demonstrated to contribute to the activation of NF-κB [15,44,45], we attempted to investigate the action of MAP kinases on the upstream of NF-κB signal transduction pathways in LPS-induced acute lung injury. Data from the present study indicate that inhibition of JNK suppresses LPS-induced increases in the DNA binding activity of NF-κB, through down-regulation of phosphorylation and degradation of IκB-α. Recently, Leonardi et al. [46] have reported that CIKS, a NF-κB essential modulator (NEMO)/IκB-α kinase (IKK)γβ-associated protein, connects to both the IKK and JNK signaling complexes, and activates an NF-κB-dependent reporter. Consequently, our and Leonardi et al.'s data support the possibility of JNK's role as an upstream activator of NF-κB, although the apparent complexity may reflect the fact that many diverse signals affect these two pathways. However, inhibition of ERK did not influence LPS induction of NF-κB activation, or the phosphorylation and degradation of IκB-α. The action of MAP kinases on the upstream of NF-κB pathways remains controversial in the context of in vitro experiments [20,47-51]. For instance, over-expression of either MEK1 or ERK1 resulted in a constitutive nuclear localization of NF-κB DNA binding activity [47]. Conversely, transfection with dominant negative MEK1 suggested that the ERK pathway does not regulate NF-κB DNA binding, stimulated by Helicobacter pylori [49]. Considering, by inference, a critical role of NF-κB in the LPS-induced inflammatory cascade, we speculate that ERK may exert effects on gene expression of these inflammatory mediators by modulating TATA-binding protein activation without affecting DNA binding activity [21]. Further study is absolutely necessary, however, to clarify the pathways of these MAP kinases associated with NF-κB, as well as other transcription factors, including AP-1 and cyclic adenosine 5'-monophosphate response element-binding protein (CREB).
Conclusion
Data from this study suggest that JNK or ERK activation would play a detrimental role in LPS-induced acute lung injury. The inhibition of JNK or ERK should effectively block amplification of the LPS-induced catalytic cascade, through significant reductions in protein leakage, LDH release and neutrophil influx into the lung, and also in levels of CINC, MMP-9 and NO after LPS treatment. In addition, the inhibition of JNK activity, but not ERK activity, led to a decrease in NF-κB DNA binding activity through the suppression of phosphorylation and degradation of IκB-α. Based on these findings, we propose that inhibition of JNK or ERK may be an effective therapeutic strategy against the early stages of lung injury, via attenuation of the inflammatory cascade.
Abbreviations
MAP; mitogen-activated protein, JNK; c-Jun NH2-terminal kinase, ERK; extracellular signal-regulated kinase, LPS; lipopolysaccharide, NF-κB; nuclear factor-kappa B, TNF; tumor necrosis factor, IL; interleukin, CINC; cytokine-induced neutrophil chemoattractant, MMP; matrix metalloproteinase, ECM; extracellular matrix NO; nitric oxide, ARDS; acute respiratory distress syndrome, IT; intratracheal, LDH; lactate dehydrogense, BAL; bronchoalveolar lavage, FBS; fetal bovine serum, DMEM; Dulbecco's modified Eagle's medium, EMSA; Electrophoretic Mobility Shift Assay, NEMO; NF-κB essential modulator (NEMO), IKK;IκB-α kinase, AP; activator protein, CREB; cyclic adenosine 5'-monophosphate response element-binding protein
Authors' contributions
HSL, HJK and CSK carried out animal studies. HSL performed the statistical analysis. YHC participated in the zymographic analysis. JLK conceived of the study, participated in its design and coordination, and drafted and edited the manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by Korea Research Foundation Grant (KRF-2002-041-E00021).
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| 15566575 | PMC538282 | CC BY | 2021-01-04 16:47:22 | no | Respir Res. 2004 Nov 27; 5(1):23 | utf-8 | Respir Res | 2,004 | 10.1186/1465-9921-5-23 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-481556938410.1186/1471-2202-5-48Research ArticleFoxo3a induces motoneuron death through the Fas pathway in cooperation with JNK Barthélémy Catherine [email protected] Christopher E [email protected] Brigitte [email protected] UMR 623, Developmental Biology Institute of Marseille, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Université de la Méditerranée, Assistance Publique Marseille, Campus de Luminy-Case 907, 13288 Marseille, France2004 29 11 2004 5 48 48 11 5 2004 29 11 2004 Copyright © 2004 Barthélémy 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
Programmed cell death of motoneurons in the developing spinal cord is thought to be regulated through the availability of target-derived neurotrophic factors. When deprived of trophic support, embryonic spinal motoneurons in vitro over-express FasL, a ligand activating a Fas-mediated death pathway. How trophic factors regulate the expression of FasL is presently unclear, but two regulators of FasL, FOXO3a (FKHRL1) and JNK have been described to play a role in other cell types. Thus, their potential function in motoneurons was investigated in this study.
Results
We show here that as a result of removal of neurotrophic factors and the consequent reduction in signalling through the PI3K/Akt pathway, Foxo3a translocates from the cytoplasm to the nucleus where it triggers cell death. Death is reduced in Fas and FasL mutant motoneurons and in the presence of JNK inhibitors indicating that a significant part of it requires activation of the Fas/FasL pathway through JNK.
Conclusions
Therefore, in motoneurons as in other cell types, FOXO transcriptional regulators provide an important link between other signalling pathways and the cell death machinery.
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Background
During development of higher vertebrates, motoneurons within the spinal cord are generated in excess, and about half the cells initially generated undergo programmed cell death (PCD) during the days following target muscle contact [1]. The most frequently proposed explanation for this death is that motoneurons compete for access to limiting quantities of neurotrophic factors produced by their target tissue, and that only those which are successful survive (reviewed in [2]). Primary motoneurons purified from embryonic spinal cords and cultured in the absence of neurotrophic support mimic this process; many of them undergo programmed cell death over a period of 2–3 days [3,4]. Cell death in these conditions results from lack of activation of the survival pathways which normally inhibit the PCD machinery (reviewed in [5]). Thus, it is essential to identify the precise mechanisms by which motoneurons die, and the ways in which removal of trophic factors leads to their activation.
We have shown that a major driving force for the death of motoneurons deprived of neurotrophic factors in vitro is activation of the Fas/CD95 death receptor by its cognate ligand, FasL [6]. Fas and FasL are expressed by embryonic motoneurons at the stage at which naturally-occurring PCD is about to occur [6]. While levels of Fas are not affected by the presence or absence of neurotrophic factors, FasL expression is strongly upregulated in motoneurons cultured for 3 days without neurotrophic factors [6], as in cerebellar granule neurons [7]. Moreover, reagents such as Fas-Fc which prevent FasL from activating Fas save a majority of motoneurons from death in the absence of trophic support, presumably by blocking interactions in cis between FasL and Fas on individual motoneurons [6]. Understanding how expression of FasL is upregulated in motoneurons is thus an important step in linking neurotrophic signalling to cell death mechanisms.
The transcription factor Foxo3a (also known as FKHRL1) was a clear candidate. In conditions in which the PI3K/Akt survival and growth pathway is activated, Foxo3a is phosphorylated by Akt and exported to the cytoplasm where it is sequestrated by the 14-3-3 protein [8]. Overexpression of a constitutively activated form of Foxo3a (mutated at the three Akt phosphorylation sites and therefore unable to be phosphorylated) leads to PCD of many cell types in culture, including primary cerebellar granule neurons [8-14]. The FasL promoter contains three FOXO DNA-binding sites, and Foxo3a-induced apoptosis of cerebellar neurons is decreased when Fas/FasL interaction is blocked by the decoy fusion protein Fas-Fc [8]. Thus, in these cells, Foxo3a induces apoptosis in part by its ability to induce the expression of the FasL gene.
The JNK pathway has also been shown to regulate FasL expression in some neuronal cells, through its effects on the transcriptional activity of the AP-1 complex. Although this pathway can play different roles, in neurons it is involved in apoptosis in response to several stresses, including withdrawal of survival factors [15]. In cerebellar granule neurons, FasL upregulation in neurons induced to die results from JNK activation and phosphorylation of c-Jun [7]. Moreover, in cerebellar neurons derived from gld mice, which are defective for FasL, the killing effect induced by trophic deprivation is reduced compared to wt mice.
We therefore wished to study the function of Foxo3a in motoneurons and its relation to JNK signalling. We show that, in the absence of survival signalling through the Akt pathway, Foxo3a is translocated to the nucleus where it triggers motoneuron death. Using appropriate mouse mutants, we show that Fas signalling is required for roughly half of the cell death induced by Foxo3a, suggesting that FasL activation both directly and through JNK is a major target of its actions.
Results
Control of subcellular localization of Foxo3a in motoneurons
We first analysed the subcellular localization of Foxo3a in motoneurons using an antibody that recognizes Foxo3a independently of its phosphorylation state. Motoneurons were purified from mouse embryonic spinal cord at E12.5, at the beginning of the period of naturally-occurring cell death, and cultured in the presence of a cocktail of neurotrophic factors (NTFs; see Methods) to strongly activate the PI3K/Akt pathway (Perez-Garcia et al., 2004; Dolcet et al., 2001; Dolcet et al., 1999). In these conditions, Foxo3a is predominantly detected in the cytoplasm (Fig. 1). To confirm that exogenous Foxo3a adopts the same subcellular localization, we electroporated purified motoneurons with a vector encoding HA-tagged wildtype Foxo3a (HA-wt-Foxo3a) together with a vector encoding GFP to identify transduced neurons. When motoneurons were grown in the presence of NTFs, only cytoplasmic staining for HA-wt-Foxo3a was detected (Fig. 2A).
We then tested the effects of inhibiting survival signalling through the PI3K/Akt pathway by treatment with the PI3K inhibitor LY294002 either in the presence (not shown) or the absence (Fig. 2A) of neurotrophic factors. In both cases, the majority of HA-wt-Foxo3a becomes localized in the nucleus of motoneurons (Fig. 2A and not shown). This pharmacological evidence suggests that Foxo3a localization reflects its phosphorylation by Akt. To confirm this, we studied the cellular distribution of the non-phosphorylatable form of Foxo3a, triple mutant (TM-) Foxo3a, in which all three Akt phosphorylation sites are mutated (Brunet et al ref). In the absence of survival signalling, HA-TM-Foxo3a showed a very similar nuclear distribution to HA-wt-Foxo3a (Fig. 2B). However, in contrast with the wildtype form, no redistribution of HA-TM-Foxo3a to the cytoplasm was observed in the presence of the cocktail of neurotrophic factors (Fig. 2B).
Results in all conditions were quantified by counting 2 independent experiments (Fig. 2C). They clearly show that, in motoneurons as in other cell types, Akt-induced phosphorylation of Foxo3a is required to prevent its accumulation in the nucleus.
Activated Foxo3a triggers motoneuron death
To investigate the ability of Foxo3a to trigger death of cultured motoneurons, we took advantage of the electroporation technique we recently developed for high-efficacy transduction of primary neurons [16]. We first confirmed that electroporation did not intrinsically inhibit motoneuron survival, by overexpressing a constitutively active form of Akt (Akt ca) in which the PH domain is replaced by a myristyl moiety which constitutively targets Akt to the membrane. Survival of electroporated motoneurons was reproducibly increased by overexpression of Akt ca as compared to the empty vector (Fig. 3A). Indeed, survival with Akt ca was greater than with NTFs alone. This was a result of increased Akt activity, since motoneurons electroporated with wild-type Akt showed survival values no greater than those with empty vector (not shown).
We therefore used electroporation to analyse the effects of Foxo3a on motoneuron survival. Overexpression of HA-wt-Foxo3a with GFP had no significant effect as compared to GFP alone or GFP with the empty vector (GFP: 100 ± 10; GFP + HA-wt-Foxo3a: 116 ± 12). To mimic Foxo3a activation and nuclear translocation, we then overexpressed HA-TM-Foxo3a together with GFP. The triple mutant triggered significant death of motoneurons cultured with or without NTFs (Fig. 3B). In the presence of NTFs, survival was reduced by 65%, which is similar to the proportion of motoneurons that die in the absence of trophic support (Fig. 3B).
FasL is a target of Foxo3a for motoneuron death
One candidate gene downstream of Foxo3a was FasL, known to be regulated by Foxo3a and shown by us to be able to trigger motoneuron death. Motoneurons were therefore isolated from mice bearing mutations that lead to reduced signalling through the Fas pathway: gld mice (point mutation in FasL) and lpr mice (regulatory defect in Fas). As before, electroporation of HA-TM-Foxo3a in control motoneurons led to loss of 60 to 70 % of them compared to HA-wt-Foxo3a (Fig. 4). This figure was reduced from 70 to 50 % in gld motoneurons, and from 60 to 35% in lpr mutants. Thus, induction of Fas signalling is responsible for approximately half of the cell killing induced by Foxo3a.
Foxo3a acts partly through JNK to trigger motoneuron death
In other cell types, Foxo3a can upregulate FasL either by direct interaction with the Foxo3A promoter [8,17] or indirectly through JNK activation [18], which itself leads to upregulation of FasL [7]. Motoneurons were therefore electroporated with either wild-type or TM-Foxo3a, and treated immediately after plating with an inhibitor of JNK, L-JNKI1, which has been shown to inhibit the interaction between JNK and its substrates. In the presence of NTFs, JNKI1 had no effect on the survival of motoneurons electroporated with wt Foxo3a or the empty vector. In contrast, inhibition of JNK led to a reduction of 20% in the number of motoneurons triggered to die by TM-Foxo3a (Fig. 5). Since this percentage is lower than the fraction saved by reduced Fas signalling, it is likely that both direct and indirect (through JNK) mechanisms are used by Foxo3a to trigger Fas-dependent death of motoneurons.
Discussion
Death of motoneurons as a result of insufficient trophic support was one of the first examples of developmental PCD to be discovered, but we still only partially understand the underlying mechanisms. We show here that as a result of removal of neurotrophic factors and the consequent reduction in signalling through the PI3K/Akt pathway, Foxo3a translocates from the cytoplasm to the nucleus where it triggers cell death. A significant part of this death requires activation of the Fas/FasL pathway through JNK. Thus, in motoneurons as in other cell types, FOXO transcriptional regulators provide an important link between other signalling pathways and the cell death machinery.
We overexpressed the constitutively active mutant TM-Foxo3a in the presence of neurotrophic factors to mimic the nuclear translocation of Foxo3a in their absence. We observed a 50% reduction in death induced by TM-Foxo3a when we used motoneurons that were mutant for Fas/FasL signalling. This could in theory result through several indirect mechanisms. However, the literature on direct regulation of FasL by Foxo3a [8] and our earlier demonstration of upregulation of FasL in motoneurons deprived of trophic support [6] make it likely that TM-Foxo3a is acting through FasL in these cells. This may not only reflect direct upregulation of the FasL gene. The partial protection obtained using a JNK inhibitor suggests that in some motoneurons, Foxo3a acts to upregulate FasL through a parallel pathway already described in other cell types, involving JNK and the AP-1 complex [7,18]. The importance of the Fas/FasL pathway in motoneuron death during development remains to be determined. However, both in vitro and in vivo, recent studies clearly demonstrate a potential role in pathological motoneuron loss. After axotomy of the facial nerve in neonates, there is a massive loss of motoneurons over the following week. The numbers of surviving motoneurons are increased 2-fold in mice deficient for the Fas/FasL pathway [19]. In vitro, Fas engagement leads to activation of a motoneuron-specific signalling pathway involving p38 kinase and neuronal nitric oxide synthase. Embryonic motoneurons purified from mouse models of familial amyotrophic lateral sclerosis (ALS) show greatly exacerbated death responses to activation of this pathway [16]. It will therefore be of interest to determine whether regulation of Foxo3a plays a role in determining the responsiveness of motoneurons to cell death activation in pathological situations as well.
The killing effect of Foxo3a was not totally abolished in motoneurons from lpr or gld mice. This may in part reflect the fact that these mutants are strong hypomorphs, rather than complete nulls. For example, in lpr mice, some Fas is still synthesized following splicing-out of the inhibitory transposon from primary transcripts [20]. However, it is equally likely that Foxo3a induces death of some motoneurons through other, Fas-independent mechanisms. Foxo3a has been shown to regulate the expression of the cyclin-dependent kinase inhibitor p27kip1 [11], the glucocorticoid-induced leucine zipper protein [21], transforming growth factor-b2, [22], the DNA damage-induced protein Gadd45a [23] and the ubiquitin ligase atrogin-1 [24]. Of particular interest is its well-characterized effect on expression of Bim (Bcl-2-interacting mediator of cell death), a proapoptotic member of the Bcl-2 family that contains only the BH3 domain, which allows it to bind anti-apoptotic Bcl-2 family members and neutralize their function.
During death of sympathetic neurons induced by NGF deprivation, Foxo3a directly activates Bim expression and thereby triggers cell death [14]. Moreover, JNKs have been shown to potentiate trophic deprivation-induced apoptosis in cerebellar granule cells, through phosphorylation of Bim (Putcha et al., 2003). The following unpublished results from our laboratory make it probable that a similar mechanism occurs in purified motoneurons. Three isoforms of Bim are produced by alternative splicing in both mouse and human: BimS, BimL and BimEL [25]. We detected all three isoforms by RT-PCR in motoneurons isolated from E12.5 ventral spinal cord of embryonic mice ([see Additional file 1], part A). Moreover, >50% of motoneurons are induced to die by overexpression of BimL and 89% by overexpression of BimS ([see Additional file 1], part B). Therefore, expression of Bim induced by Foxo3a would be likely to trigger motoneuron death and provides the most likely explanation for the Fas-independent actions of Foxo3a in these cells.
Conclusions
In conclusion, one of the upstream events now known to trigger death of neurons deprived of trophic support is the failure of Akt to phosphorylate Foxo3a and thereby prevent it from entering the nucleus. The fact that this mechanism is found to occur in motoneurons, a classical system for the study of neuronal cell death, opens the door to a better understanding of its role during development and in neurodegenerative pathology.
Methods
Animals
Normal CD1 and C57BL/6 mice were obtained from Iffacredo (L'Arbresle, France). lpr/lpr and gld/gld mice were purchased from the Jackson Laboratory (Bar Harbor, ME). lpr/lpr mutants present an insertion of an early transposon into the fas gene, resulting in fas transcriptional repression [20]. gld/gld mutants show a loss-of-function mutation in the fasL gene [26]. All mutants were maintained on a C57BL/6 genetic background. Controls were done in either C57BL/6 or in CD1 mice (we previously showed that their response to Fas activation was identical).
Reagents
LY 294002 was purchased from Calbiochem and used at a final concentration of 100 μM. L-JNKl1 was purchased from Alexis Biochemicals and used at a final concentration of 1 μM. Rabbit polyclonal antibody to HA-tag was from Clontech. Antibodies against Foxo3A were as described previously [8] and were a generous gift from A. Brunet (Stanford).
Expression constructs
We are grateful to Anne Brunet for donating vectors used as the basis for our expression constructs. The vectors encoding HA-tagged Akt and Akt ca were as described previously [27]. The vectors encoding HA-tagged wt and triple-mutant forms of Foxo3A were developed by Brunet et al[8] The cDNAs were excised from the original clones and subcloned in the pCAGGS expression vector at ClaI site, since this vector gives higher expression levels in motoneurons.
Motoneuron purification and culture
Motoneuron cultures were prepared from E12.5 mouse spinal cords essentially as described [28], except that the magnetic column step was omitted and motoneurons in the enriched metrizamide fraction were identified by morphological criteria. Motoneurons were plated in the presence or not of a cocktail of neurotrophic factors (referred to as "NTFs": 1 ng/mL BDNF, 100 pg/mL GDNF, 10 ng/mL CNTF), added at the time of cell seeding. L-JNKl1 was added at the time of seeding, and LY294002 was added for 90 mins after 22 hours of culture.
Electroporation of motoneurons
Cells dissociated from E12.5 mouse ventral spinal cords were centrifuged over a 6.5% (v/v) Metrizamide cushion at 2000 rpm for 15 min. Cells at the interface were collected and washed on a BSA cushion at 1500 rpm for 5 min. Cells were resuspended in electroporation buffer at a density of 50,000 cells per 100 μL. 100 μL aliquots of the suspension were transferred to 4 mm gap cuvettes (Eppendorf) and five μg of the pCAGGS-GFP vector as well as the same molar amount of the vector of interest were added. After 15 min of incubation at room temperature, cells were electroporated using three pulses of 5 ms at 200 V with intervals of 1 s. Immediately after electroporation, 1.5 ml of culture medium was added to dilute the cells which were plated in four four-wells plates [16].
Immunocytochemistry and HA-tag localization
For anti-HA staining, motoneurons electroporated with HA-tagged wt or TM Foxo3A were seeded on polyornithine-laminin-coated 12-mm diameter glass coverslips and cultured for 24 hr at 37°C in complete Neurobasal medium supplemented or not with NTFs. Motoneurons cultured without NTFs were treated with LY294002 for the last 90 min. They were then fixed in 3.6% (v/v) formaldehyde for 30 min, washed in PBS-50 mM L-Lysine, and blocked for 1 hr in 5% donkey serum, 4% BSA, 0.1% Triton X-100 in PBS-50 mM L-Lysine. Cells were incubated with the anti-HA antibody (dilution 1:500) in blocking buffer, followed by fluorochrome-conjugated anti-rabbit secondary antibody. The cells were then observed under a fluorescence microscope. Cells with typical motoneuron morphology that were both GFP- and HA-positive were analyzed. The fraction of motoneurons expressing HA-wt-Foxo3A or HA-TM-Foxo3A exclusively in the nucleus, exclusively in the cytoplasm or in both compartments was evaluated in a total of 20 cells per well.
Staining for endogenous Foxo3A (dilution 1:100) was performed using the same method on non-electroporated motoneurons cultured for 24 hr with NTFs.
Survival assays
Motoneurons were counted 48 hr after electroporation under a fluorescence microscope. Only green cells with healthy motoneuron morphology, i.e. with large cell bodies and long non-fragmented neurites, were taken into consideration. Two different wells for each condition were counted (between 50 and 350 green motoneurons) in all the experiments. Cross percentages of test versus control (being set at 100%) were calculated yielding 4 values, used to calculate the medium and the SD for each experiment.
Authors' contributions
C.B. conducted most of the experiments, B.P. some. C.H and B.P. conceived the experiments and the experimental design. C.B., B.P. and C.H. wrote the paper.
Supplementary Material
Additional File 1
A – The 3 forms of Bim are expressed in motoneurons: RT-PCR was performed on extracts of 80,000 motoneurones cultured for 3 days in the presence of NTFs, in a 35 mm diameter dish. Primers used for Bim were the following: 5'-GTGACAGAGAAGGTGGACAAT-3' and 5'-ATACCAGACGGAAGATAAAGC-3'. The 3 products were:BimS 284 bp, BimSL 374 bp, BimL 542 bp; B – Overexpression of BimS or BimL kills a major proportion of motoneurons: motoneurons purified from E12.5 mice embryos have been electroporated with a vector coding GFP alone or coelectroporated with a vector coding GFP and a vector coding either BimL or BimS, obtained by direct cloning of the PCR products described above first cloned into pGEMTeasy, then subcloned into pcDNA3 into EcoRI sites. Conditions of electroporation were as described in Material and Methods. The surviving electroporated motoneurons were counted after 2 days in culture in the presence of NTFs.
Click here for file
Acknowledgements
We are very grateful to Anne Brunet (Stanford) for providing essential tools to conduct this study. We thank all members of UMR623 as well as Cedric Raoul (EPFL, Lausanne, Switzerland) for helpful comments throughout this work, which was funded by Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Association Française contre les Myopathies (AFM), European Commission contract QLG3-CT-1999-00602, Amyotrophic Lateral Sclerosis Association (ALSA) and GIP-Aventis. C.B. is recipient of a PhD fellowship awarded by the Ministère de l'Education Nationale et de la Recherche.
Figures and Tables
Figure 1 Presence of endogenous Foxo3a in motoneurons. Motoneurons from E12.5 mice spinal cords were cultured in the presence of a cocktail of neurotrophic factors (NTFs) and immunostained using an antibody against total Foxo3a, demonstrating a clearly cytoplasmic localization of endogenous Foxo3a in these conditions. (A) Immunolabelling of Foxo3a; (B) Immunolabelling combined with DAPI staining to visualize the nuclei. Scale bar: 5 microns
Figure 2 Subcellular localization of Foxo3a reflects its state of phosphorylation by Akt. (A, B): Motoneurons were electroporated with constructs encoding HA-tagged version of wildtype Foxo3a (wt Foxo3a; A) or the constitutively active triple-mutant form (TM Foxo3a; B), together with another vector encoding GFP. They were cultured for 24 hr either in the presence of NTFs, or in their absence. In the latter case, they were treated with the PI3K inhibitor LY 294002 (100 μM) for the last 90 min before fixation. HA-wt-Foxo3a and HA-TM-Foxo3a were detected staining for the HA tag. Characteristic labelled cells are illustrated for each condition, together with nuclear labelling using DAPI. Scale bar = 5microns. (C) Quantification of subcellular localization in A and B. Approximately 20 motoneurons were analysed in each condition in duplicate wells; results of two independent experiments are shown as mean ± range.
Figure 3 Non-phosphorylatable triple-mutant Foxo3a triggers motoneuron death. (A) Purified motoneurons were coelectroporated with constructs for constitutively active (ca) Akt or empty vector, together with another vector encoding GFP. The survival of transduced motoneurons was evaluated after 2 d in culture with NTFs. Akt ca further enhances the effects of NTFs on motoneuron survival. Results are mean ± SD of 2 independent experiments. (B) Using the same protocol, motoneurons were electroporated with vectors encoding either HA-wt-Foxo3a or HA-TM-Foxo3a, together with the GFP vector. They were cultured in the presence or the absence of NTFs and survival was evaluated 2 d later. Overexpression of TM-Foxo-3a leads to reduced survival in each condition. Results are mean ± SD of 12 wells from 6 independent experiments. The 100 % corresponds to the number of motoneurons electroporated with HA-wt-Foxo3a. Differences between overexpression of HA-TM-Foxo3a and HA-wt-Foxo3a were found significant using Student's t-test (p < 0.01).
Figure 4 Constitutively active Foxo3a induces motoneuron death in a Fas-dependent manner. HA-wt-Foxo3a and HA-TM-Foxo3a were overexpressed by electroporation in control motoneurons. In parallel, HA-TM-Foxo3a was expressed in mutant motoneurons with reduced capacity for Fas signalling, lpr mice being deficient for Fas and gld mice for FasL. Cells were cultured in the presence of NTFs and grown for 2 d before counting numbers of surviving transduced motoneurons. The absence of Fas signalling reduces the ability of HA-TM Foxo3a to trigger cell death. Results are means ± SD of 4–6 wells in 2–3 independent experiments. The 100 % corresponds to the number of wt or mutant motoneurons electroporated with HA-wt-Foxo3a. Differences with the TM-Foxo3a effect in lpr and gld compared to control neurons were found significant using Student's t-test (p < 0.001 in lpr and in gld).
Figure 5 The killing effect of TM Foxo3a involves the JNK pathway.Motoneurons were electroporated with HA-wt-Foxo3a and cultured for 2 d with NTFs in the presence or not of a JNK inhibitor, L-JNKl1 (1 μM). Blocking the JNK pathway in motoneuron reduces the killing effect of TM-Foxo3a by about 20 %. Results are mean ± SD of 6 wells in 3 independent experiments. The 100 % corresponds to the number of motoneurons electroporated with HA-wt-Foxo3a and untreated. Differences with the TM-Foxo3a effect in L-JNKl1-treated compared with control neurons were found significant using Student's t-test (p < 0.001).
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| 15569384 | PMC538283 | CC BY | 2021-01-04 16:03:45 | no | BMC Neurosci. 2004 Nov 29; 5:48 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-48 | oa_comm |
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Malar JMalaria Journal1475-2875BioMed Central London 1475-2875-3-431554470310.1186/1475-2875-3-43ResearchIncrease of malaria attacks among children presenting concomitant infection by Schistosoma mansoni in Senegal Sokhna Cheikh [email protected] Hesran Jean-Yves [email protected] Pape A [email protected] Jean [email protected] Pape [email protected] Mamadou [email protected] Abdoulaye [email protected] Pierre [email protected] UR Paludisme afro-tropical, IRD, Dakar, Sénégal2 UR Santé de la mere et 1'enfant en milieu tropical, IRD, Dakar, Sénégal3 Région médicale de Saint Louis, Programme de lutte contre la bilharziose, Sénégal4 District médical de Richard-Toll, Sénégal5 Unité de Parasitologie BioMédicale, Institut Pasteur, 28, rue du Dr Roux, 75015 Paris, France2004 15 11 2004 3 43 43 6 9 2004 15 11 2004 Copyright © 2004 Sokhna et al; licensee BioMed Central Ltd.2004Sokhna 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.
Helminthic infections concomitant with malaria are common in inter-tropical areas. A recent study showed that mice co-infected with Schistosoma mansoni and Plasmodium chabaudi develop higher P. chabaudi parasitaemia and had a higher mortality rate. This important observation deserved to be further investigated among human populations.
Malaria attacks were recorded in 512 children aged 6–15 years living in Richard Toll (Northern Senegal) among whom 336 were infected by S. mansoni, and 175 were not. The incidence rate of malaria attacks was significantly higher among S. mansoni-infected individuals, particularly those carrying the highest worm loads, as compared to uninfected subjects (26.6% versus 16,4 %). In contrast, the rate of malaria attacks was lower, without reaching significance, in medium grade S. mansoni infections. Thus, infection by S. mansoni affects susceptibility to malaria, but this can vary according to the intensity of parasite load. The immunological mechanisms underlying this dual effect need to be further explored.
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Introduction
Malaria is prevalent in many parts of tropical Africa, where other concomitant parasitic infections are common. However, little is known about how concurrent infections affect the expression to and/or pathogenesis of each other. A recent study showed that Schistosoma mansoni and Plasmodium chabaudi co-infected mice develop higher P. chabaudi parasitemia and higher mortality rate [1]. This observation was supported in the case of human infections in subjects carrying intestinal helminths who were given Levamisole which led to the reduction of the malaria attack rate [2] and by further studies in helminth-free and helminth-carrying malaria-exposed individuals, which confirmed the deleterious effect of worm carriage upon an individual's susceptibility to malaria [3].
In the Senegal river basin, where malaria is hypoendemic, extensive irrigation programmes have been developed, promoting a spread of S. mansoni infection [4]. The exposure of the inhabitants to these two infections, in a situation where other helminthic infections were rare, provided an opportunity to study the expression of malaria in a schistosoma exposed population.
Patients and methods
Study area
The study took place in Gallo Malick, a district of Richard Toll, which is located near a canal ensuring the irrigation of rice and sugar cane fields. The population was estimated in 1998 to reach 3,685 inhabitants. Annual rainfall amounts to around 250 mm water. The district is approximately 1 km long and 500 m wide. A dispensary is set up in the centre of the district and is thus easily accessible.
Population study
In September 1998, after obtaining agreement from the parents, two stools examinations were carried out in all children aged between 5 and 15 years. From 25 mg of stools, two slides were prepared, according to the modified Kato-Katz method [5]. The parasitic burden was obtained by multiplying the average value of the burden of the two slides by 40 to express the result in the number of eggs per gram of stools. Children were given a card that entitled them to have free access to the health centre. For each consultation motivated by an episode of fever or an history of fever within 24 hours, a thick smear was performed. Thick smears prepared from capillary blood were examined over 200 microscopic fields. The average number of leucocytes per field was estimated in 10 fields. Parasite density was evaluated based on an average 8,000 leukocytes per μl of blood. Following national recommendations, any fever attack was considered suspect of malaria and treated by chloroquine (25 mg/kg over three days in a 10-10-5 posology), as the drug which was still effective in this area at that time.
The follow-up was implemented between September 1998 and April 1999. In January 1999, a further stool examination and a urine filtration for the detection of Schistosoma haematobium eggs, were performed. Ten ml of urine were filtered through a 12 μ millipore membrane and the eggs on the whole filter surface were counted. A questionnaire was issued to check habits with regard to protection against mosquitoes and usage of anti-malarial drug-prophylaxis. It was also ascertained that none of the included children had been administered anti-S. mansoni treatment (praziquantel) over the past year (September 97 – September 98). In March 99, all children who presented with at least one S. mansoni positive stool examination were treated with praziquantel (40 mg/kg).
Any child presenting with a body temperature > 38°C or having a history of fever-in the 24 hours preceding the consultation and with a parasite density ≥ 5000 parasites/μl of blood was defined as suffering from a malaria attack.
Any child whose tool tested positive for S. mansoni at least once was defined as S. mansoni positive. If both examinations proved positive, the highest parasitic burden was kept for further analysis. Parasite loads were classified into four categories: A) 1 to 100, B) 101 to 400, C) 401 to 1000, and D) > 1000 eggs per one gram of stools.
In order to exclude possible interactions with other helminthic infections, children from the group not infected by S. mansoni but presenting other intestinal parasitic infection were excluded from the analysis.
When houses where children had a malaria attack and those where the children were carrying schistosomiasis were located on a map, the two pathologies were found to be distributed in a uniform way in the whole district, without evidence of clustering of double infection malaria-S. mansoni. This is in agreement with the distinct sources of infection, Anopheles larvae breeding in small water collections in gardens, and Schistosoma vectors in rice fields and irrigation canals.
Comparison between children presenting at least one malaria attack and children not having presented any malaria attack was performed using a forward logistic regression model using STATA statistical software. The variables included in the model were sex, S. mansoni categories eggs load and age.
The period between the beginning of the study and the date of a malaria attack was compared between groups of S. mansoni infections using a lifetime estimate table from the EGRET programme (Statistics and Epidemiology Research Corporation, Seattle, Washington). Any p value lower than 0.05 was considered significant.
Results
In September 1998, 525 children underwent two stool examinations. Three-hundred and thirty-six-were found to carry S. mansoni eggs and 189 were negative for both stool examinations.
Urine filtration detected only 13 children excreting eggs of Schistosoma haematobium (2.7%). Among them, 11 were also positive for S. mansoni. The stool examination showed the presence of 31 other intestinal parasitic infections (Ascaris and Trichuris). Patients negative for S. mansoni and carriers of another intestinal (12 subjects) or urinary parasite (2 subjects) were excluded of the analysis.
In total, 511 children were included in the analysis (336 infected by S. mansoni (65.7%) and 175 (34.3%) not infected). The prevalence increased as a function of age, reaching 80% in children older than 11 years with > 30% of children presenting with high schistosome loads, i.e. > 1,000 eggs/ 1 g of stools.
The malaria index was 11.2% in September, 8.1 % in October, 8.6 % in November and 5% in February. Malaria attacks were detected by passive case detection, ie as out-patients at the dispensary. Among 262 of the cohort patients consulting for fever, 107 cases (40.8 %) were attributed to a malaria attack, according to the criteria described. Only 10 children underwent two malaria attacks. In total, 18.9 % (97/511) of the children included in the analysis presented with a malaria attack at least once.
Chemoprophylaxis for children is not recommended by the National Control Programme, but 7.9 % of the parents reported that they were giving a chemoprophylactic treatment to their children. 58.9 % stated they used a mosquito bednet. These proportions were the same in the two groups of children, infected by S. mansoniior not. However, the malaria attack prevalence was also the same whether or not the children were sleeping under a mosquito bednet.
The incidence rate of malaria attacks was 20.2 % (68/336) in the group of children concomitantly infected by S. mansoni and 16.6% (29/175) in those non-infected (p = 0.40). The malarial incidence, however, varied depending on the load of eggs. It was high in all groups, but the highest (26.6 %) in those carrying the highest worm loads (> 1,000), except in subjects presenting medium loads (>100 and < 400 eggs/g of stools) where malarial incidence was lower than in S. mansoni negative individuals (9.4 %). Sex and age had no significant influence, which is not surprising since this is a mesoendemic area. Using a logistic regression model and taking the negative group as reference, the difference was significant with the group carrying a high load of eggs (RR = 2.24 (1.2 – 4.2) (Table I). Among all S. mansoni positive individuals, and using the group with medium egg load as reference (lower incidence of malaria attacks), the malaria incidence was significantly increased for low S. mansoni egg carriage (1–100) (RR = 2.51 (1.05–6) as well as high S. mansoni egg carriage (>1,000) RR = 3.12(1.33–7.29).
Table 1 Logistic regression model for malaria attack adjusting for sex, age and load in eggs of S. mansoni/g of stools, n = 511, Richard Toll, Senegal, 1999
Odds Ratio P>|z| [95% Conf. Interval]
Sex* .72 0.16 .46–1.13
Egg's load**
1–100 (n = 99) 1.82 0.06 .97 – 3.42
101–400 (n = 73) .72 0.46 .31 – 1.70
401–1000 (n = 55) 1.46 0.35 .66 – 3.25
> 1000 (n = 109) 2.24 0.01 1.20–4.20
Age .99 0.79 .92–1.07
*Female, ** Non-infected children as reference, n = number of subjects in each group
Out of 10 children who presented two malaria attacks, nine were infected by S.m.; four presented a schistosome load higher than 1,000 eggs/g of stools, and three > 400 and <1,000.
The cumulative incidence of malaria attacks based on the Kaplan-Meier analysis according to the day of follow-up shows that the difference between subjects carrying high S. mansoni loads and other groups increased over time during the follow-up (Fig 1). This difference is particularly clear over the first ten weeks.
Figure 1 Probability of not having had a malaria attack in children presenting S. mansoni infection or without S. mansoni infection
Discussion
The high prevalence of S. mansoni and the low prevalence of urinary schistosomiasis in the area studied are in agreement with previous reports [4,6,7]. The prevalence of intestinal helminths, such as Ascaris and hookworms, was also low. The high prevalence of S. mansoni, together with a low prevalence of other helminthic infections, including S. haematobium, make this area suitable for studying the influence of S. mansoni upon P. falciparum infection. Malarial indices were consistently lower than 20 %, in agreement with previous studies which classified this northern Sahel area as meso-endemic [8,9]. Entomological studies suggest that this low endemicity could to be due to the dominance of Anopheles pharoensis, which is a poor vector in view of its short life-expectancy [10,11].
The analysis of the incidence of the malaria attacks by amount of parasite load in S. mansoni eggs suggests a more complex mechanism than a simple linear link between the frequency of malaria infection and the degree of infestation by S. mansoni. Indeed, the data show a greater rate of malaria attacks in children with either a high load (> 400 and > 1,000) or a low load of eggs (1–100), whereas a lower attack rate was observed in children presenting a medium egg load (>100 and <400 eggs/g), although this opposite trend did not reach significance.
Protection against falciparum malaria has been found to be associated with the preferential production of the cytophilic classes, IgG1 and IgG3, of antibodies [12], this being related to their ability to cooperate with blood monocytes in an ADCC-like (Antibody-Dependant Cellular cytotoxicity) mechanism [13]. Conversely, the very long delay needed to reach a state of protection was associated with the preferential production of non-cytophilic classes of antibodies, such as IgG2, IgG4 and IgM [13]. This also immediately raised the question of why the immune response to falciparum malaria is channeled to non-cytophilic classes in children and led to formulate the hypothesis that it could be related to helminthic co-infections, which are known to induce a Th2-like type of response. Indeed, children are prone to much higher helminthic loads than are adults. In Madagascar, a study conducted in children carrying intestinal helminths and treated with levamisole, suggested after a two-years follow-up, that a three-fold decrease in malaria attack rate was induced in helminth-treated subjects, as compared to non-treated paired controls [2]. These initial results were confirmed by more recent studies in which levamisole was not used [3,14].
In the present study, this observation has been confirmed with yet another worm infection, S mansoni, but only for those having the highest loads. The opposite effect was found in individuals carrying medium schistosome infections. As immunological studies could not be performed, one can only speculate about this biphasic effect. The production of cytophilic classes against malaria requires that the T-cells helper effect is provided by Th1 type of T-cells, however cytokines produced by T-cells specific for schistosome eggs or worms can influence responses to malaria. It has been reported that, during the first phase of S. mansoni infection, T-cells are stimulated which secrete cytokines belonging, in majority, to the Th1 type. The switch towards Th2 type cytokines occurs later on and is dependent on egg production [15]. In the case of this study, medium grade egg deposition may not be sufficient to modify the initial Th1 type of response, which would be dominant, contribute to accentuate the Th1 response to malaria and, therefore, to increase protection against malaria. Conversely, in subjects with high egg production, Th2 would become dominant and contribute to drive the antimalarial immune response towards non-cytophilic classes. An alternative hypothesis could be that lower egg-output actually reflects strong granuloma formation which, itself, is Th1 related, and conversely the high egg-output would indicate a Th2 type of response. Whereas this may account for the difference observed between medium and high worm load, it still does not provide an explanation for the increased susceptibility to malaria in the group excreting the lowest number of S. mansoni eggs.
Obviously, further studies, particularly of T-cell responses to both malarial and schistosome antigens are required to sort out this issue.
Conclusions
Helminthic infections are a fact of life in malaria endemic areas and their influence on the course of infection and the epidemiology of malaria is a fascinating though neglected area of research. This study shows that S. mansoni infection can increase the susceptibility to malaria in subjects excreting high schistosome egg loads.
Contribution of authors
CS, PM, JA, PC, MD were responsible for field and laboratory studies. JYL was responsible for the statistical analysis. PD inspired and designed the study
Acknowledgements
We thank all the population of the Gallo-Mallick district who have accepted to collaborate with us. We also thank Jules Lamballe, nurse of the dispensary for his contribution to this study. This programme was supported by IRD (Institut de Recherche pour le Développement) by PAL+ programme and by the Region médicale of St Louis (Senegal).
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Jambou R Rasamoel P Ralamboranto L Milijoana R Raharimalala L Pecarere JL Druilhe P Change in response to malaria induced by repeated treatment of children with Levamisole Gordon Conferences, Oxford, 1998, 26–30 July 1998
Spiegel A Tall A Raphenon G Trape JF Druilhe P Increased frequency of malaria attacks in subjects co-infected by intestinal worms and falciparum malaria Trans Roy Soc Trop Med Hyg 2003 97 198 199 14584377 10.1016/S0035-9203(03)90117-9
Talla I Belot S Kongs A Verle P Belot J Sarr S Cool AM Outbreak of intestinal schistosomiasis in the Senegal river basin Ann Soc Belg Med Trop 1990 70 173 180 2122819
Katz N Chaves A Pelegino J A simple device for quantitative stool thick-smear technique in Schistosoma mansoni Rev Inst Med Trop Sao Paulo 1972 14 397 400 4675644
Picquet M Ernoult JC Vercruysse J Southgate VR Mbaye A Sambou B Niang M Rollinson D The epidemiology of human schistosomiasis in the Senegal river basin Trans Roy Soc Trop Med Hyg 1996 90 340 346 8882173 10.1016/S0035-9203(96)90501-5
Stelma FF Talla I Polman K Niang M Sturrock RF Deelder AM Gryseels B Epidemiology of Schistosoma mansoni infection in a recently exposed community in northern Senegal Am J Trop Med Hyg 1993 49 701 706 8279638
Sy N Contribution á l'étude du paludisme dans la vallée du fleuvc Sénégal. Impact des aménagements hydro-agricoles sur la transmission dans la basse vallée au Sénégal PhD Thesis n° 35 UCAD 1998 74
Faye O Gaye O Herve JP Diack PA Diallo S Le paludisme en zone sahélienne du Sénégal. 2. Indices parasitologiques Ann Soc Belg Med Trop 1993 73 31 36 8323406
Gillies MT De Meillon B The Anophelinae of Africa south of Sahara Publications of the South African Institute for Medical Research 1968 54 343
Faye O Fontenille D Gaye O Sy N Molez JF Konate L Hebrard G Herve JP Trouillet J Diallo S Paludisme et riziculture irriguée dans le delta du fleuve Sénégal Ann Soc Belg Med Trop 1995 75 179 189 8849295
Bouharoun-Tayoun H Attanath P Sabchareon A Chongsuphajaisiddhi T Druilhe P Antibodies which protect man against P. falciparum blood stages do not on their own inhibit parasite growth and invasion in vitro but act in cooperation with monocytes J Exp Med 1990 172 1633 1641 2258697 10.1084/jem.172.6.1633
Bouharoun-Tayoun H Druilhe P P. falciparum malaria: Evidence for an isotype imbalance, which may be responsible for the delayed acquisition of protective immunity Infect Immun 1992 60 1473 1481 1548071
Nacher M Singhasivanon P Yimsamran S Manibunyong W Thanyavanich N Wuthisen R Looareesuwan S Intestinal helminth infections are associated with increased incidence of Plasmodium falciparum malaria in Thailand J Parasitol 2002 88 55 58 12053980
Pierce EJ Caspar P Grzych JM Lewis FA Cheikh A Down regulation of Th1 cytokine production accompanies induction of Th2 responses by a parasitic helminth, Schistosoma mansoni J Exp Med 1991 73 159 166 10.1084/jem.173.1.159
| 15544703 | PMC538284 | CC BY | 2021-01-04 16:37:27 | no | Malar J. 2004 Nov 15; 3:43 | utf-8 | Malar J | 2,004 | 10.1186/1475-2875-3-43 | oa_comm |
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BMC Fam PractBMC Family Practice1471-2296BioMed Central London 1471-2296-5-281557419310.1186/1471-2296-5-28Research ArticleCaregivers' practices, knowledge and beliefs of antibiotics in paediatric upper respiratorytract infections in Trinidad and Tobago: a cross-sectional study Parimi Neeta [email protected] Lexley M Pinto [email protected] P [email protected] Bishop Anstey High School, Port of Spain, Trinidad2 Department of Paraclinical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad3 Caribbean Epidemiology Center (PAHO/ WHO), Port of Spain, Trinidad4 RNA Center, Case Western Reserve University, Cleveland, OHIO, USA2004 1 12 2004 5 28 28 21 4 2004 1 12 2004 Copyright © 2004 Parimi et al; licensee BioMed Central Ltd.2004Parimi 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
Antibiotic overuse and misuse for upper respiratory tract infections in children is widespread and fuelled by public attitudes and expectations. This study assessed knowledge, beliefs, and practices regarding antibiotic use for these paediatric infections among children's caregivers' in Trinidad and Tobago in the English speaking Caribbean.
Methods
In a cross-sectional observational study, by random survey children's adult caregivers gave a telephone interview from November 1998 to January 1999. On a pilot-tested evaluation instrument, respondents provided information about their knowledge and beliefs of antibiotics, and their use of these agents to treat recent episodes (< previous 30 days) of upper respiratory tract infections in children under their care. Caregivers were scored on an antibiotic knowledge test and divided based on their score. Differences between those with high and low scores were compared using the chi-square test.
Results
Of the 417 caregivers, 70% were female and between 18–40 years, 77% were educated to high school and beyond and 43% lived in urban areas. Two hundred and forty nine (60%) respondents scored high (≥ 12) on antibiotic knowledge and 149 (34%) had used antibiotics in the preceding year. More caregivers with a high knowledge score had private health insurance (33%), (p < 0.02), high school education (57%) (p < 0.002), and had used antibiotics in the preceding year (p < 0.008) and within the last 30 days (p < 0.05). Caregivers with high scores were less likely to demand antibiotics (p < 0.05) or keep them at home (p < 0.001), but more likely to self-treat with antibiotics (p < 0.001). Caregivers administered antibiotics in 241/288 (84%) self-assessed severe episodes of infection (p < 0.001) and in 59/126 (43%) cough and cold episodes without visiting a health clinic or private physician (p < 0.05).
Conclusions
In Trinidad and Tobago, caregivers scoring low on antibiotic knowledge have erroneous beliefs and use antibiotics inappropriately. Children in their care receive antibiotics for upper respiratory tract infections without visiting a health clinic or a physician. Educational interventions in the community on the consequences of inappropriate antibiotic use in children are recommended. Our findings emphasise the need to address information, training, legislation and education at all levels of the drug delivery system towards discouraging self-medication with antibiotics in children.
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Background
Acute respiratory infections and diarrhoeal diseases are the leading causes of childhood mortality, resulting in 25–33% of all deaths in children in developing countries. Worldwide, antibiotics are the most commonly prescribed and abused drugs for upper respiratory tract infections (URTIs) [1,2]. In surveys on antibiotic use, about 20 % of prescriptions were inappropriate [3] and a high proportion of patients received antibiotics during clinic visits [4].
A significant force driving the occurrence and the spread of antibiotic resistance is the inappropriate use of antibiotics in primary and ambulatory care settings. Streptococcus pneumoniae, Hemophilus influenzae and Moraxella catarrhalis, the most common bacterial pathogens causing acute bacterial rhinosinusitis (ABRS) in children, in both the developed and developing countries, have already demonstrated resistance to the first line antibiotics [5-9]. The Sinus and Allergy Health Partnership (SAHP) guidelines for the treatment of ABRS observe antibiotics prescribed for ABRS are not only ineffective, but may contribute to the development of antibiotic-resistant bacterial infections [10] which is supported by the increasing resistance of Streptococcus pneumoniae and the increasing prevalence of strains resistant to the beta-lactams and co-trimoxazole. Unnecessary antibiotic use in viral respiratory illnesses in humans is a key factor influencing the emergence and spread of resistant pneumococci. Inappropriate antibiotic use may be consequent to misdiagnosis of the illness (viral and bacterial URTIs present with similar symptoms), patients' expectations, and their demands which induce physicians to prescribe antibiotics [11].
In the United States, a higher proportion of infections due to penicillin resistant pnuemococci among young children and whites have been attributed to an overuse of antibiotics [12,13]. Epidemiological studies demonstrate recent antibiotic use is strongly associated with carriage of resistant pneumococci in the community and the individual, and in patients with invasive pneumococcal disease, recent antibiotic use has been associated with increased risk of infection with a resistant strain [14].
Previous community-oriented studies suggest an irrational use of antibiotics, particularly in the developing and lesser-developed countries. Factors influencing antibiotic resistance are the higher incidence of infectious diseases in children, the lack of access to health care, costs and poor regulatory controls on the use of prescription drugs such as antibiotics, coupled with low antibiotic knowledge prompting increased self medication with these drugs [15-17]. As the first health-decision makers in deciding when to initiate antibiotic treatment and limit their unnecessary use, mothers and caregivers must have the appropriate knowledge to enable correct decisions. This first Caribbean study investigated the knowledge, beliefs and practices of children's caregivers in Trinidad and Tobago regarding antibiotic utilisation and explored these beliefs in self-administration of antibiotics in childhood URTIs. Gaining a better understanding of caregivers' management of childhood URTIs and factors that influence their use of antibiotics will allow appropriate educational interventions and reduce unnecessary antibiotic use in children.
Methods
Setting
Trinidad and Tobago, a twin island republic in the Caribbean located off the Venezuelan coast with a population of 1.3 million people is the second largest country in the English speaking Caribbean. About 74% of the country's population live in the urban areas and 60% of the population is aged between 15–64 years. The literacy rate in Trinidad and Tobago is 98% and at least 70% of the country's adult population has completed secondary (high) school to the pre-university level. Medical care in Trinidad and Tobago is publicly financed through three (3) regional health authorities (North West, South West and Eastern regions) in Trinidad, and one in Tobago. Secondary and tertiary care are provided at one general hospital in Port-of-Spain, one in San Fernando (1,245 beds), and at two county hospitals in Trinidad (111 beds), and at one hospital in Tobago (96 beds), besides institutions for specialised services. Primary health care is provided at 82 health centers in Trinidad and 19 in Tobago. The ratio of the population to a health center ranges from < 3000 per center in Tobago to > 21,000 per center in the north-west region. There are 33 private hospitals and approximately 45% of the population preferentially uses the private sector services. Private general practitioners are concentrated in the cities and larger towns and the estimated ratio of physician per population is 7.5 per 10, 000 inhabitants [18].
Design
This prospective cross sectional observational study was conducted in Trinidad and Tobago from November 1998 to January 1999 in randomly selected subjects interviewed over the telephone. The study design and methods have been described previously [19]. Briefly, a sample size of 800 participants with a working telephone was calculated based on 80% power to detect a difference of at least 3% use of antibiotics giving an error of 0.05. This being the first such telephone survey in the country, with no experience of the rejection rate, 1,600 telephone numbers were randomly obtained from a sampling frame of 167,272 telephone subscribers of The Telecommunication Services of Trinidad and Tobago, the only telephone service provider in the country. Of 824 respondents, 753 agreed to participate with a response rate of 91.4%. At the outset of the study participants were questioned if they were caring for a child ≤ 12 years and the term 'antibiotic' was explained in a simple sentence : "Antibiotics are drugs that are prescribed for the treatment of diseases caused by germs".
The pilot-tested questionnaire consisted of 42 items in three parts, designed to investigate knowledge, beliefs and practices of antibiotics [19]. The first part on the caregiver's demographic data included the employment status, health insurance and educational background. The second part inquired about caregivers' knowledge and beliefs. To determine antibiotic knowledge participants were asked to identify 4 antibiotics from a list of 8 commonly used drugs, and could attain a maximum score of 16. Caregivers' beliefs about antibiotics were determined using the following three questions: 1. Do you think antibiotics can cure all infections? 2. Do you believe antibiotics are free from side-effects? 3. Do you think antibiotics are generally safe? From our earlier report and the pilot project of the current study respondents differentiated their understanding of 'side-effects' and 'safety'. The former was associated with unwanted disturbances from drug therapy on the quality of life and the latter was associated with life-threatening issues like organ toxicity and death. The third part of the questionnaire ascertained caregivers' practices of antibiotic use. They were asked about symptoms which children in their care had in the past 30 days, their assessment of the symptom severity, whether they sought medical assistance and if they administered any antibiotic to the child. Respondents were not asked to name the antibiotic. Information on suspected side-effects or allergic reactions was excluded from the final questionnaire as these responses in the pilot study were uncertain and subject to memory recall.
Analysis
Four hundred and seventeen of the 753 respondents were adult caregivers with children in the family and their responses were analysed. An antibiotic knowledge score was created based on the caregivers' responses to eight common drugs, which included four antibiotics. A high Antibiotic Knowledge Score (AKS) was defined as that at or above the median score. Data were analysed using SPSS version 11.0 (Chicago), and associations were determined by the Chi square test for antibiotic knowledge and caregiver's education, beliefs and practices and recent and past antibiotic use for URTIs.
Results
The majority of respondents was female (70%), ≥ 31 years, (72%) and had completed high school (pre-university) education (77%). There was a marginally high (57%) representation from the rural area (Table 1), but residence showed no relation to the determinants of the study. The ratio of respondents with African and Indian heritage bore close similarity to the ethnic profile of the population in Trinidad and Tobago. Seventy one percent of caregivers with children did not have private health insurance. Comparable proportions of caregivers, 35% and 34% respectively reported using antibiotics recently (< 30 days) and in the past (within the last one year). The median antibiotic knowledge score was 12 and was determined by the correct identification of penicillin, tetracycline, 'Augmentin', and 'Bactrim' as antibiotics from a list of 8 common drugs. Two hundred and forty-nine (60%) caregivers scored at or above the median score. The significant predictors of high antibiotic knowledge in caregivers were those who were employed, had private health insurance and, high school education. More caregivers with a high AKS had used antibiotics recently (p < 0.037) and in the past (p < 0.008) (Table 2).
Table 1 Characteristics of children's caregivers in Trinidad and Tobago.
Characteristics Caregivers with children (n = 417)
Number of people in the house (mean +/- SD) 4.89 +/- 1.9
Number of children (mean +/- SD) 2.26 +/- 1.38
Gender:
Males 126 (30)%
Females 291 (70%)*
Age group
18–30 yrs 112 (28%)
31–40 yrs 165 (42%)
> 41 yrs 129 (30%)
Residence
Rural 235 (57%)
Urban 176 (43%)
Ethnicity
African 142 (35%)
Indian 158 (39%)
Others 110 (29%)
Education
Primary school 96 (23%)
High school 228 (55%)*
College 90 (22%)
Health Insurance
With Private Health insurance 114 (29%)
Without Private Health insurance 281 (71%)*
Antibiotics used in previous year 143 (34%)
Antibiotics used in last 30 days 149 (35%)
* Significant at p < 0.05
Table 2 Caregivers' Antibiotic Knowledge Score (AKS) and associated factors
Factors AKS <12(n = 168) (%) AKS ≥ 12(n = 249) (%) p VALUE
Gender
Males 51 (30) 51 (30) 0.96
Females 117 (70) 117 (70)
Residence
Rural 75 (46) 101 (41) 0.29
Urban 88 (54) 147 (59)
Health Insurance
Has insurance 35 (22) 79 (33) 0.02*
Does not have 122 (78) 159 (67)
Age Groups
18–30 yrs 55 (34) 57 (23) 0.07
31–40 yrs 60 (37) 105 (43)
>41 yrs 47 (29) 82 (34)
Ethnicity
African 57 (35) 85 (35) 0.70
Asian 67 (41) 91(37)
Others 41 (24) 69 (28)
Education
Primary 53 (37) 43 (17) 0.002*
High School 87 (52) 141 (57)
Tertiary Education 28 (18) 62 (26)
Employment
Employed 69 (43) 130 (54) 0.09
Self Employed 28 (18) 40 (17)
Housewife/retired/unemployed 62 (39) 71 (29)
Recent and past antibiotic use
Used in the last 12 months 45 (27) 98 (40) 0.008*
Not used in the last 12 months 118 (70) 150 (60)
Used in the last 30 days 50 (30) 99 (40) 0.037*
Not used in the last 30 days 122 (73) 150 (60)
Significant at (p < 0.05)
A majority of respondents had correct beliefs regarding whether 'antibiotics cure all infections' (54% [227/417]) and 'antibiotics are free from side effects' (61% [253/417]). Few (11% [49/417]) respondents believed antibiotics are generally safe. and some caregivers (18%–24%) remained non-responsive to all questions. The AKS of caregivers did not influence their beliefs (Table 3). Eighty six caregivers (22%) admitted to demanding antibiotics from a doctor. More caregivers (28%) with a low AKS demanded antibiotic prescriptions (p < 0.05) and kept these drugs at home (33%) (p < 0.001), to treat illnesses. Self-initiation of treatment for URTIs with antibiotics was more frequent (p < 0.05) among caregivers who had a high AKS. Caregiver's knowledge scores were not associated with the use of antibiotics given by relatives and / or friends or with compliance with the course whether recommended by the pharmacist or the doctor. (Table 3).
Table 3 Antibiotic beliefs and practices of Caregivers in Trinidad
FACTORS AKS <12 (%) AKS ≥ 12 (%) p value
1. Antibiotic Beliefs:
a. Cure all infections
Agree 48 (37) 63 (30) 0.17
Disagree 81 (63) 146 (70)
b. Free from side -effects
Agree 21 (18) 44 (21) 0.30
Disagree 99 (82) 153 (79)
c. Generally safe
Agree 21 (17) 28 (13) 0.40
Disagree 105 (83) 183 (87)
2. Antibiotic Practices
Demands from doctor for URTI in children
Yes 41 (28) 45 (19) 0.05*
No 106 (72) 188 (81)
Keeps at home
Yes 53 (33) 45 (19) 0.001**
No 107 (67) 195 (91)
Self treatment
Yes 33 (12) 69 (32)
No 154 (88) 150 (68) 0.001**
Given by friends and relatives
Yes 16 (10) 25 (11)
No 142 (90) 213 (89) 0.9
Compliance
Yes 89 (63) 152 (71)
No 52 (37) 63 (29) 0.13
*Significant at p < 0.05, ** Significant at p < 0.001
Caregivers reported as many as 450 episodes of URTIs in children within the previous 30 days (1.07 episodes per family) and 149 (35%) caregivers self-administered antibiotics in 64% (288/450) of these episodes. Cough and cold was the most frequently reported URTI symptom (48%, 214/450) followed by fever (28%,128/450) and sore throat (24%,108 / 450). Caregivers were more likely to give children antibiotics when they perceived URTIs to be severe [241/288 (84%)] (p < 0.001), and administered these drugs for the common cold [112/136, (82%)] fever [75/87 (83%)] and sore throat [54/65 (86%)] (Table 4). In 16% of episodes which caregivers deemed to be of mild severity, they self-administered antibiotics. Children received antibiotics without visiting a health clinic or a private physician for 126 (44%) URTI episodes, and more frequently for the common cold 59 (43.7%) compared with fever and sore throat (p < 0.05).
Table 4 Antibiotic administration by caregivers for severe URTIs (n = 288) and visits to health provider
URTI Episodes Assessed severe by caregiver (%) Visited a health clinic/private physician (%)
Yes No Total Yes No Total
Cough and Cold 112(82) 24(18) 136 77 (57) 59(43)* 136
Fever 75 (83) 12(17) 87 55 (63) 32(37) 87
Sore throat 54 (86) 11(14) 65 30 (46) 35(54) 65
Total 241(84)** 47 (16) 288 162(56) 126(44) 288
* Significant at p < 0.05
** Significant at p < 0.001
Discussion
This cross sectional study in Trinidad and Tobago determined the antibiotic knowledge of children's caregivers and the influence of this knowledge on their beliefs and use of these agents for URTIs in children under their care. We found high school education and higher socio-economic status (income permitted private health insurance) was significantly associated with higher knowledge scores. Similar associations between knowledge and antibiotic use were reported in a study in the Indian state of Kerala [20]. In the present study more caregivers who scored high on antibiotic knowledge, had used antibiotics (recently and in the past) compared with those who attained a low knowledge score.
A significant proportion of caregivers in the present study had misconceptions that could contribute to the inappropriate use of antibiotics. Equal proportions of caregivers with high and low knowledge scores believed that antibiotics cure all infections and are free from side-effects. Even though URTIs are generally of viral aetiology [21,22], these mistaken beliefs may have steered antibiotic abuse from self treatment or over the counter demands at the pharmacy which are fostered from easy availability of these drugs at community pharmacies in Trinidad and Tobago [19]. In a survey from the United States, 48% of paediatricians reported parents do pressure them to prescribe antibiotics [23], and 78% of the sample believed educating parents on appropriate indications for antibiotic use was the single most important factor to promote suitable prescribing, suggesting effective communication between physicians and parents may reduce inappropriate antibiotic prescribing.
Practices such as demanding a prescription for antibiotics from a physician, and keeping antibiotics at home (hoarding) were higher in caregivers with a low AKS. In Israel Shlomo et al [24] found lower education was a predictor of parents' expectations to receive antibiotics for URTIs and in Trinidad, Mohan et al reported that general practitioners attributed antibiotic over-prescribing in general practice to parents' demands [25]. A proclivity to demand antibiotics was associated with decreased knowledge and in children from insured families higher rates of antibiotic use were associated with low antibiotic knowledge and a tendency to demand antibiotics [26]. In Hong Kong educated respondents and working guardians had higher knowledge scores, and those who knew the viral aetiology of URTIs were less likely to demand antibiotics [27]. In the present study, the rate of self-treatment with antibiotics by caregivers, was higher in those who had a high AKS (32%vs12%). This may be consequent to caregivers needing to report for work following quicker recovery of children whom they care for. Braun and Fowles found a correlation between the expectation to get antibiotic treatment and parents' occupation and parents who worked full time had higher expectations to get antibiotic treatment, assuming perhaps that antibiotics shorten disease duration and allow an earlier return to work [28]. In Trinidad and Tobago at least 25% of the population demand a prescription for antibiotics from a doctor and 21% keep antibiotics in the house for emergency purposes [19]. Educational campaigns for the public can correct the widespread misconceptions on antibiotic use and storage.
Earlier in describing the prescribing practices of Caribbean physicians we reported that respiratory tract infection was the most frequent reason for antibiotic prescriptions by physicians in the English and Dutch speaking Caribbean [29]. The influence of caregivers' (parents and relatives) knowledge on antibiotic use in children with URTIs has not been studied in any Caribbean region. Proportionately more children received antibiotics from caregivers for severe episodes of cold and cough, than for sore-throat and fever. Even for what they considered mild episodes (16%) of URTIs, caregivers administered antibiotics which as is current practice, probably obtained from community pharmacies on request [19]. In Malta parents gave antibiotics to their children without a prescription particularly for sore throat and the community pharmacy made the drugs available [30]. Caregivers in our study treated 44% of URTIs in children with antibiotics without consulting a physician or attending a health facility. We did not ascertain if the child suffered any unwanted effects of the drug, which information may have been important to discuss the consequences of freely giving antibiotics to children. We believe caregivers with health insurance, education beyond the primary level and a high AKS had obtained antibiotics informally at community pharmacies and those with a low knowledge score initiated medication with antibiotics from the home storage or given them by relatives and friends. A call for strict vigilance and enforced controls regarding 'over-the-counter' availability of antibiotics without a physician's prescription, despite being controlled drugs in Trinidad and Tobago, has been made in an earlier report [19].
Inappropriate antibiotic use is a common practice in the out patient setting in a clinic, or a physician's office [31,32], and has been attributed to the combination of time pressures of outpatient practice, diagnostic uncertainty, and physicians' misconceptions of patient expectations [33]. The cost and time spent for a visit to the health center or a physician's office and a genuine concern about the children's health could have pushed caregivers in Trinidad and Tobago to purchase antibiotics without a prescription which is a widespread practice here, and initiate treatment for common childhood respiratory tract illnesses. In Africa, Asia and Latin America antibiotics are obtained from pharmacies, hospitals and even from untrained vendors at the market place [21,34-36]. In Bavi, Vietnamese children were treated with antibiotics frequently by caregivers without physician consultation, resulting in a high prevalence of multi drug-resistant strains (MDR) among respiratory pathogens [37]. Contributing to the existence of the reservoir of MDR genes among bacterial pathogens undermines the effectiveness and success of antibiotic therapy in childhood respiratory tract infections in low-income countries.
Conclusions
Inappropriate antibiotic use for paediatric URTIs in Trinidad and Tobago may have been facilitated by low knowledge, erroneous beliefs and easy availability of these drugs without the required prescription, at a retail pharmacy. A combination of education and communication to combat patients' expectations for treatment, and the physicians' appropriate prescription for antibiotics can halt inappropriate antibiotic use in children. Using narrow-spectrum antibiotics, promoting dialogue with caregivers to discuss symptom relief and antibiotic resistance, and encouraging active management of the child's illness with follow-up calls is recommended. Pharmacists have a serious responsibility not to dispense these agents without prescriptions and to discourage patients from obtaining these drugs for self-treatment. Widespread educational campaigns targeting the general public in Trinidad and Tobago, particularly parents and caregivers of young children should focus on the difference in bacterial and viral infections and the futility of treating viral infections with antibiotics. A multidisciplinary approach to rational antibiotic use, dispensing these drugs as 'prescription only medicine' and educating the public can halt inappropriate use and contain resistance.
List of abbreviations
ABRS = Acute Bacterial Rhinosinusitis
AKS = Antibiotic Knowledge Score
SAHR = Sinus and Allergy Health Partnership
URTI = Upper Respiratory Tract Infection
Competing interests
The author(s) declare that they have no competing interests.
Authors'contributions
PP conceptualised the study and drafted the protocol, NP did data collection, and contributed to the draft manuscript, LMPP prepared the final manuscript. All authors contributed to the statistical analysis and the literature search.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
Dr.Elaine Borawski and Dr C C Whalen, Case Western Reserve University critically reviewed the manuscript and made helpful comments. The Principal and Teachers of Bishop Anstey High School, Trinidad supported the study.
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| 15574193 | PMC538285 | CC BY | 2021-01-04 16:29:00 | no | BMC Fam Pract. 2004 Dec 1; 5:28 | utf-8 | BMC Fam Pract | 2,004 | 10.1186/1471-2296-5-28 | oa_comm |
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BMC GastroenterolBMC Gastroenterology1471-230XBioMed Central London 1471-230X-4-241545857010.1186/1471-230X-4-24Research ArticleEffect of straining on diaphragmatic crura with identification of the straining-crural reflex. The "reflex theory" in gastroesophageal competence Shafik Ahmed [email protected] Ali A [email protected] Sibai Olfat [email protected] Randa M [email protected] Department of Surgery and Experimental Research, Faculty of Medicine, Cairo University, Cairo, Egypt2 Department of Surgery, Faculty of Medicine, Menoufia University, Shebin El-Kom, Egypt3 Department of Physiology, Faculty of Medicine, Banha University, Egypt2004 30 9 2004 4 24 24 19 1 2004 30 9 2004 Copyright © 2004 Shafik et al; licensee BioMed Central Ltd.2004Shafik 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 role of the crural diaphragm during increased intra-abdominal pressure is not exactly known. We investigated the hypothesis that the crural diaphragm undergoes reflex phasic contraction on elevation of the intra-abdominal pressure with a resulting increase of the lower esophageal pressure and prevention of gastro-esophageal reflux.
Methods
The esophageal pressure and crural diaphragm electromyographic responses to straining were recorded in 16 subjects (10 men, 6 women, age 36.6 ± 11.2 SD years) during abdominal hernia repair. The electromyogram of crural diaphragm was recorded by needle electrode inserted into the crural diaphragm, and the lower esophageal pressure by a saline-perfused catheter. The study was repeated after crural anesthetization and after crural infiltration with saline.
Results
The crural diaphragm exhibited resting electromyographic activity which showed a significant increase on sudden (coughing, p < 0.001) or slow sustained (p < 0.01) straining with a mean latency of 29.6 ± 4.7 and 31.4 ± 4.5 ms, respectively. Straining led to elevation of the lower esophageal pressure which was coupled with the increased electromyographic activity of the crural diaphragm. The crural response to straining did not occur during crural diaphragm anesthetization, while was not affected by saline infiltration. The lower esophageal pressure declined on crural diaphragm anesthetization.
Conclusions
Straining effected an increase of the electromyographic activity of the crural diaphragm and of the lower esophageal pressure. This effect is suggested to be reflex in nature and to be mediated through the "straining-crural reflex". The crural diaphragm seems to play a role in the lower esophageal competence mechanism. Further studies are required to assess the clinical significance of the current results in gastro-esophageal reflux disease and hiatus hernia.
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Background
Swallowing is a physiologic process by which the food bolus is transmitted from the pharynx to the stomach without esophagopharyngeal or gastro-esophageal reflux [1]. A sphincteric action exists within the lower 4 cm of the esophagus which prevents reflux of gastric contents into the esophagus [2,3]. The mechanism of gastro-esophageal competence is complex and incompletely understood [4-7]. A true anatomical sphincter could not be demonstrated at the lower end of the esophagus, and the sphincter is considered a physiological one [8-11]. The resting pressure within the lower esophageal sphincter (LES) normally exceeds the intragastric pressure by 15–25 cm H2O due to tonic contraction of the esophageal musculature [10]. The LES squeeze increases by gastrin and decreases by cholecystokinin, secretin, and glucagons [5,6]. Cholinergic and ∝ – adrenergic stimuli enhance while β – adrenergic stimuli inhibit sphincter contraction11. The LES contributes to the prevention of gastric reflux into the esophagus [2,3]; however, the mechanism of action is not exactly known [2-6].
The diaphragm is believed to play a contributory role in the barrier function of the lower esophagus. This auxiliary function seems to be carried out by the crural and not the costal diaphragm. The latter contracts and relaxes with respiration. Crural diaphragm (CD) contraction effects LES pressure increase which is directly proportional to the depth of inspiration at the force of diaphragmatic contraction [12]. Pressure gradients across the esophagogastric junction during expiration is counteracted by the smooth muscle relaxation of the LES, and increases in the gastrocrural pressure gradient caused by the skeletal muscle activity of the diaphragm and abdominal wall are counteracted by the CD [13]. Crural diaphragm has been demonstrated to contribute actively in the process of deglutition [14]. Thus, on crucial balloon distension the CD relaxed, while gastric distension effected CD contraction [14]; this sphincter-like CD action was found to be mediated through the esophago-crural inhibitory and the gastro-esophageal excitatory reflexes, respectively [14].
The role of the CD during increased intra-abdominal pressure is not completely understood. We hypothesized that the CD, upon increase in intra-abdominal pressure by coughing, sneezing or straining, undergoes reflex phasic contraction with a resulting augmentation of the lower esophageal pressure and inhibition of stress reflux of the gastric contents into the esophagus. This hypothesis was investigated in the current communication.
Methods
Subjects
Sixteen subjects were enrolled in the study. Ten were men and six women with a mean age of 36.6 ± 11.2 SD years, (range 27–43). The tests were performed during operative repair of an upper abdominal ventral hernia in 9 patients and of incisional hernia after cholecystectomy for calculous cholecystitis in 7 patients. The patients did not complain of swallowing problems in the past or at the time of enrollment. They gave an informed consent after having been fully informed about the nature of the tests to be done and their role in the study.
Physical examination results, including neurologic assessment, were normal. Also barium swallow studies and upper gut endoscopy yielded normal findings. The results of laboratory work including blood count, renal and hepatic function tests as well as electrocardiography were unremarkable.
The study was approved by the Review Board and Ethics Committee of the Cairo University Faculty of Medicine.
Methods
The EMG activity of the CD was recorded during coughing and during straining. The subjects had received general anesthesia using 5% halothane/ 95% oxygen for their above mentioned hernia operations.
EMG activity of the CD
A concentric electromyographic needle electrode of 40 mm in length and 0.65 mm in diameter (Type 13 L 49 Disa, Copenhagen) was introduced into the CD as it encircled the lower end of the esophagus. A ground electrode was applied to the thigh.
A standard electromyographic (EMG) apparatus (Type MES, Medelic, Woking, UK) was used to amplify and display the potentials recorded. Films of the potentials were taken on light-sensitive paper (Linagraph type 1895, Kodak, London, UK) from which measurements of the motor unit action potentials' duration were obtained. The electromyopraphic signals were also stored on an FM tape recorder (type 7758 A, Hewlett-Packard, Waltham, MA) for further analysis as required.
Before performing the experiment, the normality of the EMG activity of the CD was tested by stimulating it with a needle electrode introduced into the CD and registering the motor unit action potentials from the already inserted needle electrode. The CD had normal EMG activity in all examined subjects.
Manometric studies
A manometric 6-F catheter was introduced into the esophagus to lie in the high pressure zone at its lower end. The catheter with 2 side ports and a metallic clip applied to its distal closed end for fluoroscopic control was connected to a pneumohydraulic capillary infusion system (Arndorfer Medical Specialities, Greendale, Wis). The pump delivered saline solution continuously via the capillary tube at a rate of 0.6 ml / min. The transducer outputs were registered on a rectilinear recorder (model RS-3400, Gould Inc). Occlusion of the recording orifice produced a pressure elevation rate that was greater than 250 cm H2O/s. During pressure measurements, the catheter was rotated so as to record anteroposterior and lateral pressures.
Induction of cough and straining
Near the end of the operation when the effect of muscle relaxant had waned, the anesthetist was asked to induce coughing and straining via laryngeal and tracheal stimulation by moving the endotracheal tube while lying in the trachea. The EMG response of the CD to increased intra-abdominal pressure was registered. Readings were recorded during two types of straining: the sudden forcible straining as that induced by coughing, and the slow sustained straining which simulates that occurring during defecation or micturition. The latency of the crural response was measured from the stimulus (straining) to the first deflection of the muscle action potential complex. The millisecond latencies were calculated when the movement artifact associated with straining appeared on the crural EMG and then the time to the first muscle action potential was measured as an index of latency.
Crural anesthetization
To define whether the effect of coughing or straining on the crural diaphragm was direct or reflex action, the following lest was done. In 8 subjects (5 men and 3 women), the CD was infiltrated with 5 ml of 2% lidocaine to anesthetize the crura around the needle electrode. The crural response to sudden and slow sustained straining was recorded after 10 minutes and after 2 hours when the anesthetic effect had waned. Similarly, normal saline was injected and the crural response to straining was registered.
The results were analyzed statistically using the Student's t test and values were given as the mean ± standard deviation. Differences assumed significance at p < 0.05.
Results
The CD in all of the subjects showed a basal activity with a mean of 112.3 ± 16.3 μV (range 86–123, fig 1). Upon sudden straining (coughing), the CD exhibited an increase in the EMG activity to a mean of 553.6 ± 54.2 μV (range 480–675 μV, p < 0.001, fig 1). The basal activity was resumed after cessation of straining. Slow sustained straining induced increase of the crural EMG activity to a mean of 482.7 ± 42.5 μV (range 366–610, p < 0.01, fig 2).
Figure 1 Electromyographic activity of the crural diaphragm a) at rest and b) on sudden straining (coughing). ↑ = coughing
Figure 2 Electromyographic activity of the crural diaphragm a) at rest and b) on slow sustained straining. ↑ = straining
The crural response to straining (sudden or slow sustained) was reproducible in all studied subjects. It was weaker in women than men, and in the elderly than in the young subjects, though the difference was insignificant (p > 0.05). The CD response disappeared when straining was sustained for more than 15–18 seconds (mean 16.8 ± 1.2) and was not evoked after frequent successive straining. The latency of the response recorded a mean of 29.6 ± 4.7 ms (range 21–33, fig 1) for the sudden straining (fig 1) and 31.4 ± 4.5 ms (range 22–36) for the slow sustained straining (fig 2) with no significant difference between the 2 latencies.
In the 8 subjects in whom the CD was anesthetized, the crural response to straining did not occur, except after 2 hours when the effect of lidocaine had waned; the response after 2 hours was similar to that before anesthetization with no significant difference (p > 0.05). Saline injection of the crura did not affect the crural response to straining.
Lower esophageal pressure response to straining
The pressure at rest in the LES recorded a mean of 25.4 ± 6.3 cm H2O (table 1). On sudden straining (coughing), we registered a mean of 96.6 ± 10.8 cm H2O (table 1), while with slow sustained straining a mean of 82.6 ± 8.3 cm H2O (table 1). The elevated esophageal pressure was coupled with the increased EMG activity of the CD and was sustained along with the increased motor unit action potentials.
Table 1 The pressure in the lower esophageal sphincter at rest and on straining+.
Pressure (cm H2O)
Mean Range
Basal 25.4 ± 6.3 17 – 32
Sudden straining 96.6 ± 10.8 * 72 – 124
Sustained straining 82.6 ± 8.3 * 58 – 97
+ values were given as the mean ± standard deviation
* p < 0.01
P values were compared to the basal value.
On CD anesthetization, the lower esophageal pressure dropped to a mean of 14.2 ± 2.4 cm H2O (table 1). It rose significantly (p > 0.01) to a mean of 63.7 ± 10.4 cm H2O on sudden straining and to a mean of 56.2 ± 7.5 cm H2O (p < 0.01, table 2) on slow sustained straining. The pressure returned to the pre-anesthetic level after 2 hours when the anesthetic effect had worn off.
Table 2 The pressure in the lower esophagus upon crural anesthetization at rest and on straining+.
Pressure (cm H2O)
Mean Range
Basal 14.2 ± 2.4 9 – 18
Sudden straining 63.7 ± 10.4 * 49 – 84
Sustained straining 56.2 ± 7.5 * 43 – 73
+ values were given as the mean ± standard deviation
* p < 0.01
P values were compared to the basal values.
Discussion
The current study seems to shed some light on the effect of coughing-or-straining-induced intra-abdominal pressure increase on the CD and the lower esophagus. The CD has a respiratory rhythm but is not a respiratory muscle. It surrounds the lower end of the esophagus, which is an intra-abdominal structure and is continuously exposed to variations in the intra-abdominal pressure. The lower esophagus contains a physiologic sphincter, which is the LES. In contrast to the CD which consists of striated muscle fibers, the LES is composed of smooth fibers.
The resting electric activity exhibited by the CD most likely denotes that the CD possesses a resting tone which presumably shares in inducing the high pressure within the LES. The high pressure zone in the lower esophagus appears to be created not only by the effect of the LES but also by the muscle tone of the CD. This is evidenced by the reduced lower esophageal pressure on the CD anesthetization. The increased crural electric activity and the elevated esophageal pressure upon straining presumably denote crural contraction. The CD tone at rest and crural contraction on straining probably share in preventing gastro-esophageal reflux under resting and stress conditions. The disappearance of the crural response on prolonged straining and the non-response after frequent successive straining appear to be due to the fact that the CD consists of striated muscle fibers which are easily fatigable and cannot remain contracted for long periods.
On CD anesthetization, the lower esophageal pressure dropped from the mean basal pressure of 25.4 ± 6.3 cm H2O to 14.2 ± 2.4 cm H2O. This denotes that the CD has a share of approximately 44% in the basal lower esophageal pressure against 54 % of the lower esophageal sphincter. On straining while the CD was anesthetized, the lower esophageal pressure recorded values significantly below those before anesthetization. These findings would indicate that the CD shares the formation of the lower esophageal high pressure zone with the LES.
The question that needs to be discussed is whether the crural response to straining is the result of a direct action or reflex in nature.
The straining-crural reflex
The current study have demonstrated that the CD contracts on straining as evidenced by increase of both the crural EMG activity and the lower esophageal pressure. The crural contraction on straining could be a direct or reflex action; it seems to be reflex in nature as became evident from its absence when the CD, a suggested arm of the reflex arc was anesthetized. This reflex relationship was reproducible and we call it the "straining – crural reflex". Lidocaine blocks the sensory fibers (C and A delta – fibers) which are responsible for pain and reflex activity [15,16]. The straining-crural reflex appears to be evoked in conditions of increased intra-abdominal pressure as occurs during coughing, squeezing and during straining at defecation or micturition.
Role of the straining-crural reflex in lower esophageal competence: The "reflex theory", a new concept
The mechanism of gastroesopageal competence is vague and incompletely understood [2-7]. There are several factors claimed to maintain the lower esophageal competence. These include the "diaphragmatic pinchcock", a circular anatomic sphincter and a flap valve [17,18]. However, in spite of the general acceptance that the circular fibers at the lower esophagus acts as a sphincter, there is so far no anatomical evidence to support the presence of a true sphincter [17-21].
Meanwhile, it is highly probable in the light of the findings of our study that the prevention of gastro-esophageal reflux is a "reflex process" rather than an anatomical entity. We have previously demonstrated that gastric distension by food or an increase in the intra-abdominal pressure would evoke the "gastroesophageal reflex" which acts to tighten the LES [22]. The more voluminous the gastric distension or the higher the intra-abdominal pressure, the tighter the LES.
The current study presumably denotes that the CD shares reflexly in the competence mechanism of the gastroesophageal junction. Thus, upon increase of the intra-abdominal pressure, the straining-crural reflex seems to be evoked effecting crural contraction and increase of the lower esophageal pressure.
In view of the aforementioned results and discussion, we believe that the "reflex theory" plays a more important role in gastroesophageal competence than the diaphragm pinchock, the flap valve mechanism or other possible anatomical factors.
Conclusion
The CD appears to play a role in the lower esophageal competent mechanism. Straining effected an increase in the EMG activity of the CD and in the lower esophageal pressure. This effect is suggested to be reflex in nature and to be mediated through the "straining-crural reflex". Further studies are needed to evaluate the clinical significance of the current results in the pathogenesis and treatment of gastresophageal disease and hiatus hernia.
List of abbreviations
lower esophageal sphincter (LES)
crural diaphragm (CD)
electromyographic (EMG)
Competing interests
The author(s) declare that they have no competing interests.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgment
Margot Yehia assisted in preparing the manuscript.
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| 15458570 | PMC538286 | CC BY | 2021-01-04 16:29:54 | no | BMC Gastroenterol. 2004 Sep 30; 4:24 | utf-8 | BMC Gastroenterol | 2,004 | 10.1186/1471-230X-4-24 | oa_comm |
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Nutr JNutrition Journal1475-2891BioMed Central London 1475-2891-3-201556662510.1186/1475-2891-3-20ResearchAn adaptogenic role for omega-3 fatty acids in stress; a randomised placebo controlled double blind intervention study (pilot) [ISRCTN22569553] Bradbury Joanne [email protected] Stephen P [email protected] Chris [email protected] Australian Centre for Complementary Medicine, Education and Research, a joint venture between University of Queensland and Southern Cross University, PO Box 157, Lismore, NSW 2480, Australia2 School of Pharmacy, University of Queensland, St Lucia, QLD 4067, Australia3 School of Natural and Complementary Medicine, Southern Cross University, PO Box 157, Lismore NSW 2480, Australia4 Blackmores Research Institute, PO Box 157, Lismore NSW 2480, Australia2004 28 11 2004 3 20 20 23 7 2004 28 11 2004 Copyright © 2004 Bradbury et al; licensee BioMed Central Ltd.2004Bradbury 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
There is evidence for an adaptive role of the omega -3 fatty acid, docosahexaenoic acid (DHA) during stress. Mechanisms of action may involve regulation of stress mediators, such as the catecholamines and proinflammatory cytokines. Prevention of stress-induced aggression and hostility were demonstrated in a series of clinical trials. This study investigates whether perceived stress is ameliorated by DHA in stressed university staff.
Methods
Subjects that scored ≥ 17 on the Perceived Stress Scale were randomised into a 6-week pilot intervention study. The diet reactive group was supplemented with 6 g of fish oil containing 1.5 g per day DHA, while the placebo group was supplemented with 6 g a day of olive oil. The groups were compared with each other and a wider cross sectional study population that did not receive either active or placebo intervention.
Results
There was a significant reduction in perceived stress in both the fish oil and the placebo group from baseline. There was also a significant between-group difference between the fish oil group and the no-treatment controls in the rate of stress reduction (p < 0.05). However, there was not a significant between-group difference between the fish oil and the placebo group, nor the placebo group and the control group.
These results are discussed in the context of several methodological limitations. The significant stress reductions in both the fish oil and the placebo group are considered in view of statistical regression, an effect likely to have been exaggerated by the time course of the study, a large placebo effect and the possibility of an active effect from the placebo.
Conclusion
There were significant differences (p < 0.05) in the fish oil group compared with no-treatment controls. This effect was not demonstrated in the placebo group. As a pilot study, it was not sufficiently powered to find the difference between the fish oil group and the placebo group significant. Further work needs to be undertaken to conclusively demonstrate these data trends. However, the findings from this research support the literature in finding a protective or 'adaptogenic' role for omega-3 fatty acids in stress.
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Background
Rousseau and Moreau et al [1] demonstrated an ameliorated cardiac response to a mild socio-social stress in DHA (the omega-3 fatty acid, docosahexaenoic acid) fed rats. The feeding schedule induced mild increases in heart rate in the sunflower oil fed group but not the DHA group. A corresponding increase in norepinephrine was significant only in the sunflower oil group. DHA also decreased systolic and diastolic blood pressure. The beneficial cardiovascular alterations, evident within a few weeks of supplementation, corresponded with high cardiac phospholipid membrane levels of DHA found on post-mortem examination.
Mills and Prkachin et al [2] found an effect from borage oil (found to rapidly increase membrane dGLA, the omega-6 dihomogammalinolenic acid) but not fish oil (rich in DHA) in cardiac parameters of stress reactivity in humans. Unfortunately, the potential confounds were not adequately discussed, bringing into question the reliability of these results. For instance, there was no mention of subject withdrawals or dropouts, a flush-out period, background diets, or background stress levels. Chronic stress levels have subsequently been demonstrated to influence reactivity to an acute stressor [3] and the effectiveness of DHA to reduce stress [4].
A research group in Japan have shown a protective effect of DHA during stress. A multi-centred randomised, placebo-controlled, double blind study involving 53 medical students and 3 months supplementation with 1.5 g/d was timed to coincide with a period of intense stress. They found that aggression towards others was significantly increased in the control group by 8.9% from baseline (p < 0.007) during the final examinations. There was no difference in aggression in the DHA group [5]. DHA prevented an increase in aggression during the examination period.
The second study was modelled on the previous study with the major difference being timing [6]. A similar but non-stressed sample 46 of university students were tested for aggression. The second study was designed to not coincide with any periods of academic stress. It commenced at the start of the summer holidays. The researchers found that DHA does not affect aggression of normal volunteers under non-stressful conditions.
Hostility was also found to increase significantly during psychological stress[7]. In a randomised, placebo-controlled, double blind study, 41 students took either 1.5 g/d DHA or placebo (soy oil) for 3 months. Hostile responses were significantly increased by from 27% (baseline) to 92% (during exams) in the control group, where there were no significant changes in the DHA group (p < 0.01). There were highly significant between-group differences (p < 0.002). The same researchers demonstrated that hostility levels significantly decreased in a population of university staff taking DHA supplementation compared with no change in hostility levels in subjects taking the placebo [7]. DHA appears to have an adaptive effect on hostility.
Sawazaki and Hamazaki et al [8] investigated the effect of DHA on various physiological parameters during psychological stress. Fourteen medical students took either 1.5 g/d DHA or placebo (47% olive oil, 25% rapeseed oil, 25% soy oil and 3% fish oil) for 9 weeks, culminating in a period of intense stress. While there were no significant differences between groups in epinephrine, cortisol, glucose or insulin, DHA significantly reduced plasma norepinephrine (NE) concentrations from baseline (-13%, p < 0.03). This reduction corresponded with a 78% increase in the ratio of epinephrine (E) to NE in the DHA group (p < 0.02). The higher E:NE ratios were interpreted as a favourable adaptive response to stress. This claim was substantiated by citations of studies which reported a failure to normalise the E:NE ratio during psychological stress observed in patients with duodenal ulcer. This ratio is believed to be protective as it has been associated with lower death rates in 412 older men. Thus, the authors concluded, a possible adaptive mechanism for DHA during stress may be to regulate the E:NE ratio.
Another possible mechanism whereby omega-3 fatty acids may be protective in stress is by modulation of proinflammatory cytokines. Maes and Christophe et al [9] found that exam stress in 27 university students significantly increased the stimulated production of many proinflammatory cytokines ex vivo. Subjects with low serum omega-3 fatty acid levels had significantly higher stimulated production of interleukin-6 at baseline compared with the subjects with high serum levels (p = 0.026) and a trend towards a significant difference during academic stress (p = 0.1). Stimulated production of interferon-γ and tumour necrosis factor-α was significantly greater in subjects with low serum omega-3 fatty acids (p = 0.02). The higher serum omega 3 levels were believed to be protective in academic stress because they were associated with lower levels of pro-inflammatory cytokines
The primary aim of the present study is to investigate whether manipulation of dietary fats has an effect on perceived stress, as measured by the Perceived Stress Scale (PSS). The hypothesis is that DHA will ameliorate stress in moderate to highly stressed university staff.
Methods
A small intervention study was nested within a larger prospective cross sectional study. Figure 1 illustrates the research design. The cross sectional study compared three stress measures, and correlated the respective measures with measures of mood and dietary fats intake [10]. This was repeated after a 10-week interval.
Figure 1 A synoptic overview of the research design showing the smaller intervention study nested within the larger cross sectional study. Note: PSS = Perceived Stress Study [11]; VAS = visual analogue scale; OSI-R = Occupational Stress Inventory-Revised [21]; PANAS=Positive and Negative Affect Scales [22].
Moderately stressed subjects were randomised into a 6-week intervention study. The nutritional intervention study was designed as a double blind randomised placebo-controlled clinical trial (pilot), involving three groups. The three groups were (1) active (1.5 g/d DHA from fish oil); (2) placebo (olive oil); and (3) control (no treatment). The supplementation period was 6-weeks.
Study Population
All procedures and processes were subject to the prior approval of the Human Research Ethics Committee at Southern Cross University.
Sample size
A power calculation was conducted using the variability data on the PSS [11]. The α and β values were set at 0.05 and 0.8, respectively. The resulting sample size requirement was for 50 subjects in each arm to demonstrate a change of 20%, 70 subjects to demonstrate a change of 15% and 175 subjects to demonstrate a change of 10%. For logistical reasons (the research was part of an honours project), the sample size for the study was chosen to be 15 subjects in each arm. Whilst inadequately powered, it was hoped that there were enough subjects to provide data that may indicate data trends.
Recruitment
All staff members of Southern Cross University were invited via intra-staff email to participate in the study on the effects of dietary fats in stress. Staff that responded to the initial recruitment email had the questionnaire personally delivered. They were instructed to complete the questionnaire and return it via internal mail. Staff members were contacted by phone and invited to participate in the nutritional intervention study if the score on the PSS was greater than or equal to 17.
Time course for recruitment and study
Recruitment for the intervention study commenced in June 2002 and was conducted over a 4-week period. Scores from the PSS at Time 1 formed the screening for the intervention study. The PSS was re-administered at baseline. The supplementation period commenced at the end of June and was completed by mid July 2002. Time 2 questionnaires were then sent out to all subjects that had participated at Time 1. Questionnaires were returned and scored in September 2002.
Inclusion/exclusion criteria
Subjects were included if they were 18–60 years, had not taken a course of fish oil in the past three months, refrained from taking other nutritional supplements and/or aspirin and from radically changing their diet for the duration of the trial, and had a normal physical examination. Subjects were excluded for medical history of coronary heart disease, any type of clotting disorder, clinically diagnosed depression, psychiatric history, diabetes mellitus, or in female subjects, pregnancy or lactation. Suitable candidates undertook a brief clinical assessment.
Randomisation and blinding
Subjects gave informed consent for the study and were subsequently randomised into two groups. A computer program was used to generate the stratified randomisation schedule. The investigators involved with the study had no knowledge of the details or results of randomisation, the study participants had no knowledge of the group to which they had been allocated, all investigators and statisticians associated with the research were blinded regarding ongoing results.
The Nutritional Intervention
Each capsule contains 1000 mg tuna oil, with 10 mg d-alpha-Tocopherol (vitamin E). The tuna oil was standardised to contain docosahexaenoic acid (DHA) 252 mg per 1000 mg, and eicosapentaenoic acid (EPA) 60 mg per 1000 mg oil. The placebo capsules contained 1000 mg olive oil, consisting predominantly of monounsaturated fatty acids. The placebo capsules were identical to the DHA capsules in every way, including size, shape, colour and smell.
There was a 2-week wash out period prior to the commencement of the supplementation period that applied to subjects taking any form of natural or complementary medicine. All subjects were instructed to take 3 capsules with breakfast and 3 for dinner for 6 weeks.
Compliance was measured by collecting the bottles with any remaining supplements at the completion of the study. The number of remaining supplements was divided by the total number of supplements dispensed. Less than 85% compliance resulted in the withdrawal of subject data from analysis.
Outcome
The primary outcome was the differences between groups in changes over time in perceived stress, as measured by the Perceived Stress Scale (PSS) [11]. The PSS-10 consists of 10 questions designed to measure subjective appraisal of life stress, taking into account appraisal of the ability to cope with the stress. It has adequate reliability and validity as a stress appraisal (perceived stress) measure [12]. Scores on the PSS have been correlated with other mental and physical health outcomes [13].
Statistical (multi-level) analysis
A two-level structure was used, where level-one units were measurement occasions, consisting of Time 1 and Time 2. Level-two units were the treatment groups, consisting of T1 =the group of subjects that did not go into the intervention study; T2 = the placebo (olive oil) group and T3 = the active (fish oil) group.
Results
Study population
Response rate to the recruitment email was 13.6% of full-time equivalent university staff. The response rate for the return of personally delivered questionnaires was 85%. The flow of participants through the intervention study is given in figure 2. There were 47 staff members that scored ≥ 17 on the PSS. After the phone screen, 39 staff members were invited for an interview, which involved further inclusion/exclusion criteria, a clinical assessment, and obtaining informed consent.
Figure 2 Flow of participants through each stage of the recruitment process and intervention study.
A further 11 potential subjects were excluded as a result of the interview process; 9 because they did not want to cease other complementary medicines for the duration of the trial, 1 because of positive findings in the clinical assessment and 1 was a female subject still occasionally lactating.
Withdrawals
A high percentage (30%) of subjects that began the intervention study were withdrawn; 3 subjects were lost to follow-up and 6 discontinued intervention; 5 due to illness related reasons and 1 because taking the supplements caused discomfort with swallowing. This resulted in 70% (n = 21) of subjects that commenced the trial participated until completion of the trial.
Multilevel analysis uniquely allows the use of unbalanced data sets. The variance component model using restricted maximum likelihood estimation was used in the hierarchical model building strategy. Absent data were assumed to be 'missing at random', implying that the reason for the missing data is not relevant to the phenomena being investigated [14].
Randomisation
The success of randomisation was verified by dividing the subjects into groups to assess whether the distribution of group characteristics was evenly balanced. The group was divided by age, gender, BMI and gender and is given in table 1. The randomisation process resulted in an uneven distribution of group characteristics according to occupation level and gender. The numbers and proportions of the distribution of staff by occupation level are provided in table 2. The majority, 71%, of the staff randomised into the placebo group were administration staff, while the fish oil group was fairly evenly distributed. In addition, males seem to be under-represented in the placebo group.
Table 1 Distribution of group characteristics for subjects randomised into intervention study.
Treatment group
Control: no-treatment Placebo: olive oil Active: fish oil
Mean SD Mean SD Mean SD
Age 44.18 8.127 44.43 9.387 40.69 7.391
Gender .74 .443 .79 .426 .63 .500
Body Mass Index .00 .00 28.051 9.2384 26.684 5.8819
Occupation Status .40 .494 .29 .469 .56 .512
Note: Dummy variables given to categorical variables: gender is coded 1 = female and 0 = males, and occupation status is coded 0 = administration staff and 1 = academic staff.
Table 2 Distribution of staff by occupation and gender between the three treatment groups at baseline.
Control Placebo Active
No-treatment Olive Oil Fish Oil
n % n % n %
Academic 26 40 4 29 9 56
Administration 37 60 10 71 7 44
Total 63 100 14 100 16 100
Male 17 26 3 21 6 38
Female 46 74 11 79 10 63
Total 63 100 14 100 16 100
Note: % = relative proportion for each subcategory (occupation and gender), n = number of subjects
These frequencies and distributions are not outside the range of expected results from the randomisation process. The main concern is that the numbers are very low in some groups. For instance, 3 male subjects in the placebo group and 4 academic staff in the placebo group may not be enough to be sensitive to significant effects, increasing the risk of a Type II error. However, the results of multivariate analysis of variance found that gender and occupational status did not have any effects on perceived stress. Therefore, the unbalanced distribution of group characteristics was not relevant.
Preliminary and multilevel analysis
Correlations between PSS scores at Time 1 and baseline were high (0.9, p < 0.05) and there was no significant difference between group means on paired-samples t-tests. Therefore all subsequent analysis treated Time 1 PSS as baseline scores. Preliminary analysis involved testing for any main effects of occupation level, gender and age on the various stress measures. A two-way between-groups multivariate analysis of variance was used to test for significant interactions between the active (fish oil) and placebo (olive oil) groups over time. Age, gender and occupation levels were all added to the model with no significant effects. Therefore all models were reduced to test for the two main effects of treatment and time on perceived stress.
When the fish oil group was compared with the placebo group (olive oil), variance components analysis found no difference. No treatment effect was found but there was a trend towards an effect for the fish oil group (Wald statistic = 1.24). There was a main effect for time in both groups.
Because of the very small numbers of subjects on the study, results of the comparison between the fish oil and the placebo groups were then compared with the group of subjects that did not participate in the intervention study. Means and standard deviations for the three groups on the PSS are given in table 3. The means were much higher for subjects on the intervention study. A hierarchical model, given in table 4, investigated the effects of time, treatment and the interaction of time on treatment for the three treatment groups, where T1 = no treatment, T2 = placebo (olive oil), and T3 = active (fish oil).
Table 3 Means and standard deviations for the three groups on the PSS
No-treatment Placebo (olive oil) Active (fish oil)
Mean (SD) Mean (SD) Mean (SD)
n 63 14 16
PSS 15.83 (4.79) 23.93 (2.62) 23.94 (4.33)
Note: n = number of subjects
Table 4 Parameter estimates for models of variance components, time, treatment (T1 and T2) and interaction (of time on treatment) main effects on the Perceived Stress Scale (PSS) group means.
Variance Components Time Main Effects Treatment Main Effects Interaction Main Effects
Fixed effects Coeff. (S.E.) Coeff. (S.E.) Coeff. (S.E.) Coeff. (S.E.)
β Constant 17.639 (0.533) 18.444 (0.597) 16.479 (0.598) 15.836 (0.614)
Time -1.999 (0.669)* -2.223 (0.665)* -0.500 (0.770)
T2 6.958 (1.241)* 8.092 (1.441)*
T3 5.372 (1.164)* 8.101 (1.365)*
T2 × time -2.786 (1.679)
T3 × time -6.059 (1.554)*
Random effects
Subject level residual variance 15.498 (4.260) 16.639 (4.200) 8.226 (3.063) 9.867 (2.956)
Time level residual variance 18.496 (3.115) 16.599 (2.801) 16.721 (2.801) 13.930 (2.334)
-2 log likelihood 1020.264 1011.772 974.964 960.204
* Denotes significance (Wald statistic ≥ 1.96, where p ≤ 0.05). Note: T2 = olive oil group, T3 = fish oil group, Coeff. = coefficient, S.E. = standard error, -2 log likelihood statistic = -2LL.
The effects of time were significantly greater for subjects on the intervention study. There was a significant effect of treatment in the respective active and the placebo groups. Once the effects of time were accounted for, only the fish oil group estimated means were significantly different. The changes in the group means over time are provided in table 5 and illustrated in figure 3. There were substantial changes on the PSS for the two groups in the intervention study over time, but the changes in the no-treatment group were not different over time.
Table 5 Predicted changes over time in estimates of Perceived Stress Scale (PSS) means for each treatment group, 95% confidence intervals, and chi square value.
Control Placebo Fish oil
Estimate -0.500 -3.285 -6.559
95% CI sep 1.509 2.923 2.646
95% CI joint 2.152 4.170 3.755
Chi square 0.421 4.852* 23.590*
* significance determined by chi sq ≥ 3.84, the critical value of chi square for df = 1 when p ≤0.05. Note: 95% CI sep = 95% confidence interval considering each item separately, 95% CI joint = 95% confidence interval, considering the estimates of the three treatment groups collectively, df = degrees of freedom
Figure 3 Changes over time in predicted means on the Perceived Stress Scale (PSS) for the three treatment groups.
The change over time in PSS means were not the same for the two groups in the intervention study. Inclusion in the fish oil group predicted a larger reduction in estimates of perceived stress than the placebo group. Estimated means at Time 2 on the PSS were 17.4 for the fish oil group, where those for the placebo group were 20.6.
The within-group changes over time are given in table 6. The changes over time were significant for both the placebo and fish oil groups but not the no-treatment group. The difference between the groups in the changes over time are given in table 7. The differences between the fish oil group and the no-treatment group is the only between-group difference to reach statistical significance (p < 0.05). The change over time in the placebo group was not significantly different from those in the no-treatment group. Changes in the placebo group over time were also not significantly different from changes in the fish oil group over time.
Table 6 Predicted means and 95% confidence intervals of the Perceived Stress Scale (PSS) for the treatment groups over time.
Control Placebo Fish oil
Time 1
Mean 15.836 23.929 23.938
95% CI sep 1.203 2.555 2.390
95% CI joint 1.716 3.645 3.409
Time 2
Mean 15.337 20.643* 17.379*
95% CI sep 1.416 2.725 2.455
95% CI joint 2.021 3.888 3.502
Note: 95% CI sep = 95% confidence interval considering each item separately, 95% CI joint = 95% confidence interval, considering the estimates of the three treatment groups collectively. *denotes significant difference from baseline (p < 0.05).
Table 7 Estimated between-group differences in changes over time for predicted means on the Perceived Stress Scale (PSS), with confidence intervals and chi square statistic.
Placebo vs Control Fish oil vs Control Fish oil vs Placebo
Estimate 2.786 6.059 3.273
95% CI sep 3.289 3.046 3.943
95% CI joint 4.692 4.345 5.625
Chi square 2.754 15.193* 2.647
* Significance determined by observed value of chi sq ≥ 3.84, the critical value of chi square for df = 1 when p ≤ 0.05 Note: 95% CI sep = 95% confidence interval considering each item separately, 95% CI joint = 95% confidence interval, considering the estimates of the three treatment groups collectively, chi sq = chi square, vs = versus, the difference between groups
Discussion
There were significant reductions in stress for both the fish oil and the placebo (olive oil) groups from baseline (both p < 0.05). The stress reduction for the fish oil group was significantly different from the no-treatment controls (p < 0.05). The stress reduction in the placebo group was not significantly different from the no-treatment controls. The fish oil group had more substantial stress reductions than the olive oil group, but the differences between the fish oil and the placebo groups did not reach statistical significance.
All subjects taking the nutritional intervention reported significantly less perceived stress at Time 2 (p < 0.05). Arguably, the key factor influencing these results is 'regression to the mean', an effect that may have been exaggerated by methodological limitations such as timing, a large placebo effect, and the possibility that the placebo was not a true placebo. These issues are discussed in the following sections.
Statistical regression
Perhaps staff applied and were selected for the intervention study at a time when their stress levels were peaking. If this were the case, 'regression to the mean' predicts that stress levels would decrease. Factors which would exaggerate this effect are the difference in the means between the groups on the intervention study and the no-treatment controls, and the study time course corresponding with the mid-year break.
Because only 30 out of a possible 47 candidates were randomised into the intervention study, the 17 potential candidates remaining in the group receiving no intervention were thought to increase the mean of the no-treatment group. That is, 37% of the no-treatment group had high scores on the PSS (≥ 17). Therefore, this group was considered a potential control group for the intervention study.
The time course of the study may have noticeably influenced the regression to the mean. The academic year at Southern Cross University consists of two semesters. This study commenced mid-way through the first semester (Time 1). Questionnaires were reissued at the beginning of the second semester (Time 2), immediately after the mid-year break. Staff may generally have been more relaxed and less stressed after the break. The variation of stress levels associated with time may have been minimised if Time 2 had been at mid-semester 2, a time that corresponds more closely with Time 1.
Evidence of a treatment effect beyond statistical regression will be demonstrated in the between-group differences. The only such difference was between the fish oil group and the no-treatment controls. This evidence supports the hypothesis that fish oil ameliorates chronic stress.
Methodological limitations
The present study has several important limitations, including (1) selection bias (2) unsuccessful blinding (3) inadequate power and (4) the possibility of an active effect from the 'placebo'.
Sampling bias
Invariably, recruitment involving self-selection entails some degree of selection bias [15]. In this instance, the advertisement was aimed at university staff interested in stress research involving the beneficial omega-3 fatty acids. Staff may have excluded themselves for weight-watching reasons, or included themselves because they were interested in the intervention. Perhaps very stressed and/or busy staff did not apply, or perhaps staff that were not stressed did not apply. Because the study ran into the mid year break, perhaps staff did not apply because of issues relating to availability during the break.
Power
While the results demonstrated a strong trend towards a difference between the fish oil and the olive oil groups, this difference failed to reach statistical significance with the current sample size. Post-hoc power calculations were conducted using the PSS means and standard deviations. The study had 90% power to find a 20% difference between the fish oil and the placebo groups, and only 40% power to find a difference of 10% significant. The study was underpowered to find the difference between the groups statistically significant.
Blinding
Although the capsules appeared in every way identical, there was one distinct difference. The fish oil capsules were often accompanied by very mild gastrointestinal disturbances in the form of a slight after taste in the mouth, which was unmistakably fishy. All the subjects taking the fish oil suspected as much because they had the taste of fish in their mouth after ingestion of the capsules.
Interestingly, half (50%) the subjects taking the placebo during this study also believed they were taking the active because they 'felt better'. The placebo group did actually report significantly less stress levels. This effect may be due regression to the mean, as previously discussed. Other factors that must be considered are the 'placebo effect' and the possibility of an active effect from the placebos.
The placebo effect
Half (50%) of the subjects taking the placebo believed themselves to be taking the active. If these subjects believed in the treatment and expected to benefit from it, then it is likely they reported the improvements, which may have influenced the mean of the group. If half the group were reporting an exaggerated stress reduction, then the mean of the group would show a trend towards a treatment effect. Indeed, this was observed: The placebo group demonstrated significant stress reduction; however, the change over time was not significantly different from reductions observed in the no-treatment controls.
Perhaps the most unbiased way to estimate true placebo effects is to observe the difference between a placebo group and a group of no-treatment controls in a three-arm clinical study [16]. In the present study, the change over time for the placebo group, like the fish oil group, was significant (p < 0.05), where the change over time for the no-treatment controls was not significant. This apparent placebo effect, however, may have been inflated by statistical regression or an active effect from the placebo.
The placebo
The use of olive oil as a placebo is not uncommon in essential fatty acid research [17]. However, olive oil may not be an inert substance in brain lipid chemistry. Oleic acid, the major lipid in olive oil, is related to an endogenous sleep-inducing substance. Isolation of a chemical from the cerebrospinal fluid of sleep deprived cats, led to its identification as cis-9,10-octadecenoamide (oleamide), an analogue of 9-octadecenoic acid (oleic acid) [18]. Oleamide, but not oleic acid, was found to improve the action of serotonin (5-hydroxytryptamine), which implies a role for this molecule in mood, alertness and sleep [19]. Cell membranes have been shown to catalyse the synthesis of oleamide from oleic acid [20]. In addition, the rat brain demonstrated control of increased levels of cis-9,10-octadecenoamide (oleamide) by conversion into oleic acid [18]. Understanding of the lipid chemistry involved with the neuromodulation of sleep and mood is still incomplete. However, the assumption that oleic acid is neutral in lipid neurochemistry is questionable.
The issue of a correct choice of placebo for essential fatty acid research is difficult. Most studies use omega-6 rich oil, such as corn oil, soy oil, or safflower seed oil. However, this practice is not completely unbiased as the omega-6 oil competes with omega-3 fatty acids for metabolism. Given in high enough doses, the omega-6 oils will overwhelm delta-6 desaturase and inhibit the metabolism of the omega-3 fatty acids.
This methodology results in a systematic measurement error, where the omega-3 metabolism in the placebo group may be suppressed. Further, the placebo is not a true placebo if it has a specific effect on the measurement outcome. Between-group differences in these studies may be enhanced. Alternatively, soy oil has been shown to increase omega-3 levels in omega-3 deficiency. As the regulation of the end products of n-3:n-6 blood levels is complex and not fully understood, it is probably best to avoid using these fatty acids as a placebo. This practice may increase the variance in the measurement error to an unknown extent. The monounsaturated fatty acid found in olive oil was chosen here to avoid further imbalances between the essential fatty acids.
Conclusions
Perceived stress was significantly reduced from baseline after 6 weeks supplementation with 1.5 g/d DHA from fish oil (p < 0.05). Furthermore, the difference was significant compared with no-treatment controls (p < 0.05). The placebo group also demonstrated significant reductions in perceived stress compared to baseline levels (p < 0.05). However, when compared with the no-treatment control group, the differences in perceived stress were not significant for the placebo group.
The study may have demonstrated an exaggerated regression to the mean due to its timing, a strong placebo effect or the placebo itself may have had an active effect. The question that olive oil may have a subtle but protective effect in stress has nevertheless been raised. Perhaps 6 g per day of olive oil was not sufficient dietary intake to find a difference between the placebo and no-treatment controls significant, especially with such a small sample.
This research has provided preliminary findings suggesting that DHA ameliorates perceived stress. Future research is required to conclusively substantiate the ameliorating effects of DHA in stress, and further investigate the role of olive oil and/or other dietary fats in stress reduction.
Competing interests
This research was purely academic research and has no commercial interest. However, Blackmores Ltd, a company that sells fish oil capsules, also employs one of the authors, Chris Oliver.
Acknowledgments
The authors would like to thank all the participating staff members of Southern Cross University that gave their time to this research.
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| 15566625 | PMC538287 | CC BY | 2021-01-04 16:39:29 | no | Nutr J. 2004 Nov 28; 3:20 | utf-8 | Nutr J | 2,004 | 10.1186/1475-2891-3-20 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-441556373510.1186/1475-925X-3-44ResearchA quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep Cysarz Dirk [email protected] Henrik [email protected] Silke [email protected] Daniel [email protected] Leeuwen Peter [email protected] Department of Clinical Research, Gemeinschaftskrankenhaus Herdecke D-58313 Herdecke, Germany2 Institute of Mathematics, University of Witten/Herdecke D-58455 Witten, Germany3 Department of Biomagnetism, Research and Development Center for Microtherapy (EFMT) D-44799 Bochum, Germany2004 25 11 2004 3 44 44 2 9 2004 25 11 2004 Copyright © 2004 Cysarz et al; licensee BioMed Central Ltd.2004Cysarz 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 univariate approaches used to analyze heart rate variability have recently been extended by several bivariate approaches with respect to cardiorespiratory coordination. Some approaches are explicitly based on mathematical models which investigate the synchronization between weakly coupled complex systems. Others use an heuristic approach, i.e. characteristic features of both time series, to develop appropriate bivariate methods.
Objective
In this study six different methods used to analyze cardiorespiratory coordination have been quantitatively compared with respect to their performance (no. of sequences with cardiorespiratory coordination, no. of heart beats coordinated with respiration). Five of these approaches have been suggested in the recent literature whereas one method originates from older studies.
Results
The methods were applied to the simultaneous recordings of an electrocardiogram and a respiratory trace of 20 healthy subjects during night-time sleep from 0:00 to 6:00. The best temporal resolution and the highest number of coordinated heart beats were obtained with the analysis of 'Phase Recurrences'. Apart from the oldest method, all methods showed similar qualitative results although the quantities varied between the different approaches. In contrast, the oldest method detected considerably fewer coordinated heart beats since it only used part of the maximum amount of information available in each recording.
Conclusions
The method of 'Phase Recurrences' should be the method of choice for the detection of cardiorespiratory coordination since it offers the best temporal resolution and the highest number of coordinated sequences and heart beats. Excluding the oldest method, the results of the heuristic approaches may also be interpreted in terms of the mathematical models.
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Background
The time intervals between successive heartbeats (e.g. the RR tachogram or, equivalently, the series of instantaneous heart rates) may be analyzed with different tools to obtain information about e.g. heart rate variability (HRV) [1], regularity of the time series [2-7], or large-scale correlations in the time series [8-10]. Each technique provides different information about the 'time-structure' contained in the series of successive heartbeats. Some of them, like information obtained from HRV, may be linked to the sympathetic and parasympathetic activity of the autonomic nervous system [1,11]. Hence, they offer the possibility to interpret the derived quantities in terms of physiology. Furthermore, when applied to data obtained from patients suffering from heart diseases, these different information may be combined and used for risk stratification [12,13]. A large body of work concentrates on these univariate time series analysis and standards have been established [1].
To gain information that cannot be extracted from a single time series, the interaction between two (or more) physiological (sub-)systems has been investigated. This is often done on the basis of simultaneously recorded time series of each system. Hence, new bivariate techniques have been developed that aim to quantify the imprint of the physiological interaction between the two systems [14-17]. Especially, techniques which analyze the cardiorespiratory interaction have been developed recently [18-24]. Among these techniques the coordination between the series of successive heartbeats and the series of successive respiratory cycles, further denoted as cardiorespiratory coordination, is one focus of attention.
In fact, physiologists had already investigated cardiorespiratory coordination in the human organism as early as the 1960ies [25]. Calculating the distance between an inspiratory onset and its preceding R-peak they found intermittent coordination between heartbeat and respiration. In the 1970ies this interesting topic was no longer followed up (except in one study [26]), presumably because the physiological interpretation of the results was limited, although the last reviews of this era appeared in the late 1980ies [27-29]. The investigation of cardiorespiratory coordination has recently been revived mainly by physicists and mathematicians. They first studied the interaction of two weakly coupled chaotic oscillators and found synchronization of the phases whereas the amplitudes of the oscillators remained uncorrelated [30]. This class of mathematical models may serve as an approximate qualitative model for the interaction of heartbeat and respiration [19]. On the basis of these models new techniques for the analysis of bivariate time series data recorded from these physiological systems have been developed [22,31,32]. Other approaches have also been used to develop methods to analyze the cardiorespiratory interaction since the significance of this topic was deemed high [23,33-41].
The similarities or differences between the definitions of the different techniques and the advantages of each technique are not known yet. Unfortunately, a complete review and a (qualitative and quantitative) comparison of the recent literature is too extensive and beyond the scope of this paper. This study is restricted to the comparison of the performance of six different techniques in the detection of cardiorespiratory coordination. Although the comparison is limited, the insight that is drawn from it provides valuable information with regard to techniques that are not considered in this comparison. A second aim of this study is to investigate the cardiorespiratory interaction during night-time sleep since previous studies only dealt with relatively short recordings (no more than one hour).
Methods
Data acquisition and pre-processing
The electrocardiogram (ECG, standard lead II) and the uncalibrated nasal airflow (derived by a thermistor-technique) were recorded simultaneously in 20 healthy subjects (7 female, median age: 34.9 years, interquartile range: 13.7 years) using an ambulatory device (Medikorder, Tom-Signal, Graz). In each subject the night-time sleeping period was recorded, in some subjects the complete evening before the sleep period was also recorded. Hence, the total recording time varied from 7–16 hours. The device's internal sampling rate of the ECG was 3000 Hz. Thus, the times of the automatically identified R-peaks had an accuracy <1 ms and were written into a file. To save memory, the ECG was recorded using a rate of 250 Hz. The data were transferred to a PC and analyzed using Matlab (The Mathworks, Natick, Mass, USA) and C routines. The times of the automatically identified R-peaks were visually controlled, i.e. the times of the R-waves were marked in the ECG, and edited if the times of the automatically identified R-peaks did not match the R-peak in the ECG (<0.1% of all R-peaks). The times of the edited R-peaks had an accuracy of 4 ms because the recorded ECG had a lower sampling rate. Ectopies and artefacts were marked and excluded from the analysis.
The device's sampling rate of the nasal airflow was 100 Hz. The respiratory trace was saved into a file. Low frequency baseline trends were avoided using an internal filter with a 0.01 Hz cut-off frequency. The trace did not need any further pre-processing or filtering because it was smooth (cf. example in Figure 1). Inspiratory onsets were defined as local minima in the respiratory trace since local minima in the respiratory trace are due to the change from exhaling warm air to inhaling colder environmental air. They were extracted with an accuracy of 10 ms. For further analysis only the times of R-waves Ri (i = 1,...,nR) and the times of the inspiratory onsets Ij (j = 1,...,nI) were necessary and saved into a file. These data served as the basis for further calculations, see Figure 2.
Figure 1 Example of the recordings. Example of a short sequence of the simultaneously recorded electrocardiogram (ecg) and respiratory trace (thermistor) during night time sleep.
Figure 2 Relevant data in the recordings. The times of the R-peaks Ri and the times of the inspiratory onsets Ij (defined as local minima in the respiratory trace) derived from the recordings serve as event markers for further calculations.
Note that in the following the term 'cardiorespiratory coordination' is used descriptively in the sense that the times of the R-waves and the times of the inspiratory onsets show some kind of temporal incidence. The temporal incidence does not imply a physiological coupling between heartbeat and respiration, i.e. 'cardiorespiratory synchronization'.
The first step towards the analysis of cardiorespiratory coordination with respect to different m:n-coordination ratios (m: number of heart beats, n: number of respiratory cycles) is the calculation of temporal distances between the event markers of the two time series. Generally, two different useful possibilities have to be considered. (1) The temporal distance between inspiratory onsets and successive R-peaks, or, (2) the temporal distance between an inspiratory onset and the preceding R-peak.
1. Temporal distance between inspiratory onsets and successive R-peaks:
absolute distance: ti = Ri - Ij (1)
2. temporal distance between an inspiratory onset and the preceding R-peak:
absolute distance: tj = Ij - Ri-1 (3)
Note that for the calculation of the absolute distances ti and n > 1 the appropriate settings of the indices i and j have to be considered. Furthermore, the relative distance φi corresponds to a definition of a phase angle of an oscillator on the interval [0, n]. This phase (multiplied by 2π) is equivalent to the cyclic phase calculated via the Hilbert-transformation [32,42]. Similar phases may also be calculated using e.g. the Fourier-transformation [43]. Hence, with respect to the variable φi, the terms 'relative distance' and 'phase' are used synonymously throughout this paper. Compared to φi and ti the calculation of tj and βj only needs the timings of the R-peaks before and after the inspiratory onset. Thus, these variables contain less information. They have been mainly used in early studies on cardiorespiratory coordination and are not common at present [44,45]. In order to keep this comparison practicable, the calculation of the absolute distances tj and the subsequent analysis of these quantities is left out in this study.
In Figure 3 (a) an example of a succession of absolute distances ti is shown, i.e. each point represents the temporal distance of the R-peak to its preceding inspiratory onset (n = 1). In Figure 3 (b) these distances are calculated for two consecutive respiratory cycles (n = 2). This kind of representation is known as 'Post Event Time Series' [34,46] and has already been used in some early studies of cardiorespiratory coordination [26]. Since n = 1 in Figure 3 (a), the structures of parallel horizontal lines reveal 4:1- and 3:1-coordination (see marked points). In Figure 3 (b) the arrows indicate 7:2-, 8:2- and 6:2-coordination because n = 2 was used for the calculation of the distances. Figures 3 (c) and 3 (d) show the relative distances φi of the same sequence for n = 1 and n = 2 respiratory cycles, respectively. This kind of representation is called 'Synchrogram' [19]. Obviously, it shows structures of parallel horizontal lines for the same epochs as the absolute distances ti (see markers in Figure 3). The structures with horizontal lines reveal epochs in which absolute distances ti or relative distances φi, respectively, of each m-th R-peak recurs after one (n = 1) or two (n = 2) respiratory cycles: the cardiorespiratory interaction is coordinated. If the number of respiratory cycles n is increased (n > 2) these m:n-coordination may be observable in the same manner. In Figure 3 (e) the relative distances βj for the same sequence are shown. As mentioned above, this diagram contains less information compared to the other diagrams. Still, in the case of cardiorespiratory coordination, a short horizontal structure appears, cf. the markers in Figure 3 (e). These structures show the presence of m:1-coordination, similar to the diagrams in Figure 3 (a) and 3 (c).
Figure 3 Example of cardiorespiratory coordination. (A) and (B) show examples of a 'Post Event Time Series' for 1 and 2 respiratory cycles, respectively. The corresponding 'Synchrograms' are shown in (C) and (D). (E) depicts the 'Synchrogram' for phases βj. In (A) and (C) the arrows indicate a sequence with 4:1- and a sequence with 3:1-coordination. In (B) and (D) the arrows point to sequences with 7:2-, 8:2- and 6:2-coordination. The arrows in (E) indicate sequences with coordinated inspiratory onsets that correspond to the coordinated sequences in (A) and (C).
The fact that cardiorespiratory coordination is present if structures with horizontal lines occur leads to the task of detecting these structures in the different diagrams. This task can be carried out by different techniques based on mathematical models or heuristic approaches. Some relevant approaches are described in the following.
Detection of horizontal structures in Synchrograms or Post Event Time Series
1. Detection via 'Synchronization-λ'
Along with the development of the Synchrogram a quantification on the basis of a mathematical model has been proposed [32,47,48]. Consider two coupled oscillators with appropriately defined phases φ1 and φ2 that may intermittently show a 1:1-synchronization (for simplicity both phases are defined on the interval [0, 2π]). In the synchronized case the difference φ1 - φ2 is constant. In real world data the phases φ1 and φ2 are contaminated by noise. Thus, even during synchronization the difference φ1 - φ2 is not constant. Rather it fluctuates around a constant. To take these influences into account a 'stroboscopic technique' has proven to be useful. Each time the phase φ1 exceeds a pre-defined value θ the phase φ2 is registered. During a synchronization the different values of φ2 are almost constant whereas during complete de-synchronization φ2 spreads equally over the interval [0, 2π]. This approach may easily be generalized for m:n-synchronization if the phases φ1 and φ2 are replaced by φ1/m and φ2/n. The distribution of φ2 may be quantified by the first Fourier mode.
The formalization of this idea is as follows: Observe the phase φ2 at the times t the phase φ1 is φ1= θ :
Next, calculate the first Fourier mode of the distribution of ξi which is known as circular variance [49] (M values of φ2 have been observed):
If both oscillators are synchronized λ = 1, the completely de-synchronized case yields λ = 0. To get a more reliable result, λ may be calculated for different values of θ and subsequently averaged.
This method is easily applicable to the detection of cardiorespiratory coordination. Define the phases φ1,2 as follows: φ1 increases linearly on the interval [0, 2π] between two successive R-peaks and φ2 between two successive inspiratory onsets, respectively. To detect coordination for different n:m-ratios this method has to be applied for each desired m:n-ratio. In practice, coordination is present if λ exceeds a pre-defined threshold λ ≥ thresλ. In this case the respective R-peaks are marked as coordinated.
Note that although this method deals with synchronization of two coupled oscillators it is not able to detect cardiorespiratory synchronization in a physical sense. A detection of synchronization on the basis of two simultaneously recorded time series would require supplementary information, e.g. the variation of the strength of coupling. Such supplementary information is not available for the cardiorespiratory interaction. Thus, the term cardiorespiratory coordination denotes a temporal incidence of events in both time series without claiming that this incidence is based on a coupling between both systems.
In this study, the window length M is set to M = 20. To obtain a better time resolution of the λ-values the window is forwarded one R-peak or accompanying phase, respectively. To obtain more stable values of λ the average of 10 different values of θ, equally distributed over the interval [0, 2π], is used. The threshold is set to thresλ = 0.85. This procedure is successively carried out for the following m:n-coordinations: for n = 1: m = 2,...,8 and for n = 2: m = 5,7,9,11,13,15.
2. Detection via 'Phase Recurrences'
This method is based on a heuristic approach. Consider a synchrogram containing a sequence with m parallel horizontal lines indicating a m:n-coordination. In this sequence the relative distance φi of each m-th R-peak has to be approximately the same. Otherwise parallel horizontal lines would not appear. This 'recurrence of phases' may be used to identify coordinated sequences [36] (a slightly different approach is presented in [50]).
The formalization of this concept is straightforward. For a m:n-coordination check whether the phase difference between the phase of R-peak i + m and the phase of R-peak i is within a pre-defined tolerance ε. This condition has to be fulfilled for at least k successive R-peaks:
Nr is the total number of R-peaks. In principle, the parameter k is not pre-defined. But, to be compatible with the description of 'parallel horizontal lines' during coordination, k ≥ m needs to be fulfilled. This procedure allows a detection of a structure of parallel horizontal lines already with a length of 2m successive relative distances φi. E.g. a 4:1-coordination may be identified already in a minimal sequence of 8 R-peaks. In the case coordination is detected, the respective R-peaks are marked as coordinated. To detect horizontal structures for different m:n-ratios this method has to be applied for each ratio. It is easy to implement and it can be used for absolute distances ti analogously.
In this paper the Phase Recurrence is analyzed with respect to absolute distances ti and relative distances φi for the following m:n-coordinations: n = 1: m = 2,...,8 and n = 2: m = 5,7,9,11,13,15. In the case of absolute distances ti the tolerance ε is set to ε = 0.075, i.e. 75 ms, and in the case of relative distances φi the tolerance is set to ε = 0.025.
3. Detection via 'Quantification of Histograms'
Another technique to detect cardiorespiratory coordination makes use of a sliding window comprising nF successive phases φi that is moved over the entire series of phases [34,46]. First, a distribution of the phases φi is calculated for each window. If cardiorespiratory coordination, i.e. a structure with parallel horizontal lines, is present the distribution of phases φi shows some distinct equidistant local maxima (e.g. four local maxima in the case of 4:1 coordination). In the un-coordinated case the phases φi are randomly distributed and local maxima do not appear. Next, each distribution is quantified by means of a Fourier Transformation. In the case of coordination, the distribution of the phases φi contains several local maxima resulting in a huge maximum in the power spectrum. In the un-coordinated case the power spectrum does not contain any pronounced maximum since the phases φi are equally distributed. Thus, the appearance of pronounced local maxima in the power spectrum is used to detect cardiorespiratory coordination.
This idea may be formalized as follows [46]. First, choose the length nF of the sliding window and the number of bins k to calculate the distribution. In the completely un-coordinated case the value of each bin xl is nF/k. If a perfect m:n-coordination is present the following distribution is obtained:
Here, l0 is the index of the bin that contains the first local maximum of the distribution. This distribution is quantified by means of a Fourier transformation:
The average of all bins nF/k is subtracted from each bin to avoid a dc-component in the power spectrum. In the un-coordinated case Px(f) = 0 for all frequencies f. In the case of a m:n-coordination m local maxima at the frequencies f = a m, a ≤ k/(2m) appear. These local maxima are conserved even if the parallel horizontal lines are not exactly mapped into m bins but also some surrounding bins are filled. Thus, the distinction between coordinated and un-coordinated cardiorespiratory interaction may be carried out using the difference between the maximum and the minimum of the power spectrum.
diff(Px) = max(Px) - min(Px) (10)
In the coordinated case this difference is large whereas in the un-coordinated case the difference is small. In practice, the R-peaks in the analyzed window are being marked as coordinated if diff (Px) exceeds a pre-defined threshold, i.e. if diff (Px) ≥ thresF. This procedure may analogously be applied to absolute distances ti.
Practically, as a compromise between the temporal resolution and a practicable estimation of the distribution, the window length is set nF = 20 and the window is forwarded 1 R-peak each time. The distribution has a bin-width of 0.1 sec for absolute distances ti and 0.025 for phases φi, the number of bins k is adjusted accordingly. The threshold for identifying coordination is set to thresF = 12 for both, absolute distances ti and phases φi. Notice that this implementation does not require a pre-selection of the integers m and n of a m:n-ratio.
4. Detection via 'Quantification of the distribution of inspiratory onsets in RR-intervals'
In this study, the relative distances βj are also analyzed. They were used in the early studies of the 1960ies and in the subsequent studies of these research groups [25,27-29]. Although this technique does not use all the information available in the recording it is successfully used at present by one research group [44,51-53]. It has to be kept in mind that this technique yields coordinated inspiratory onsets instead of coordinated R-peaks. Thus, in order to get results which are comparable with the previously described techniques, the coordinated inspiratory onsets are replaced by R-peaks with the following rule: all R-peaks in a respiratory cycle that follow a coordinated inspiratory onset are marked as coordinated.
The quantification of the distribution of βj is carried out as follows. Since the values of βj are in the interval [0,1] they may be mapped onto the interval [0,2π] by multiplication with 2π. In this study, the distribution of βj in a data window of length nF is quantified by the calculation of the first Fourier mode of the distribution (the circular variance):
Analogously to the 'Synchronization-λ' defined above, the range of γ is 0 ≤ γ ≤ 1. If γ = 0 βj is equally distributed indicating the completely de-coordinated case. If γ = 1 the βj is constant in the data window indicating the coordinated case. Practically, the inspiratory onsets in a data window are marked as being coordinated if γ ≥ thresγ. Subsequently, the R-peaks in the respiratory cycle that follow a coordinated inspiratory onset are marked as coordinated. The window length nF is set to 10 and thresγ = 0.5. The data window is forwarded one inspiratory onset to achieve the maximal temporal resolution.
Quantitative comparison of the methods, Statistics
The goal of the study was to compare quantitatively the results of the different techniques to analyze cardiorespiratory coordination. This was achieved in two steps. In the first step the cardiorespiratory coordination of each recording was analyzed with the following techniques: (1) 'Synchronization-λ', (2) 'Phase Recurrence' of relative distances φi, (3) 'Phase Recurrence' of absolute distances ti, (4) 'Quantification of Histograms' of relative distances φi, (5) 'Quantification of Histograms' of absolute distances ti, and (6) 'Quantification of Distribution of relative distances βj'. The detected R-peaks in horizontal structures, i.e. coordinated R-peaks, are marked with their corresponding number m of the m:n-ratio (e.g. all R-peaks in a sequence of 4:1 coordination were marked with 4). This allowed the distinction of coordinated R-peaks with respect to their m:n-ratio. Next, to reduce the amount of information, the percentage of coordinated R-peaks with a certain m:n-ratio in a window of 500 consecutive R-peaks was calculated. To get an adequate temporal resolution, the window was forwarded by 100 R-peaks until the entire series of R-peaks is covered. The percentage of coordinated R-peaks was coded as a greyscale plot because the percentage was plotted versus time for all analyzed m:n-ratios. This plot is called 'coordination diagram' (see Figure 4). In this diagram the amount of cardiorespiratory coordination and its respective m:n-ratio is easily accessible.
Figure 4 Example of a 'coordination diagram'. The 'coordination diagram' shows the course of the relative number of coordinated R-peaks (in a window of 500 R-peaks) at their respective m:n-ratio (method: 'Phase recurrences'). In this examples the 4:1-coordination prevails. The absolute maximum of 68% of coordinated R-peaks turns up at about midnight. The line in the diagram depicts the 'total coordination', i.e. the amount of coordination regardless of the m:n-ratio. Obviously, the cardiorespiratory coordination seems to oscillate.
The total number of coordinated sequences and the total number of coordinated R-peaks during night-time sleep, i.e. from 0:00 to 6:00 assuming that all subjects were continuously asleep, served as quantitative measures of the performance of each method. They were calculated for each recording. Furthermore, the matrix of Pearson's correlation coefficients r between the coordination diagrams of the six methods was obtained as follows. Each correlation is calculated comparing the percentages of coordinated R-peaks with a m:n-ratio of two different methods for each data window. Furthermore, to enhance the validity of this comparison, only those pairs of percentages are used in which at least one percentage is greater than zero. Otherwise the large number of pairs with (0%,0%), i.e. the white area that both coordination diagrams have in common, would lead to a strong correlation even if the other parts of the coordination diagrams show apparent divergent m:n-coordination ratios.
Results
An example of a 'coordination diagram' is shown in Figure 4. This example was analyzed with the method of 'Phase Recurrences' of relative distances φi. Since the heart rate decreases during night-time sleep whereas the respiratory rate remains almost constant the m:n-ratios of cardiorespiratory coordination also decrease during this period. Thus the sleeping period may be estimated from the 'coordination diagram' by looking at the low m:n-ratios. In the example, the subject slept from 23:30 to 6:30 and had a predominant 4:1-coordination during night-time sleep. At midnight the maximum of approximately 68% is reached, i.e., 68% of all R-peaks in a window of 500 R-peaks showed a 4:1-coordination with respect to respiration. A second local maximum of 4:1-coordination is observable at 1:30. The rest of the night shows sequences with 4:1-, 7:2- (shown in between 4:1- and 3:1-coordination) and also some 3:1-coordination. Although not shown, it has to be added that qualitatively similar results were obtained with the other techniques. Furthermore, the analysis of the other subjects showed that the predominant m:n-coordination ratio during night-time sleep of each subject varies inter-individually and is one of the following ratios: 3:1, 4:1, 5:1, 7:2 or 9:2.
The additional line drawn in the coordination diagram represents the 'total coordination', i.e., the percentage of coordinated R-peaks in a window of 500 consecutive R-peaks regardless of the m:n-ratio. As indicated by the time course of the separate m:n-coordinations the total coordination is not constant or a simple function of time. Instead, the total coordination seems to oscillate. In the course of the night-time sleep the frequency of the oscillation increases whereas the amplitude decreases. Similar qualitative results were obtained using the other techniques.
The total number of coordinated R-peaks NR and the total number of coordinated sequences Nseq during night-time sleep contain important quantitative information about the performance of the different methods (see Table 1). The group's median total number of coordinated R-peaks ranges from NR = 1673 R-peaks for the 'Quantification of inspiratory onsets in RR-intervals' (Hist βj) to NR = 6158 R-peaks for the detection via 'Phase Recurrences' of phases φi (PhRec φi), i.e. 7.7% to 30.4% of the total number of R-peaks during night-time sleep are coordinated with respiration. The 'Phase Recurrences' of times ti and the 'Quantification of Histograms' of phases φi also detect a high number of coordinated R-peaks during night-time sleep (NR = 5465 and NR = 5146, respectively). Notice that the number of coordinated sequences is highest for the 'Phase Recurrences' (Nseq = 416 for relative distances and Nseq = 398 for absolute distances) compared to other methods. Hence, the coordinated sequences detected by the 'Phase Recurrences' are shorter than the sequences detected by other methods. Although not shown, it has to be added that the high number of coordinated sequences and R-peaks for the 'Phase Recurrences' is due to the setting k ≥ m for equation (7). If this setting is changed to k ≥ m + 8, i.e. the requirement |φi - φi+m| <ω has to be fulfilled at least m + 8 times, the median number of coordinated epochs decreases to Nseq = 206 and the median number of coordinated R-peaks decreases to NR = 4273.
Table 1 Number of detected sequences with cardiorespiratory coordination. Median and inter-quartile range during night-time sleep of the following parameters: NR total number of coordinated R-peaks, Nseq total number of coordinated sequences, % NR relative amount of coordinated R-peaks with respect to the total number of R-peaks.
Sync λ PhRec ti PhRec φi Hist ti Hist φi Hist βj
NR 3134 (2957) 5465 (3776) 6158 (2696) 3502 (4818) 5146 (3795) 1673 (1288)
Nseq 123 (98) 398 (266) 416 (126) 140 (176) 191 (118) 31 (21)
% NR 14.6 (11.2) 27.8 (14.0) 30.4 (10.8) 16.8 (17.6) 24.3 (12.0) 7.7 (5.9)
An example of the correlation between the coordination diagrams of the different methods is shown in Figure 5. For this subject the correlation coefficient r ranges from r = 0.63 to r = 0.91 indicating a good agreement between the different coordination diagrams. The coordination diagrams for 'Synchronization-λ' and 'Quantification of Histograms' of relative distances φi correlate strongest (r = 0.91). And also the coordination diagrams for 'Phase Recurrence' of absolute distances ti and 'Phase Recurrence' of relative distances φi show a strong correlation (r = 0.90).
Figure 5 Example of the correlations between the different methods. Example of the correlations between the different methods to detect cardiorespiratory coordination.
The matrix of correlation coefficients was calculated for each subject. Table 2 lists the median and the interquartile range of each item in the matrix. Again, the results of the analysis of the 'Synchronization-λ' and the 'Quantification of Histograms' of relative distances φi correlate strongest (median correlation r = 0.89), followed by the correlation between the 'Phase Recurrences' and the 'Quantification of Histograms' of relative distances φi (r = 0.82) and the correlation between the 'Phase Recurrences' of relative distances φi and the 'Phase Recurrences' of absolute distances ti (r = 0.81). In all rows of the matrix except the last the correlation coefficients are r > 0.64 indicating a strong correlation between results of the different techniques. Since all correlation coefficients in the last row are r < 0.5 the correlation between the results of the 'Quantification of inspiratory onsets in RR-intervals' (Hist βj) and all other techniques is weaker.
Table 2 Correlations between the different methods to detect cardiorespiratory coordination. Median and inter-quartile range of the correlations coefficients between the different methods to detect cardiorespiratory coordination.
Sync λ PhRec ti PhRec φi Hist ti Hist φi Hist βj
Sync λ 1.00
PhRec ti 0.66 (0.20) 1.00
PhRec φi 0.78 (0.18) 0.81 (0.10) 1.00
Hist ti 0.60 (0.21) 0.75 (0.16) 0.70 (0.18) 1.00
Hist φi 0.89 (0.11) 0.72 (0.14) 0.82 (0.09) 0.64 (0.26) 1.00
Hist βj 0.45 (0.36) 0.35 (0.28) 0.37 (0.30) 0.45 (0.34) 0.49 (0.28) 1.00
Discussion
In recent years a variety of different methods have been proposed to detect cardiorespiratory interaction on the basis of simultaneously recorded time series of heart beat and respiration. In this study, six different methods to identify cardiorespiratory coordination based on mathematical models or heuristic approaches have been quantitatively compared. The main issue of the different methods is the recognition of coordination in synchrograms and post event time series, i.e. the detection of structures with horizontal lines in these representations. Thus, the procedures permit the quantification of qualitative information that is contained in these representations and may provide new information that is not extractable from the isolated univariate time series.
The quantitative comparison of the six different methods to detect cardiorespiratory coordination showed that the group's median total number of coordinated R-peaks and the median total number of coordinated sequences varied depending on the detection method used. The largest number of coordinated R-peaks (30.4% of all R-peaks during night-time sleep) and coordinated sequences was detected by the 'Phase Recurrences' of phases φi. Since the coordinated R-peaks are distributed in the largest number of coordinated sequences this method has the best temporal resolution compared to all other methods used in this study. Furthermore, it is able to detect even very short and intermittent cardiorespiratory coordination because the implementation used in this study only required at least 2m R-peaks to detect a m:n-coordination (typically, m = 2,3,...,8). But, if the minimal number of required coordinated R-peaks is increased to e.g. 2(m + 8), this method shows approximately the same number of coordinated epochs and R-peaks as e.g. the 'Quantification of Histograms'. Hence, if the 'window length' of this method is increased, the results are similar to those of the methods with a window length of 20 R-peaks. Since the 'Phase Recurrences' of absolute distances ti also showed a large number of coordinated R-peaks and coordinated sequences, respectively, and the correlation to the results of the 'Phase Recurrences' of phases φi is strong, these procedures showed approximately the same characteristic features. All other methods were less sensitive in the detection of cardiorespiratory coordination because the identification of cardiorespiratory coordination required a minimal window length of 20 R-peaks. The method of 'Quantification of inspiratory onsets in RR-intervals' had an even longer window length, provided that the window length of 10 inspiratory onsets is equivalent to approximately 40 R-peaks since the average ratio of heart rate and respiratory rate, irregardless of any cardiorespiratory coordination, is 4 [35]. Thus, these methods are not as suitable for the detection of cardiorespiratory coordination as the method of 'Phase Recurrences'. Nevertheless, although all methods had different definitions and different implementation details, they revealed similar qualitative features of cardiorespiratory coordination during night-time sleep, i.e., the coordination diagrams showed approximately the same structure and the 'total coordination' (percentage of all coordinated R-peaks regardless of the m:n- ratio) was not constant but oscillated during night-time sleep. Since this oscillation correlates with heart rate variability (HRV) [36] and different sleep stages also correlate with HRV [54,55], this oscillation may have its origin in the sleep architecture.
Another quantitative comparison was carried out by intra-individually correlating the 'coordination diagrams' of each method. The correlation coefficients served as a measure of similarity between the different diagrams. The strongest correlation was observed between the coordination diagrams derived from the 'Synchronization-λ' and the 'Quantification of Histograms' of relative distances φi. Furthermore, the 'Phase Recurrences' of relative distances φi and of absolute distances ti correlated strongly. These results indicate that the amount of information contained in the relative distances φi, i.e. the relative timings of the R-peaks in a full respiratory cycle, and in the absolute distances ti, i.e. the timings of R-peaks with regard to the preceding inspiratory onset, was approximately the same. Furthermore, the results of the 'Synchronization-λ' showed strong correlations to the 'Phase Recurrences' and the 'Quantification of Histograms'. Thus, the latter methods, which have heuristic origins, may also be explained mathematically by the mathematical models used to develop the method of 'Synchronization-λ'. However, the correlation coefficients concerning the 'Quantification of Histograms' of relative distances βj, i.e. the relative timing of an inspiratory onset in the respective RR-interval, were lower. Thus, these distances seem to contain different information. This difference is attributed to the fact that the calculation of the relative distances βj is not based on the full amount of information available in the recording.
Taken these findings together, it is apparent that the different methods may lead to similar results if the maximum amount of information is used. It is important to keep in mind that all quantifications are based on the 'Post Event Time Series' or the 'Synchrogram'. Thus, although some methods have heuristic origins and some have a mathematical model as its origin and although the different approaches differ considerably with respect to their calculations, the relevant information of the 'Post Event Time Series' and the 'Synchrogram' is captured by the different methods.
A debate exits whether the cardiorespiratory coordination is due to physiological interaction between the involved systems or other mechanisms might be responsible, like e.g. a reduced variability in at least one of the involved systems [36,56]. Surrogate data have been used to give at least a quantitative answer to this debate [34,38,57]. The analysis of surrogate data suggests that cardiorespiratory coordination is sometimes due to some kind of physiological interaction. Another approach that may be of importance is the stochastic phase synchronization, i.e. the synchronization between coupled systems due to stochastic stimuli [58,59]. However, this kind of mechanism has not been explored for the cardiorespiratory system and the implications are not clear yet. Irrespective of this debate, it is an intriguing phenomenon that heart rate and respiratory frequency show a 4:1-ratio on average during night-time sleep [35,60]. The present results also indicate that there seem to be some constraints with respect to variability and other features of the involved systems to achieve certain m:n-ratios. Further methods and models have to be developed to explore these features of the cardiorespiratory system.
In conclusion, the different methods to detect cardiorespiratory coordination show similar qualitative results. The method 'Phase Recurrences' has the ability to detect most sequences of heart beats coordinated with respiration. It is able to maximize the temporal resolution because even very short sequences may be detected. Thus, this method should be preferred. The method of 'Quantification of Histograms' of relative distances βj contains different quantitative information since only parts of the full amount of information available is used. Hence, this method is not recommended because a comparison with the other methods is limited. Generally, using appropriate methods, cardiorespiratory coordination is detectable in approximately 20–25% of all heart beats during night-time sleep. Future work should focus on the prerequisites of the appearance of cardiorespiratory coordination during night-time sleep and whether this appearance is linked to sleep stages. Furthermore, a preliminary study has shown the loss of cardiorespiratory coordination in patients after acute myocardial infarction [61,62]. Hence, the gain of prognostic information of this bivariate analysis should be explored further.
Authors' contributions
DC and HB designed the study, recruited the subjects and collected the data. DC carried out the analysis and drafted the manuscript. SL, DG and PvL were involved in the interpretation of the data and participated in the final revision.
Acknowledgements
DC and HB acknowledge financial support from the Weleda AG, Schwäbisch Gmünd, Germany. The authors would like to thank Roland Zerm and Matthias Kröz for providing data.
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| 15563735 | PMC538288 | CC BY | 2021-01-04 16:37:31 | no | Biomed Eng Online. 2004 Nov 25; 3:44 | utf-8 | Biomed Eng Online | 2,004 | 10.1186/1475-925X-3-44 | oa_comm |
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Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-441556373510.1186/1475-925X-3-44ResearchA quantitative comparison of different methods to detect cardiorespiratory coordination during night-time sleep Cysarz Dirk [email protected] Henrik [email protected] Silke [email protected] Daniel [email protected] Leeuwen Peter [email protected] Department of Clinical Research, Gemeinschaftskrankenhaus Herdecke D-58313 Herdecke, Germany2 Institute of Mathematics, University of Witten/Herdecke D-58455 Witten, Germany3 Department of Biomagnetism, Research and Development Center for Microtherapy (EFMT) D-44799 Bochum, Germany2004 25 11 2004 3 44 44 2 9 2004 25 11 2004 Copyright © 2004 Cysarz et al; licensee BioMed Central Ltd.2004Cysarz 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 univariate approaches used to analyze heart rate variability have recently been extended by several bivariate approaches with respect to cardiorespiratory coordination. Some approaches are explicitly based on mathematical models which investigate the synchronization between weakly coupled complex systems. Others use an heuristic approach, i.e. characteristic features of both time series, to develop appropriate bivariate methods.
Objective
In this study six different methods used to analyze cardiorespiratory coordination have been quantitatively compared with respect to their performance (no. of sequences with cardiorespiratory coordination, no. of heart beats coordinated with respiration). Five of these approaches have been suggested in the recent literature whereas one method originates from older studies.
Results
The methods were applied to the simultaneous recordings of an electrocardiogram and a respiratory trace of 20 healthy subjects during night-time sleep from 0:00 to 6:00. The best temporal resolution and the highest number of coordinated heart beats were obtained with the analysis of 'Phase Recurrences'. Apart from the oldest method, all methods showed similar qualitative results although the quantities varied between the different approaches. In contrast, the oldest method detected considerably fewer coordinated heart beats since it only used part of the maximum amount of information available in each recording.
Conclusions
The method of 'Phase Recurrences' should be the method of choice for the detection of cardiorespiratory coordination since it offers the best temporal resolution and the highest number of coordinated sequences and heart beats. Excluding the oldest method, the results of the heuristic approaches may also be interpreted in terms of the mathematical models.
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Background
The time intervals between successive heartbeats (e.g. the RR tachogram or, equivalently, the series of instantaneous heart rates) may be analyzed with different tools to obtain information about e.g. heart rate variability (HRV) [1], regularity of the time series [2-7], or large-scale correlations in the time series [8-10]. Each technique provides different information about the 'time-structure' contained in the series of successive heartbeats. Some of them, like information obtained from HRV, may be linked to the sympathetic and parasympathetic activity of the autonomic nervous system [1,11]. Hence, they offer the possibility to interpret the derived quantities in terms of physiology. Furthermore, when applied to data obtained from patients suffering from heart diseases, these different information may be combined and used for risk stratification [12,13]. A large body of work concentrates on these univariate time series analysis and standards have been established [1].
To gain information that cannot be extracted from a single time series, the interaction between two (or more) physiological (sub-)systems has been investigated. This is often done on the basis of simultaneously recorded time series of each system. Hence, new bivariate techniques have been developed that aim to quantify the imprint of the physiological interaction between the two systems [14-17]. Especially, techniques which analyze the cardiorespiratory interaction have been developed recently [18-24]. Among these techniques the coordination between the series of successive heartbeats and the series of successive respiratory cycles, further denoted as cardiorespiratory coordination, is one focus of attention.
In fact, physiologists had already investigated cardiorespiratory coordination in the human organism as early as the 1960ies [25]. Calculating the distance between an inspiratory onset and its preceding R-peak they found intermittent coordination between heartbeat and respiration. In the 1970ies this interesting topic was no longer followed up (except in one study [26]), presumably because the physiological interpretation of the results was limited, although the last reviews of this era appeared in the late 1980ies [27-29]. The investigation of cardiorespiratory coordination has recently been revived mainly by physicists and mathematicians. They first studied the interaction of two weakly coupled chaotic oscillators and found synchronization of the phases whereas the amplitudes of the oscillators remained uncorrelated [30]. This class of mathematical models may serve as an approximate qualitative model for the interaction of heartbeat and respiration [19]. On the basis of these models new techniques for the analysis of bivariate time series data recorded from these physiological systems have been developed [22,31,32]. Other approaches have also been used to develop methods to analyze the cardiorespiratory interaction since the significance of this topic was deemed high [23,33-41].
The similarities or differences between the definitions of the different techniques and the advantages of each technique are not known yet. Unfortunately, a complete review and a (qualitative and quantitative) comparison of the recent literature is too extensive and beyond the scope of this paper. This study is restricted to the comparison of the performance of six different techniques in the detection of cardiorespiratory coordination. Although the comparison is limited, the insight that is drawn from it provides valuable information with regard to techniques that are not considered in this comparison. A second aim of this study is to investigate the cardiorespiratory interaction during night-time sleep since previous studies only dealt with relatively short recordings (no more than one hour).
Methods
Data acquisition and pre-processing
The electrocardiogram (ECG, standard lead II) and the uncalibrated nasal airflow (derived by a thermistor-technique) were recorded simultaneously in 20 healthy subjects (7 female, median age: 34.9 years, interquartile range: 13.7 years) using an ambulatory device (Medikorder, Tom-Signal, Graz). In each subject the night-time sleeping period was recorded, in some subjects the complete evening before the sleep period was also recorded. Hence, the total recording time varied from 7–16 hours. The device's internal sampling rate of the ECG was 3000 Hz. Thus, the times of the automatically identified R-peaks had an accuracy <1 ms and were written into a file. To save memory, the ECG was recorded using a rate of 250 Hz. The data were transferred to a PC and analyzed using Matlab (The Mathworks, Natick, Mass, USA) and C routines. The times of the automatically identified R-peaks were visually controlled, i.e. the times of the R-waves were marked in the ECG, and edited if the times of the automatically identified R-peaks did not match the R-peak in the ECG (<0.1% of all R-peaks). The times of the edited R-peaks had an accuracy of 4 ms because the recorded ECG had a lower sampling rate. Ectopies and artefacts were marked and excluded from the analysis.
The device's sampling rate of the nasal airflow was 100 Hz. The respiratory trace was saved into a file. Low frequency baseline trends were avoided using an internal filter with a 0.01 Hz cut-off frequency. The trace did not need any further pre-processing or filtering because it was smooth (cf. example in Figure 1). Inspiratory onsets were defined as local minima in the respiratory trace since local minima in the respiratory trace are due to the change from exhaling warm air to inhaling colder environmental air. They were extracted with an accuracy of 10 ms. For further analysis only the times of R-waves Ri (i = 1,...,nR) and the times of the inspiratory onsets Ij (j = 1,...,nI) were necessary and saved into a file. These data served as the basis for further calculations, see Figure 2.
Figure 1 Example of the recordings. Example of a short sequence of the simultaneously recorded electrocardiogram (ecg) and respiratory trace (thermistor) during night time sleep.
Figure 2 Relevant data in the recordings. The times of the R-peaks Ri and the times of the inspiratory onsets Ij (defined as local minima in the respiratory trace) derived from the recordings serve as event markers for further calculations.
Note that in the following the term 'cardiorespiratory coordination' is used descriptively in the sense that the times of the R-waves and the times of the inspiratory onsets show some kind of temporal incidence. The temporal incidence does not imply a physiological coupling between heartbeat and respiration, i.e. 'cardiorespiratory synchronization'.
The first step towards the analysis of cardiorespiratory coordination with respect to different m:n-coordination ratios (m: number of heart beats, n: number of respiratory cycles) is the calculation of temporal distances between the event markers of the two time series. Generally, two different useful possibilities have to be considered. (1) The temporal distance between inspiratory onsets and successive R-peaks, or, (2) the temporal distance between an inspiratory onset and the preceding R-peak.
1. Temporal distance between inspiratory onsets and successive R-peaks:
absolute distance: ti = Ri - Ij (1)
2. temporal distance between an inspiratory onset and the preceding R-peak:
absolute distance: tj = Ij - Ri-1 (3)
Note that for the calculation of the absolute distances ti and n > 1 the appropriate settings of the indices i and j have to be considered. Furthermore, the relative distance φi corresponds to a definition of a phase angle of an oscillator on the interval [0, n]. This phase (multiplied by 2π) is equivalent to the cyclic phase calculated via the Hilbert-transformation [32,42]. Similar phases may also be calculated using e.g. the Fourier-transformation [43]. Hence, with respect to the variable φi, the terms 'relative distance' and 'phase' are used synonymously throughout this paper. Compared to φi and ti the calculation of tj and βj only needs the timings of the R-peaks before and after the inspiratory onset. Thus, these variables contain less information. They have been mainly used in early studies on cardiorespiratory coordination and are not common at present [44,45]. In order to keep this comparison practicable, the calculation of the absolute distances tj and the subsequent analysis of these quantities is left out in this study.
In Figure 3 (a) an example of a succession of absolute distances ti is shown, i.e. each point represents the temporal distance of the R-peak to its preceding inspiratory onset (n = 1). In Figure 3 (b) these distances are calculated for two consecutive respiratory cycles (n = 2). This kind of representation is known as 'Post Event Time Series' [34,46] and has already been used in some early studies of cardiorespiratory coordination [26]. Since n = 1 in Figure 3 (a), the structures of parallel horizontal lines reveal 4:1- and 3:1-coordination (see marked points). In Figure 3 (b) the arrows indicate 7:2-, 8:2- and 6:2-coordination because n = 2 was used for the calculation of the distances. Figures 3 (c) and 3 (d) show the relative distances φi of the same sequence for n = 1 and n = 2 respiratory cycles, respectively. This kind of representation is called 'Synchrogram' [19]. Obviously, it shows structures of parallel horizontal lines for the same epochs as the absolute distances ti (see markers in Figure 3). The structures with horizontal lines reveal epochs in which absolute distances ti or relative distances φi, respectively, of each m-th R-peak recurs after one (n = 1) or two (n = 2) respiratory cycles: the cardiorespiratory interaction is coordinated. If the number of respiratory cycles n is increased (n > 2) these m:n-coordination may be observable in the same manner. In Figure 3 (e) the relative distances βj for the same sequence are shown. As mentioned above, this diagram contains less information compared to the other diagrams. Still, in the case of cardiorespiratory coordination, a short horizontal structure appears, cf. the markers in Figure 3 (e). These structures show the presence of m:1-coordination, similar to the diagrams in Figure 3 (a) and 3 (c).
Figure 3 Example of cardiorespiratory coordination. (A) and (B) show examples of a 'Post Event Time Series' for 1 and 2 respiratory cycles, respectively. The corresponding 'Synchrograms' are shown in (C) and (D). (E) depicts the 'Synchrogram' for phases βj. In (A) and (C) the arrows indicate a sequence with 4:1- and a sequence with 3:1-coordination. In (B) and (D) the arrows point to sequences with 7:2-, 8:2- and 6:2-coordination. The arrows in (E) indicate sequences with coordinated inspiratory onsets that correspond to the coordinated sequences in (A) and (C).
The fact that cardiorespiratory coordination is present if structures with horizontal lines occur leads to the task of detecting these structures in the different diagrams. This task can be carried out by different techniques based on mathematical models or heuristic approaches. Some relevant approaches are described in the following.
Detection of horizontal structures in Synchrograms or Post Event Time Series
1. Detection via 'Synchronization-λ'
Along with the development of the Synchrogram a quantification on the basis of a mathematical model has been proposed [32,47,48]. Consider two coupled oscillators with appropriately defined phases φ1 and φ2 that may intermittently show a 1:1-synchronization (for simplicity both phases are defined on the interval [0, 2π]). In the synchronized case the difference φ1 - φ2 is constant. In real world data the phases φ1 and φ2 are contaminated by noise. Thus, even during synchronization the difference φ1 - φ2 is not constant. Rather it fluctuates around a constant. To take these influences into account a 'stroboscopic technique' has proven to be useful. Each time the phase φ1 exceeds a pre-defined value θ the phase φ2 is registered. During a synchronization the different values of φ2 are almost constant whereas during complete de-synchronization φ2 spreads equally over the interval [0, 2π]. This approach may easily be generalized for m:n-synchronization if the phases φ1 and φ2 are replaced by φ1/m and φ2/n. The distribution of φ2 may be quantified by the first Fourier mode.
The formalization of this idea is as follows: Observe the phase φ2 at the times t the phase φ1 is φ1= θ :
Next, calculate the first Fourier mode of the distribution of ξi which is known as circular variance [49] (M values of φ2 have been observed):
If both oscillators are synchronized λ = 1, the completely de-synchronized case yields λ = 0. To get a more reliable result, λ may be calculated for different values of θ and subsequently averaged.
This method is easily applicable to the detection of cardiorespiratory coordination. Define the phases φ1,2 as follows: φ1 increases linearly on the interval [0, 2π] between two successive R-peaks and φ2 between two successive inspiratory onsets, respectively. To detect coordination for different n:m-ratios this method has to be applied for each desired m:n-ratio. In practice, coordination is present if λ exceeds a pre-defined threshold λ ≥ thresλ. In this case the respective R-peaks are marked as coordinated.
Note that although this method deals with synchronization of two coupled oscillators it is not able to detect cardiorespiratory synchronization in a physical sense. A detection of synchronization on the basis of two simultaneously recorded time series would require supplementary information, e.g. the variation of the strength of coupling. Such supplementary information is not available for the cardiorespiratory interaction. Thus, the term cardiorespiratory coordination denotes a temporal incidence of events in both time series without claiming that this incidence is based on a coupling between both systems.
In this study, the window length M is set to M = 20. To obtain a better time resolution of the λ-values the window is forwarded one R-peak or accompanying phase, respectively. To obtain more stable values of λ the average of 10 different values of θ, equally distributed over the interval [0, 2π], is used. The threshold is set to thresλ = 0.85. This procedure is successively carried out for the following m:n-coordinations: for n = 1: m = 2,...,8 and for n = 2: m = 5,7,9,11,13,15.
2. Detection via 'Phase Recurrences'
This method is based on a heuristic approach. Consider a synchrogram containing a sequence with m parallel horizontal lines indicating a m:n-coordination. In this sequence the relative distance φi of each m-th R-peak has to be approximately the same. Otherwise parallel horizontal lines would not appear. This 'recurrence of phases' may be used to identify coordinated sequences [36] (a slightly different approach is presented in [50]).
The formalization of this concept is straightforward. For a m:n-coordination check whether the phase difference between the phase of R-peak i + m and the phase of R-peak i is within a pre-defined tolerance ε. This condition has to be fulfilled for at least k successive R-peaks:
Nr is the total number of R-peaks. In principle, the parameter k is not pre-defined. But, to be compatible with the description of 'parallel horizontal lines' during coordination, k ≥ m needs to be fulfilled. This procedure allows a detection of a structure of parallel horizontal lines already with a length of 2m successive relative distances φi. E.g. a 4:1-coordination may be identified already in a minimal sequence of 8 R-peaks. In the case coordination is detected, the respective R-peaks are marked as coordinated. To detect horizontal structures for different m:n-ratios this method has to be applied for each ratio. It is easy to implement and it can be used for absolute distances ti analogously.
In this paper the Phase Recurrence is analyzed with respect to absolute distances ti and relative distances φi for the following m:n-coordinations: n = 1: m = 2,...,8 and n = 2: m = 5,7,9,11,13,15. In the case of absolute distances ti the tolerance ε is set to ε = 0.075, i.e. 75 ms, and in the case of relative distances φi the tolerance is set to ε = 0.025.
3. Detection via 'Quantification of Histograms'
Another technique to detect cardiorespiratory coordination makes use of a sliding window comprising nF successive phases φi that is moved over the entire series of phases [34,46]. First, a distribution of the phases φi is calculated for each window. If cardiorespiratory coordination, i.e. a structure with parallel horizontal lines, is present the distribution of phases φi shows some distinct equidistant local maxima (e.g. four local maxima in the case of 4:1 coordination). In the un-coordinated case the phases φi are randomly distributed and local maxima do not appear. Next, each distribution is quantified by means of a Fourier Transformation. In the case of coordination, the distribution of the phases φi contains several local maxima resulting in a huge maximum in the power spectrum. In the un-coordinated case the power spectrum does not contain any pronounced maximum since the phases φi are equally distributed. Thus, the appearance of pronounced local maxima in the power spectrum is used to detect cardiorespiratory coordination.
This idea may be formalized as follows [46]. First, choose the length nF of the sliding window and the number of bins k to calculate the distribution. In the completely un-coordinated case the value of each bin xl is nF/k. If a perfect m:n-coordination is present the following distribution is obtained:
Here, l0 is the index of the bin that contains the first local maximum of the distribution. This distribution is quantified by means of a Fourier transformation:
The average of all bins nF/k is subtracted from each bin to avoid a dc-component in the power spectrum. In the un-coordinated case Px(f) = 0 for all frequencies f. In the case of a m:n-coordination m local maxima at the frequencies f = a m, a ≤ k/(2m) appear. These local maxima are conserved even if the parallel horizontal lines are not exactly mapped into m bins but also some surrounding bins are filled. Thus, the distinction between coordinated and un-coordinated cardiorespiratory interaction may be carried out using the difference between the maximum and the minimum of the power spectrum.
diff(Px) = max(Px) - min(Px) (10)
In the coordinated case this difference is large whereas in the un-coordinated case the difference is small. In practice, the R-peaks in the analyzed window are being marked as coordinated if diff (Px) exceeds a pre-defined threshold, i.e. if diff (Px) ≥ thresF. This procedure may analogously be applied to absolute distances ti.
Practically, as a compromise between the temporal resolution and a practicable estimation of the distribution, the window length is set nF = 20 and the window is forwarded 1 R-peak each time. The distribution has a bin-width of 0.1 sec for absolute distances ti and 0.025 for phases φi, the number of bins k is adjusted accordingly. The threshold for identifying coordination is set to thresF = 12 for both, absolute distances ti and phases φi. Notice that this implementation does not require a pre-selection of the integers m and n of a m:n-ratio.
4. Detection via 'Quantification of the distribution of inspiratory onsets in RR-intervals'
In this study, the relative distances βj are also analyzed. They were used in the early studies of the 1960ies and in the subsequent studies of these research groups [25,27-29]. Although this technique does not use all the information available in the recording it is successfully used at present by one research group [44,51-53]. It has to be kept in mind that this technique yields coordinated inspiratory onsets instead of coordinated R-peaks. Thus, in order to get results which are comparable with the previously described techniques, the coordinated inspiratory onsets are replaced by R-peaks with the following rule: all R-peaks in a respiratory cycle that follow a coordinated inspiratory onset are marked as coordinated.
The quantification of the distribution of βj is carried out as follows. Since the values of βj are in the interval [0,1] they may be mapped onto the interval [0,2π] by multiplication with 2π. In this study, the distribution of βj in a data window of length nF is quantified by the calculation of the first Fourier mode of the distribution (the circular variance):
Analogously to the 'Synchronization-λ' defined above, the range of γ is 0 ≤ γ ≤ 1. If γ = 0 βj is equally distributed indicating the completely de-coordinated case. If γ = 1 the βj is constant in the data window indicating the coordinated case. Practically, the inspiratory onsets in a data window are marked as being coordinated if γ ≥ thresγ. Subsequently, the R-peaks in the respiratory cycle that follow a coordinated inspiratory onset are marked as coordinated. The window length nF is set to 10 and thresγ = 0.5. The data window is forwarded one inspiratory onset to achieve the maximal temporal resolution.
Quantitative comparison of the methods, Statistics
The goal of the study was to compare quantitatively the results of the different techniques to analyze cardiorespiratory coordination. This was achieved in two steps. In the first step the cardiorespiratory coordination of each recording was analyzed with the following techniques: (1) 'Synchronization-λ', (2) 'Phase Recurrence' of relative distances φi, (3) 'Phase Recurrence' of absolute distances ti, (4) 'Quantification of Histograms' of relative distances φi, (5) 'Quantification of Histograms' of absolute distances ti, and (6) 'Quantification of Distribution of relative distances βj'. The detected R-peaks in horizontal structures, i.e. coordinated R-peaks, are marked with their corresponding number m of the m:n-ratio (e.g. all R-peaks in a sequence of 4:1 coordination were marked with 4). This allowed the distinction of coordinated R-peaks with respect to their m:n-ratio. Next, to reduce the amount of information, the percentage of coordinated R-peaks with a certain m:n-ratio in a window of 500 consecutive R-peaks was calculated. To get an adequate temporal resolution, the window was forwarded by 100 R-peaks until the entire series of R-peaks is covered. The percentage of coordinated R-peaks was coded as a greyscale plot because the percentage was plotted versus time for all analyzed m:n-ratios. This plot is called 'coordination diagram' (see Figure 4). In this diagram the amount of cardiorespiratory coordination and its respective m:n-ratio is easily accessible.
Figure 4 Example of a 'coordination diagram'. The 'coordination diagram' shows the course of the relative number of coordinated R-peaks (in a window of 500 R-peaks) at their respective m:n-ratio (method: 'Phase recurrences'). In this examples the 4:1-coordination prevails. The absolute maximum of 68% of coordinated R-peaks turns up at about midnight. The line in the diagram depicts the 'total coordination', i.e. the amount of coordination regardless of the m:n-ratio. Obviously, the cardiorespiratory coordination seems to oscillate.
The total number of coordinated sequences and the total number of coordinated R-peaks during night-time sleep, i.e. from 0:00 to 6:00 assuming that all subjects were continuously asleep, served as quantitative measures of the performance of each method. They were calculated for each recording. Furthermore, the matrix of Pearson's correlation coefficients r between the coordination diagrams of the six methods was obtained as follows. Each correlation is calculated comparing the percentages of coordinated R-peaks with a m:n-ratio of two different methods for each data window. Furthermore, to enhance the validity of this comparison, only those pairs of percentages are used in which at least one percentage is greater than zero. Otherwise the large number of pairs with (0%,0%), i.e. the white area that both coordination diagrams have in common, would lead to a strong correlation even if the other parts of the coordination diagrams show apparent divergent m:n-coordination ratios.
Results
An example of a 'coordination diagram' is shown in Figure 4. This example was analyzed with the method of 'Phase Recurrences' of relative distances φi. Since the heart rate decreases during night-time sleep whereas the respiratory rate remains almost constant the m:n-ratios of cardiorespiratory coordination also decrease during this period. Thus the sleeping period may be estimated from the 'coordination diagram' by looking at the low m:n-ratios. In the example, the subject slept from 23:30 to 6:30 and had a predominant 4:1-coordination during night-time sleep. At midnight the maximum of approximately 68% is reached, i.e., 68% of all R-peaks in a window of 500 R-peaks showed a 4:1-coordination with respect to respiration. A second local maximum of 4:1-coordination is observable at 1:30. The rest of the night shows sequences with 4:1-, 7:2- (shown in between 4:1- and 3:1-coordination) and also some 3:1-coordination. Although not shown, it has to be added that qualitatively similar results were obtained with the other techniques. Furthermore, the analysis of the other subjects showed that the predominant m:n-coordination ratio during night-time sleep of each subject varies inter-individually and is one of the following ratios: 3:1, 4:1, 5:1, 7:2 or 9:2.
The additional line drawn in the coordination diagram represents the 'total coordination', i.e., the percentage of coordinated R-peaks in a window of 500 consecutive R-peaks regardless of the m:n-ratio. As indicated by the time course of the separate m:n-coordinations the total coordination is not constant or a simple function of time. Instead, the total coordination seems to oscillate. In the course of the night-time sleep the frequency of the oscillation increases whereas the amplitude decreases. Similar qualitative results were obtained using the other techniques.
The total number of coordinated R-peaks NR and the total number of coordinated sequences Nseq during night-time sleep contain important quantitative information about the performance of the different methods (see Table 1). The group's median total number of coordinated R-peaks ranges from NR = 1673 R-peaks for the 'Quantification of inspiratory onsets in RR-intervals' (Hist βj) to NR = 6158 R-peaks for the detection via 'Phase Recurrences' of phases φi (PhRec φi), i.e. 7.7% to 30.4% of the total number of R-peaks during night-time sleep are coordinated with respiration. The 'Phase Recurrences' of times ti and the 'Quantification of Histograms' of phases φi also detect a high number of coordinated R-peaks during night-time sleep (NR = 5465 and NR = 5146, respectively). Notice that the number of coordinated sequences is highest for the 'Phase Recurrences' (Nseq = 416 for relative distances and Nseq = 398 for absolute distances) compared to other methods. Hence, the coordinated sequences detected by the 'Phase Recurrences' are shorter than the sequences detected by other methods. Although not shown, it has to be added that the high number of coordinated sequences and R-peaks for the 'Phase Recurrences' is due to the setting k ≥ m for equation (7). If this setting is changed to k ≥ m + 8, i.e. the requirement |φi - φi+m| <ω has to be fulfilled at least m + 8 times, the median number of coordinated epochs decreases to Nseq = 206 and the median number of coordinated R-peaks decreases to NR = 4273.
Table 1 Number of detected sequences with cardiorespiratory coordination. Median and inter-quartile range during night-time sleep of the following parameters: NR total number of coordinated R-peaks, Nseq total number of coordinated sequences, % NR relative amount of coordinated R-peaks with respect to the total number of R-peaks.
Sync λ PhRec ti PhRec φi Hist ti Hist φi Hist βj
NR 3134 (2957) 5465 (3776) 6158 (2696) 3502 (4818) 5146 (3795) 1673 (1288)
Nseq 123 (98) 398 (266) 416 (126) 140 (176) 191 (118) 31 (21)
% NR 14.6 (11.2) 27.8 (14.0) 30.4 (10.8) 16.8 (17.6) 24.3 (12.0) 7.7 (5.9)
An example of the correlation between the coordination diagrams of the different methods is shown in Figure 5. For this subject the correlation coefficient r ranges from r = 0.63 to r = 0.91 indicating a good agreement between the different coordination diagrams. The coordination diagrams for 'Synchronization-λ' and 'Quantification of Histograms' of relative distances φi correlate strongest (r = 0.91). And also the coordination diagrams for 'Phase Recurrence' of absolute distances ti and 'Phase Recurrence' of relative distances φi show a strong correlation (r = 0.90).
Figure 5 Example of the correlations between the different methods. Example of the correlations between the different methods to detect cardiorespiratory coordination.
The matrix of correlation coefficients was calculated for each subject. Table 2 lists the median and the interquartile range of each item in the matrix. Again, the results of the analysis of the 'Synchronization-λ' and the 'Quantification of Histograms' of relative distances φi correlate strongest (median correlation r = 0.89), followed by the correlation between the 'Phase Recurrences' and the 'Quantification of Histograms' of relative distances φi (r = 0.82) and the correlation between the 'Phase Recurrences' of relative distances φi and the 'Phase Recurrences' of absolute distances ti (r = 0.81). In all rows of the matrix except the last the correlation coefficients are r > 0.64 indicating a strong correlation between results of the different techniques. Since all correlation coefficients in the last row are r < 0.5 the correlation between the results of the 'Quantification of inspiratory onsets in RR-intervals' (Hist βj) and all other techniques is weaker.
Table 2 Correlations between the different methods to detect cardiorespiratory coordination. Median and inter-quartile range of the correlations coefficients between the different methods to detect cardiorespiratory coordination.
Sync λ PhRec ti PhRec φi Hist ti Hist φi Hist βj
Sync λ 1.00
PhRec ti 0.66 (0.20) 1.00
PhRec φi 0.78 (0.18) 0.81 (0.10) 1.00
Hist ti 0.60 (0.21) 0.75 (0.16) 0.70 (0.18) 1.00
Hist φi 0.89 (0.11) 0.72 (0.14) 0.82 (0.09) 0.64 (0.26) 1.00
Hist βj 0.45 (0.36) 0.35 (0.28) 0.37 (0.30) 0.45 (0.34) 0.49 (0.28) 1.00
Discussion
In recent years a variety of different methods have been proposed to detect cardiorespiratory interaction on the basis of simultaneously recorded time series of heart beat and respiration. In this study, six different methods to identify cardiorespiratory coordination based on mathematical models or heuristic approaches have been quantitatively compared. The main issue of the different methods is the recognition of coordination in synchrograms and post event time series, i.e. the detection of structures with horizontal lines in these representations. Thus, the procedures permit the quantification of qualitative information that is contained in these representations and may provide new information that is not extractable from the isolated univariate time series.
The quantitative comparison of the six different methods to detect cardiorespiratory coordination showed that the group's median total number of coordinated R-peaks and the median total number of coordinated sequences varied depending on the detection method used. The largest number of coordinated R-peaks (30.4% of all R-peaks during night-time sleep) and coordinated sequences was detected by the 'Phase Recurrences' of phases φi. Since the coordinated R-peaks are distributed in the largest number of coordinated sequences this method has the best temporal resolution compared to all other methods used in this study. Furthermore, it is able to detect even very short and intermittent cardiorespiratory coordination because the implementation used in this study only required at least 2m R-peaks to detect a m:n-coordination (typically, m = 2,3,...,8). But, if the minimal number of required coordinated R-peaks is increased to e.g. 2(m + 8), this method shows approximately the same number of coordinated epochs and R-peaks as e.g. the 'Quantification of Histograms'. Hence, if the 'window length' of this method is increased, the results are similar to those of the methods with a window length of 20 R-peaks. Since the 'Phase Recurrences' of absolute distances ti also showed a large number of coordinated R-peaks and coordinated sequences, respectively, and the correlation to the results of the 'Phase Recurrences' of phases φi is strong, these procedures showed approximately the same characteristic features. All other methods were less sensitive in the detection of cardiorespiratory coordination because the identification of cardiorespiratory coordination required a minimal window length of 20 R-peaks. The method of 'Quantification of inspiratory onsets in RR-intervals' had an even longer window length, provided that the window length of 10 inspiratory onsets is equivalent to approximately 40 R-peaks since the average ratio of heart rate and respiratory rate, irregardless of any cardiorespiratory coordination, is 4 [35]. Thus, these methods are not as suitable for the detection of cardiorespiratory coordination as the method of 'Phase Recurrences'. Nevertheless, although all methods had different definitions and different implementation details, they revealed similar qualitative features of cardiorespiratory coordination during night-time sleep, i.e., the coordination diagrams showed approximately the same structure and the 'total coordination' (percentage of all coordinated R-peaks regardless of the m:n- ratio) was not constant but oscillated during night-time sleep. Since this oscillation correlates with heart rate variability (HRV) [36] and different sleep stages also correlate with HRV [54,55], this oscillation may have its origin in the sleep architecture.
Another quantitative comparison was carried out by intra-individually correlating the 'coordination diagrams' of each method. The correlation coefficients served as a measure of similarity between the different diagrams. The strongest correlation was observed between the coordination diagrams derived from the 'Synchronization-λ' and the 'Quantification of Histograms' of relative distances φi. Furthermore, the 'Phase Recurrences' of relative distances φi and of absolute distances ti correlated strongly. These results indicate that the amount of information contained in the relative distances φi, i.e. the relative timings of the R-peaks in a full respiratory cycle, and in the absolute distances ti, i.e. the timings of R-peaks with regard to the preceding inspiratory onset, was approximately the same. Furthermore, the results of the 'Synchronization-λ' showed strong correlations to the 'Phase Recurrences' and the 'Quantification of Histograms'. Thus, the latter methods, which have heuristic origins, may also be explained mathematically by the mathematical models used to develop the method of 'Synchronization-λ'. However, the correlation coefficients concerning the 'Quantification of Histograms' of relative distances βj, i.e. the relative timing of an inspiratory onset in the respective RR-interval, were lower. Thus, these distances seem to contain different information. This difference is attributed to the fact that the calculation of the relative distances βj is not based on the full amount of information available in the recording.
Taken these findings together, it is apparent that the different methods may lead to similar results if the maximum amount of information is used. It is important to keep in mind that all quantifications are based on the 'Post Event Time Series' or the 'Synchrogram'. Thus, although some methods have heuristic origins and some have a mathematical model as its origin and although the different approaches differ considerably with respect to their calculations, the relevant information of the 'Post Event Time Series' and the 'Synchrogram' is captured by the different methods.
A debate exits whether the cardiorespiratory coordination is due to physiological interaction between the involved systems or other mechanisms might be responsible, like e.g. a reduced variability in at least one of the involved systems [36,56]. Surrogate data have been used to give at least a quantitative answer to this debate [34,38,57]. The analysis of surrogate data suggests that cardiorespiratory coordination is sometimes due to some kind of physiological interaction. Another approach that may be of importance is the stochastic phase synchronization, i.e. the synchronization between coupled systems due to stochastic stimuli [58,59]. However, this kind of mechanism has not been explored for the cardiorespiratory system and the implications are not clear yet. Irrespective of this debate, it is an intriguing phenomenon that heart rate and respiratory frequency show a 4:1-ratio on average during night-time sleep [35,60]. The present results also indicate that there seem to be some constraints with respect to variability and other features of the involved systems to achieve certain m:n-ratios. Further methods and models have to be developed to explore these features of the cardiorespiratory system.
In conclusion, the different methods to detect cardiorespiratory coordination show similar qualitative results. The method 'Phase Recurrences' has the ability to detect most sequences of heart beats coordinated with respiration. It is able to maximize the temporal resolution because even very short sequences may be detected. Thus, this method should be preferred. The method of 'Quantification of Histograms' of relative distances βj contains different quantitative information since only parts of the full amount of information available is used. Hence, this method is not recommended because a comparison with the other methods is limited. Generally, using appropriate methods, cardiorespiratory coordination is detectable in approximately 20–25% of all heart beats during night-time sleep. Future work should focus on the prerequisites of the appearance of cardiorespiratory coordination during night-time sleep and whether this appearance is linked to sleep stages. Furthermore, a preliminary study has shown the loss of cardiorespiratory coordination in patients after acute myocardial infarction [61,62]. Hence, the gain of prognostic information of this bivariate analysis should be explored further.
Authors' contributions
DC and HB designed the study, recruited the subjects and collected the data. DC carried out the analysis and drafted the manuscript. SL, DG and PvL were involved in the interpretation of the data and participated in the final revision.
Acknowledgements
DC and HB acknowledge financial support from the Weleda AG, Schwäbisch Gmünd, Germany. The authors would like to thank Roland Zerm and Matthias Kröz for providing data.
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| 15555083 | PMC538289 | CC BY | 2021-01-04 16:38:57 | no | World J Surg Oncol. 2004 Nov 23; 2:38 | latin-1 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-38 | oa_comm |
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World J Surg OncolWorld Journal of Surgical Oncology1477-7819BioMed Central London 1477-7819-2-411557162410.1186/1477-7819-2-41Case ReportEctopic paraesophageal mediastinal parathyroid adenoma, a rare cause of acute pancreatitis Foroulis Christophoros N [email protected] Sotirios [email protected] Christos [email protected] Dimitrios [email protected] Georgia [email protected] Athanassios [email protected] Larissa University Hospital, Department of Cardio-thoracic Surgery, 41110 Larissa, Greece2 Volos General Hospital, Department of General Surgery, 38221 Volos, Greece3 Volos General Hospital, 1st Department of Internal Medicine, 38221 Volos, Greece2004 30 11 2004 2 41 41 21 5 2004 30 11 2004 Copyright © 2004 Foroulis et al; licensee BioMed Central Ltd.2004Foroulis 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 manifestation of primary hyperparathyroidism with acute pancreatitis is a rare event. Ectopic paraesophageal parathyroid adenomas account for about 5%–10% of primary hyperparathyroidism and surgical resection results in cure of the disease.
Case presentation
A 71-year-old woman was presented with acute pancreatitis and hypercalcaemia. During the investigation of hypercalcemia, a paraesophageal ectopic parathyroid mass was detected by computerized tomography (CT) scan and 99mTc sestamibi scintigraphy. The tumor was resected via a cervical collar incision and calcium and parathormone tumor levels returned to normal within 48 hours.
Conclusions
Acute pancreatitis associated with hypercalcaemia should pose the suspicion of primary hyperparathyroidism. Accurate preoperative localization of an ectopic parathyroid adenoma, by using the combination of 99mTc sestamibi scintigraphy and CT scan of the neck and chest allows successful surgical treatment.
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Background
Acute pancreatitis occurring secondary to hypercalcemia is rare. Most of the time adenomas are located in the neck. However, in 10–20% cases the parathyroid adenomas are found to be located within the mediastinum [1,2]. The lower parathyroid glands develop from the third pharyngeal pouch in close association with the thymus and they may migrate along with the thymus during development. As a result they may be found commonly within the anterosuperior mediastinum. On the other hand, the superior parathyroid glands are not associated with the thymus and may even be located in the posterior mediastinum [1-3]. Paraesophageal or retroesophageal parathyroid tumors arise from superior parathyroid glands, have normal blood supply from a branch of the inferior thyroid artery and are not embryologically considered ectopic [1-3].
We present a case of paraesophageal parathyroid adenoma clinically presenting as acute pancreatitis and successfully managed surgically by the collaboration of thoracic and general surgeons, via a cervical incision.
Case presentation
A 71-year-old female was admitted with epigastric pain and vomiting lasting for more than 12 hours. She had a history of arterial hypertension and cholecystectomy two years previously. On examination she had tenderness of the upper abdomen. Blood tests showed leucocytosis (14,500 / mm3), increased serum levels of amylase (1,100 IU/L), LDH (550 IU/L) and calcium (14.8 mg/dl). On ultrasonography of the upper abdomen a non homogeneous appearance of the head of the pancreas was noted with a common bile duct diameter of 8.5 mm. CT scan of the abdomen confirmed the diagnosis of acute exudative pancreatitis. Acute pancreatitis subsided within 72 hours after conservative treatment. Further laboratory investigation of the hypercalcaemia revealed increased 24-hours urine calcium (465 mg), decreased serum phosphorus levels at 1.3 mg/dl, increased serum parathyroid hormone levels (771 pg/ml), normal levels of serum free T3 (FT3), free T4 (FT4), thyroid stimulating hormone (TSH), calcitonine, carcino embryonic antigen (CEA), carcinoma antigen (CA) 15-9, CA 125, alpha feto protein (AFP). Ultrasonography of the thyroid gland and the neck showed a suspicious prevertebral mass. CT scan of the thorax and neck detected a paraesophageal mediastinal mass close to the thoracic inlet. (Figure 1) 99mTc sestamibi scintigraphy confirmed the diagnosis of parathyroid adenoma. (Figure 2).
Figure 1 CT scan showing a paraesophageal, retrotracheal mass, close to the thoracic inlet
Figure 2 Imaging of the parathyroid paraesophageal adenoma by Tc-99m scintigraphy
After the complete subsidence of acute pancreatitis and the return of calcium serum levels at values less than 12 g/dl by hydration, the patient underwent endoscopic retrograde cholangiopancreaticography (ERCP) which failed to show bile duct lithiasis. With a diagnosis of parathyroid adenoma, resection of the parathyroid adenoma via a collar cervical incision was carried out. The tumor was easily separated from the surrounded structures (vertebra, trachea, and esophagus) by blunt dissection and the feeding vessels were found and ligated anteriorly. (Figure 3) The patient had a 6 days uneventful hospital stay. Calcium and parathyroid hormone serum levels returned to normal within 48 hours from the end of operation. A slight serum hypocalcaemia was observed over the following days and the patient received oral therapy with calcium and vitamin D to restore serum calcium levels within normal range values.
Figure 3 The parathyroid adenoma after resection, measuring 40 mm in its maximal dimension. The silk suture ligating the feeding vessels is shown.
Discussion
Recurrent episodes of acute pancreatitis secondary to hypercalcemia are an uncommon presentation of primary hyperparathyroidism [4-7]. Acute pancreatitis is reported to be associated with primary hyperparathyroidism in 1% – 8% of cases in some large published series [4-7]. Sporadically reported cases of acute pancreatitis induced by primary hyperparathyroidism, in both the recent and past medical literature, suggest that the relationship between the two clinical conditions is not incidental [8-15]. Carnaille et al found significantly elevated serum calcium levels to be of major importance in the development of pancreatitis in patients with primary hyperparathyroidism [7].
Increased levels of serum calcium at the first episode of acute pancreatitis should pose the suspicion of primary hyperparathyroidism. In patients with a history of cholecystectomy (as in the presented case), where the main cause of an episode of acute pancreatitis is bile duct lithiasis, the diagnosis could be missed if serum calcium levels ranged within normal values. The main causes of primary hyperparathyroidism are single or double parathyroid adenoma (80%), hyperplasia of all four or more existing parathyroid glands (15–20%) and rarely cancer of the parathyroid gland (2%) [1,2,16].
Richards et al reported 5%–10% of the parathyroid glands to be located in the posterior mediastinum, 20% are found substernally within the thymic tissue in the anterior mediastinum (1–2%) while 1% of the glands are located in the carotid sheath and 5% into the thyroid gland. [2] Other rare sites of ectopic parathyroid tissue are the vagus nerve sheath, the thyrothymic ligament and the pericardium [3]. By reviewing 112 patients who underwent re-operation for primary hyperparathyroidism, Wang found 39% of missing adenomas to be located in the retrotracheal space [17]. Parathyroid glands, are now found with increasing frequency in the visceral compartment of the mediastinum (aortopulmonary window and right pulmonary artery, close to the tracheal bifurcation), because of the improvement of the imaging techniques (99mTc sestamibi scintigraphy). The frequency of this occurrence at the moment is uncertain [2].
Many investigators advocate the need for the concordance of at least two diagnostic modalities before surgical excision. The combination of 99mTc sestamibi scintigraphy and CT scan of the chest and neck gives important information to proceed with surgery and to minimize the risk of re-operation for recurrent hyperparathyroidism in the future [3,16,18,19]. The combination of both techniques had 100% sensitivity and 97.4% positive predictive value for the detection of the cause of primary hyperparathyroidism [18]. The spectrum of diseases demonstrated with 99mTc scintigraphy includes eutopic parathyroid disease, ectopic parathyroid disease, solitary, double or multiple parathyroid adenoma, cystic adenoma, lipoadenoma, multiple endocrine neoplasia, entities with atypical washout and non-parathyroid entities that take up 99mTc sestamibi (normal and pathologic cervical, supraclavicular, axillary lymph nodes, hyperplastic thymus, focal soft tissue uptake from a sarcoid or carcinoid tumor) [3]. The addition of early lateral views to the conventional 99mTc sestamibi scintigraphy gives more information to the surgeon, concerning the depth of the lesion in atypical sites [20].
CT scan with intravenously injected contrast material has a low overall sensitivity of 45%–55% in primary hyperparathyroidism, but it is helpful mainly in the detection of ectopic mediastinal parathyroid adenomas [2,3]. Magnetic resonance imaging (MRI) of the neck and chest has a sensitivity of about 80%. The sensitivity of MRI is higher for the detection of ectopic mediastinal parathyroid adenomas (88%) [3]. Selective angiography combined with venous parathyroid hormone sampling has sensitivity between 60% and 85%. However, selective angiography is an aggressive and complicated approach and it is not advised as the initial approach in primary hyperaparathyroidism [2,3]. Single photon-emission computed tomographic (SPECT) sestamibi scintigraphy of the neck and thorax has the capability of three-dimensional assessment and it is considered to be the optimal method for the evaluation of parathyroid disease, especially that of mediastinum for ectopic parathyroid glands [21-24]. Fusion of sestamibi SPECT images onto the CT images using a software package, as described by Patrick et al, gives excellent information on the exact localization of ectopic parathyroid tissue [19]. FDG-PET was found to have higher sensitivity than the sestamibi-SPECT in a prospective study by Neumann et al for preoperative detection and localization of parathyroid adenomas; high cost and limited availability of the scanners restrict its use as first-line examination in primary hyperparathyroidism [25].
Paraesophageal mediastinal adenomas are resected via a cervical incision in the majority of cases [1,2,16,26]. By retracting the thyroid gland and trachea to the opposite side, a finger can be inserted into the pretracheal space, even down to the mediastinum, to palpate the tumor. If the tumor is localized by finger palpation, it is easy to mobilize by blunt (finger) dissection and to expose it into the operating field. The vascular pedicle is the only structure that needs to be ligated. When an ectopic cervical or paraesophageal parathyroid adenoma is detected preoperatively by imaging studies, intraoperative frozen section of the adenoma and of a homolateral parathyroid gland, on which normal parathyroid tissue will be confirmed, precludes diffuse parathyroid hyperplasia. A targeted operation can then be chosen, which has the advantage of minimizing the time of operation and avoiding serious hypocalcemia in the immediate postoperative period. [26]
Conclusions
An ectopic paraesophageal parathyroid adenoma may be manifested with an episode of acute pancreatitis. Preoperative investigation for exact localization of an adenoma should include two imaging studies, preferably Tc-99m sestamibi scintigraphy or sestamibi-SPECT scintigraphy of the neck and chest and CT scan of the neck and chest. Resection of an ectopic paraesophageal adenoma is easily accomplished via a cervical incision and blunt mobilization of the tumor.
Competing interests
The author(s) declare that they have no competing interests.
Authors contributions
CNF has made contribution to the conception, design and drafting of the article, was involved in the critical revision of the article.
S R has made contribution to the conception, design and drafting of the article.
C L has made contribution to the conception and design of the article.
D K has made contribution to the conception and design of the article.
G K has made contribution to the conception and design of the article, was involved in the critical revision of the article.
AL has made contribution to the conception and design of the article, was involved in the critical revision of the article.
All the authors have read and approved the final version of the manuscript.
Acknowledgements
Written consent was obtained from the relative of the patient for publication of the case.
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| 15571624 | PMC538290 | CC BY | 2021-01-04 16:38:38 | no | World J Surg Oncol. 2004 Nov 30; 2:41 | utf-8 | World J Surg Oncol | 2,004 | 10.1186/1477-7819-2-41 | oa_comm |
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Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-791556373910.1186/1477-7827-2-79ResearchMelatonin blocks inhibitory effects of prolactin on photoperiodic induction of gain in body mass, testicular growth and feather regeneration in the migratory male redheaded bunting (Emberiza bruniceps) Trivedi Amit K [email protected] Sangeeta [email protected] Vinod [email protected] Department of Zoology, University of Lucknow, Lucknow 226 007, India2004 26 11 2004 2 79 79 24 8 2004 26 11 2004 Copyright © 2004 Trivedi et al; licensee BioMed Central Ltd.2004Trivedi 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.
Little is known about how hormones interact in the photoperiodic induction of seasonal responses in birds. In this study, two experiments determined if the treatment with melatonin altered inhibitory effects of prolactin on photoperiodic induction of seasonal responses in the Palearctic-Indian migratory male redheaded bunting Emberiza bruniceps. Each experiment employed three groups (N = 6–7 each) of photosensitive birds that were held under 8 hours light: 16 hours darkness (8L:16D) since early March. In the experiment 1, beginning in mid June 2001, birds were exposed to natural day lengths (NDL) at 27 degree North (day length = ca.13.8 h, sunrise to sunset) for 23 days. In the experiment 2, beginning in early April 2002, birds were exposed to 14L:10D for 22 days. Beginning on day 4 of NDL or day 1 of 14L:10D, they received 10 (experiment 1) or 13 (experiment 2) daily injections of both melatonin and prolactin (group 1) or prolactin alone (group 2) at a dose of 20 microgram per bird per day in 200 microliter of vehicle. Controls (group 3) received similar volume of vehicle. Thereafter, birds were left uninjected for the next 10 (experiment 1) or 9 days (experiment 2). All injections except those of melatonin were made at the zeitgeber time 10 (ZT 0 = time of sunrise, experiment 1; time of lights on, experiment 2); melatonin was injected at ZT 9.5 and thus 0.5 h before prolactin. Observations were recorded on changes in body mass, testicular growth and feather regeneration.
Under NDL (experiment 1), testis growth in birds that received melatonin 0.5 h prior to prolactin (group 1) was significantly greater (P < 0.05, Student Newman-Keuls test) than in those birds that received prolactin alone (group 2) or vehicle (group 3). Although mean body mass of three groups were not significantly different at the end of the experiment, the regeneration of papillae was dramatically delayed in prolactin only treated group 2 birds. Similarly, under 14L:10D (experiment 2) testes of birds receiving melatonin plus prolactin (group 1) and vehicle (group 3) were significantly larger (P < 0.05, Student Newman-Keuls test) than those receiving prolactin alone (group 2). Also, birds of groups 1 and 3, but not of group 2, had significant (P < 0.05, 1-way repeated measures Analysis of Variance) gain in body mass. However, unlike in the experiment 1, the feather regeneration in birds of the three groups was not dramatically different; a relatively slower rate of papillae emergence was however noticed in group 2 birds. Considered together, these results show that a prior treatment with melatonin blocks prolactin-induced suppression of photoperiodic induction in the redheaded bunting, and suggest an indirect role of melatonin in the regulation of seasonal responses of birds.
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Background
In many birds, day length regulates seasonal changes in fattening and body mass gain, gonadal growth and development, molt, and plasma levels of several hormones, including luteinizing hormone (LH), prolactin and melatonin [1-4]. There occurs some degree of phase-relationship among various photoinduced events. For example, photoperiodically induced rise in LH coincides with the onset of breeding [1,2], and rise in prolactin coincides with the late breeding and early post-breeding periods [4,5]. During laying and incubation stages of the reproductive cycle plasma prolactin levels increase dramatically by 100 to 150 folds [6]. High prolactin levels in late breeding season are implicated in the development of reproductive photorefractoriness and postnuptial molt in birds [4,7]. Circulating melatonin levels also undergo seasonal changes. High melatonin levels in the summer months and low melatonin levels in the winter months coincide, respectively, with the breeding and non-breeding phases of the reproductive cycle in long day breeding birds [3]. Although not known in birds, Lincoln and Clarke [8] provide evidence that melatonin acts directly within the pituitary to regulate photoperiod-induced changes in prolactin secretion in seasonally breeding Soay sheep.
Previous studies on how hormones interact in photoperiodic induction of seasonal responses in birds have yielded conflicting results. A number of early findings show prolactin acting both as pro- and anti-gonadal in birds exposed to stimulatory day lengths [9-11]. However, in many birds high plasma prolactin levels are associated with decreased gonadal activity and LH levels [6,10-13]. In their recent review Blache and Sharp [4] conclude that prolactin is involved in the regulation of avian reproduction by providing inhibitory inputs to the hypothalamo-hypophyseal-gonadal axis.
The production and secretion of melatonin encodes a photoperiodic calendar to birds as they exhibit changes in both the duration and amplitude of melatonin secretion corresponding to the duration of night length/ day length [3,14,15]. Also, melatonin is part of the birds' multioscillatory circadian system and helps maintain a robust and stable phase relationship among different internal circadian oscillators, and photoperiodic induction of seasonal responses in birds is mediated by the circadian system [16-21]. Interestingly, however, a direct role of melatonin in avian photoperiodism is not explicitly found. Most studies negate the role of pineal/ melatonin in photoperiodic induction of seasonal responses in birds (for references see [17]). We propose that the role of melatonin in avian photoperiodism is indirect. Melatonin modulates sensitivity of the circadian response system to a stimulatory photoperiod, and/ or influences downstream the photoperiod-induced effects by interacting with other hormones released in response to stimulatory photoperiods [17,19]. We sought to investigate this by examining the effects of exogenous melatonin on prolactin-induced suppression of photoperiodic response in a migratory bird species, the redheaded bunting (Emberiza bruniceps), in which melatonin is not directly involved in the photoperiodic time measurement based on circadian rhythm of photosensitivity [22,23]. Previous studies show that prolactin administered subcutaneously at a dose of 100 μg day-1 suppresses ovarian response in buntings subjected to long days [10]. In this study, we specifically determined if the administration of melatonin 0.5 h before prolactin blocks the prolactin-induced suppression of the photoperiodic induction of gain in body mass, testicular growth and development, and feather regeneration in the redheaded bunting exposed to stimulatory day lengths.
Methods
We used adult male redheaded bunting caught in late February from the overwintering flock at 25°N. Buntings are migratory finch that breed in summer in west Asia and east Europe (~ 40°N) and overwinter in India. Birds were held outdoors and acclimatized to captive conditions for two weeks, and then brought indoors and maintained on short days (8 hours light: 16 hours darkness, 8L:16D) until subjected to experiments. Under short days, buntings do not fatten, and remain reproductively immature and responsive to photostimulation [24]; birds pretreated with short days are referred to as the photosensitive birds throughout this manuscript. Two experiments were performed as per experimental design detailed in the figure 1, and in accordance with the guidelines in the Principles of Animal Care.
Figure 1 Experimental design. Both experiments had three phases: phase 1- pretreatment with 8 hours light: 16 hours darkness (8L:16D); phase 2- injection phase under natural day length (experiment 1) or 14L:10D; phase 3- uninjected phase. Arrows on top indicate time of injections (left: zeitgeber time (ZT) 9.5 – melatonin; right: ZT 10 – prolactin or vehicle; ZT 0 = the time of sunrise for the experiment 1, the time of onset of light for the experiment 2). Lines at the base of phase 2 indicate number of days of injection. The experiment 1 was terminated after 23 days and the experiment 2 was terminated after 22 days from the beginning of the phase 2.
Experiment 1
This experiment began in mid June 2001. Photosensitive birds maintained indoors on 8L:16D since early March were brought outdoors in the aviary and exposed to natural day lengths (NDL) at 27°N (day length = ~ 13.8 h, sunrise to sunset). After three days of acclimatization, they were divided in three groups (N = 6 each). Beginning on day 4, they received subcutaneous injections once daily for 10 days as follows: group 1- first melatonin and 0.5 h later prolactin; group 2- prolactin alone; group 3- vehicle (control). After 10 consecutive injections (days 4–13), birds were left uninjected for the next 10 days (day 14–23). The experiment was terminated on day 24.
Experiment 2
To confirm results of the experiment 1, we performed the experiment 2 under artificial conditions providing light-dark (LD) cycles corresponding to that was available outdoors (NDL) to birds of the experiment 1. This experiment began in the second week of April 2002. Three groups (N = 6–7) of photosensitive birds were subjected to 14L:10D. Beginning on day 1, they received 13 injections (days 1–13) as in the experiment 1: group 1- first melatonin and 0.5 h later prolactin; group 2- prolactin alone; group 3- vehicle (control). Thereafter, birds were left uninjected for the next 9 days (days 14–22). The experiment was terminated on day 23.
Melatonin was administered at zeitgeber time 9.5 (ZT 0 = the time of sunrise in the experiment 1; the time of light on in the experiment 2) in view of our previous study [23] and several other observations [17] showing that melatonin or vehicle given alone at this time of day does not affect photoperiodic induction of gain in body mass and testicular recrudescence in the redheaded bunting, although around this time of day melatonin administration affects photoperiodic induction in mammals [25]. The prolactin was administered at ZT 10; group 1 birds thus received prolactin 0.5 h after melatonin. Vehicle was administered to birds of group 3 at ZT 10. Thus, the timing of prolactin and vehicle injections was decided in relation to the timing of the melatonin injection that itself was timed in relation to the timing of sunrise or the timing of light on.
In both experiments, melatonin and prolactin were administered each at a dose of 20 μg bird-1 day-1 in 200 μl injection volume. Prolactin was obtained from Sigma Chemical Co. USA (Luteotropic hormone; L-6520, Lot 120K1606) and melatonin from Genzyme Fine Chemicals Ltd., Haverhill, Suffolk, UK). Melatonin injections were prepared as described by Kumar [26]. Briefly, a known amount of melatonin was dissolved in 100% ethanol and diluted in saline (0.9 % NaCl) such that each injection in 200 μl volume was 0.1 % ethanolic saline containing 20 μg of hormone. Prolactin was dissolved directly in saline yielding 20 μg per 200 μl of injection volume. Controls received 200 μl injection of 0.1% ethanolic saline (vehicle).
We measured the effects on changes in body mass, testis size and regeneration of feather papillae. Body mass and testis size were measured at the beginning (day 0, the day before injections began), in the middle (body mass only) and at the end of the experiments. Birds were weighed on a top pan balance providing accuracy nearest to 0.1 g. In view of the findings that the fattening accounts for the most of the gain in body weight in photostimulated passerine birds [27,28], in the present study we considered body mass of photostimulated redheaded bunting, a passeriform, reflecting the fat deposition. The dimensions of the left testis were recorded when birds were laparotomised under local anesthesia (for details see [29]), and testis volume was calculated from 4/3πab2, where a and b denote half of the long and short axes, respectively. Feather papillae regeneration was recorded as follows. On day 1 of the experiment, feathers in a specific area on the left chest were plucked. A permanent ink marker marked an area on bare epidermis measuring 1 cm2. Beginning 24 h after the first injection, the number of papillae emerged from the epidermis were counted daily throughout the experiment 1 or till 4 days after the last injection in the experiment 2, and scored subjectively as outlined by Boswell [30]. Briefly the scoring was done as follows: 0- missing feather, 1- a papilla emerging, 2- a papilla grown up to one-third of full size, 3- a papilla grown up to two-third of full size, 4- a papilla grown more than two-third of full size but still not complete, and 5- a completely grown feather papilla. The feather papillae scores were limited to first 50 feathers emerged within the marked area, and so total papillae score for an individual bird ranged from 0–250.
Food and water were available ad libitum. In an artificial LD cycle, light was provided by white compact fluorescent lamps at ~ 500 lux. Data are presented as mean ± SE. They were analyzed using one-way analysis of variance (1-way ANOVA) with or without repeated measures, followed by post-hoc tests if ANOVA indicated a significance of difference. 1-way repeated measure ANOVA was used to compare data generated from the same group as a function of time, and 1-way ANOVA was used to compare data of different groups at one observation. Two-way (2-way) ANOVA was used to analyze data when two factors were considered together, for example the effect of the treatment and duration of the treatment. Significance was taken at P < 0.05. In the experiment 1, one bird of group 1 and two birds of group 2 died, and their data are excluded from the statistical analyses.
Results
Experiment 1
Results are shown in figure 2a,2b,2c. There was no significant change in body mass in birds of all the three groups during the treatment period (Fig. 2a). Testes were however stimulated in all birds but the mean testis volume at the end of the experiment was different among the three groups (F(2,12) = 4.656, P = 0.0319; 1-way ANOVA). Testes were significantly larger (P < 0.05, Newman-Keuls test) in birds that received melatonin prior to prolactin (group 1) compared to those that received prolactin alone (group 2) or vehicle (group 3) (Fig. 2b). The rate of regeneration of feathers was not significantly different between birds of groups 1 and 3 (F(1,180) = 3.3.19, P = 0.0701; 2-way ANOVA) although in group 1 birds the emergence of the first papilla was delayed by at least a day (Fig. 2c). Whereas in the group 1 the first papilla in a bird was found on day 5 and in all birds by day 11, in the group 3 the first papilla in a bird was found on day 4 and in all birds by day 10. Also, mean papillae scores during first some days were relatively smaller in group 1 compared to group 3. In group 2 birds that received prolactin alone the first papilla emergence in a bird was found on day 12, and hence papillae regeneration was dramatically delayed as compared to birds in group 1 (F(17,144) = 15.26, P < 0.0001; 2-way ANOVA) and group 3 (F(17,144) = 16.49, P < 0.0001; 2-way ANOVA) (cf. Fig. 2c).
Figure 2 Mean (± SE) body mass, testis volume and feather papillae regeneration in response to treatment with melatonin and prolactin (group 1), prolactin alone (group 2), or vehicle (group 3) in the redheaded bunting (Emberiza bruniceps) subjected to natural day lengths at 27°N (experiment 1: a-c) for 23 days (from mid-June to early July) or to artificial day length (14L:10D; experiment 2: d-f) for 22 days. Birds were injected with exogenous hormones at a dose of 20 μg bird-1 day-1 in 200 μl of vehicle daily for first 10 days in the experiment 1 and for first 13 days in the experiment 2, and thereafter they were left uninjected. Controls received similar volume of vehicle. All injections except melatonin were made at the zeitgeber time 10 (ZT 0 = the time of sunrise for the experiment 1; the time of onset of light for the experiment 2); melatonin was injected at ZT 9.5. Day 0 on X-axis refers to the day before first injection. Under NDL, one bird of group 1 and two birds of group 2 died, and their data are excluded. Asterisk indicates the significance of difference at P < 0.05.
Experiment 2
Figure 2d,2e,2f show results from the experiment 2. There was a significant gain in body mass in birds of groups 1 and 3 which received melatonin plus prolactin and vehicle, respectively (group 1: F(2,10) = 12.16, P = 0.0021; group 3: F(2,12) = 6.978, P = 0.0098; 1-way RM ANOVA; Fig. 2d), but not in birds of group 2 which received prolactin alone (F(2,12) = 0.2839, P = 0.7379; 1-way RM ANOVA; Fig. 2d). Hence at the end of the experiment, there was a significant difference in the response of body mass among the three groups (F(2,17) = 3.857, P = 0.0416; 1-way ANOVA). Although testes were stimulated in all groups (Fig. 2e), the size attained at the end of the experiment was different among different groups (F(2,17) = 4.343, P = 0.0299; 1-way ANOVA). Testes grew to full size in birds of groups 1 and 3, and hence were significantly larger (P < 0.05, Newman-Keuls test) than those of group 2 in which they grew to less than half-maximal size (Fig. 2e). Unlike the experiment 1, the feather regeneration was not dramatically different among the three groups (Fig. 2f). Nonetheless, the rate of papillae regeneration in birds of group 2 that received prolactin alone was slower as compared to group 1 (F(14,150) = 3.9550, P < 0.0485; 2-way ANOVA) and group 3 (F(14,165) = 24.76, P < 0.0001; 2-way ANOVA) (cf. Fig. 2f). By day 17, however, all individuals regardless of the treatment had shown papillae emergence (Fig. 2f).
Discussion
The present results confirm a previous finding on female redheaded buntings [10] that exogenous prolactin suppresses the photoperiodic induction of body mass gain and testis recrudescence under long days. In general, the effects of prolactin on body mass and testes found in the present study are consistent with the evidence that high prolactin levels during late breeding phase decrease fat stores [31] by affecting lipid metabolism via increasing lipoprotein lipase activity in the adipocytes [32] and induce testicular regression by inhibiting the hypothalamo-hypophyseal-gonadal axis [4,7,13]. Prolactin-induced suppression of papillae emergence in buntings is also consistent with the suggested role of high prolactin inducing defeathering and postnuptial molt [4,7]. High prolactin levels induced by prolactin administration thus seem producing a physiological condition comparable to late phase of the gonadal cycle, both in terms of declining body mass, reduced testicular activity and defeathering indicated by suppression of the emergence of feather papillae (Fig. 2).
Of more interest is however that a prior treatment with melatonin blocks prolactin-induced suppression of photoperiodic induction in the redheaded bunting (Fig. 2). Birds administered with melatonin 0.5 h prior to prolactin showed photoperiodic induction similar to that of controls (experiment 2; Fig. 2d,2e,2f). Data on feather regeneration also support this. Melatonin administration blocked the suppression of papillae emergence by exogenous prolactin (cf. Fig. 2e). It is not understood however from these experiments how melatonin acts to restore photoperiodic response in prolactin-treated birds, but we can offer some plausible explanations. One is that melatonin reduces circulating prolactin levels either directly by acting on pituitary prolactin producing cells, as reported in fish [33,34], or indirectly by affecting the release of hypothalamic dopamine (DA) and vasoactive intestinal peptide (VIP) [35,36]. There is increasing evidence from both in vivo and in vitro experiments that hypothalamic VIP acts as prolactin releasing factor [35] and its secretion is photoperiodically regulated in birds [35,36]. Both the DAergic and VIPergic systems interact in regulation of prolactin secretion in birds [35]. It is to be investigated if there is a relationship between the melatonin and VIP, but from studies on avian retinal system there is evidence for an inverse relationship between the melatonin and dopamine (DA) [37]. Additionally, melatonin acts directly within the pituitary to regulate prolactin secretion in seasonally breeding photoperiodic Soay sheep [8], and melatonin stimulates dopamine release from tuberoinfundibular dopaminergic neurons resulting into the suppression of serum prolactin levels in rats [38]. Current results (Fig. 2) that when photostimulated buntings receiving both melatonin and prolactin had greater body mass and larger testes than those receiving prolactin are consistent with one or the other of the above explanations.
A second possibility is that exogenous melatonin changes phase-relationship between daily rhythms of endogenous endocrine rhythms, and this somehow enhances sensitivity of the circadian response system to stimulatory effects of long day lengths. Testicular response in birds of the experiment 1 supports this (Fig. 2b). Birds that received both melatonin and prolactin (group 1) had significantly larger testes (P < 0.05, Student Newman-Keuls test) than those received prolactin alone (group 2) or vehicle (group 3). A role of melatonin in enhancing responsiveness of the photoperiodic response system is shown in an experiment on the blackheaded bunting (Emberiza melanocephala), an allied species that shares breeding and wintering grounds with the redheaded bunting. In blackheaded buntings exposed to 11.75L:11:25D of red light (650 nm), testes grew significantly larger in individuals that carried implants filled with melatonin compared with those that carried empty implants [39].
However, one observation of the present study is not entirely consistent. Controls of the experiment 1 had significantly (P < 0.05, Student t-test) smaller testes than those of the experiment 2. This occurred perhaps because of one or both of the following reasons. First, there was a difference in the lighting environment between two experiments, both in the shape of LD cycle (saw-tooth shape in NDL versus square-wave shape in 14L:10D) and intensity of light period (gradually changing intensity for ~ 13.8 h daylight outdoors underneath opaque roof of the aviary in NDL (light intensity in the aviary during the experiment ranged from 92.3 ± 13.4 lux at sunrise to several thousand lux during day to 56.2 ± 5.9 lux at the time of sunset) versus a continuous ~ 500 lux intensity for 14 h within the photoperiodic chambers in 14L:10D). It is reported that the duration of photoperiod and light intensity do affect photoperiodic induction of body mass and testis recrudescence in the blackheaded bunting [24]. Second, high temperatures (> 40°C) outdoors during mid June – early July may have caused rise in endogenous prolactin levels [40], similar to those in group 2 that received exogenous prolactin, and this may have suppressed the photoperiodic induction (cf. Fig 2a,2b).
The present study indicates that melatonin could be involved indirectly in regulation of photoperiod-induced seasonal responses in the redheaded bunting by modulating effects of other hormones such as the prolactin. This appears consistent with another finding on this species [23] suggesting effects of melatonin on temporal phasing of the testicular cycle (individuals that carried implant filled with melatonin peaked in testicular growth one-month later compared to those that carried empty implant) and not on the initiation of testicular recrudescence. However, unlike in several birds (for references see [17]) in which melatonin fails to produce an effect on testicular growth, a few reports do exist in the literature showing direct effects of melatonin administration on gonadal activity. In migratory European quail (Coturnix coturnix), for example, melatonin given in drinking water influences the reproductive cycle [41]. Daily melatonin injections inhibited testicular recrudescence in lal munia (Estrilda amandava) [42], and caused significant involution of enlarged testes of breeding season in both the blossomheaded parakeet (Psittacula cyanocephala) and the Indian weaver bird (Ploceus philippinus) [43]. Inhibitory effects of pineal on hypothalamo-hypophyseal-gonadal axis is reported in the Indian weaver bird (Ploceus philippinus) [44] although a recent study in which reproductively active individuals were implanted with melatonin did not support the antigonadal effect of melatonin [23]. Differences in the effects of melatonin probably reflect diversity of the avian photoperiodic system. It will be interesting therefore to further examine species showing divergent effects of melatonin to unravel the diversity of the role of melatonin in photoperiod-induced seasonal responses in birds.
Conclusion
The present results show that a prior treatment with melatonin blocks prolactin-induced suppression of photoperiodic induction in the migratory redheaded bunting. How melatonin acts to negate the effects of prolactin is unclear. Whatever is the actual mechanism of action, the current result provide evidence that melatonin modulates photoperiodic induction of seasonal responses in birds by interacting with prolactin. Whether such effect will vary during different seasons of the year remains to be investigated.
Authors' contributions
AKT and SR carried out the experiments and prepared the first draft of the manuscript. VK supervised the experiments and the final version of the manuscript. The study was conceived by VK but then the experiments were discussed jointly. All the authors approved the final manuscript.
Acknowledgements
We gratefully acknowledge the financial assistance for this study from the Department of Science and Technology, New Delhi through a grant to VK.
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| 15563739 | PMC538291 | CC BY | 2021-01-04 16:36:43 | no | Reprod Biol Endocrinol. 2004 Nov 26; 2:79 | utf-8 | Reprod Biol Endocrinol | 2,004 | 10.1186/1477-7827-2-79 | oa_comm |
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Popul Health MetrPopulation Health Metrics1478-7954BioMed Central London 1478-7954-2-91551859410.1186/1478-7954-2-9ResearchThe impact of antidepressant treatment on population health: synthesis of data from two national data sources in Canada Patten Scott B [email protected] Departments of Community Health Sciences and Epidemiology, University of Calgary, 3300 Hospital Drive NW, Calgary, Alberta, CANADA. T2N 4N12004 1 11 2004 2 9 9 5 4 2004 1 11 2004 Copyright © 2004 Patten; licensee BioMed Central Ltd.2004Patten; 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 randomized, controlled trials, antidepressant medications have been shown to reduce the duration of major depressive episodes and to reduce the frequency of relapse during long-term treatment. The epidemiological impact of antidepressant use on episode duration and relapse frequency, however, has not been described.
Methods
Data from two Canadian general health surveys were used in this analysis: the National Population Health Survey (NPHS) and the Canadian Community Health Survey (CCHS). The NPHS is a longitudinal study that collected data between 1994 and 2000. These longitudinal data allowed an approximation of episode incidence to be calculated. The cross-sectional CCHS allowed estimation of episode duration. The surveys used the same sampling frame and both incorporated a Short Form version of the Composite International Diagnostic Interview.
Results
Episodes occurring in antidepressant users lasted longer than those in non-users. The apparent incidence of major depressive episodes among those taking antidepressants was higher than that among respondents not taking antidepressants. Changes in duration and incidence over the data collection interval were not observed.
Conclusions
The most probable explanation for these results is confounding by indication and/or severity: members of the general population who are taking antidepressants probably have more highly recurrent and more severe mood disorders. In part, this may have been due to the use of a brief predictive diagnostic interview, which may be prone to detection of sub-clinical cases. Whereas antidepressant use increased considerably over the data-collection period, differences in episode incidence and duration over time were not observed. This suggests that the impact of antidepressant medications on population health may have been less than expected.
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Background
Depressive disorders are among the most important contributors to disease burden at the population level . While primary prevention for this condition has remained an elusive goal, provision of treatment has been viewed as having the capacity to reduce its impact on population health. Randomized, controlled clinical trials confirm that treatment with antidepressant medications can favorably impact the course of major depressive disorder. Clinical practice guidelines recommend acute treatment to reduce episode duration, continuation treatment to prevent relapse and maintenance treatment for those at high risk of recurrence [1].
Direct generalization of clinical trial data to the general population results in an expectation that depressive episode frequency and episode duration should be reduced in those receiving antidepressant treatment. However, outcomes reported in clinical trials are not necessarily reflected in "real world" outcome data. Since treatments are not assigned randomly in clinical settings, those treated are likely to have more severe illness (both in terms of episode duration and recurrence risk) than those who do not seek or receive treatment. In such circumstances, the effect of treatment may become confounded with the effect of the underlying disorder itself, and/or its severity [2].
Despite the plausible occurrence of confounding by indication and confounding by severity, population-based studies examining outcomes in relation to treatment status in the general population have not been reported. The objective of the current study was to estimate the incidence and duration of major depressive episodes in members of the general population who are receiving or not receiving antidepressant treatment.
Methods
The National Population Health Survey (NPHS) is a longitudinal general health survey conducted by the Canadian government's statistical agency, Statistics Canada The NPHS sample consists of 17,262 subjects selected in 1994 and 1995 (hereafter denoted 1994/95) who have been followed prospectively with interviews every two years since. Data has been released for the first four cycles (1994/95 to 2000/01). The Canadian Community Health Survey (CCHS) is another general health survey conducted by Statistics Canada. The CCHS has a very large sample size (n = 130,880) and employed a cross-sectional study design. Data collection for the CCHS occurred in 2000.
Both the NPHS and the CCHS utilized probability samples, based on the same sampling frame, from the Canadian general population. The sampling procedures incorporated both clustering and unequal selection probabilities. Valid inference therefore requires the use of sampling weights and statistical procedures accounting for non-independence within clusters. In order to deal with these methodological issues, a bootstrap procedure for variance estimation developed by Statistics Canada was employed in this analysis. This procedure accounts for the design effects.
Both the NPHS and the CCHS recorded medication use with a series of self-report items. The item relevant to this analysis asked whether the respondent had taken antidepressant medications during the month preceding the interview. Each interview also included the Composite International Diagnostic Interview Short Form for Major Depression (CIDI-SFMD), which was developed and validated by Kessler et al. [3]. This is a brief, fully structured instrument derived from a set of modified CIDI items. The CIDI-SFMD was designed to provide an operationalization of the DSM-IV [4] diagnostic criteria for major depression and is sufficiently brief that it can be included in general health surveys. The instrument detects symptoms indicative of major depression, and identification of five such symptoms (one of which must be depressed mood or loss of interest) indicates a high probability that the subject fulfilled DSM-IV criteria for major depression in the 12-months preceding the interview. It should be noted, however, that the Short Form does not contain all of the clinical significance probes and organic exclusion items that are included in the full CIDI, and may therefore detect some subclinical episodes [5]. A component of the CIDI-SFMD is an item that asks (of subjects reporting a probable episode of major depression) the number of weeks in the preceding year that were characterized by depressive symptoms.
In calculating incidence, pairs of observations across NPHS longitudinal data collection cycles were used. There were three suitable intervals covered by the four data collection rounds: 1994/95 to 1996/97, 1996/97 to 1998/99 and 1998/99 to 2000/01.
Attrition rates have generally been modest in the NPHS, with 76.6% of subjects having been successfully followed over the first four cycles [6]. In each instance, the subjects with major depression at the baseline interview were excluded and the remaining subjects were regarded as the population at risk of having a new or recurrent episode. It was not possible to differentiate between new and recurrent episodes, as lifetime history was not available. The proportion of subjects not reporting an episode in the 12-months preceding an interview who subsequently reported an episode in the 12-months preceding their interview 2 years later was used as an approximation of episode incidence. With the four available NPHS cycles, incidence could be estimated in this way across the three intervals.
Results
Estimates of the number of weeks depressed in the past year derived from the CCHS were computed from 126,715 subjects who provided valid responses both to the CIDI-SFMD (including the duration item) and the antidepressant use survey item. There were 9729 subjects with an episode of major depression in the year preceding the interview and 9508 (97.7%) of these provided valid duration data. Figure 1 shows the number of weeks depressed in the past year as reported by subjects with major depression in the CCHS, depending on whether or not they were taking antidepressants. Weeks depressed in the past year is presented in Figure 1 as a cumulative frequency; the Figure depicts the proportion (on the 'y' axis) of subjects reporting a duration less than or equal to the number of weeks specified (on the 'x' axis). Figure 1 shows that subjects reporting antidepressant use had a greater number of weeks depressed in the year preceding the interview. Analogous plots were generated for the each of the NPHS cycles, and each presented a similar pattern. Figure 2 presents weeks depressed in the past year from the 1994/95 NPHS. In the CCHS, the mean reported number of weeks depressed in the past year was 10.8 weeks among those not taking antidepressants and 18.7 weeks among subjects who reported taking antidepressants.
Figure 1 Cumulative proportion reporting ≤ specified number of weeks depressed in the past year for CIDI-SFMD positive subjects (n = 9508) in the Canadian Community Health Survey, by antidepressant use.
Figure 2 Cumulative proportion reporting ≤ specified number of weeks depressed in the past year for CIDI-SFMD positive subjects (n = 1030) in the 1994/95 National Population Health Survey, by antidepressant use.
Figure 3 presents the weeks depressed in the past year data from the NPHS and CCHS, without reference to antidepressant use. The reported weeks depressed in the past year is virtually identical in each of the four NPHS data collection cycles and the CCHS.
Figure 3 Proportion with ≤ specified weeks depressed in past year, by year of data collection.
Table 1 presents approximate episode incidence, as calculated for the three intervals between the four NPHS data collection interviews. The point estimate for the 1996/97 to 1998/99 interval was slightly lower than the other two, but the confidence intervals associated with these estimates suggest that this difference could be due to chance. The incidence of new episodes in subjects reporting the use of antidepressant medication was approximately three times that of subjects not using antidepressants.
Table 1 Approximate incidence* in subjects taking or not taking antidepressants in the baseline year
Baseline year Follow-up interview Taking antidepressants Not taking antidepressants
Approximate incidence* (unweighted proportion) 95% confidence interval Approximate incidence* (unweighted proportion) 95% confidence interval
1994 1996 12.7% (20/172) 6.0 – 19.4 3.4% (277/9055) 2.9 – 4.0
1996 1998 7.8% (21/234) 3.5 – 12.0 3.5% (375/9127) 3.0 – 4.1
1998 2000 12.7% (47/352) 8.6 – 16.8 3.5% (317/8994) 2.9 – 4.0
Average 11.1% 3.5%
*Proportion of subjects free from a major depressive episode in the year preceding the baseline interview who developed major depression in the year preceding the follow-up interview. These estimates are weighted to account for design effects. The unweighted raw proportions have been placed beside the weighted estimates.
Discussion
Direct generalization of clinical trial data to the general population would lead to an expectation that subjects in the general population who report antidepressant treatment should have briefer episodes of major depression than those who do not receive such treatment. Similarly, one might hypothesize that subjects reporting antidepressant treatment should have a reduced incidence of episodes. In the current analysis of national survey data, neither expectation was found to hold true. Subjects reporting an episode of major depression and reporting receipt of antidepressant treatment reported more, rather than fewer, weeks depressed in the preceding year. Similarly, subjects who did not have an episode of major depression in the year preceding an interview but who nevertheless reported using antidepressant medications had a higher risk of having an incident episode than similar subjects who did not report taking antidepressants. The most obvious explanation for these findings is that of confounding by indication or severity.
Some of these results may have been due to inadequacies involving the data sources. One of these is the brief, and therefore somewhat crude [5] nature of the CIDI-SFMD as a measure of major depression. As this instrument does not contain detailed clinical significance probes, some sub-clinical episodes may have been detected. As the CIDI-SFMD does not contain organic exclusions, the instrument may have detected some episodes characterized by organic symptoms. The 12-month prevalence of major depression in the CCHS was 7.4% which is higher than most recently published estimates of major depression prevalence, consistent with the possibility of non-specificity. This draws into question whether these results would be replicated with the use of a more detailed diagnostic instrument. However, this concern should not be overstated. Some studies using the full CIDI have reported higher estimated 12-month prevalence [7,8]. Severe depressive disorders that are treated with antidepressants may have longer episode durations and higher relapse rates than less severe and untreated disorders.
Items evaluating antidepressant use in each survey referred to use of the medications during the past month, whereas probable major depressive episodes occurring during the past year are detected by the CIDI-SFMD. Therefore, even though subjects who reported taking antidepressants were found to be more likely to have a subsequent episode of major depression, the data did not allow a determination of whether some of these subjects may have stopped or started antidepressants at some time during the follow-up interval.
Another limitation of the data sources used in this project was the lack of comorbidity data. Many of the subjects taking antidepressants may have been doing so for indications such as the prophylaxis of migraine headaches or for treatment of anxiety disorders. Both migraine headaches [9,10] and anxiety disorders [11] are frequently associated with major depression. To the extent that these disorders impact upon the risk and prognosis of major depressive episodes, they could also confound associations between antidepressant use, major depression episode incidence and episode duration.
While confounding by indication and severity offer, perhaps, the most appealing explanation for these results it is important to emphasize that the attractiveness of these explanations is based on a set of assumptions: that antidepressants are efficacious in reducing episode duration and the risk of relapse and recurrence. An altered set of initial assumptions leads to other possible interpretations. For example, some authors have hypothesized that antidepressant treatment may lead to a deterioration in the long-term course of mood disorders [12]. This hypothesis, although not widely accepted, predicts that episode duration and incidence would be higher in those reporting antidepressant use than in those not using these medications. A finding consistent with this idea was a meta-analysis by Baldessarini et al., which found that subjects taking antidepressant medications for longer periods were more likely to relapse upon discontinuation [13].
Generally, the public health challenges associated with major depression have been conceptualized in "common sense" terms, and in a way that does not depend on underlying etiological features of the disorder. For example, the idea that preventing relapse (episode incidence) should translate into reduced prevalence and reduced disease burden seems on the surface to be a common-sense belief. However, it has been hypothesized that depression can assume an adaptive role [14]. If an adaptive role for depression involves, for example, limiting an individual's interaction with a stressful environment (e.g. if depressive symptoms serve the adaptive purpose of discouraging interaction with elements of the environment that trigger them), then treatment of depression may indirectly lead to increased stress exposure. If this is true, then antidepressant treatment may increase people's comfort and capacity to function in a stressful environment, but may also increase the intensity of stress in the environment that surrounds them. Such complex dynamics could obscure an expected decline in prevalence due to increased treatment.
Conclusions
Most of the literature concerned with major depression and public health has emphasized that the disorder is under-treated, although more recent studies have begun to move beyond the frequency of treatment to address the issue of treatment adequacy [15]. There is an emerging need to address the lack of epidemiological evidence confirming the population-health benefits of increased antidepressant treatment. Specifically, it will be necessary to determine the extent to which negative outcomes in association with antidepressant treatment in observational data sources represent a methodological artifact.
List of Abbreviations
CIDI-SFMD Composite International Diagnostic Interview Short Form for Major Depression
DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition.
MDE Major Depressive Episode
NPHS National Population Health Survey
CCHS Canadian Community Health Survey (iteration 1.1)
Competing Interests
The author(s) declare that they have no competing interests.
Acknowledgement
This study was funded by a grant from the Canadian Institutes of Health Research, The author is a Health Scholar with the Alberta Heritage Foundation for Medical Research.
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| 15518594 | PMC538292 | CC BY | 2021-01-04 16:37:41 | no | Popul Health Metr. 2004 Nov 1; 2:9 | utf-8 | Popul Health Metr | 2,004 | 10.1186/1478-7954-2-9 | oa_comm |
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BMC NeurolBMC Neurology1471-2377BioMed Central London 1471-2377-4-211557419510.1186/1471-2377-4-21Research ArticleThrombomodulin Ala455Val Polymorphism and the risk of cerebral infarction in a biracial population: the Stroke Prevention in Young Women Study Cole John W [email protected] Stacy C [email protected] Margaret [email protected] Wayne H [email protected] Braxton D [email protected] Karen K [email protected] Marcella A [email protected] Richard F [email protected] Laurie J [email protected] Steven J [email protected] Department of Neurology, University of Maryland School of Medicine, Baltimore, Maryland, USA2 Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA3 Department of Medicine University of Maryland School of Medicine, Baltimore, Maryland, USA4 Geriatrics Research, Education, and Clinical Center, Department of Veterans Affairs Medical Center, Baltimore, Maryland, USA5 Molecular Biology Branch, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA6 Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA7 Coordinating Center for Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia, USA2004 1 12 2004 4 21 21 12 7 2004 1 12 2004 Copyright © 2004 Cole et al; licensee BioMed Central Ltd.2004Cole 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 genes encoding proteins in the thrombomodulin-protein C pathway are promising candidate genes for stroke susceptibility because of their importance in thrombosis regulation and inflammatory response. Several published studies have shown that the Ala455Val thrombomodulin polymorphism is associated with ischemic heart disease, but none has examined the association with stroke. Using data from the Stroke Prevention in Young Women Study, we sought to determine the association between the Ala455Val thrombomodulin polymorphism and the occurrence of ischemic stroke in young women.
Methods
All 59 hospitals in the greater Baltimore-Washington area participated in a population-based case-control study of stroke in young women. We compared 141 cases of first ischemic stroke (44% black) among women 15 to 44 years of age with 210 control subjects (35% black) who were identified by random digit dialing and frequency matched to the cases by age and geographical region of residence. Data on historical risk factors were collected by standardized interview. Genotyping of the thrombomodulin Ala455Val polymorphism was performed by pyrosequencing.
Results
The A allele (frequency = 0.85) was associated with stroke under the recessive model. After adjustment for age, race, cigarette smoking, hypertension, and diabetes, the AA genotype, compared with the AV and VV genotypes combined, was significantly associated with stroke (odds ratio 1.9, 95% CI 1.1–3.3). The AA genotype was more common among black than white control subjects (81% versus 68%) but there was no significant interaction between the risk genotype and race (adjusted odds ratio 2.7 for blacks and 1.6 for whites). A secondary analysis removing all probable (n = 16) and possible (n = 15) cardioembolic strokes demonstrated an increased association (odds ratio 2.2, 95% CI 1.2–4.2).
Conclusions
Among women aged 15 to 44 years, the AA genotype is more prevalent among blacks than whites and is associated with increased risk of early onset ischemic stroke. Removing strokes potentially related to cardioembolic phenomena increased this association. Further studies are needed to determine whether this polymorphism is functionally related to thrombomodulin expression or whether the association is due to population stratification or linkage to a nearby functional polymorphism.
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Background
Thrombosis is a dynamic balance between factors that promote clot formation, antithrombotic mechanisms, and fibrinolysis. Central to this balance is the thrombomodulin-protein C antithrombotic mechanism. Thrombomodulin forms a 1:1 complex with thrombin on the vascular endothelium, thereby inhibiting the procoagulant actions of thrombin and converting protein C to activated protein C [1]. Activated protein C promotes fibrinolysis, inhibits thrombosis by inactivating clotting factors Va and VIIIa, and reduces inflammation by decreasing white blood cell and nuclear factor kappa-B activation [2-5]. These relationships are demonstrated in Figure 1. Because of the central role that the thrombomodulin-protein C pathway plays in thrombosis regulation and inflammatory response, the genes encoding these pathway proteins are promising candidate genes regarding stroke susceptibility.
Figure 1 Thrombomodulin / Protein-C relationships and function
The thrombomodulin gene (THBD) maps to chromosome 20p11.2, contains a single exon and no introns, and spans 4 kb (OMIM 188040, UniGene NM_000361, Locus Link 7056). The thrombomodulin protein is expressed primarily on the luminal surface of vascular endothelial cells and consists of 557 amino acids (aa) (60,300 Dalton): an N-terminal lectin-like module (aa 1–154), a hydrophobic region (aa 155–222), six epidermal growth factor (EGF)-like modules (aa 223–462), a serine and threonine rich region (aa 463–497), a single transmembrane segment (aa 498–521), and a short cytoplasmic tail (aa 522–557) [6]. A single nucleotide polymorphism (C→T) at position +1418 (C1418T) encodes for an aa change from alanine to valine at protein position 455 (Ala455Val) [7]. The location of this aa variation corresponds to the sixth EGF region of the thrombomodulin protein as seen in Figure 2. This location has been shown to be responsible for the high-affinity binding of thrombin and for the suspension of thrombin at a specific position above the endothelial surface in relation to other cofactors, thereby producing optimal protein C activation by thrombin [2,8].
Figure 2 Thrombomodulin protein
A few studies have shown that the THBD Ala455Val polymorphism is associated with ischemic heart disease [9,10], but we know of no prior reports examining this polymorphism's association with stroke. Using data from the Stroke Prevention in Young Women Study [11], we sought to determine the association between the THBD Ala455Val polymorphism and the occurrence of ischemic stroke in young women. In addition, because cardioembolic stroke has a lesser degree of familial aggregation [12], we performed a secondary analysis excluding cases with cardioembolic etiologies.
Methods
The Stroke Prevention in Young Women Study (SPYW) is a population-based case-control study that was initiated to examine risk factors for ischemic stroke in young women. In that study the term "population-based" means that cases and their comparison group were identified from the same defined population. The study area included all of Maryland (except the far Western panhandle), Washington DC, and the southern portions of both Pennsylvania and Delaware. Cases were female patients 15 to 44 years of age with a first cerebral infarction as identified by discharge surveillance at 59 regional hospitals and through direct referral by regional neurologists. The methods for discharge surveillance, chart abstraction, and case adjudication have been described previously [11,13,14]. The adjudication of stroke cases was performed blinded to genetic information. Stroke cases were classified as having a probable, possible or undetermined etiology as per prior description [13,14]. Control subjects were women without a history of stroke. They were identified by random digit dialing and were frequency matched to the cases by age and geographic region of residence. The original SPYW study consisted of 227 cases and 342 controls. DNA samples were available for a subset of this population consisting of 141 cases and 210 controls.
We performed THBD genotyping at the Ala455Val polymorphism for 141 cases and 210 control subjects. This included all case and control samples that were available at that time. Genotyping was performed blinded to case-control status. Genomic DNA was extracted from stored peripheral blood lymphocytes by using standard protocols (Gentra Systems, Minneapolis, MN). The THBD Ala455Val polymorphism was determined by pyrosequencing. The single-nucleotide polymorphism region of the gene was amplified by polymerase chain reaction (PCR) with the use of published primers [10] except that we labeled the reverse primer with biotin. PCR was performed in 40 μl reactions containing 40 ng of genomic DNA, 15 pmol each of forward and reverse primer, 1.5 U of Amplitaq (Applied Biosystems, Foster City, CA) and MasterAmp PCR PreMix D (Epicenter, Madison, WI). The resulting biotinylated PCR product was bound to streptavidin-coated Sepharose HP beads (Amersham Pharmacia Biotech, Uppsala, Sweden) and the product was denatured according to the manufacturer's protocol (PSQ 96 Sample Preparation Kit, Pyrosequencing AB, Uppsala, Sweden). Following denaturation, an internal sequencing primer (5'-CGACTCGGC CCT T-3') was annealed to the bound single-stranded DNA. We used an automated pyrosequencing instrument (PSQ96, Pyrosequencing AB, Uppsala, Sweden) to perform the genotyping [15,16]. The reactions were performed at 28°C and contained the bound single-stranded DNA with annealed sequencing primer, enzymes (DNA polymerase, apyrase, luciferase, and activating transcription factor sulfurylase), nucleotides (dTTP, dGTP, dCTP, or dATPαS), and substrate (luciferin) supplied by the manufacturer. We monitored continuously the output from the charge-coupled device as a pyrogram, and we analyzed manually the results from the completed sequencing reactions by visually inspecting each program. The validity of the method was confirmed by fluorescent dye terminator sequencing of a subset of samples using standard protocols on an ABI 3100 genetic analyzer (Applied Biosystems, Foster City, CA).
We assessed the following potential confounders of the association between the alleles of the THBD Ala455Val polymorphism and stroke: age, race, current cigarette smoking, hypertension, diabetes mellitus, history of angina or myocardial infarction (angina/MI), use of oral contraceptive pills (OCP) or hormone replacement therapy (HRT), sickle cell disease, and sickle cell trait. Age, race, current cigarette smoking status, use of OCP or HRT was determined by subject reports (or proxy report, if a participant was unable to answer). Hypertension and diabetes mellitus, sickle cell disease or sickle cell trait were determined by asking study participants (or a proxy) if a physician had ever told them that they had the condition.
We compared means by t tests and proportions by χ2 tests. The probability values presented are based on two-sided tests. Because of the low frequency of the V455 allele, we compared the frequency of the combined AV/VV genotype between cases and controls. Adjusted odds ratios derived from logistic regression were used to determine whether the presence of the Ala455Val test allele was associated with an increased risk for stroke after differences in age, race, current cigarette smoking, hypertension, and diabetes mellitus were controlled for. Additional analyses included: 1). adding ischemic heart disease (angina/MI) into the logistic regression model; 2). evaluation for interactions between genotype and OCP/HRT 3). an analysis excluding sickle cell trait, and 4). an analysis excluding cardioembolic strokes.
Results
Subject characteristics
Characteristics by case-control status are described in Table 1. The mean age of the cases (i.e., women with a first cerebral infarction) was 35.5 years and the mean age of control subjects was 36.1 years. Cases were more likely than control subjects to be black (44.0% versus 34.8%, p = 0.12), and were significantly more likely to currently smoke cigarettes (p < 0.001), to have hypertension (p < 0.01), diabetes (p < 0.001) and history of angina/MI (p < 0.001). No study subjects reported sickle cell disease, however 6 cases and 5 controls reported sickle cell trait (non-significant difference). Twenty cases and 34 controls reported use of oral contraceptive pills (OCP) or hormone replacement therapy (non-significant difference).
Table 1 Characteristics, by case-control status
Case (N = 141) Control (N = 210) p-value
Mean age (years) 35.5 36.1 .31
Black (%) 44.0 34.8 .12
Current Smokers (%) 45.4 26.7 <.001
Hypertension (%) 27.7 13.3 <.01
Diabetes mellitus (%) 13.5 3.3 <.001
Angina/MI (%) 14.9 4.3 <.001
Genotype and vascular risk factor distributions
The distribution of genotypes was in Hardy Weinberg equilibrium for the pooled set of cases and controls, both in total and by race.
Among control subjects, the prevalence of the AA genotype was 81% (59/73) for blacks and 68% (93/137) for whites. The relationship between the Ala455Val genotypes and selected stroke risk factors in control subjects is summarized in Table 2. Blacks were significantly more likely to have the AA genotype than the AV and VV genotypes combined (38.8% vs. 24.1%, p < 0.05). In contrast, there were no significant differences in prevalence of hypertension, diabetes, angina/MI, or sickle cell trait between carriers and non-carriers of the V allele, nor did the frequency of cigarette smoking or OCP/HRT use differ significantly between the two groups.
Table 2 Characteristics among control subjects, by thrombomodulin genotype status
AA (n= 152) AV/VV (n= 58) p-value
Mean age (years) 36.5 34.9 0.17
Black (%) 38.8 24.1 <.05
Current Smokers (%) 26.3 27.6 0.86
Hypertension (%) 11.2 19.0 0.18
Diabetes Mellitus (%) 4.0 1.7 0.34
Angina/MI (%) 2.4 1.9 0.26
Genotype risk
Table 3 shows the association of the AA genotype with stroke, stratified by race and other vascular risk factors. The association between the AA genotype and stroke was 2.7 (95% CI 0.9–8.0) among blacks and 1.6 (95% CI 0.8–3.2) among whites. Since logistic regression analysis did not show a significant interaction by race (i.e., the effect of the AA genotype did not differ significantly between blacks and whites), subsequent analyses were conducted on the combined sample. After adjustment for age, race, cigarette smoking, hypertension, and diabetes, the AA genotype was found to be significantly associated with stroke compared with the AV and VV genotypes (OR 1.9, 95% CI 1.1–3.3).
Table 3 Frequency of the THBD Ala455Val AA genotype in cases and controls (proportion with AA genotype in parentheses) as stratified by race and other stroke risk factors; with associated crude and adjusted odds ratios
Risk Factor Percentage of cases with the AA genotype (proportion) Percentage of Controls with the AA genotype (proportion) Crude OR ^ (95% CI) Adjusted OR*^ (95% CI)
White 79% (62/79) 68% (93/137) 1.7 (0.9–3.3) 1.6 (0.8–3.2)
Black 87% (54/62) 81% (59/73) 1.8 (0.4–7.9) 2.7 (0.9–8.0)
Current smoking 84% (54/64) 71% (40/56) 2.2 (0.9–5.3) 3.0 (1.1–7.8)
No current smoking 81% (62/77) 73% (112/154) 1.6 (0.8–3.0) 1.5 (0.7–2.9)
Hypertension 85% (33/39) 61% (17/28) 3.6 (1.1–11.3) 5.7 (1.4–22.6)
No hypertension 81% (83/102) 74% (135/182) 1.5 (0.8–2.8) 1.6 (0.8–3.0)
Diabetes** 84% (16/19) 86% (6/7) Not performed Not performed
No Diabetes 82% (100/122) 72% (146/203) 1.8 (1.0–3.1) 1.9 (1.1–3.4)
Angina/MI ** 86% (18/21) 56% (5/9) Not performed Not performed
No Angina/MI 82% (98/120) 73% (147/201) 1.6 (0.9–2.9) 1.7 (.95–3.1)
Overall 82% (116/141) 72% (152/210) 1.8 (1.1–3.0) 1.9 (1.1–3.3)
* Each variable adjusted for age, race, smoking, hypertension, and diabetes (less the stratified variable). Overall model and Angina/MI adjusted for age, race, smoking, hypertension, and diabetes. Including Angina/MI in adjusted overall model demonstrated no change in association (OR = 1.9 95% CI = 1.1–3.3).
** Insufficient sample size to perform diabetic or angina/MI analyses.
^ The combined AV and VV genotypes within each strata serve as the reference group in all analyses, with the crude OR and adjusted OR assigned a reference value of 1.0.
The strength of association between the AA genotype and stroke remained unchanged including history of angina or myocardial infarction in the logistic regression model (OR 1.9, 95% CI 1.1–3.3). Neither OCP/HRT use, nor sickle cell trait demonstrated an interaction with genotype and additional adjustment for these factors did not alter the association between the AA genotype and stroke.
Stroke subtype
Among the 141 stroke patients, 70 (50%) had a least 1 probable cause, 30 (21%) had no probable cause but a least one possible cause, and 41 (29%) were indeterminate. Table 4 shows the distribution of probable and possible causes. "Other determined causes" of stroke included hematologic disorders, nonatherosclerotic vasculopathy (eg, vasculitis and dissection), migraine, drug abuse and stroke associated with oral contraceptive or exogenous estrogen use.
Table 4 Etiologies among cases with a probable or possible cause of stroke
Probable Causes1 (n = 70) Possible Causes2 (n = 30)
Large-artery autherosclerosis 9 8
Cardioembolism* 16 14
Lacune 7 3
Other determined cause** 38 5
1 One patient had 2 probable causes, but only 1 cause is listed according to the following hierarchy: large-artery atherosclerosis > cardioembolism > lacune> other determined cause.
2 Most patients had multiple possible causes, but only 1 cause is listed per patient according to the same hierarchy as for probable causes.
* Note one probable case attributed to "other determined cause", also had possible cardioembolism as an etiology, this case was removed from the secondary analysis. A total of 31 cases were removed from the secondary analysis on the basis of either probable (n = 16) or possible (n = 15) cardioembolism as the stroke etiology.
** Other determined causes included: Probable = 38, (10 non-atherosclerotic vasculopathy, 13 hematologic, 4 migraine, 6 oral contraceptive or exogenous estrogen use, 5 other drug related). Possible = 5, (3 hematologic, 2 migraine).
A secondary analysis removing all probable (n = 16) or possible (n = 15) cardioembolic strokes was performed using the same adjusted model including age, race, smoking, hypertension, and diabetes. An increased association between non-cardioembolic stroke and the AA genotype was demonstrated (odds ratio 2.2, 95% CI 1.2–4.2).
Discussion
In our study of the THBD Ala455Val polymorphism, the prevalence of the AA genotype among our control population was similar to that previously reported for the Atherosclerosis Risk in Communities (ARIC) Study population [10]. Our results indicate a positive association between the AA genotype and stroke among women aged 15 to 44 years. Furthermore, an increased association was demonstrated with the removal of all probable or possible cardioembolic strokes, a finding consistent with a recent meta-analysis demonstrating that cardioembolic stroke appears to have a smaller familial (or genetic) component that other subtypes of ischemic stroke [12]. Vascular risk factors were not significantly associated with specific genotypes in either analysis.
Several recent studies evaluating the THBD Ala455Val polymorphism and coronary artery disease (CAD) have yielded conflicting results. A Swedish case-control study found the alanine allele was associated with CAD [9]. In contrast, the American prospective ARIC study found the valine allele (AV plus VV) was associated with an increase in CAD risk in both blacks (OR 4.4, 95% CI 1.5–12.9) and whites (OR 1.4, 95% CI 0.9–2.1), although the association attained statistical significance only in blacks [10]. A British case-control study found no association at all between the THBD Ala455Val polymorphism and CAD [17]. Consistent with the Swedish results [9], we observed an association between the alanine allele at this locus and stroke onset at a young age. It is unclear whether the conflicting information regarding the THBD Ala455Val polymorphism, ours included, is due to population-stratification bias, a functionally neutral polymorphism that serves as a marker for a nearby functional mutation (linkage disequilibrium), or the true existence of different associations in the different study populations.
Population-stratification bias is due to confounding by population admixture [18]. An unidentified subpopulation can confound the association between a genotype and disease if the subpopulation is associated with the genotype under study and the risk of disease. Because our results indicate that blacks have a higher prevalence of the AA genotype and have an increased risk of early-onset stroke, the AA genotype might be a marker for African ancestry in general rather than a marker for increased stroke susceptibility.
The THBD Ala455Val locus may be in linkage disequilibrium with an unobserved "high-risk" susceptibility locus. Linkage disequilibrium is a function of the history of the population, and thus true associations can occur in one population and not another.
Our results are also consistent with a causal association between stroke and the THBD Ala455Val polymorphism, thereby defining a susceptibility locus for the disease. An important criterion for a true susceptibility locus is that the polymorphism is associated with a change in protein expression or function. The THBD Ala455Val polymorphism has not been associated with variation in soluble thrombomodulin concentrations [19], but soluble thrombomodulin levels do not necessarily indicate the functional status of thrombomodulin on the endothelial surface. The Ala455Val polymorphism resides within a critical region for thrombomodulin function, specifically within the sixth EGF region. Epidermal growth factor (EGF) regions 4, 5, and 6 within the thrombomodulin molecule (see Figure 1) appear to play critical roles in the activation of protein C by thrombin [2,8,20,21]. Furthermore, this contiguous EGF segment is the minimal functional fragment of the thrombomodulin cofactor that can switch the specificity of thrombin from a procoagulant to an anticoagulant enzyme [21,22]. Furthermore, two polymorphisms close to the Ala455Val polymorphism, Arg385Ser and Pro477Ser, have been shown to influence the expression and function of thrombomodulin in a tissue culture model [23].
Conclusions
Thrombomodulin has not previously been examined as a candidate gene for stroke susceptibility. We found that among women aged 15 to 44 years, the AA genotype is more prevalent among blacks than whites and is associated with increased risk of early-onset ischemic stroke. Removing strokes potentially related to cardioembolic phenomena increased this association. Further studies are needed to determine whether this association is due to population stratification, linkage to a nearby functional polymorphism, or variation in thrombomodulin expression or function.
Competing interest
The author(s) declare that they have no competing interests.
Authors' contributions
All authors certify that they participated in the conceptual design of this work, the analysis of the data, and the writing of the manuscript to take public responsibility for it. All authors reviewed the final version of the manuscript and approve it for publication. J.W.C., S.J.K., B.D.M., M.A.W. and R.F.M. participated in the writing of the initial draft. M.G., K.K.S., W.H.G. and S.C.R. participated in the genotyping. J.W.C., S.J.K., B.D.M., and L.J.R. participated in the data analysis. All authors provided critiques of the final manuscript.
Funding acknowledgments
Dr. Cole's effort on this project was supported in part by an American Academy of Neurology Clinical Research Training Fellowship, by the National Institutes of Health Research Training in the Epidemiology of Aging (Grant T32-AG00262-04), and by the Department of Veterans Affairs, Baltimore, Office of Research and Development, Medical Research Service, and Stroke Research Enhancement Award Program. Dr. Kittner was supported in part by the Department of Veterans Affairs, Baltimore, Office of Research and Development, Medical Research Service, Geriatrics Research, Education and Clinical Center, and Stroke Research Enhancement Award Program; a Cooperative Agreement with the Division of Adult and Community Health, Centers for Disease Control and Prevention; the National Institute of Neurological Disorders and Stroke and the NIH Office of Research on Women's Health; the National Institute on Aging Pepper Center Grant P60 12583; and the University of Maryland General Clinical Research Center (Grant M01 RR 165001), General Clinical Research Centers Program, National Center for Research Resources, NIH.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgments
We are indebted to the following members of the Stroke Prevention in Young Women research team for their dedication: Anne Epstein, Barbara Feeser, James Gardner, Mary Keiser, Ann Maher, Jennifer Rohr, Mary J. Seipp, Susan Snyder, Mary J. Sparks, and Nancy Zappala.
The authors would like to acknowledge the assistance of the following individuals who have sponsored the Stroke Prevention in Young Women Study at their institution: Frank Anderson, MD; Clifford Andrew, MD, PhD; Christopher Bever, MD; Nicholas Buendia, MD; Young Ja Cho, MD; James Christensen, MD; Remzi Demir, MD; Terry Detrich, MD; John Eckholdt, MD; Nirmala Fernback, MD; Jerold Fleishman, MD; Benjamin Frishberg, MD; Stuart Goodman, MD, PhD; Norman Hershkowitz, MD, PhD; Luke Kao, MD, PhD; Mehrullah Khan, MD; Ramesh Khurana, MD; John Kurtzke, MD; William Leahy, MD; William Lightfoote II, MD; Bruce Lobar, MD; Micheal Miller, MD, PhD; Harshad Mody, MBBS; Marvin Mordes, MD; Seth Morgan, MD; Howard Moses, MD; Sivarama Nandipati, MD; Mark Ozer, MD; Roger Packer, MD; Thaddeus Pula, MD; Phillip Pulaski, MD; Naghbushan Rao, MD; Marc Raphaelson, MD; Solomon Robbins, MD; David Satinsky, MD; Elijah Saunders, MD; Micheal Sellman, MD, PhD; Arthur Siebens, MD (Deceased); Harold Stevens, MD, PhD; Dean Tippett, MD; Roger Weir, MD; Micheal Weinrich, MD; Richard Weisman, MD; Don Wood, MD (Deceased); and Mohammed Yaseen, MD.
In addition, the study could not have been completed without the support from the administration and medical records staff at the following institutions: In Maryland, Anne Arundel Medical Center, Atlantic General Hospital, Bon Secours Hospital, Calvert Memorial Hospital, Carroll County General, Church Hospital Corporation, Department of Veterans Affairs Medical Center in Baltimore, Doctors Community Hospital, Fallston General Hospital, Franklin Square Hospital Center, Frederick Memorial Hospital, The Good Samaritan Hospital of Maryland, Inc., Greater Baltimore Medical Center, Harbor Hospital Center, Hartford Memorial Hospital, Holy Cross Hospital, Johns Hopkins Bayview, Inc., The Johns Hopkins Hospital, Howard County General Hospital, Inc. Kennedy Krieger Institute, Kent and Queen Anne Hospital, Laurel Regional Hospital, Liberty Medical Center, Inc., Maryland General Hospital, McCready Memorial Hospital, Memorial Hospital at Easton, Mercy Medical Center, Montebello Rehabilitation Hospital, Montgomery General Hospital, North Arundel Hospital, Northwest Hospital Center, Peninsula Regional Medical Center, Physician's Memorial Hospital, Prince George's Hospital Center, Saint Agnes Hospital, Saint Joseph Hospital, Saint Mary's Hospital, Shady Grove Adventist Hospital, Sinai Hospital of Baltimore, Southern Maryland Hospital Center, Suburban Hospital, The Union Memorial Hospital, Union Hospital, University of Maryland Medical System, Washington Adventist Hospital and Washington County.
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| 15574195 | PMC538749 | CC BY | 2021-01-04 16:28:50 | no | BMC Neurol. 2004 Dec 1; 4:21 | utf-8 | BMC Neurol | 2,004 | 10.1186/1471-2377-4-21 | oa_comm |
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BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-591558143010.1186/1471-2458-4-59Research ArticleInteraction among general practitioners age and patient load in the prediction of job strain, decision latitude and perception of job demands. A Cross-sectional study Vanagas Giedrius [email protected] Susanna [email protected] Kaunas University of Medicine, dept. Preventive Medicine, Kaunas, Lithuania2004 7 12 2004 4 59 59 7 7 2004 7 12 2004 Copyright © 2004 Vanagas and Bihari-Axelsson; licensee BioMed Central Ltd.2004Vanagas and Bihari-Axelsson; 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 is widely recognized and accepted that job strain adversely impacts the workforce. Individual responses to stressful situations can vary greatly and it has been shown that certain people are more likely to experience high levels of stress in their job than others. Studies highlighted that there can be age differences in job strain perception.
Methods
Cross-sectional postal survey of 300 Lithuanian general practitioners. Psychosocial stress was investigated with a questionnaire based on the Reeder scale. Job demands were investigated with the Karasek scale. The analysis included descriptive statistics; logistic regression beta coefficients to find out predictors and interactions between characteristics and predictors.
Results
Response rate was 66% (N = 197). Logistic regression as significant predictors for job strain assigned – duration of work in primary care; for job demands- age and duration of working in primary care; for decision latitude- age and patient load.
The interactions with regard to job strain showed that GP's age and job strain are negatively associated to a low patient load. Lower decision latitude for older GP age is strongly related to higher patient load. Job demands and GP age are slightly positively related at low patient load.
Conclusions
Lithuanian GP's have high patient load and are at risk of stress, they have high job demands and low decision latitude. Older GP's perceive less strain, lower job demands and higher decision latitude in case of low patient load. Young GP's decision latitude has week association to patient load. Regarding to the changes in patient load younger GP's perceive it more sensitively as changes in job demands.
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Background
The issue of job stress is of utmost important to the public health community and working people because it adversely impacts the workforce. Strain has been considered as an environmental condition, as an appraisal of an environmental condition, as a response to an environmental condition, and as a form of relationship between environmental demands and a person's abilities to meet these demands. Although there are a lot of controversies about the epistemology of job strain, there is an agreement about it as a complex phenomenon related to health. In considering workplace-related stress, it should be recognized that stressors may occur because of individual characteristics of the worker as well as the work environment [1-5].
In general, physicians are at risk of stress. The main experienced pressures at work were uncertainty and insecurity, isolation, poor relationships with other doctors, disillusion with the role of the general practitioner and awareness of changing demands [6,7]. It has been demonstrated that negative feelings of tension, lack of time, excessive paper work among physicians take turnover to quality of care and was associated with poor clinical performance and patient's dissatisfaction [8-10].
The importance of job strain understanding as a problem for the general practitioners (GP's) was yielded by Appleton [11] in a study among 406 GP's. There was found that the prevalence of stress was 52%. Other studies also showed, that general practice is one of the most stressful workplaces among health care workers [12-15]. The specific characteristics that make general practice stressful are largely unknown. Sociodemographic factors such as age were depicted as independent predictors of vulnerability to GP's [16-21].
The personal and social conditions have influences on the relationship between age and stress. Continuing problems at work and job strain mostly affects young GP's [20,22]. On the contrary some studies showed that as a result of the age interaction, the total effects on job strain are twice larger in the sample of old persons as in the sample of young persons [21] and the age impact on job strain increases in successively in older age groups until retirement age [23]. The results of different studies showed that age also attribute to stress, anxiety, job satisfaction and quality of life for GP's [22-24]. It is shown that GP age and patient load have additive effects and increase vulnerability to stress [25] but still unknown how it interact with decision latitude and perception of job demands in general practice?
The aim of this study was to investigate physician's age, duration of work in primary care and patient load interactions with job strain, decision latitude and perception of job demands.
Methods
Target group
Lithuanian GP's.
Study design
Cross – sectional study. A mailed survey of random national samples. Computerized random sampling was performed from the registry of Lithuanian physicians. The data collected through the questionnaires filled-in by the GP's.
Sample size
Total number of GP's in Lithuania at the time was 1007 GP's. Sample size was calculated using EpiInfo 2000 Statcalc software which argued the sample size of 192 GP's with the 95% confidence level. From the previous studies the expected response rate was 63%. Therefore, it was decided to send questionnaires to 300 Lithuanian GP's. Our observed response rate was 66%. We collected 197 filled-in questionnaires.
Assessment of Psychosocial Stress
Psychosocial stress in this study was investigated by a questionnaire based on the Reeder scale [26,27]. The Reeder scale uses four statements experienced in everyday stressful situations as "usually tense or nervous", "daily activities are extremely trying and stressful". The respondents should indicate whether each of the statements describe them. Each question has four alternative responses, which were coded using Likert-like scale.
A simple inversion of the Coulson scoring system (table 1) was used, giving a score of between 0 and 8 [28]. We have previously found analyses based on the Coulson approach to give very similar results to analyses based on the simple summation of scores [29].
Table 1 Coulson scoring system
Score Description
0 No response on one or more statements.
1 Not at all' for all four statements.
2 'Not at all' for any three statements with any other response on the fourth.
3 'Not at all' for any two statements with 'Not very accurately' for the other two.
4 'Not at all' for any one or two statements with any other responses for the remainder but not those for a score of 3.
5 All other response sets not specified under 0, 1, 2, 3, 4, 6, 7, or 8.
6 'To some extent' to all four statements, or 'To some extent' for three statements with 'Exactly' for the fourth.
7 'Exactly' for any three statements with 'To some extent' or 'Not very accurately' for the fourth. Or 'Exactly' for two statements with 'To some extent' for two.
8 'Exactly' in response to all statements.
Assessment of stressful work characteristics
Work characteristics were measured by the Karasek's Job Content Questionnaire. This instrument has two scales that measure stressful job character – job decision latitude and psychological workload demands. This model, also known as the "job strain" model [30-32].
Psychological workload demands were defined by questions such as "working very fast," "working very hard," "doing so many things".
Job decisions latitude was measured within questions as: "always must learn for new skills", "working a lot".
A four point Likert – like scale was used with the coding from 4 to 1 for series, so that the responses were summarised to give a score [33].
Statistical analysis
Data were computed, coded and analyzed using Statistical Package for the Social Sciences for Windows version 11.0 (SPSS Inc) and Microsoft Excel 2000. The analysis included descriptive statistics; logistic regression beta coefficients were used to assess physician's age, duration of work in primary care and patient load impact on job strain, job demands and decision latitude. Results differences at the p = 0.05 level were considered as statistically significant.
Results
Descriptive statistics
Of the 197 respondents, 162 (82.2%) GP's were female, and 35 (17.8%) male. This is very similar to whole GP population in Lithuania. The GP ages ranged from 31 to 66 years (mean 44.2 years, 95% CI 42.9 – 45.4). GP's were investigated in 3 age groups: < 44 yr – N = 90 (45.7%); 45–54 yr – N = 85 (43.1%); 55 and > – N = 22 (11.2%).
Regarding to our data in general Lithuanian GP's have high patient load and are at risk of stress, they have high job demands and low decision latitude (table 2).
Table 2 Descriptive analysis of measured characteristics
Characteristics Values
Mean SD 95% CI
Age 44.2 9.0 42.9–45.4
Patient load 23.8 6.7 22.8–24.7
Duration of work in primary care 17.6 10.0 16.2–19.0
Job demands 37.1 6.8 36.2–38.1
Decision latitude 23.5 6.5 22.6–24.4
Psychosocial stress 5.0 1.2 4.8–5.2
Logistic regression
The logistic regression beta coefficients showed that job strain development and higher job demands could be predicted by the shorter duration of GP practice. Otherwise older age for GP's can predict lower job demands and higher decision latitude. We found that lower decision latitude can be predicted by high patient load (table 3).
Table 3 Predicting coefficients of psychosocial stress, job demands and decision latitude
Predictor Psychosocial stress Psychological workload demands Job decisions latitude
Beta p-value Beta p-value Beta p-value
Age 0.009 0.13 0.008 0.05 -0.008 0.01
Duration of work in primary care -0.012 0.03 -0.009 0.02 0.004 0,14
Patient load -0.003 0.40 0.003 0.21 -0.003 0.05
In figures the interactions are graphically presented according to the method described by Aiken [34] and recognized in psychological research [35]. In terms of interactions we analysed job strain, job demands and decision latitude with respect to age and patients load. Values of the predictor variables were chosen one standard deviation below and above the mean.
The interactions with regard to job strain (fig. 1) shows that GP's age and job strain are negatively associated to a low patient load. In other words, for older GP's job strain development have stronger associations with high patient load than young GP's.
Figure 1 Interaction among general practitioner age and patient load in the prediction of job strain.
The age interactions with respect to psychological job demands (fig. 2) shows that job demands and GP age are slightly positively related at low numbers of patients per day. It shows that young GP's in terms of job demands more sensitively perceive increase in patient load that those in older age group.
Figure 2 Interaction among general practitioner age and patient load in the prediction of job demands.
Regarding to job decision latitude (fig. 3), the interaction terms shows that higher decision latitude and older general practitioner's age are strongly related to a lower patient load, which means that these variables are positively but inversely associated with patient load. Decision latitude and patient load for younger GP's has week associations.
Figure 3 Interaction among general practitioner age and patient load in the prediction of decision latitude.
Discussion
In the current social and political climate Lithuanian GPs face many stressors that are peculiar to the medical profession. However there are many stressors that are also attributed to the personality. GPs are the professionals who are at the forefront of helping patients to manage urgent health problems, and as gatekeepers they have to make decisions on patient's health; whether to send them to hospitals. Sometimes it can interfere with personal life that can cause negative feelings about work, frustration, tension and lack of time to make appropriate decisions [23].
Our study has highlighted a matrix of issues contributing to elevated levels of job strain. These issues are rarely attributable to a simple cause and effect formula but there are complex problems with the many linkages. Lithuanian GP's has indicated twofold age interaction with job strain because it depends on patient load. Work related stress development was hardly related to duration of working in primary care. GP's perceive higher job strain and higher job demands when they have shorter duration of GP practice. Older GP's are more vulnerable to job strain, when age interaction compared among low and high patient load groups. This also means different workload and job demands. It seems to be the confirmation of Cox definition of work related stress, where the concept includes an external demand and an internal perception that the response to the demand is uncomfortable: "Work related stress is a person's recognition of his/her inability to cope with demands relating to work, and his/her subsequent experience of discomfort" [34]. We found differences in perceived job demands and in objectively measured workload units. It can be explained within growing psychological adaptation to working environment with increasing duration of GP practice. We can see the same in fig 2. younger GP's are more vulnerable in perception of the increase in workload.
Peterson's substantial review found that detrimental work environments had social and psychological consequences for all [35]. He mentioned that the extent of decision-making power, decisions latitude, as well as overwork is related to job strain development. We can say more, namely that higher patients load can be a predictor of lower decision latitude and it seems also to be related to GP age. Our results highlighted that high patient load can cause decrease in decision latitude for the older age GP's and has only week associations to younger GP's.
Several weaknesses of the present study have to be mentioned. As main weakness of our study we see its cross-sectional nature, which precludes an evaluation of temporal precedence and causality of the observed associations. Karasek Job Strain model guided our hypothesis about causal relationships between age, patient load and work characteristics, explored causal relations should be interpreted carefully and longitudinal studies should be carried out in the future research.
Another limitation is the Karasek's Job Content Questionnaire it self. It was designed to be broadly applicable to a wide range of occupations. However, this generalisability inevitably means that factors that are specific to particular occupations may be overlooked. For example, job demands as it has been conceptualized and operationalised in this survey would not take into account some emotional demands that could be source of stress to general practitioners such as dealing with difficult patients or caring for the dying patients [35,36].
Third limitation is our exclusive reliance on self-reported rating scales, which raises the issue of systematic positive or negative response tendencies. Furthermore, as no scale is perfectly reliable, the associations between self-reported measures and self-reported workload appear to be weaker than they could be in reality. Several authors have argued that this phenomenon is not a major threat if interactions has been found [7,37].
On the positive side, our results were obtained among a sample of people working in general practice. Respondents were with similar education level that can be seen as strength of the investigation. The sample was sufficient regarding to sample size calculation and allow exploration of tendencies. The participation rate was acceptable, and the scales we used were previously validated instruments that retained their psychometric properties in our population [26]. Otherwise it is important to mention that generalisability of Karasek's model allow to us comparisons among different medical and non medical occupational groups and this is important factor selecting job strain model. One of the principal outputs of this article is a categorization of the characteristics into a series of domains, in order to provide consistent information on the prediction of job strain, job demands and decision latitude perception. Findings from this research have hopefully emphasized the importance of examining changes and associations between work characteristics and job strain among GP's before health care reform in Lithuania will be definitely implemented.
Conclusions
Lithuanian GP's have high patient load and are at risk of stress, they have high job demands and low decision latitude. Job strain development and higher job demands can be influenced by shorter duration of general practice. Older GP's perceive less strain, lower job demands and higher decision latitude in case of low patient load. Young GP's decision latitude has week association to patient load. Regarding to changes in patient load younger GP's perceive it more sensitively as changes in job demands.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
GV designed the study, abstracted data, made data analysis, drafted and revised the manuscript.
SBA participated in initial study design, participated in data analysis and revised 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 for Collegium of General Practitioners of Lithuania for expressed kind interest in this study.
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| 15581430 | PMC538750 | CC BY | 2021-01-04 16:28:47 | no | BMC Public Health. 2004 Dec 7; 4:59 | utf-8 | BMC Public Health | 2,004 | 10.1186/1471-2458-4-59 | oa_comm |
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BMC DermatolBMC Dermatology1471-5945BioMed Central London 1471-5945-4-161552750810.1186/1471-5945-4-16Research ArticleTongue lesions in psoriasis: a controlled study Daneshpazhooh Maryam [email protected] Homayoon [email protected] Maryam [email protected] Marjan [email protected] Department of Dermatology, Tehran University of Medical Sciences, RAZI Hospital, Vahdate-Eslami Sq. 11966 Tehran, Iran2004 4 11 2004 4 16 16 17 6 2004 4 11 2004 Copyright © 2004 Daneshpazhooh et al; licensee BioMed Central Ltd.2004Daneshpazhooh 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
Our objective was to study tongue lesions and their significance in psoriatic patients.
Methods
The oral mucosa was examined in 200 psoriatic patients presenting to Razi Hospital in Tehran, Iran, and 200 matched controls.
Results
Fissured tongue (FT) and benign migratory glossitis (BMG) were the two most frequent findings. FT was seen more frequently in psoriatic patients (n = 66, 33%) than the control group (n = 19, 9.5%) [odds ratio (OR): 4.69; 95% confidence interval (CI): 2.61–8.52] (p-value < 0.0001). BMG, too, was significantly more frequent in psoriatic patients (28 cases, 14%) than the control group (12 cases, 6%) (OR: 2.55; 95% CI: 1.20–5.50) (p-value < 0.012). In 11 patients (5.5%), FT and BMG coexisted.
FT was more frequent in pustular psoriasis (7 cases, 53.8%) than erythemato-squamous types (56 cases, 30.4%). On the other hand, the frequency of BMG increased with the severity of psoriasis in plaque-type psoriasis assessed by psoriasis area and severity index (PASI) score.
Conclusions
Nonspecific tongue lesions are frequently observed in psoriasis. Further studies are recommended to substantiate the clinical significance of these seemingly nonspecific findings in suspected psoriatic cases.
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Background
The occurrence of psoriatic lesions on oral mucous membranes was a subject of controversy [1,2]. Some investigators stated that they do not occur [3]; others, have claimed that they are uncommon. Still others say that they occur only in generalized pustular psoriasis (GPP) [4,5]. Nowadays, there is sufficient evidence that a subset of patients have oral lesions in association with skin disease [2].
Oppenheim, in 1903, was the first to substantiate oral psoriasis with biopsy [6]. Since then, various lesions have been described, including grey, yellowish, white or translucent plaques or annular forms, diffuse areas of erythema, geographic tongue and fissured tongue [7-16].
In all the cases reported in the literature, a positive biopsy showing a psoriasiform pattern has been the crucial component of the diagnosis [5,17-20]. Thus hyperkeratosis, parakeratosis, and an inflammatory infiltrate consisting of lymphocytes, polymorphonuclear leukocytes and histiocytes have been noted as well as Munro's microabscesses and spongiform pustules of Kogoj. In addition, many investigators believe that the presence of cutaneous lesions with a course parallel to that of oral lesions is necessary for establishing the diagnosis of oral psoriasis [2,5,20].
However, it is impossible to perform an oral biopsy in psoriatic cases in everyday clinical practice. On the other hand, some of the lesions seen more frequently in psoriatic patients are not specific histologically. In fact, similar changes are seen in otherwise healthy people (although with a lower frequency) leading to an underestimation of the value of these findings in psoriatic patients.
In order to substantiate further the relationship between these oral disorders and psoriasis, we compared 200 patients with psoriasis to a matched control group.
Methods
Two hundred psoriatic patients (70 women and 130 men) attending the dermatology clinics of Razi Hospital, a major referral center in Tehran, from September 2000 till February 2001, were enrolled in this study using simple nonrandom (sequential) sampling. The diagnosis was made mainly on clinical data. The control group included 200 healthy subjects among the visitors of Surgery wards in a general hospital, matched one by one for age and sex. The skin and oral mucosa were examined in the two groups and, in addition to demographic and clinical data, PASI score [21] was recorded in plaque-type psoriasis. The data were analyzed by Epi-Info (version 6) software, and frequency, mean, standard deviation, OR and p-value were calculated.
Results
The mean age of the patient group was 33.8+/- 18.2 years (4–79 years). The mean age of onset of disease was 26+/-17.7 years (0–74 years), 23 +/-18.8 years in women and 27.6 +/- 17.0 years in men. Age and sex were matched between patients and control subjects. Family history of psoriasis was positive in 34 patients. Different clinical types of psoriasis were as follows: Chronic plaque-type psoriasis (n = 140); generalized pustular psoriasis (n = 10); flexural psoriasis (n = 10); erythrodermic psoriasis (n = 9); localized pustular psoriasis (n = 3); guttate psoriasis (n = 9); palmoplantar psoriasis (n = 15); scalp (n = 95); nail alone (n = 3).
Oral findings were detected in 87 (43.5%) and 39 (19.5%) cases in the psoriatic and control groups, respectively. They are presented in table 1. FT was seen more frequently in psoriatic patients (66 patients, 33%) than the control group (19, 9.5%) (OR: 4.69; 95% CI: 2.61–8.52) (p-value < 0.0001). BMG, too, was significantly more frequent in psoriatic patients (28 cases, 14%) than the control group (12, 6%) (OR: 2.55; 95% CI: 1.20–5.50) (p-value < 0.012). BMG was seen in 18.2% of patients with FT, and 42.9% of patients with BMG suffered from FT. In other words, in 12 patients (6%) FT and BMG coexisted. In the control group, FT and BMG coexisted in 2 cases (1%).
Table 1 The frequency of oral findings in psoriasis patients and control group
Psoriasis Control
Fissured tongue 66 19
Benign migratory glossitis 28 12
Diffuse oral and tongue erythema 11 3
Hairy tongue 2 4
Rhomboid glossitis 2 3
Depapillated tongue 2 3
One hundred eighty-four patients (92%) suffered from erythemato-squamous lesions and 13 cases (6.5%) from pustular lesions. The frequency of FT in the erythemato-squamous and pustular groups was 30.4% (56 cases) and 53.8% (7 cases), respectively. On the other hand, the frequency of BMG in the erythemato-squamous and pustular groups was 14.1% (26 cases) and 15.4% (2 cases), respectively.
The severity of chronic plaque-type psoriasis cases assessed by PASI score was as follows: mild, 53 cases (37.9%); moderate, 60 cases (42.9%); and severe 27 cases (19.3%). The corresponding frequency of FT and BMG in the three severity groups is presented in table 2. The frequency of BMG increased with the severity of skin lesions (p-value < 0.001).
Table 2 Frequency of fissured tongue and benign migratory glossitis according to severity in plaque-type psoriasis
Fissured tongue Benign migratory glossitis
Mild 14 (26.4%) 3 (5.7%)
Moderate 23 (38.3%) 10 (16.7%)
Severe 7 (25%) 9 (32.1%)
Discussion
In general oral lesions in psoriasis can be divided into two major categories. The first one includes authentic psoriatic lesions proved by biopsy and with a parallel clinical course with skin lesions. It's not known whether these lesions are truly rare, or they remain undetected, as mucosal biopsy is seldom done in known psoriatic cases. The second group comprises the majority of oral findings in psoriasis and includes nonspecific lesions such as FT and psoriasiform lesions such as BMG [22]. These lesions are underestimated in the literature, but deserve more attention due to their high frequency.
We will discuss the main oral findings observed in our study as well as those reported in the literature.
Fissured tongue, also termed lingua fissurata, lingua plicata, scrotal tongue, and grooved tongue is recognized clinically by an antero-posteriorly oriented fissure, often with branch fissures extending laterally. It's believed by most authors to be an inherited trait. The frequency of FT increases with age and has been associated with Down's syndrome and the Melkerson-Rosenthal syndrome [5,20].
According to our study, FT was the most common oral finding in the psoriasis group: Nearly one-third of patients suffered from FT. It was significantly more frequent in psoriasis patients than the control group (9.5%) (p-value < 0.0001). The previously reported figures of the frequency of FT in the general population vary markedly in the literature depending on the study design and the target study. Axell reported a figure of 6.5% [10] and Morris found FT in 20.3% of its target study [17]. Aboyans et al reported a frequency of 2.56% in Iran [23]. On the other hand, FT was reported in 6–16.7% of psoriatic patients in different studies [3,10,14,15,20].
BMG or geographic tongue presents clinically as one or more erythematous patches with a raised white or yellow serpiginous border. Lesions may migrate across the tongue by healing on one edge while extending on another. BMG has no known cause, but it has been associated with atopic conditions, diabetes mellitus, reactive bronchitis, anemia, stress [20], hormonal disturbances, Down's syndrome and lithium therapy [24]. Lesions identical to BMG have been described in patients with Reiter's syndrome and psoriasis. The association of both psoriasis and BMG with HLA-CW6 provides further evidence that the two disorders are related [25].
In our study, BMG was significantly more frequent in psoriatic patients (14%) than the control group (6%) (p-value < 0.012). According to the literature, the estimated frequency of BMG in the general population is from 1–5% [1,10,20,26] and varies from 1–10.3% in psoriatic patients [3,10,13-15,20,26]. Only Hietanen found a figure of 1% in psoriasis [10].
FT was more frequent in patients with pustular lesions compared with the erythemato-squamous types. Contrary to previous studies, this finding was not seen for BMG, a disease generally considered accompanied with GPP [27,28]. This may be due to the low frequency of GPP in our study group. On the other hand, the frequency of BMG increased with the severity of psoriasis in plaque-type disease, a finding not seen in Morris's study [20], perhaps due to different definition for the severity of the disease. According to our study, the frequency of FT didn't increase by increasing severity of psoriasis.
SAM was first described by Cooke in 1955 as an idiopathic inflammatory condition of the nonlingual oral mucosa [10,15]. It is also denoted using different terms: Geographic stomatitis, ectopic geographic tongue, erythema circinate migrans, and migratory stomatitis. These lesions are similar in appearance to BMG, but occur on the oral mucosal surfaces as well as the dorsum of the tongue [1,29,30].
As seen in Van der Wal's study, [14] we didn't find SAM in psoriatic patients. The reported frequency of this oral finding in psoriatic patients in the literature is between 0–19% [15,20]. Furthermore, this lesion seems to be very rare in the general population, too: Bouquot found no patients with SAM in 231616 white American dental patients [20].
Diffuse oral and tongue erythema was another positive finding in the psoriasis group with a frequency of 5.5%. This lesion, too, was reported previously in the literature, although with a lower frequency (1%) [10].
An association between FT and BMG is well established in the literature [31,32]. In our study, BMG was seen in 18.2% of patients with FT (results consistent with Pindorf's) [14,16].
Conclusions
Overall, although oral lesions might not be considered authentic oral psoriasis unless proven histologically and with a parallel clinical course, nonspecific tongue lesions are significantly more frequent in psoriatic cases. Further studies are recommended to evaluate the clinical significance of these seemingly nonspecific lesions in a suspected psoriatic case. Furthermore, more thorough studies are recommended regarding the relationship of oral psoriasis and disease severity in plaque-type psoriasis.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors contributed equally in the study design, literature search, data analysis and manuscript preparation. All 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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| 15527508 | PMC538751 | CC BY | 2021-01-04 16:29:15 | no | BMC Dermatol. 2004 Nov 4; 4:16 | utf-8 | BMC Dermatol | 2,004 | 10.1186/1471-5945-4-16 | oa_comm |
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BMC Med EthicsBMC Medical Ethics1472-6939BioMed Central London 1472-6939-5-710.1186/1472-6939-5-7Research ArticleDisclosure of cancer diagnosis and prognosis: a survey of the general public's attitudes toward doctors and family holding discretionary powers Miyata Hiroaki [email protected] Hisateru [email protected] Miyako [email protected] Tami [email protected] Ichiro [email protected] Department of Social Gerontology, School of Health Sciences and Nursing, Graduate School of Medicine, University of Tokyo, Japan2 National Institute of Mental Health, National Center of Neurology and Psychiatry, Japan2004 1 12 2004 5 7 7 17 6 2004 1 12 2004 Copyright © 2004 Miyata et al; licensee BioMed Central Ltd.2004Miyata 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 study aimed to ask a sample of the general population about their preferences regarding doctors holding discretionary powers in relation to disclosing cancer diagnosis and prognosis.
Methods
The researchers mailed 443 questionnaires to registered voters in a ward of Tokyo which had a socio-demographic profile similar to greater Tokyo's average and received 246 responses (response rate 55.5%). We describe and analysed respondents' attitudes toward doctors and family members holding discretionary powers in relation to cancer diagnoses disclose.
Results
Amongst respondents who wanted full disclosure about the diagnosis without delay, 117 (69.6 %) respondents agreed to follow the doctor's discretion, whilst 111 (66.1 %) respondents agreed to follow the family member's decision. For respondents who preferred to have the diagnosis and prognosis withheld, 59 (26.5 %) agreed to follow the doctor's decision, and 79 (35.3 %) of respondents agreed with following family member's wishes.
Conclusions
The greater proportion of respondents wants or permits disclosure of cancer diagnosis and prognosis. In patients who reveal negative attitudes toward being given a cancer disclosure directly, alternative options exist such as telling the family ahead of the patient or having a discussion of the cancer diagnosis with the patient together with the family. It is recommended that health professionals become more aware about the need to provide patients with their cancer diagnosis and prognosis in a variety of ways.
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Background
Cancer ranks as the third leading cause of death worldwide, accounting for approximately 12 % of all recorded deaths [1]. As cancer is sometimes fatal and its treatment often involves invasive medical procedures and medication, it has a great impact on patients' lives. The extent to which physicians should inform patients of their diagnosis and prognosis poses a difficult decision in clinical settings. Previous studies show that a patient's cancer diagnosis is not routinely disclosed in many cultures in Africa [2], Eastern and Southern Europe [3-6], and the Middle East [7]. Even in the United States, where most doctors follow informed consent guidelines which includes informing patients of their diagnosis as standard clinical practice, problems still exist regarding the accurate provision of prognosis information [8].
In Japan, historically, physicians have withheld discussing cancer diagnoses directly with patients [9]. However, since the early 1990s, due to the increased understanding and adoption of informed consent policy and practice, physicians have gradually begun to inform patients of their cancer diagnosis in clinical practice [10,11]. In many cases, however, details regarding prognosis are still concealed from patients, especially if the condition is incurable [12,13]. While some physicians provide full information from the outset, others provide no information at all, even withholding basic diagnosis information [14]. The National Cancer Centre (The core national institution for developing cancer treatment, research and policy) has compiled a set of guidelines for cancer disclosure. However, each hospital has deferring policy and practice [9]. No law or regulation stipulates that doctors are required to obtain informed consent from patients. Given this context, there are demonstrated needs to develop concrete guidelines and to promote cancer disclosure based on patients' preferences.
In Japan, the patient, family and doctor are the main players in cancer disclosure. According to legal precedents in Japan, doctors are given a wide range of discretionary powers regarding disclosure [14-16]. As a rationale for holding discretionary power, doctors report a number of compelling reasons such as the need to protect patients from psychological distress caused by disclosure of the diagnosis, families' wishes for non-disclosure to patients, and the fact that most patients themselves do not wish to be told the truth [9,17,18]. However, several case-control studies report that there is no relationship between cancer disclosure and mental harm [19-21]. As family members are more reluctant than patients to disclose the truth [11,22], patients' needs for information are often unsatisfied in Japan where physicians often discuss the cancer diagnosis with family prior to informing the patient [23,24]. Doctors' discretionary powers and families' powers of attorney need to be reconsidered in the light of patients' preferences. This study's aim was to ask the general population whether they, in the event of developing cancer, preferred doctors' (or family members') discretionary powers regarding disclosure of the cancer diagnosis and prognosis.
Methods
This study was a cross-sectional, stratified random sampling survey of the general population in their 40s to 50s. As people over 60 years old are epidemiologically more at risk of having cancer, we excluded them not only because it seemed harmful to ask about these experiences, but also because there was a possibility that their responses would be affected by their experiences. Participants were selected from eligible voters in 'A' ward in the Tokyo Metropolitan Area. We chose 'A' ward as a representative area of Tokyo because various social indices such as the proportion of the elderly population, average length of education, and population growth rate were consistent with the Tokyo average [25]. The researchers mailed 443 questionnaires in October 2002 and received 246 responses (response rate 55.5 %). Amongst the respondents, 26 (10.5%) people had been diagnosed with cancer sometime in the past. As there were no significant differences in the responses of those who had been diagnosed with cancer and those who had not, we included these 26 respondents in the analysis. There were also no significant differences between those who were relatives of a cancer patient or were not and those who had dealt with cancer in their role as medical staff or had not. The sample size was determined by the need to provide adequate numbers to be able to detect differences among disclosure preferences with some degree of statistical certainty.
The questionnaire was developed in consultation with 6 medical staff and 19 patients. The questionnaire presented a hypothetical scenario in which "The doctor discovers terminal cancer, but the patient does not know yet." to each respondent, and asked about preferences regarding diagnosis and prognosis disclosure; "How would you want to be told, if you were in such a situation". Answer choices for disclosure preferences regarding diagnosis were: 1."I would not want to be given any information regarding my diagnosis [non-disclosure]", 2. "I would like to obtain information regarding my diagnosis of a general nature but not in detail" 3." "I would like to be given all information regarding my diagnosis [full-disclosure]". Choices for disclosure on the prospects of complete recovery (CR) and expected length of survival (LS) were: 1. "I would not want to be given any information about the prospects of CR and LS [non-disclosure]", 2." I would like to obtain information on the prospects of CR and LS of a general nature but not in detail. [partial-disclosure]", 3. "I would like to be told about my prospects of CR and LS eventually. However, I would like to receive only general information on the prospects of CR (LS) when I am initially informed about the disease [postponed full-disclosure]", and 4. "I would like to be told about my prospects of CR and LS without delay. [immediate full-disclosure]". The reason for providing s answers allowing partial-disclosure was based on research by Akabayashi[26] which indicated that many Japanese were accustomed to and commonly preferred ambiguous or graded answers rather than polarised ones.
Respondents were asked about their attitudes toward doctors and family members holding discretionary powers regarding cancer diagnosis disclosure. In order to compare the attitudes and characteristics of respondents who preferred immediate diagnosis and prognosis and those who did not, analysis was carried out twice. In the first analysis we included respondents who did not choose "full diagnosis and prognosis without delay", and we included the data from the remaining respondents that explained the reason for allowing to receive immediate diagnosis and prognosis. In the second analysis we included respondents who did want to receive diagnosis and prognosis, and we included data from the rest of the respondents about their reasons for preferring the withholding of diagnosis and prognosis. We also asked about preferences regarding the cancer disclosure process, such as whether people would like to obtain information ahead of their family.
The questionnaire also included the trait part of the Japanese version of the State-Trait Anxiety Inventory (STAI), which assesses the personality predisposition to anxiety [27-29]. The Japanese version of STAI is a widely used and standardized test. In the present sample, the trait part of STAI for Cronbach's α = 0.90.
Firstly, we calculated all respondents' disclosure preferences regarding diagnosis, CR and LS. Secondly, we calculated the attitudes toward doctors and family members holding prognosis discretion of respondents who preferred to be given diagnosis information directly, and those who did not. Wilcoxon's test was used to examine the differences between the attitudes held toward doctors and family members holding discretionary powers between these two groups. Statistical analyses were conducted using SPSS Version 11.5J.
Results
The socio-demographic characteristics of the respondents are shown in Table 1. The mean age of the 246 respondents was 49.8 years (± 6.2 years). More than half (N = 143: 58.1 %) were female, 78 (31.7 %) had graduated from college, and 32 (13.0 %) were living alone.
Table 1 Characteristics of the respondents. (N = 246)
Mean SD
Age (yr) 49.8 6.2
STAI (total score) 41.4 9.9
N %
Sex (female) 143 58.1 %
College graduates 78 31.7 %
Living alone 32 13.0 %
Married 186 75.6 %
Living with adult child 85 26.1 %
Living with infant child 98 39.8 %
Principal household earner 133 54.1 %
Non-religious 185 75.2 %
Respondents' preferences regarding diagnosis and prognosis disclosure are shown in Table 2. Regarding diagnosis, 85.4 % of respondents wanted full-disclosure, 11.3 % wanted partial disclosure and 2.9 % wanted non-disclosure. In the case of the prospect of a complete recovery; 35.7 % of respondents wanted an immediate full-disclosure, 17.2 % wanted a postponed full-disclosure, 39.2 % wanted partial-disclosure, and 2.9 % wanted no disclosure. Regarding the expected length of survival; 32.2 % of respondents wanted an immediate full-disclosure, 11.4 % wanted a postponed full-disclosure, 50.0 % wanted partial-disclosure, and 6.4 % wanted no disclosure.
Table 2 Disclosure preferences regarding diagnosis and prognosis
Non-disclosure Partial-disclosure Full-disclosure
Diagnosis (N = 239) 7 (2.9 %) 27 (11.3 %) 204 (85.4%)
Non-disclosure Partial-disclosure Postponed Full-disclosure Immediate Full-disclosure
Prospect of Complete recovery (N = 238) 7 (2.9 %) 105 (39.2 %) 41 (17.2 %) 85 (35.7 %)
Expected Length of Survival (N = 236) 15 (6.4 %) 118 (50.0 %) 27 (11.4 %) 76 (32.2 %)
Regarding the contextual reason for wanting to receive full diagnosis and prognosis information without delay, 117 (69.6 %) respondents agreed to follow the doctor's initiative and 111 (66.1 %) of the respondents agreed to follow the with family member's decision [Figure 1]. The Wilcoxon test found no significant difference between these two groups (z = 0.186, p = 0.853). As for the reason for wanting the diagnosis and prognosis information to be withheld, 59 (26.5 %) of the respondents agreed to follow the doctor's initiative, and 79 (35.3 %) of respondents agreed to follow family member's wishes [Figure 2]. Wilcoxon test found significant differences between these two groups (z = 6.470, p < 0.001).
Figure 1 Preference for who should decide whether to give immediate diagnosis and prognosis. N = 175.
Figure 2 Preferences for who should decide whether to withhold diagnosis and prognosis. N = 240.
Regarding the cancer disclosure process, more than half the respondents (N = 136; 55.3 %) answered that they would like to obtain diagnosis and prognosis information ahead of their family, a third (N = 82: 32.3 %) answering that would like to receive information with their family together at the same time. Only 26 (32.3%) respondents preferred to obtain this information after the doctor had already informed their family.
Discussion
Regarding preferences relating to diagnosis and prognosis, only 2.7 % of the respondents wanted no information regarding a cancer diagnosis. In addition to considering to tell or not to tell, the extent to which physicians should inform patient of diagnosis and prognosis poses a difficult decision in clinical settings. However, more than two-thirds (68.7 %) wanted full diagnostic and general prognostic information in a general nature but not in detail or wanted to be told about their prognosis eventually. Less than a third (28.9 %) wanted full information regarding diagnosis and prognosis without delay. These results suggest that a disclosure policy which provides patients with full information on diagnosis and general information on prognosis can satisfy the majority of patients' preferences. The results also suggest that any disclosure policy should also try to acknowledge and meet patients' wishes of being informed together with their families, and of being given information at a later time.
Nevertheless, some patients do not want any information regarding their cancer diagnosis. In the clinical setting, medical staff needs to develop policy and procedures that can deal with the needs of patients who do not want any information as well as those patients and who want complete information immediately. The priority in identifying these types of patients over-rides other factors which affect patients preferences regarding diagnosis and prognosis such as patient characteristics and seriousness of cancer (previous research conducted by the authors [30]). Regarding those respondents who did not want to be given a diagnosis directly, those who preferred to follow a family member's decision were significantly larger than those who would prefer a doctor to decide. If patients reveal negative attitudes toward being given a cancer diagnosis at the time of initial consultation and testing, it may still be effective to tell the patient's family ahead of the patient or to have a discussion of cancer disclosure together with the family.
Despite the data that indicates a mix of patients' preferences regarding cancer diagnosis, it may not be necessary for doctors to make choices regarding diagnosis by actually knowing individual patients' preferences. As opposed to those who would prefer no information, a greater proportion of respondents wanted to receive full information, even contrary to their preferences. Two other surveys with the general public show a similar tendency of patients wanting more information regarding cancer diagnosis than they used to. Asahi Newspaper found that regarding one's own cancer diagnosis and prognosis, in 1989, 59% of respondents wanted disclosure, which increased to 76%) in 2000 [31]. Similarly, Yomiuri Newspaper found that in 1994, 70% of respondents preferred being given information about a cancer diagnosis that increased to 78% in 2001 [32]. Thus the importance of providing information is widely supported by the majority of the general community.
To simulate the fact that cancer results in a variety of disease outcomes for patients, we used scenarios with a range of severities in the outcomes of the cancer. As a result, there is little difference between respondents who had experienced cancer disclosure as a patient and those who did not, and the diagnosis preferences revealed in this study (full-disclosure, 85.4%) are consistent with previous studies (Seo [18], 85.7%: Miura [33], 88.1%). These findings suggest that this study's method succeeded in simulating a situation that reflected some degree of reality for respondents who had been given a cancer diagnosis in the past.
This study has several limitations. Although the response rate to this study was moderate for a general population survey, we acknowledge that the characteristics of the respondents might not be wholly representative of the general population. Also, because we restricted participants to adult inhabitants in an urban area in Japan, further research is required to test the validity of these findings.
It is recommended that health professionals become more aware about the need to provide patients with options to be given their cancer diagnosis and prognosis in a variety of ways. The greater proportion of respondents wants or permits disclosure of cancer diagnosis and prognosis. However, in patients who reveal negative attitudes toward being given a cancer disclosure directly, alternative options should be made available such as telling the family ahead of the patient or having a discussion of the cancer diagnosis with the patient together with the family. Further research with people aged over-sixty is needed to test the applicability of these findings to older age groups.
Competing interests
Although partial funding for this study was provided by the Education Ministry within the Japanese government, the views and opinions expressed in this report are those of the authors and not those of the funding organisation.
Authors' contributions
HM planned and conducted the survey, carried out the analysis, and wrote this paper.
HT, MT, TS and IK made close supervision and extensive support. All 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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BMC Plant BiolBMC Plant Biology1471-2229BioMed Central London 1471-2229-4-181555016810.1186/1471-2229-4-18Research ArticleMolecular cloning and functional expression of geranylgeranyl pyrophosphate synthase from Coleus forskohlii Briq Engprasert Surang [email protected] Futoshi [email protected] Makoto [email protected] Yukihiro [email protected] Graduate School of Pharmaceutical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan2 Department of Applied Bioscience and Biotechnology, Faculty of Life and Environment Science, Shimane University, Matsue 690-8504, Japan2004 18 11 2004 4 18 18 20 8 2004 18 11 2004 Copyright © 2004 Engprasert 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
Isopentenyl diphosphate (IPP), a common biosynthetic precursor to the labdane diterpene forskolin, has been biosynthesised via a non-mevalonate pathway. Geranylgeranyl diphosphate (GGPP) synthase is an important branch point enzyme in terpenoid biosynthesis. Therefore, GGPP synthase is thought to be a key enzyme in biosynthesis of forskolin. Herein we report the first confirmation of the GGPP synthase gene in Coleus forskohlii Briq.
Results
The open reading frame for full-length GGPP synthase encodes a protein of 359 amino acids, in which 1,077 nucleotides long with calculated molecular mass of 39.3 kDa. Alignments of C. forskohlii GGPP synthase amino acid sequences revealed high homologies with other plant GGPP synthases. Several highly conserved regions, including two aspartate-rich motifs were identified. Transient expression of the N-terminal region of C. forskohlii GGPP synthase-GFP fusion protein in tobacco cells demonstrated subcellular localization in the chloroplast. Carotenoid production was observed in Escherichia coli harboring pACCAR25ΔcrtE from Erwinia uredovora and plasmid carrying C. forskohlii GGPP synthase. These results suggested that cDNA encoded functional GGPP synthase. Furthermore, C. forskohlii GGPP synthase expression was strong in leaves, decreased in stems and very little expression was observed in roots.
Conclusion
This investigation proposed that forskolin was synthesised via a non-mevalonate pathway. GGPP synthase is thought to be involved in the biosynthesis of forskolin, which is primarily synthesised in the leaves and subsequently accumulates in the stems and roots.
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Background
Forskolin, a labdane diterpene, is a major active compound isolated from tuberous roots of Coleus forskohlii Briq. (Lamiaceae) [1]. C. forskohlii has been used as an important folk medicine in India. Futher, forskolin has been found to be a potent activator of adenylate cyclase [2], leading to an increase in levels of c-AMP, which affects heart action, blood and intraocular pressure. Recently, forskolin has become commercially available as a drug for treating heart disease in Japan. Forskolin is not available by chemical synthesis due to its complicated structure. However, two groups have reported successful total synthesis of forskolin [3,4].
Isoprenoids are essential for the normal growth and development processes in all living organisms. Isopentenyl diphosphate (IPP; C5) is a common metabolic precursor of all isoprenoids. Recently, several groups have demonstrated that two distinct pathways synthesise IPP in plants. The mevalonate (MVA) pathway occurs in the cytoplasm, and an alternative mevalonate-independent (2C-methyl-D-erythritol 4-phosphate; MEP) pathway occurs in plastids [5-7].
Geranylgeranyl diphosphate (GGPP) synthase catalyses the consecutive condensation of an allylic diphosphate with three molecules of IPP to produce GGPP, an essential linear precursor for biosynthesis of diterpenes, carotenoid, retinoids and side chain of chlorophyll [8]. GGPP synthase is an important branch point prenyltransferase enzyme in terpenoid biosynthesis.
GGPP synthase genes have been cloned in a number of organisms including; Arabidopsis thaliana [9,10], Taxus canadensis [11], Helianthus annuus [12], Scoparia dulcis and Croton sublyratus [13], Sulfolobus acidocaldarius [14], Neurospora crassa [15], and mouse and human [16]. Amino acid sequence comparison has shown that GGPP synthases contain several domains of conserved amino acid residues including the first aspartate-rich motifs (FARM) and the second aspartate-rich motif (SARM) [17]. Futhermore, recent studies suggested that two amino acids at the four and five positions before FARM in the sequence, as well as an insertion in FARM of plant GGPP synthases play important roles in product length determination [13,18].
Carotenoids arise from the coupling of two molecules of GGPP. The carotenoid biosynthetic gene cluster (crt genes) of Erwinia uredovora was elucidated [19], and is currently used to investigate the function of carotenoid related genes in a heterologous system. This crt gene cluster is composed of six genes; crtB (phytoene synthase), crtE (GGPP synthase), crtI (phytoene desaturase), crtX (zeaxanthin β-glucosidase), crtY (lycopene cyclase) and crtZ (β-carotene hydroxylase). Consequently, the production of carotenoids using E. coli harbouring the crt gene cluster can be used for the determination of GGPP synthase activity.
GGPP synthase is suggested to be a key enzyme in the biosynthesis of forskolin. Herein, we report the cDNA encoding C. forskohlii GGPP synthase and its heterologous expression in E. coli.
Results and discussion
cDNA cloning and sequencing of C. forskohlii GGPP synthase gene
The open reading frame (ORF) for full-length GGPP synthase gene encodes a protein of 359 amino acids, 1,077 nucleotides long, with a calculated molecular mass of 39.3 kDa. The amino acid sequence of C. forkohlii GGPP synthase revealed high homology throughout the entire coding region of Catharanthus roseus (75%), Arabidopsis thaliana (73%), Sinapis alba (72%), Croton sublyratus (69%), Scoparia dulcis (67%) and Mentha piperita (64%) (Fig. 1). However, comparison of the amino acid sequence with that of prokaryotic GGPP synthases showed a low level of homology (30–53%). Highly conserved residues were designated as domains I-VII. Two conserved aspartate-rich motifs, DDXX(X)D, were identifed. FARM and SARM have been shown to be important in substrate binding and catalysis [20-22].
Transient expression of putative localization signal of C. forskohlii GGPP synthase in tobacco cells
Sequence alignment of plant GGPP synthases showed that the N-terminal region has a low level of homology. It is reasonable to assume that these GGPP synthases have localization signals in their N-terminal regions to target them into specific subcellular compartments. The N-terminal region of C. forskohlii GGPP synthase was predicted to be localized in chloroplasts by the ChloroP 1.1 Prediction Server. In an effort to determine the localization of C. forskohlii GGPP synthase, the sequence coding for the 80 amino acid sequence at the N-terminus of C. forskohlii GGPP synthase was fused to the N-terminus of the GFP reporter gene and transformed into BY-2 tobacco cells. The pattern of putative localization signal of C. forskohlii GGPP synthase was identical to the positive chloroplast targeting signal [35SΩ-pt-sGFP(S65T)] (Fig. 2). The N-terminal region of C. forskohlii GGPP synthase was determined to contain a chloroplast localization signal. Recently, plant GGPP synthases have been determined to be translocated into plastids, mitochondria and cytosol [9,23].
Heterologous expression and activity of C. forskohlii GGPP synthase
In order to express C. forskohlii GGPP synthase, the gene was constructed and cloned into the plasmid pBluescript II KS-. The fusion protein of GGPP synthase with lacZ had a calculated molecular mass of 41.6 kDa, was observed in the soluble fraction of E. coli carrying pGGPPS after IPTG induction (Fig. 3).
Functional activity of expressed GGPP synthase was investigated by genetic complementation with the carotenogenic crt gene cluster. Carotenoids are produced in E. coli harbouring a crt cluster gene from E. uredovora. Replacements of a crt gene with an unknown gene with the same activity, can be used to determine the function of the gene [15]. Herein, the C. forskohlii GGPP synthase gene was cloned into pBluescript II KS- vector (pGGPPS) in order to produce a lacZ fusion protein. pGGPPS was then transformed into E. coli DH10B carrying the plasmid pACCAR25ΔcrtE in which the crtE encoding GGPP synthase had been deleted. The yellow color of carotenoid was observed in the transformant, indicating that pGGPPS carried the gene substituting the function of the crtE gene (Fig. 4). Carotenoid production of the transformants was compared with that of E. coli transformant carrying plasmid pACCAR25ΔcrtE and pBAA encoding mouse GGPP synthase (positive control) [16], and with transformant carrying plasmid pACCAR25ΔcrtE and a pBluescript II KS- (pBS) vector (negative control). This result suggested that the coding region of a cDNA of C. forskohlii GGPP synthase encodes a functional GGPP synthase.
Expression of GGPP synthase gene in organs of C. forskohlii
The expression of GGPP synthase gene was investigated by RT-PCR in different organs of C. forskohlii. Total RNA extracted from the roots, stems and leaves of an eight-month-old plant were analysed. The C. forskohlii GGPP synthase gene was strongly expressed in the leaves, whereas expression was decreased in stems and barely expressed in roots (Fig. 5). Therefore, the leaves are thought to be the primary location for forskolin synthesis. We previously reported the forskolin concentration in clonally propagated plant organs of C. forskohlii [24]. Tuberous roots and the stem base were determined to contain a higher concentration of forskolin than the organs. Moreover, the stem base, parts of the epidermis and cortex, the vascular bundle, and the pith were analysed separately. The highest concentration of forskolin was identified in the vascular bundle tissue. From these data, we proposed that GGPP synthase involved in biosynthesis of forskolin, is mainly synthesised in leaves, subsequently distributed to stems and finally accumulated in stem bases and roots.
Forskolin production via non-mevalonate pathway
In an effort to investigate the forskolin biosynthesis pathway by a non-mevalonate pathway, various concentrations of fosmidomycin, the specific inhibitor of 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) enzyme in the non-mevalonate pathway were applied to the C. forskohlii culture and the forskolin content of roots was determined (Fig. 6). Treatment led to a decrease in forskolin, whereas 10 μM fosmidomycin had no effect on forskolin production. At higher concentrations a dose-dependent inhibitory effect was observed. At 1000 μM fosmidomycin, the forskolin content was decreased by up to fifty percent in comparison to the control tissue without inhibitor treatment. Thus, forskolin was thought to be synthesised via a non-mevalonate pathway.
A recent 13C-glucose feeding experiment using 13C-NMR analytical methodology suggested the biosynthetic pathway of forskolin via a non-mevalonate pathway [25]. In addition, the DXR gene regarding the specific enzyme in the first step of the non-mevalonate pathway was cloned from C. forskohlii [26].
Conclusions
C. forskohlii GGPP synthase was cloned and its subcellular localization was determined. The N-terminal region contained a signal which was localized in chloroplasts. Functional expression of GGPP synthase was investigated by genetic complementation with the carotenogenic crt gene cluster. Carotenoids were produced when the crtE gene was replaced with C. forskohlii GGPP synthase. GGPP synthase is thought to be involved in biosynthesis of forskolin, which is primary synthesised in the leaves, subsequently distributed to stems and finally accumulated in stem bases and roots.
Methods
Plant materials and reagents
C. forskohlii plantlets were cultured in hormone-free MS (Murashige and Skoog) medium at 25°C under a 16 hours light cycle. The light intensity was 3000 lux and the relative humidity was 60%. Shoot cuttings (10 mm in length) propagated by shoot tip culture were successively cultivated in vermiculite. BY-2 tobacco single cell suspension [27] was cultured in liquid modified LS (Leinsmaier and Skoog) medium supplemented with 0.2 mg l-1 of 2,4-D (2,4-dichlorophonoxy acetic acid) under dark conditions at 25°C on an orbital incubator. Restriction enzymes, ligase, and PCR-polymerase were purchased from Takara Shuzo Co., Ltd. (Tokyo, Japan) and Toyobo Co., Ltd. (Tokyo, Japan). Fosmidomycin (FR-3154) was purchased from Molecular Probes (Oregon, USA). Chemical reagents were purchased from Sigma Chemical Company (St. Louis, USA) and Nacalai Tesque Inc. (Tokyo, Japan)
Bacterial strains and plasmids
E. coli TOP10F' and E. coli DH10B carrying the plasmid pACCAR25ΔcrtE were used in the present investigation. The pUC119 vector was used for cDNA cloning and sequencing. The pBluescript II KS- vector was used as a GGPP synthase expression plasmid. The 35SΩ-sGFP(S65T) plasmid was used as a green fluorescent protein (GFP) reporter plasmid. The pBI121 plant vector and Agrobacterium tumefaciens LBA4404 were used for transformation of GFP and GFP-fusion genes to plant cells.
cDNA cloning and sequencing of C. forskohlii GGPP synthase gene
Total RNA was prepared from roots of the C. forskohlii culture using the acid guanidium-phenol-chloroform extraction procedure [28]. Single strand cDNA was synthesised using an oligo-dT adapter primer, M-MLV reverse transcriptase and total RNA as template. Degenerate primers were designed based on highly conserved amino acid sequences of previously cloned genes encoding plant GGPP synthases [13]. A 470 bp cDNA fragment was amplified using a nested PCR with Taq DNA polymerase and degenerate primers A, B, C and D (Table 1). The 3' end of cDNA was amplified using 3' rapid amplification of cDNA ends (RACE) with gene specific primers I and J, and adapter primer F. A 522 bp product was obtained by nested PCR. For 5' RACE, the first strand cDNA was polyadenylated at its 5' end by terminal deoxynucleotidyl transferase. The first and second PCR were performed with specific primers G and H and adapter primers E and F. A 285 bp product was obtained. The entire coding region of 1,077 bp was amplified by nested PCR using specific primers K, L, M and N designed from 5' and 3' RACE products.
All amplified cDNA fragments were purified and digested with restriction enzymes at sites introduced via the PCR primers, and cloned into the vector pUC119. After transformation to E. coli TOP10F', clones harboring inserts were sequenced using a Model 310 Genetic Analyzer (PE Biosystems) using a BigDye Terminator Cycle Sequencing Kit.
The amino acid sequence deduced from the nucleotide sequence was compared with sequence databases in the Genome Net WWW server using the FASTA program. Multiple amino acid sequence alignment was performed using the CLUSTALW Multiple Sequence Alignment in the GenomeNet CLUSTALW Server.
Construction and expression of putative localization signal of C. forskohlii GGPP synthase
A 240 bp fragment of the N-terminal region of C. forskohlii GGPP synthase was PCR-amplified using primers P and Q and the PCR product was digested and cloned into the SalI-NcoI site of the 35SΩ-sGFP(S65T) plasmid. 35SΩ-pt-sGFP(S65T) was used as the positive control for chloroplast targeting [29,30]. GFP, GGPP synthase-GFP fusion and pt-GFP fusion with CaMV35SΩ promoter and NOS3' terminator [35SΩ-sGFP (S65T), 35SΩ-GGPP synthase-sGFP (S65T) and 35SΩ-pt-sGFP (S65T), respectively] were subcloned into the HindIII-EcoRI site of the pBI121 vector and then transformed into A. tumefaciens LBA4404. The transformants were cultured at 28°C for two days in YEB liquid medium containing 25 μg/ml of kanamycin and 25 μg/ml of rifampicin. The transformants were washed twice and re-suspended in YEB medium. Agrobacterium transformants (108 cells) were applied to four ml of five-day-old BY-2 suspension culture. The culture was incubated at 28°C for two days under dark conditions. GFP and GFP fusion protein were analysed by fluorescence microscopy using Nikon Eclipse TE2000-U model. Cells were observed at a 400 × magnification.
Construction of plasmid for C. forskohlii GGPP synthase expression
The coding region of a cDNA of C. forskohlii GGPP synthase was amplified by PCR using specific primers M and O. A PCR product was digested; purified and cloned into the KpnI-SalI site of pBluescript II KS- vector, namely pGGPPS. This plasmid was transformed into E. coli XL1-Blue MRF' for over-expression. The transformants were cultured in LB liquid medium containing 50 μg/ml of ampicillin and 25 μg/ml of chloramphenicol. The culture was induced with 1 mM isopropyl-1-thio-β-D-galactoside (IPTG) and incubated for six hours at 37°C. The cells were harvested and washed with 50 mM Tris-HCl pH 8.0 by centrifugation. The pellet was re-suspended, lysozyme was added and the mixture was incubated for 30 minutes. The mixture was then sonicated for four cycles of 15 seconds at one minute intervals. The soluble fraction was obtained after centrifugation at 10,000 × g for 10 minutes. SDS-PAGE was conducted in order to detect the proteins [31].
Genetic complementation expression
The pACCAR25ΔcrtE plasmid contains the gene cluster crtB, crtI, crtX, crtY and crtZ encoding carotenoid biosynthetic enzymes with the exception of crtE (encoding GGPP synthase). The plasmid pBAA containing mouse GGPP synthase (positive control plasmid) and E. coli DH10B carrying the plasmid pACCAR25ΔcrtE was provided by Dr. M. Kawamukai, Shimane University, Japan [16]. pBluescript II KS- vector, pBS, was used as negative control. pGGPPS, pBAA and pBS were transformed into E. coli DH10B carrying the plasmid pACCAR25ΔcrtE. All transformants were plated on LB agar medium containing 50 μg/ml of ampicillin and 25 μg/ml of chloramphenicol and then incubated for two to three days at 25°C.
Reverse transcriptase-PCR (RT-PCR)
An eight-month-old C. forskohlii was analysed in twelve separate parts; leaf (L1–L4), stem (S1–S5) and root (R1–R3). The numbering is based on the maturation of organs. Total RNA was extracted from each part of plant. One microgram of total RNA was used as the template for the synthesis of the first strand cDNA (using SuperScript First-Strand Synthesis System for RT-PCR, Invitrogen). Primers M and O, the first strand cDNA and KOD-polymerase were used for the amplification of C. forskohlii GGPP synthase with the condition of denaturation, 98°C, 15 seconds; annealing, 60°C, 2 seconds and extension, 74°C, 5 seconds. The 18S rRNA fragment used as an internal control was amplified using primers R and S under the same conditions of C. forskohlii GGPP synthase amplification. The amplified PCR products were analysed by 1.0% agarose gel electrophoresis.
Analysis of forskolin production
C. forskohlii plantlets were treated with various concentrations of fosmidomycin and then investigated for forskolin content using the HPLC method as previously described [26]. Forskolin was detected by comparison with the retention time of a forskolin standard (Sigma) detected by UV absorption at 202 nm.
List of abbreviations
crt, carotenogenic gene; FARM, first aspartate-rich motif; GFP, green fluorescent protein; GGPP, geranylgeranyl diphosphate; MEP, 2C-methyl-D-erythritol 4-phosphate; MVA, mevalonate; SARM, second aspatate-rich motif
Authors' contributions
SE carried out the molecular genetic studies, participated in the sequence alignment, forskolin analysis and drafted the manuscript. TF participated in the design of the study and coordination. MK participated in genetic complementation and coordination. YS conceived the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
35SΩ-sGFP(S65T) plasmid was generously provided by Dr. Yasuo Niwa, University of Shizuoka, Japan.
Figures and Tables
Figure 1 Amino acid sequence alignment of C. forskohlii GGPP synthase and other plant GGPP synthases. The residues boxed in black indicate the positions of at least five of the six compared sequences while the gray shading indicates similar amino acids. Dashes indicate gaps introduced for optimization of the alignment. Numbers of amino acids are indicated in the left and right margins. Asterisks show the seven domains that are highly conserved among prenyltransferases with two aspartate-rich motifs (domain II and VI). Species and accession numbers are C. forskohlii, AY515700; S. dulcis, AB034250_1; C. sublyratus, AB034249_1; C. roseus, T09966; S. alba, T10452; A. thaliana, F85434.
Figure 2 Transient expression of GFP and GFP fusion proteins in BY-2 tobacco cells. (A) GFP protein [35SΩ-sGFP (S65T)]; (B) Arabidopsis chloroplast targeting signal (pt)-GFP fusion protein [35SΩ-pt-sGFP (S65T)]; (C) putative localization signal of GGPP synthase-GFP fusion protein [35SΩ-GGPP synthase-sGFP (S65T)]
Figure 3 Expression of GGPP synthase in pBluescript II KS-vector analysed by SDS-PAGE. M = molecular mass standards are indicated in kDa; lane 1 = control (E. coli transformed with pBluescriptII KS-); lane 2 = total protein extract after 6 hours of IPTG induction; lane 3 = soluble cytoplasmic fraction of cells treated with IPTG; and lane 4 = insoluble fraction of cells treated with IPTG
Figure 4 Carotenoid production of E. coli harboring plasmid pACCAR25ΔcrtE and plasmid expressing. (1) mouse GGPP synthase, pBAA; (2) plasmid expressing C. forskohlii GGPP synthase, pGGPPS; and (3) pBluescript II KS- vector, pBS
Figure 5 Expression of GGPP synthase in roots, stems and leaves of C. forskohlii culture. Ten microliters of PCR product was loaded in each lane. The lower panel shows 18S rRNA fragment as an internal control.
Figure 6 Effect of fosmidomycin on forskolin production.
Table 1 Primers for cDNA cloning of GGPP synthase.
Primers Sequences (5' to 3') Designed from
A GGGGTACCYITTGYATHGCIGCITAYGA conserved sequence (LCIAACE)
B GGGGTACCGTIGARATGATHCAYACIAT conserved sequence (VEMIHTM)
C CGGGATCCTTIGTIACRTCIARDATRTC conserved sequence (DILDVTK)
D CGGGATCCARRTCYTTICCIGCIGTYTT conserved sequence (KTAGKDL)
E GACTCGTCTAGAGGATCCCGT adapter primer
F GACTCGTCTAGAGGATCCCGT17 oligo-dT adapter primer
G GGGGTACCCCTTATGGTTGGTGGGCTTC degenerated PCR product (KPTNHK)
H CGCCACGTTCTCGCCGTAGA degenerated PCR product (VYGENVA)
I CCATATTGGGTGGCGCCGAT degenerated PCR product (AILGGAD)
J GGGGTACCGATGAGGCGGTGGAGAAGCT degenerated PCR product (DEAVEKL)
K ATTTGGTGTTCGACGACGAC 5'RACE
L GGGGTACCCATATGAGCCTCATCGCGAGTC 5'RACE
M GCACGCGTCGACATTGTTCCGGTAAGCAAT 3'RACE
N AGTGAAATGGAAATTATTCA 3'RACE
O GGGGTACCGATGAGCCTCATCGCGAGTCCA 5'RACE
P GCACGCGTCGACAACAATGAGCCTCATCGCG N-terminal sequence (MSLIAS)
Q CATGCCATGGGGTTCACCCGCTCTGCCTTCT N-terminal sequence (EKAERVN)
R TTCTTGGATTTATGAAAGACGAACAAC 18S rRNA
S AAGACCAACAATTGCAATGATCTATCC 18S rRNA
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| 15550168 | PMC538753 | CC BY | 2021-01-04 16:03:51 | no | BMC Plant Biol. 2004 Nov 18; 4:18 | utf-8 | BMC Plant Biol | 2,004 | 10.1186/1471-2229-4-18 | oa_comm |
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BMC Plant BiolBMC Plant Biology1471-2229BioMed Central London 1471-2229-4-181555016810.1186/1471-2229-4-18Research ArticleMolecular cloning and functional expression of geranylgeranyl pyrophosphate synthase from Coleus forskohlii Briq Engprasert Surang [email protected] Futoshi [email protected] Makoto [email protected] Yukihiro [email protected] Graduate School of Pharmaceutical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan2 Department of Applied Bioscience and Biotechnology, Faculty of Life and Environment Science, Shimane University, Matsue 690-8504, Japan2004 18 11 2004 4 18 18 20 8 2004 18 11 2004 Copyright © 2004 Engprasert 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
Isopentenyl diphosphate (IPP), a common biosynthetic precursor to the labdane diterpene forskolin, has been biosynthesised via a non-mevalonate pathway. Geranylgeranyl diphosphate (GGPP) synthase is an important branch point enzyme in terpenoid biosynthesis. Therefore, GGPP synthase is thought to be a key enzyme in biosynthesis of forskolin. Herein we report the first confirmation of the GGPP synthase gene in Coleus forskohlii Briq.
Results
The open reading frame for full-length GGPP synthase encodes a protein of 359 amino acids, in which 1,077 nucleotides long with calculated molecular mass of 39.3 kDa. Alignments of C. forskohlii GGPP synthase amino acid sequences revealed high homologies with other plant GGPP synthases. Several highly conserved regions, including two aspartate-rich motifs were identified. Transient expression of the N-terminal region of C. forskohlii GGPP synthase-GFP fusion protein in tobacco cells demonstrated subcellular localization in the chloroplast. Carotenoid production was observed in Escherichia coli harboring pACCAR25ΔcrtE from Erwinia uredovora and plasmid carrying C. forskohlii GGPP synthase. These results suggested that cDNA encoded functional GGPP synthase. Furthermore, C. forskohlii GGPP synthase expression was strong in leaves, decreased in stems and very little expression was observed in roots.
Conclusion
This investigation proposed that forskolin was synthesised via a non-mevalonate pathway. GGPP synthase is thought to be involved in the biosynthesis of forskolin, which is primarily synthesised in the leaves and subsequently accumulates in the stems and roots.
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Background
Forskolin, a labdane diterpene, is a major active compound isolated from tuberous roots of Coleus forskohlii Briq. (Lamiaceae) [1]. C. forskohlii has been used as an important folk medicine in India. Futher, forskolin has been found to be a potent activator of adenylate cyclase [2], leading to an increase in levels of c-AMP, which affects heart action, blood and intraocular pressure. Recently, forskolin has become commercially available as a drug for treating heart disease in Japan. Forskolin is not available by chemical synthesis due to its complicated structure. However, two groups have reported successful total synthesis of forskolin [3,4].
Isoprenoids are essential for the normal growth and development processes in all living organisms. Isopentenyl diphosphate (IPP; C5) is a common metabolic precursor of all isoprenoids. Recently, several groups have demonstrated that two distinct pathways synthesise IPP in plants. The mevalonate (MVA) pathway occurs in the cytoplasm, and an alternative mevalonate-independent (2C-methyl-D-erythritol 4-phosphate; MEP) pathway occurs in plastids [5-7].
Geranylgeranyl diphosphate (GGPP) synthase catalyses the consecutive condensation of an allylic diphosphate with three molecules of IPP to produce GGPP, an essential linear precursor for biosynthesis of diterpenes, carotenoid, retinoids and side chain of chlorophyll [8]. GGPP synthase is an important branch point prenyltransferase enzyme in terpenoid biosynthesis.
GGPP synthase genes have been cloned in a number of organisms including; Arabidopsis thaliana [9,10], Taxus canadensis [11], Helianthus annuus [12], Scoparia dulcis and Croton sublyratus [13], Sulfolobus acidocaldarius [14], Neurospora crassa [15], and mouse and human [16]. Amino acid sequence comparison has shown that GGPP synthases contain several domains of conserved amino acid residues including the first aspartate-rich motifs (FARM) and the second aspartate-rich motif (SARM) [17]. Futhermore, recent studies suggested that two amino acids at the four and five positions before FARM in the sequence, as well as an insertion in FARM of plant GGPP synthases play important roles in product length determination [13,18].
Carotenoids arise from the coupling of two molecules of GGPP. The carotenoid biosynthetic gene cluster (crt genes) of Erwinia uredovora was elucidated [19], and is currently used to investigate the function of carotenoid related genes in a heterologous system. This crt gene cluster is composed of six genes; crtB (phytoene synthase), crtE (GGPP synthase), crtI (phytoene desaturase), crtX (zeaxanthin β-glucosidase), crtY (lycopene cyclase) and crtZ (β-carotene hydroxylase). Consequently, the production of carotenoids using E. coli harbouring the crt gene cluster can be used for the determination of GGPP synthase activity.
GGPP synthase is suggested to be a key enzyme in the biosynthesis of forskolin. Herein, we report the cDNA encoding C. forskohlii GGPP synthase and its heterologous expression in E. coli.
Results and discussion
cDNA cloning and sequencing of C. forskohlii GGPP synthase gene
The open reading frame (ORF) for full-length GGPP synthase gene encodes a protein of 359 amino acids, 1,077 nucleotides long, with a calculated molecular mass of 39.3 kDa. The amino acid sequence of C. forkohlii GGPP synthase revealed high homology throughout the entire coding region of Catharanthus roseus (75%), Arabidopsis thaliana (73%), Sinapis alba (72%), Croton sublyratus (69%), Scoparia dulcis (67%) and Mentha piperita (64%) (Fig. 1). However, comparison of the amino acid sequence with that of prokaryotic GGPP synthases showed a low level of homology (30–53%). Highly conserved residues were designated as domains I-VII. Two conserved aspartate-rich motifs, DDXX(X)D, were identifed. FARM and SARM have been shown to be important in substrate binding and catalysis [20-22].
Transient expression of putative localization signal of C. forskohlii GGPP synthase in tobacco cells
Sequence alignment of plant GGPP synthases showed that the N-terminal region has a low level of homology. It is reasonable to assume that these GGPP synthases have localization signals in their N-terminal regions to target them into specific subcellular compartments. The N-terminal region of C. forskohlii GGPP synthase was predicted to be localized in chloroplasts by the ChloroP 1.1 Prediction Server. In an effort to determine the localization of C. forskohlii GGPP synthase, the sequence coding for the 80 amino acid sequence at the N-terminus of C. forskohlii GGPP synthase was fused to the N-terminus of the GFP reporter gene and transformed into BY-2 tobacco cells. The pattern of putative localization signal of C. forskohlii GGPP synthase was identical to the positive chloroplast targeting signal [35SΩ-pt-sGFP(S65T)] (Fig. 2). The N-terminal region of C. forskohlii GGPP synthase was determined to contain a chloroplast localization signal. Recently, plant GGPP synthases have been determined to be translocated into plastids, mitochondria and cytosol [9,23].
Heterologous expression and activity of C. forskohlii GGPP synthase
In order to express C. forskohlii GGPP synthase, the gene was constructed and cloned into the plasmid pBluescript II KS-. The fusion protein of GGPP synthase with lacZ had a calculated molecular mass of 41.6 kDa, was observed in the soluble fraction of E. coli carrying pGGPPS after IPTG induction (Fig. 3).
Functional activity of expressed GGPP synthase was investigated by genetic complementation with the carotenogenic crt gene cluster. Carotenoids are produced in E. coli harbouring a crt cluster gene from E. uredovora. Replacements of a crt gene with an unknown gene with the same activity, can be used to determine the function of the gene [15]. Herein, the C. forskohlii GGPP synthase gene was cloned into pBluescript II KS- vector (pGGPPS) in order to produce a lacZ fusion protein. pGGPPS was then transformed into E. coli DH10B carrying the plasmid pACCAR25ΔcrtE in which the crtE encoding GGPP synthase had been deleted. The yellow color of carotenoid was observed in the transformant, indicating that pGGPPS carried the gene substituting the function of the crtE gene (Fig. 4). Carotenoid production of the transformants was compared with that of E. coli transformant carrying plasmid pACCAR25ΔcrtE and pBAA encoding mouse GGPP synthase (positive control) [16], and with transformant carrying plasmid pACCAR25ΔcrtE and a pBluescript II KS- (pBS) vector (negative control). This result suggested that the coding region of a cDNA of C. forskohlii GGPP synthase encodes a functional GGPP synthase.
Expression of GGPP synthase gene in organs of C. forskohlii
The expression of GGPP synthase gene was investigated by RT-PCR in different organs of C. forskohlii. Total RNA extracted from the roots, stems and leaves of an eight-month-old plant were analysed. The C. forskohlii GGPP synthase gene was strongly expressed in the leaves, whereas expression was decreased in stems and barely expressed in roots (Fig. 5). Therefore, the leaves are thought to be the primary location for forskolin synthesis. We previously reported the forskolin concentration in clonally propagated plant organs of C. forskohlii [24]. Tuberous roots and the stem base were determined to contain a higher concentration of forskolin than the organs. Moreover, the stem base, parts of the epidermis and cortex, the vascular bundle, and the pith were analysed separately. The highest concentration of forskolin was identified in the vascular bundle tissue. From these data, we proposed that GGPP synthase involved in biosynthesis of forskolin, is mainly synthesised in leaves, subsequently distributed to stems and finally accumulated in stem bases and roots.
Forskolin production via non-mevalonate pathway
In an effort to investigate the forskolin biosynthesis pathway by a non-mevalonate pathway, various concentrations of fosmidomycin, the specific inhibitor of 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) enzyme in the non-mevalonate pathway were applied to the C. forskohlii culture and the forskolin content of roots was determined (Fig. 6). Treatment led to a decrease in forskolin, whereas 10 μM fosmidomycin had no effect on forskolin production. At higher concentrations a dose-dependent inhibitory effect was observed. At 1000 μM fosmidomycin, the forskolin content was decreased by up to fifty percent in comparison to the control tissue without inhibitor treatment. Thus, forskolin was thought to be synthesised via a non-mevalonate pathway.
A recent 13C-glucose feeding experiment using 13C-NMR analytical methodology suggested the biosynthetic pathway of forskolin via a non-mevalonate pathway [25]. In addition, the DXR gene regarding the specific enzyme in the first step of the non-mevalonate pathway was cloned from C. forskohlii [26].
Conclusions
C. forskohlii GGPP synthase was cloned and its subcellular localization was determined. The N-terminal region contained a signal which was localized in chloroplasts. Functional expression of GGPP synthase was investigated by genetic complementation with the carotenogenic crt gene cluster. Carotenoids were produced when the crtE gene was replaced with C. forskohlii GGPP synthase. GGPP synthase is thought to be involved in biosynthesis of forskolin, which is primary synthesised in the leaves, subsequently distributed to stems and finally accumulated in stem bases and roots.
Methods
Plant materials and reagents
C. forskohlii plantlets were cultured in hormone-free MS (Murashige and Skoog) medium at 25°C under a 16 hours light cycle. The light intensity was 3000 lux and the relative humidity was 60%. Shoot cuttings (10 mm in length) propagated by shoot tip culture were successively cultivated in vermiculite. BY-2 tobacco single cell suspension [27] was cultured in liquid modified LS (Leinsmaier and Skoog) medium supplemented with 0.2 mg l-1 of 2,4-D (2,4-dichlorophonoxy acetic acid) under dark conditions at 25°C on an orbital incubator. Restriction enzymes, ligase, and PCR-polymerase were purchased from Takara Shuzo Co., Ltd. (Tokyo, Japan) and Toyobo Co., Ltd. (Tokyo, Japan). Fosmidomycin (FR-3154) was purchased from Molecular Probes (Oregon, USA). Chemical reagents were purchased from Sigma Chemical Company (St. Louis, USA) and Nacalai Tesque Inc. (Tokyo, Japan)
Bacterial strains and plasmids
E. coli TOP10F' and E. coli DH10B carrying the plasmid pACCAR25ΔcrtE were used in the present investigation. The pUC119 vector was used for cDNA cloning and sequencing. The pBluescript II KS- vector was used as a GGPP synthase expression plasmid. The 35SΩ-sGFP(S65T) plasmid was used as a green fluorescent protein (GFP) reporter plasmid. The pBI121 plant vector and Agrobacterium tumefaciens LBA4404 were used for transformation of GFP and GFP-fusion genes to plant cells.
cDNA cloning and sequencing of C. forskohlii GGPP synthase gene
Total RNA was prepared from roots of the C. forskohlii culture using the acid guanidium-phenol-chloroform extraction procedure [28]. Single strand cDNA was synthesised using an oligo-dT adapter primer, M-MLV reverse transcriptase and total RNA as template. Degenerate primers were designed based on highly conserved amino acid sequences of previously cloned genes encoding plant GGPP synthases [13]. A 470 bp cDNA fragment was amplified using a nested PCR with Taq DNA polymerase and degenerate primers A, B, C and D (Table 1). The 3' end of cDNA was amplified using 3' rapid amplification of cDNA ends (RACE) with gene specific primers I and J, and adapter primer F. A 522 bp product was obtained by nested PCR. For 5' RACE, the first strand cDNA was polyadenylated at its 5' end by terminal deoxynucleotidyl transferase. The first and second PCR were performed with specific primers G and H and adapter primers E and F. A 285 bp product was obtained. The entire coding region of 1,077 bp was amplified by nested PCR using specific primers K, L, M and N designed from 5' and 3' RACE products.
All amplified cDNA fragments were purified and digested with restriction enzymes at sites introduced via the PCR primers, and cloned into the vector pUC119. After transformation to E. coli TOP10F', clones harboring inserts were sequenced using a Model 310 Genetic Analyzer (PE Biosystems) using a BigDye Terminator Cycle Sequencing Kit.
The amino acid sequence deduced from the nucleotide sequence was compared with sequence databases in the Genome Net WWW server using the FASTA program. Multiple amino acid sequence alignment was performed using the CLUSTALW Multiple Sequence Alignment in the GenomeNet CLUSTALW Server.
Construction and expression of putative localization signal of C. forskohlii GGPP synthase
A 240 bp fragment of the N-terminal region of C. forskohlii GGPP synthase was PCR-amplified using primers P and Q and the PCR product was digested and cloned into the SalI-NcoI site of the 35SΩ-sGFP(S65T) plasmid. 35SΩ-pt-sGFP(S65T) was used as the positive control for chloroplast targeting [29,30]. GFP, GGPP synthase-GFP fusion and pt-GFP fusion with CaMV35SΩ promoter and NOS3' terminator [35SΩ-sGFP (S65T), 35SΩ-GGPP synthase-sGFP (S65T) and 35SΩ-pt-sGFP (S65T), respectively] were subcloned into the HindIII-EcoRI site of the pBI121 vector and then transformed into A. tumefaciens LBA4404. The transformants were cultured at 28°C for two days in YEB liquid medium containing 25 μg/ml of kanamycin and 25 μg/ml of rifampicin. The transformants were washed twice and re-suspended in YEB medium. Agrobacterium transformants (108 cells) were applied to four ml of five-day-old BY-2 suspension culture. The culture was incubated at 28°C for two days under dark conditions. GFP and GFP fusion protein were analysed by fluorescence microscopy using Nikon Eclipse TE2000-U model. Cells were observed at a 400 × magnification.
Construction of plasmid for C. forskohlii GGPP synthase expression
The coding region of a cDNA of C. forskohlii GGPP synthase was amplified by PCR using specific primers M and O. A PCR product was digested; purified and cloned into the KpnI-SalI site of pBluescript II KS- vector, namely pGGPPS. This plasmid was transformed into E. coli XL1-Blue MRF' for over-expression. The transformants were cultured in LB liquid medium containing 50 μg/ml of ampicillin and 25 μg/ml of chloramphenicol. The culture was induced with 1 mM isopropyl-1-thio-β-D-galactoside (IPTG) and incubated for six hours at 37°C. The cells were harvested and washed with 50 mM Tris-HCl pH 8.0 by centrifugation. The pellet was re-suspended, lysozyme was added and the mixture was incubated for 30 minutes. The mixture was then sonicated for four cycles of 15 seconds at one minute intervals. The soluble fraction was obtained after centrifugation at 10,000 × g for 10 minutes. SDS-PAGE was conducted in order to detect the proteins [31].
Genetic complementation expression
The pACCAR25ΔcrtE plasmid contains the gene cluster crtB, crtI, crtX, crtY and crtZ encoding carotenoid biosynthetic enzymes with the exception of crtE (encoding GGPP synthase). The plasmid pBAA containing mouse GGPP synthase (positive control plasmid) and E. coli DH10B carrying the plasmid pACCAR25ΔcrtE was provided by Dr. M. Kawamukai, Shimane University, Japan [16]. pBluescript II KS- vector, pBS, was used as negative control. pGGPPS, pBAA and pBS were transformed into E. coli DH10B carrying the plasmid pACCAR25ΔcrtE. All transformants were plated on LB agar medium containing 50 μg/ml of ampicillin and 25 μg/ml of chloramphenicol and then incubated for two to three days at 25°C.
Reverse transcriptase-PCR (RT-PCR)
An eight-month-old C. forskohlii was analysed in twelve separate parts; leaf (L1–L4), stem (S1–S5) and root (R1–R3). The numbering is based on the maturation of organs. Total RNA was extracted from each part of plant. One microgram of total RNA was used as the template for the synthesis of the first strand cDNA (using SuperScript First-Strand Synthesis System for RT-PCR, Invitrogen). Primers M and O, the first strand cDNA and KOD-polymerase were used for the amplification of C. forskohlii GGPP synthase with the condition of denaturation, 98°C, 15 seconds; annealing, 60°C, 2 seconds and extension, 74°C, 5 seconds. The 18S rRNA fragment used as an internal control was amplified using primers R and S under the same conditions of C. forskohlii GGPP synthase amplification. The amplified PCR products were analysed by 1.0% agarose gel electrophoresis.
Analysis of forskolin production
C. forskohlii plantlets were treated with various concentrations of fosmidomycin and then investigated for forskolin content using the HPLC method as previously described [26]. Forskolin was detected by comparison with the retention time of a forskolin standard (Sigma) detected by UV absorption at 202 nm.
List of abbreviations
crt, carotenogenic gene; FARM, first aspartate-rich motif; GFP, green fluorescent protein; GGPP, geranylgeranyl diphosphate; MEP, 2C-methyl-D-erythritol 4-phosphate; MVA, mevalonate; SARM, second aspatate-rich motif
Authors' contributions
SE carried out the molecular genetic studies, participated in the sequence alignment, forskolin analysis and drafted the manuscript. TF participated in the design of the study and coordination. MK participated in genetic complementation and coordination. YS conceived the study and participated in its design and coordination. All authors read and approved the final manuscript.
Acknowledgements
35SΩ-sGFP(S65T) plasmid was generously provided by Dr. Yasuo Niwa, University of Shizuoka, Japan.
Figures and Tables
Figure 1 Amino acid sequence alignment of C. forskohlii GGPP synthase and other plant GGPP synthases. The residues boxed in black indicate the positions of at least five of the six compared sequences while the gray shading indicates similar amino acids. Dashes indicate gaps introduced for optimization of the alignment. Numbers of amino acids are indicated in the left and right margins. Asterisks show the seven domains that are highly conserved among prenyltransferases with two aspartate-rich motifs (domain II and VI). Species and accession numbers are C. forskohlii, AY515700; S. dulcis, AB034250_1; C. sublyratus, AB034249_1; C. roseus, T09966; S. alba, T10452; A. thaliana, F85434.
Figure 2 Transient expression of GFP and GFP fusion proteins in BY-2 tobacco cells. (A) GFP protein [35SΩ-sGFP (S65T)]; (B) Arabidopsis chloroplast targeting signal (pt)-GFP fusion protein [35SΩ-pt-sGFP (S65T)]; (C) putative localization signal of GGPP synthase-GFP fusion protein [35SΩ-GGPP synthase-sGFP (S65T)]
Figure 3 Expression of GGPP synthase in pBluescript II KS-vector analysed by SDS-PAGE. M = molecular mass standards are indicated in kDa; lane 1 = control (E. coli transformed with pBluescriptII KS-); lane 2 = total protein extract after 6 hours of IPTG induction; lane 3 = soluble cytoplasmic fraction of cells treated with IPTG; and lane 4 = insoluble fraction of cells treated with IPTG
Figure 4 Carotenoid production of E. coli harboring plasmid pACCAR25ΔcrtE and plasmid expressing. (1) mouse GGPP synthase, pBAA; (2) plasmid expressing C. forskohlii GGPP synthase, pGGPPS; and (3) pBluescript II KS- vector, pBS
Figure 5 Expression of GGPP synthase in roots, stems and leaves of C. forskohlii culture. Ten microliters of PCR product was loaded in each lane. The lower panel shows 18S rRNA fragment as an internal control.
Figure 6 Effect of fosmidomycin on forskolin production.
Table 1 Primers for cDNA cloning of GGPP synthase.
Primers Sequences (5' to 3') Designed from
A GGGGTACCYITTGYATHGCIGCITAYGA conserved sequence (LCIAACE)
B GGGGTACCGTIGARATGATHCAYACIAT conserved sequence (VEMIHTM)
C CGGGATCCTTIGTIACRTCIARDATRTC conserved sequence (DILDVTK)
D CGGGATCCARRTCYTTICCIGCIGTYTT conserved sequence (KTAGKDL)
E GACTCGTCTAGAGGATCCCGT adapter primer
F GACTCGTCTAGAGGATCCCGT17 oligo-dT adapter primer
G GGGGTACCCCTTATGGTTGGTGGGCTTC degenerated PCR product (KPTNHK)
H CGCCACGTTCTCGCCGTAGA degenerated PCR product (VYGENVA)
I CCATATTGGGTGGCGCCGAT degenerated PCR product (AILGGAD)
J GGGGTACCGATGAGGCGGTGGAGAAGCT degenerated PCR product (DEAVEKL)
K ATTTGGTGTTCGACGACGAC 5'RACE
L GGGGTACCCATATGAGCCTCATCGCGAGTC 5'RACE
M GCACGCGTCGACATTGTTCCGGTAAGCAAT 3'RACE
N AGTGAAATGGAAATTATTCA 3'RACE
O GGGGTACCGATGAGCCTCATCGCGAGTCCA 5'RACE
P GCACGCGTCGACAACAATGAGCCTCATCGCG N-terminal sequence (MSLIAS)
Q CATGCCATGGGGTTCACCCGCTCTGCCTTCT N-terminal sequence (EKAERVN)
R TTCTTGGATTTATGAAAGACGAACAAC 18S rRNA
S AAGACCAACAATTGCAATGATCTATCC 18S rRNA
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| 15563372 | PMC538754 | CC BY | 2021-01-04 16:33:01 | no | BMC Psychiatry. 2004 Nov 24; 4:39 | latin-1 | BMC Psychiatry | 2,004 | 10.1186/1471-244X-4-39 | oa_comm |
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BMC Med EducBMC Medical Education1472-6920BioMed Central London 1472-6920-4-281556939510.1186/1472-6920-4-28Research ArticleLeniency and halo effects in marking undergraduate short research projects McKinstry Brian H [email protected] Helen S [email protected] Robert A [email protected] Simon C [email protected] Community Health Sciences, University of Edinburgh, Edinburgh, Scotland, UK2 Medical Teaching Organisation, College of Medicine and Veterinary Medicine, University of Edinburgh Scotland, UK3 Obstetrics and Gynaecology Section, Centre for Reproductive Biology, University of Edinburgh, Scotland, UK2004 29 11 2004 4 28 28 24 9 2004 29 11 2004 Copyright © 2004 McKinstry et al; licensee BioMed Central Ltd.2004McKinstry 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
Supervisors are often involved in the assessment of projects they have supervised themselves. Previous research suggests that detailed marking sheets may alleviate leniency and halo effects. We set out to determine if, despite using such a marking schedule, leniency and halo effects were evident in the supervisors' marking of undergraduate short research projects (special study modules (SSM)).
Methods
Review of grades awarded by supervisors, second markers and control markers to the written reports of 4th year medical students who had participated in an SSM during two full academic years (n = 399). Paired t-tests were used to compare mean marks, Pearson correlation to look at agreement between marks and multiple linear regression to test the prediction of one mark from several others adjusted for one another.
Results
There was a highly significant difference of approximately half a grade between supervisors and second markers with supervisors marking higher. (t = 3.12, p < 0.01, difference in grade score = 0.42, 95% CI for mean difference 0.18–0.80). There was a high correlation between the two marks awarded for performance of the project and the written report by the supervisor (r = 0.75), but a low-modest correlation between supervisor and second marker (r = 0.28). Linear regression analysis of the influence of the supervisors' mark for performance on their mark for the report gave a non-significant result. This suggests a leniency effect but no halo effect.
Conclusions
This study shows that with the use of structured marking sheet for assessment of undergraduate medical students, supervisors marks are not associated with a halo effect, but leniency does occur. As supervisor assessment is becoming more common in both under graduate and postgraduate teaching new ways to improve objectivity in marking and to address the leniency of supervisors should be sought.
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Background
There is compelling evidence from the literature that supervisors may be unreliable when asked to assess the performance of their own students. Effects such as the so-called 'halo' effect [1] in which a good or bad performance in one area affects the assessor's judgement in other areas and 'leniency'[2] where assessors are reluctant for a variety of reasons including fear of impairing the student-teacher relationship, fear of a negative emotional reaction from the student, or of poor reflection on the teacher's own expertise may come into play when assessing students' work. Increasingly however, particularly in medical education, teachers and supervisors are being asked to assess their own students. We describe a study to investigate to what extent effects such as halo and leniency were operating in supervisor marked Special Study Modules (SSMs) in the Edinburgh University undergraduate course.
SSMs were introduced into the fourth year of the 5-year undergraduate medical curriculum in 1995. This was in response to the recommendations from the General Medical Council's document Tomorrow's Doctors [3]. Edinburgh SSMs aim to develop students' skills in self-directed and enquiry-led learning, team working and writing a short thesis or report (of about 3000 words). The development also gives students an opportunity to choose an area of study and to pursue it in depth. Students spend 8 weeks on individual projects under the supervision of a member of the University of Edinburgh academic staff working on a wide range of projects in virtually every specialty including clinical audit, laboratory-based research and clinical projects, with over 300 supervisors involved.
For assessment an identical structured form was used by all assessors. Supervisors were asked to assess students on both their performance during the 8-week SSM and on their written report. Each component was awarded a separate grade by the supervisor and a combined grade for both was calculated by taking the mean grade. This mean grade contributed 50% to the final SSM mark. A second marker, usually another SSM supervisor working in a related area of research, with no prior knowledge of the student or the project would also assess the written report and this mark contributed 50% of the final mark. It was intended that this would permit the supervisors to be able to compare their own students' projects with others and would ensure greater consistency in the marking. Where there was a discrepancy of more than one full alphabetic grade category (e.g. A and C) between the supervisor and the second marker or where a fail grade was awarded, the report was assessed without prior knowledge of previous marks by at least one other experienced member of the Board of Examiners (control marker). The mark schemes for these assessments are described in Figure 1. Other than the guidance described there is no formal training of assessors.
Figure 1 Marking scheme
On reviewing the marks we noticed that there appeared to be a high correlation between the supervisor's marks for any one student's performance during the attachment and marks for their written report but a low correlation between the supervisor's and second marker's marks for the student's written report. This observation led us to investigate the hypothesis that the supervisors' knowledge of the students influenced their mark for the written report.
Methods
We reviewed the grades of all the students from two full academic years (n = 399) who had participated in an SSM between 1999–2001 to answer the following questions: What is the correlation between the supervisor's marks for performance and report, and if this is high is there a causal relationship? Is there a real difference in the marks awarded for the report between the supervisor and the second marker, and if so what is the cause of the difference? In cases of discrepant marks where the reports were further marked by control markers; what is the correlation between the control markers with the supervisors' and second markers?
The grades awarded for Performance and Reports were translated to a numerical scale thus: A+ = 1, A = 2, A- = 3, B+ = 4, through to E = 14. No grades below E (Marginal Fail) were awarded.
We used paired t-tests to compare mean marks, Pearson correlation for looking at agreement between markers, and multiple linear regression to test the prediction of one mark from several others adjusted for one another.
Results
Table 1 shows the mean and standard deviation expressed in a numerical scale of grades given by supervisors, second markers, and control markers.
Table 1 Mean and standard deviation of grades expressed on a numerical scale (grade score) awarded by the supervisor for performance and for the written report, and by the second marker and control markers for the written report (A+ = 1, A = 2 etc.; the lower the grade score the higher the mark)
Marker Component marked N Grade score Standard deviation
Supervisor Performance 383 4.12 2.60
Written report 383 4.64 2.50
Combined mark 389 4.45 2.48
Second marker Written report 373 5.16 2.40
Mean of Control markers Written report 98 5.86 2.21
Final mark 399 5.18 2.06
Using paired t-tests to compare mean marks for the written report between supervisors and second markers revealed a highly significant difference (t = 3.12, p < 0.01), with the supervisor scoring higher than the second marker (difference in grade score = 0.42, 95% confidence interval for mean difference 0.18 – 0.80). Correlation between the two marks was modest, r = 0.28. Control markers tended to mark the lower scoring students. While there was a numerical difference (lower) between control marks for the written report and the supervisor this failed to reach significance (t = 1.81, p = 0.07). Despite there being no significant difference between control markers and second markers, correlation was low (r = 0.11).
There was considerably higher correlation between the two marks awarded by each supervisor i.e. for the students' performance and written report r = 0.75 but again there was a highly significant difference in the mean marks t = 5.69, P < 0.001 (difference in grade score = 0.52; 95% confidence interval for mean difference 0.34 – 0.69)
Analysis of the influence of the supervisor's mark for performance on his/her mark for the report was done by linear regression. This gave a non-significant result for the performance mark adjusted for the written mark. Table 2 summarises these comparisons.
Table 2 Summary of statistical analysis of data
Supervisor Written Report Control Marker Written Report
Supervisor Performance t = 5.69, p < 0.001. Performance scoring higher than report (difference in grade score = 0.52)
Highly significant difference.
r = 0.75
Linear regression – non-significant result t = 3.07, p = 0.003.
Significant difference
Second Marker Written Report t = 3.12, p < 0.01
Highly significant difference.
Supervisor scoring higher than second marker (difference in grade score = 0.42, 95% confidence interval for mean difference 0.18 – 0.80).
r = 0.28 t = 0.68
No significant difference. r = 0.11
Control Marker Written Report t = 1.81, p = 0.07
No significant difference.
Discussion
Analysis of the grades awarded demonstrated that there is a significant difference in the mean marks awarded by the supervisors and second markers, with the supervisors marking nearly half a grade higher than the second markers. The correlation was also modest between these markers' assessments of the reports suggesting that the two groups of markers were not using the same criteria to reach their decision, despite being provided with descriptors and a mark scheme. It is important to note that most supervisors were also second markers. At the same time they were assessing their own students' project, and so had a direct and simultaneous comparison. Therefore, the same individual appeared to use different criteria depending on whether they marked their supervised student's report or others. The lack of significant difference between the mean marks awarded by the second marker and the control marker suggests that they were awarding the same range of grades overall but the modest correlations indicate that in the case of individual students there was again significant inter-marker variability. Control markers, unlike supervisors and second markers (who may only supervise one project a year) have experience of reviewing large numbers of SSM reports. There was also a significant difference in the mean marks awarded by supervisors for performance and for written reports but in this analysis there was a much higher correlation between the marks. However, further analysis of this finding by linear regression failed to demonstrate an undue influence of the performance mark on that of the report.
Although we have been unable to provide evidence that the supervisor's mark for performance has an undue influence on the mark for the written report (halo effect), we have demonstrated that the supervisors mark significantly higher than second markers, suggesting a leniency effect. This indicates that the supervisor's mark is influenced by having known and worked with the student. Such effects have been demonstrated before in many forms of education [4-8]. Some of the factors contributing to this may include insight and therefore sympathy for the student's difficulties in performing the project; inability to be objective when the student has become part of the work team; unwillingness of the supervisor to acknowledge that a piece of work emanating from his team is poor quality, or lacking the confidence or courage to feed back personally a bad assessment to the student. These factors need further exploration.
Increasingly in medical education supervisors are expected to summatively assess their students [9,10]. Assessors are unlikely to be affected equally by leniency and halo effects and this will advantage some and disadvantage others among their students. These effects are likely to be strongest on supervisors who, like some of those in our study, are assessing a relatively small number of students and are inexperienced in assessment [6]. If we are to continue to use supervisor-based assessments we must find ways to combat these effects. Other authors' suggestions for improving objectiveness and partially overcoming halo and leniency effects include detailed marking sheets [6,11], training for assessors in providing feedback of assessments [5], and also providing feedback on assessors' marking performance [6].
We are aware that the marking scheme in Figure 1, while structured, still permitted a fair degree of interpretation by examiners. Since carrying out this project we have introduced more detailed marking schemes with specific questions and detailed descriptors for each level of achievement for assessing the students' performance and report. This now includes an assessment of how the student overcame any problems which arose and how this may have affected the outcome of the project. We have also provided more detailed guidance to markers. We intend to review the inter-marker variability in light of the increased guidance given to markers.
These findings raise the ethical question as to whether or not we should continue to utilise supervisors in this assessment process. We are planning to continue to use supervisors as markers because of the expertise they bring to the specific field of study and their realistic expectation of the difficulties encountered by the student during the course of the project. Also the supervisor is sometimes the only person capable of marking the student's performance, which we consider a very valuable assessment of the students personal and professional abilities. We do realise that this is a difficult responsibility for supervisors. Better staff development of supervisors as markers and a more detailed marking schedule may help ensure appropriate marks for performance. Furthermore, we will also consider introducing 360 degree assessment to include all members of staff who have interacted with the student, particularly to improve formative feedback to students.
Conclusions
In this paper we have demonstrated the problem of inter-marker variability between the supervisor of undergraduate projects and the second marker even when using a mark scheme. This emphasises the difficulty in creating mark schemes and providing adequate staff training which ensures that markers apply the criteria in the same way in very varied reports. On average, supervisors awarded higher marks for their students' reports than the second markers but the influence of the performance mark on this was not significant. We would suggest that this difference is due to leniency in the supervisor resulting from the student being part of the supervisor's team, but these influences need further exploration.
Competing interests
SR, HC and BMcK are all involved in undergraduate teaching at the University of Edinburgh
Authors' contributions
BMcK, HC and SR contributed equally to the design of the research and the writing of the project. RE analysed the data.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
We would like to thank Miss Katy Elliot for her help with data entering.
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Dunnington G Wright K Hoffman K A pilot experience with competency-based clinical skills assessment in a surgical clerkship Am J S 1994 167 604 607 10.1016/0002-9610(94)90107-4
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Prescott LE Norcini JJ McKinlay P Rennie JS Facing the challenges of competency-based assessment of postgraduate dental training: Longitudinal Evaluation of Performance (LEP) Medical Education 2002 36 92 97 11849528 10.1046/j.1365-2923.2002.01099.x
Kinicki AJ Bannister B Hom P Denisi A Behaviorally anchored rating scales vs. summated rating scales: psychometric properties and susceptibility to rating bias Educational & Psychological Measurement 1985 45 535 549
| 15569395 | PMC538755 | CC BY | 2021-01-04 16:30:54 | no | BMC Med Educ. 2004 Nov 29; 4:28 | utf-8 | BMC Med Educ | 2,004 | 10.1186/1472-6920-4-28 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046010.1371/journal.pmed.0010034EssayOtherMedical EducationGeneral MedicineMedical EducationMedical CareersAcademic MedicinePreserving Creativity in Medicine EssayShaywitz David A *Ausiello Dennis A David A. Shaywitz is an endocrinology fellow at Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America, and is cofounder of the Harvard PASTEUR initiative. Dennis A. Ausiello is physician-in-chief of Massachusetts General Hospital, the Jackson professor of clinical medicine at Harvard Medical School, and cofounder and director of the PASTEUR initiative.
*To whom correspondence should be addressed. E-mail: [email protected]
Competing Interests: David A. Shaywitz declares that he has no competing interests. Dennis A. Ausiello is on the editorial board of PLoS Medicine.
12 2004 28 12 2004 1 3 e34Copyright: © 2004 Shaywitz and Ausiello.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.Imagination and creativity are essential traits that medicine, and medical insurers, must again learn to recognize and reward
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Galvanized by rising costs, increased calls for greater, accountability, and an Institute of Medicine (Washington, D. C., United States) report suggesting that medical errors may kill nearly 100,000 Americans every year [1], United States health care experts have tried to boost the quality of patient care by focusing on the speed and precision of service delivery. Several insurance companies have already started to place a surcharge on patients who elect to receive care from “inefficient” providers (a definition that includes most teaching hospitals), hoping to encourage patients to seek more cost-effective service, and to encourage physicians to provide it [2].
The problem is, most of these reform efforts, while critically important, only capture half the picture. Efficiency isn't everything, and unless we learn to cultivate creativity as avidly as we pursue consistency, future generations of patients may find themselves stuck with the same basic treatments they're receiving today. It will be the same medicine, just served quickly.
Benefits of Quality Reform
From its earliest days, medical training was based on an apprenticeship model, in which junior acolytes learned the art from senior practitioners. Even with the evolution of modern medical schools, which offered future physicians a rigorous common training, once doctors entered the real world they essentially did as they pleased. Consequently, there were pronounced differences in approaches to common problems from one clinician to another.
There was also little to guarantee that once doctors had hung out their shingle, they were actually competent (and remained competent) to practice their craft. While most physicians remained committed to the general professional standard—do the best that you can for each individual patient— many well-meaning doctors ultimately were not delivering their patients the best care available.
More recently, and largely due to the contagious spread of the so-called “business model,” there has been an increased emphasis on the consistency and quality of care. The clear goal is ensuring that all patients truly receive the very best care available, as defined by rigorous scientific studies.
Contemplation can provide new medical insights
(Illustration: Rusty Howson, sososo design)
This discrepancy between what patients should be receiving and what patients are actually receiving is the major focus of quality reform, and reflects the new recognition that there are truly preferred approaches—pathways—to guide disease management. These pathways are not meant to represent a rigid algorithm reflexively applied to each patient, but are intended as a summary of the best available data, a useful template to guide further medical decisions.
The renewed emphasis on quality has also resulted in a newfound appreciation for the role of experience and repetition in patient care. Study after study has shown that the best physician to treat a particular problem is the one who has treated it the most [3].
What Gets Lost: Innovation
The great paradox here is that the same reforms that are improving our current care may also be endangering our future health. As medicine has become more standardized and increasingly regulated, it turns out there is much less room for innovation. The spirited pursuit of the unknown—so long a defining quality of medicine—now seems seriously endangered. The new world of rapid throughput and endless documentation provides little time to reflect upon important clinical problems and consider fresh approaches. If anything, thinking about a patient or a question too much is now implicitly discouraged because it slows doctors down; contemplation is bad for productivity.
Academic medical centers like our own have played a particularly important role in the history of medical discovery; the hallmark of these institutions is our commitment to thinking and reflecting about the patients we see, patients who are often extremely sick and whose management is exceptionally complex. Unfortunately, many of the measurements now used by insurance companies to assess quality pay little attention—if any at all—to the complexity of a patient's illness, or to the importance of spending time trying to define the underlying malady. Insurance companies' major concern seems to be how fast a patient is “processed,” ideally with as few tests as possible. These measures provide no mechanism for distinguishing between the addled physician who inappropriately orders every test that springs to mind, and the reflective physician who is trying to get to the bottom of a patient's complaint, rather than simply throw a Band-Aid over the symptoms [4].
Situated on the front lines, clinicians have a unique opportunity to provide new medical insights and to identify critical, unanswered questions. Classic examples include Archibald Garrod, a British physician whose desire to understand why a patient produced black urine led to the hypothesis that diseases can result from defective metabolic enzymes, and Fuller Albright, a clinical investigator at Harvard whose thoughtful approach to his patients yielded insights that revolutionized the field of endocrinology. More recently, the astute clinical observations of UCLA immunologist Michael Gottlieb resulted in the original description of the Acquired Immune Deficiency Syndrome (AIDS) in 1981 [5].
Preserving Creativity in Medicine
But where are these types of insights going to come from today? It seems difficult to imagine that a medical care environment characterized by staccato-quick patient visits covering an ever-increasing number of compulsory topics will support or encourage such reflection and innovation. Our failure to nourish and sustain inquisitive physicians seems particularly tragic because medicine has traditionally attracted some of our brightest and most imaginative individuals. Even at the height of the dot-com boom, for example, there were still more medical school applicants than there were spaces to train them. But if current trends continue, many of these creative minds will head elsewhere, while those who stay will risk becoming stultified by repetitious routine.
Several medical schools and a handful of foundations have recognized this emerging problem, and have initiated programs aimed at sparking curiosity in young doctors (our own school's program is called the PASTEUR initiative—see www.pasteur.hms.harvard.edu) [6]. But as well-intentioned as these efforts are, simply changing the curriculum isn't likely to fix the underlying problem. Unless ever-savvy medical students perceive that inquisitive thinking is truly valued in clinical medicine, and unless exasperated physicians are inspired to believe that they have the ability to change some aspect of the way medicine is practiced, nothing is going to change. We may lose the best hope we have of defeating the terrible diseases that now plague us.
Even as we strive to improve the consistency of care—and striving is clearly a very good idea—we must continue to cultivate novelty and originality, rather than penalize it. Imagination is perhaps the most essential trait that medicine, and medical insurers, must again learn to recognize and reward. Even with the best algorithms and the brightest computers, the future of health care ultimately depends upon the creativity of the hardy men and women still entrusted with its delivery.
Citation: Shaywitz DA, Ausiello DA (2004) Preserving creativity in medicine. PLoS Med 1(3): e34.
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| 15630460 | PMC539041 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e34 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010034 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046110.1371/journal.pmed.0010038EssayOtherScience PolicyEpidemiology/Public HealthHealth PolicyHealth education (including prevention and promotion)Medical journalsEditorial policies (including conflicts of interest)Communication in Health CareThe Commercialisation of Medical and Scientific Reporting EssayCaulfield Timothy Timothy Caulfield is Canada research chair in Health Law and Policy, professor in the Faculty of Law and Faculty of Medicine and Dentistry, and research director of the Health Law Institute at the University of Alberta, Canada. E-mail: [email protected]
Competing Interests: The author is on the editorial board of PLoS Medicine.
12 2004 28 12 2004 1 3 e38Copyright: © 2004 Timothy Caulfield.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.Commercial influences on research results can in turn lead to "hype" in science and medicine news stories
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There is a growing recognition of the importance of the popular press in the communication of science. The media is often the primary source of science information and, as such, can have a profound impact on how the public views the risks and benefits of scientific advances. Dorothy Nelkin suggests that the “media serve as brokers between science and the public, framing the social reality for their readers and shaping the public consciousness about science related events” [1].
Because of this influential role, many commentators have been highly critical of the quality of media reporting, suggesting that reporting is “hyped”, irresponsible, and hurtful to the public's understanding of important scientific issues. In 1999, the United Kingdom House of Commons Science and Technology Committee was concerned enough to recommend that the media be governed by a Code of Practice that “stipulates that scientific stories should be factually accurate” [2].
But is it fair to point an accusing finger solely at the popular press? There are many examples of science reporting that has been less than perfect, such as the coverage of behavioural genetics and human cloning. And there is no doubt that an entertaining or controversial spin will win out over a muted message. But there is also evidence that the media does a surprisingly good job [3], often accurately conveying information found in peer-reviewed journals. A more subtle problem, and one that may have more long-term implications than simply bad reporting, is the faithful portrayal of commercially influenced research results.
A Marketing Message?
There is an expanding body of evidence that suggests that the increasingly commercial nature of biomedical research is having an impact on how science stories are portrayed. Studies have shown that papers in peer-reviewed journals are more likely to contain positive findings if the research is funded by industry [4]. A study that examined pharmaceutical research found that “among the authors of original research papers, reviews and letters to the editor that were supportive of the drugs' use, 96% had financial relationships with the drugs' manufacturers; for publications deemed neutral or critical the figure was only 60% and 37% respectively” [5,6]. To make matters worse, there is also evidence that negative results are either de-emphasised or simply not published [7,8]. This bias is picked up by the popular press and conveyed, largely uncritically, to the public [3].
Commercial influences can spin a story
Commercial influence on public representations of science has the potential to create a skewed picture of biomedical research—a picture that emphasises benefits over risks, and predictions of unrealistic breakthroughs over a tempered explanation of the incremental nature of the advancement of scientific knowledge. In the area of genetics, for example, there is concern that this commercial influence will lead to a simplistic and overly deterministic view of the role of genes in human health and may have an adverse impact on public dialogue [1]. There is also concern that it will create unrealistic expectations about a given scientific advance or product. In the context of health care, this may lead to inappropriate and expensive utilisation patterns.
Given the increasingly close connection between the media's portrayal of science and the broader agenda of commercialisation, some media representations can be viewed as a subtle form of marketing, albeit often inadvertent. One commentator has gone so far as to suggest that, to a large degree, “medical news is actually unpaid advertising” [9].
Further Reading on Media Hype
This is not to say that science reporting is part of a coordinated effort to promote a particular product. On the contrary, there is rarely a specific product to promote, and the media is just looking for an interesting and intriguing story that will help sell papers. However, in the long run, a continued, systemic trend toward positive, industry-influenced reporting may operate in much the same way as an explicit promotional campaign. In fact, optimistic media portrayals could be considered more powerful than promotional campaigns. The message is separated from an obvious marketing agenda and often includes a trusted voice, such as a university-based researcher. Paradoxically, this trust is based in part on a belief in the perceived independence of university researchers.
Balancing the Message
There is nothing inherently wrong with commercial involvement in biomedical research. After all, in most countries with an advanced biomedical research infrastructure, commercial entities, rightly or not, are an essential element of the technology-development process. Nevertheless, we need to be sensitive to the influence of market forces on how science is represented to the public. Eventually, the public will catch on. And when they do, public trust in the biomedical research enterprise may be irreparably harmed.
In an increasingly knowledge-based economy, there seems to be little doubt that private industry will continue to play a significant role in the funding of science. The research community must adjust to this inevitability by taking steps to ensure that portrayals of science remain as balanced as possible. As thoughtfully noted by Tom Wilkie: “If science is to manage the transition from its older, academic tradition to a new style, while keeping popular assent and the popular image of science as an impartial means of getting at the truth, then the scientific community itself must recognise the importance of maintaining impartial sources of public information.” [10]
What can be done? For a start, reporters should always ask for and researchers should always offer information about the nature of the funding and the financial relationship of the researchers to the sponsor. Increasingly, this information is disclosed in peer-reviewed journals. However, it may not be communicated in other popular representations of research results. As motivation, the research community should remember that the media also likes a good conflict-of-interest story [11]. Complete transparency should be the understood standard practice.
The research community should also consider the establishment of various sources of independent science information, including a venue for the publishing of negative results and a list of respected researchers who may be able to provide an alternative view. Not only would this create an outlet for results that do not correspond with commercial interests, it would also serve as a resource for the media. Reporters are often under extremely tight deadlines, and it is not always easy to find an independent second opinion, an indispensable component of balanced reporting.
Naturally, commercial pressure isn't the only source of science hype, and it is understandable that researchers may want to promote their latest findings. But commercial influence is emerging as a known source of bias, and it is a phenomenon that could have a profound impact on how the public perceives the entire research enterprise. Developing strategies, starting with the modest ones suggested above, seems to be an essential element of any communication strategy.
Citation: Caulfield T (2004) The commercialisation of medical and scientific reporting. PLoS Med 1(3): e38.
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Bubela TM Caulfield TA Do the print media “hype” genetic research? A comparison of newspaper stories and peer-reviewed research papers CMAJ 2004 170 1399 1407 15111473
Bhandari M Busse JW Jackowski D Montori VM Schunemann H Association between industry funding and statistically significant pro-industry findings in medical and surgical randomized trials CMAJ 2004 170 477 480 14970094
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van Kolfschooten F Conflicts of interest: Can you believe what you read? Nature 2002 416 360 363 11919595
Cassels A Hughes MA Cole C Mintzes B Lexchin J Drugs in the news: An analysis of Canadian newspaper coverage of new prescription drugs CMAJ 2003 168 1133 1137 12719316
Koren G Klein N Bias against negative studies in newspaper reports of medical research JAMA 1991 266 1824 1826 1890712
Zuckerman D Hype in health reporting: “Checkbook science” buys distortion of medical news Int J Health Serv 2003 33 383 389 12800894
Wilkie T Sources of science: Who can we trust? Lancet 1996 347 1308 1311 8622510
McComas K Simone L Media coverage of conflicts of interest in science Sci Commun 2003 24 395 419
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046210.1371/journal.pmed.0010040Policy ForumBioethicsBiotechnologyGenetics/Genomics/Gene TherapyEpidemiology/Public HealthHealth PolicyNutritionToxicology/Environmental HealthGeneticsNutrition and MetabolismMedicine in Developing CountriesPublic HealthHealth PolicyStrengthening the Role of Genomics in Global Health Policy ForumAcharya Tara Daar Abdallah S Thorsteinsdóttir Halla Dowdeswell Elizabeth Singer Peter A *All authors are at the University of Toronto, Toronto, Canada. Tara Acharya is a research associate in the Joint Centre for Bioethics. Abdallah S. Daar is director of ethics and policy at the McLaughlin Centre for Molecular Medicine and professor of public health sciences and surgery. Halla Thorsteinsdóttir is assistant professor and Elizabeth Dowdeswell is visiting professor in the Joint Centre for Bioethics and the Department of Public Health Sciences. Peter A. Singer is the Sun Life financial chair and director of the Joint Centre for Bioethics and professor of medicine.
Competing Interests: The authors declare that they have no competing interests.
*To whom correspondence should be addressed. E-mail: [email protected] 2004 28 12 2004 1 3 e40Copyright: © 2004 Acharya 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.How genomics and related health biotechnologies can improve the health of the poor and contribute towards meeting the Millenium Development Goals
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Development experts and policy makers agree that investment in science and technology is important for economic growth and development. The 2001 United Nations (UN) Development Programme report, Making New Technologies Work for Human Development, identified technical progress as the largest factor in reducing mortality rates and improving life expectancy from 1960 to 1990 [1]. A May 2004 report to UN Secretary-General Kofi Annan from the InterAcademy Council on Science and Technology Capacity supports the view that mobilization of sound scientific knowledge and evidence-based principles is needed to address critical world issues such as poverty, disease, and the effects of globalization and economic transformation [2]. Annan himself has drawn attention to the importance of science capacity for global development, observing that “no nation can afford to be without its own [science and technology] capacity” [3].
This capacity is essential if the world is to achieve the UN Millennium Development Goals (MDGs), which were adopted by all UN members in 2000 in a commitment to promote sustainable development and eliminate poverty in the world. As part of the Millennium Project, the UN established task forces to come up with strategies to help developing countries achieve the MDGs. One of these is the Task Force on Science, Technology, and Innovation (Task Force 10), created in recognition of the fact that many of the goals, especially those related to health and the environment, cannot be realized without a strong contribution from science and technology [4]. In a report titled Genomics and Global Health, presented recently to Task Force 10, we addressed how genomics and related health biotechnologies can improve global health and contribute towards meeting the MDGs [5]. The report shows how the world can unite in a global approach to meet these objectives and what steps developing countries themselves are taking to harness these technologies. The main findings of the report are summarized in our conclusions below.
Genomics Can Contribute to the MDGs
Genomics refers to the powerful new wave of health-related life sciences energized by the human genome project and the knowledge and tools it is spawning. It is a relatively new science that has tremendous potential to address health problems in developing countries.
The role of genomics in global health has been highlighted previously in the World Health Organization's 2002 report [6], and explored further in a technology foresight exercise by the University of Toronto Joint Centre for Bioethics [7]. Genomics-related technologies, including DNA sequencing and bioinformatics, were once considered expensive, exotic, and applicable only to wealthy nations, but this perception has been changing over the past few years. Through the efforts of companies and institutions worldwide, certain applications have become simpler and cheaper to the point that they can start replacing older technologies that are currently used for health care in poorer nations. Such simple and easy-to-use tests are being developed for tuberculosis, hepatitis C, HIV, malaria, and other diseases (e.g., the OptiMAL rapid malaria test [http://www.malariatest.com/]). Recombinant vaccines, a result of genetic engineering, promise to be safer, cheaper, and possibly easier to store and transport than traditional vaccines [7]. Microorganisms with remarkable biochemical properties show promise of being able to reduce pollution, making water safer to drink [8]. Table 1 provides a snapshot of how genomics and related biotechnologies can support some important MDGs [9]; a more complete discussion can be found in our report [5].
Table 1 Genomics and Related Biotechnologies Can Support the MDGs
aSource: Global Fund to Fight AIDS, Tuberculosis, and Malaria (http://www.theglobalfund.org)
GM, genetically modified; STD, sexually transmitted disease
What the International Community Can Do
In Genomics and Global Health
[5], we argue that genomics knowledge should be considered a global public good [10]. We need to establish a governance mechanism that fosters a balance between genomics knowledge as a public good and the application of this knowledge to foster private-sector interests. We propose the creation of a global partnership, the Global Genomics Initiative (GGI), to promote genomics for health. We see this as a network of industry leaders, academics, concerned citizens, non-governmental organizations, and government officials, with strong representation from the developing world. The proposed GGI would highlight broad actions that should be taken at the global level to apply genomics to development issues in this new era of globalization. Participation in the GGI would strengthen capacity in biotechnology worldwide by increasing international and inter-sectoral exchange of knowhow, and encouraging partnerships between countries.
The GGI could also facilitate the sharing of good practices. For example, the Canadian Prime Minister, Paul Martin, in his February 2004 reply to the Speech from the Throne, set a long-term target for Canada to devote 5% of its research and development spending to the challenges of developing countries. If successfully implemented and replicated by other industrialized countries, this target could make a real difference to global health.
What Developing Countries Can Do
Our report concludes that the key actors are developing countries themselves. We explore how to put genomics and related technologies to work in developing countries within the next 5–10 years. Developing countries with the scientific capacity and institutional arrangements that allow creation, utilization, adaptation, and diffusion of genomics are well positioned to harness this new science for development (Figure 1). Learning is important for building genomics capacity, and is central to the creation of national systems of innovation (NSIs) in biotechnology in developing countries.
Figure 1 A Rapid Test for HIV Used in Jaipur, India
Biotechnology has a vital role to play in developing better diagnostic tools for diseases such as HIV, tuberculosis, and malaria. (Photo: World Health Organization/P. Virot)
Today there are examples of strategies that some developing countries, including China, Cuba, Brazil, India, and South Africa, have followed to institute learning processes that are helping them to build NSIs in biotechnology. China seized the opportunity to take part in the Human Genome Project and quickly set up major institutions in genomics, such as the Beijing Genomics Institute, equipped with state-of-the-art sequencing facilities and computers. It has also followed a strategy of private-sector development in line with the NSI framework. Because of a government policy encouraging their return, Chinese expatriates are increasingly active in setting up small health biotechnology firms, bringing to the country knowledge from the world's major centers for genomics and related technologies.
The government in Cuba became interested in biotechnology in the early 1980s when the field was still in its infancy and created an interdisciplinary group, the Biological Front, to explore the possibilities of the technology for Cuba. It has continued to support biotechnology even during periods of economic hardship, set up institutions with research, development, and manufacturing facilities, and encouraged linkages between these institutions by setting up a biotechnology cluster, the West Havana Scientific Pole. Encouraging linkages has been a core policy of the government in Cuba, and its health biotechnology development has benefited from the ties with a strong public health system.
Brazil has a relatively long history of supporting science and technology, and the country is increasingly focusing on genomics and related technologies. The lack of commercialization of its cutting edge science and technology has been a weakness of the system in Brazil, but the country is now trying to overcome this weakness by proposing an “Innovation Law” that encourages cooperation between universities and the private sector.
Since its independence in 1947, India has followed a vision to improve the quality of life of its people by emphasizing science and technology. Limited resources and a patenting system that did not allow patenting of pharmaceutical products but only patenting of processes encouraged firms to come up with low-cost process innovation. This has resulted in health products such as the Shanvac-B hepatitis B vaccine, which is produced in India for a fraction of the cost in developed countries.
The South African government's Biotechnology Advisory Committee recognizes that successful commercialization of public-sector-supported research and development requires strong linkages within the NSI. The committee has recommended the creation of several Regional Innovation Centres to act as nuclei for the development of biotechnology platforms that can effectively launch new products and services. These strategies will provide important lessons for many other developing nations as they begin participating in the genomics revolution.
Six Conclusions to Our Report
First, the development gap between developing countries and the industrialized world continues to grow. The international community is beginning to promote science and technology to reduce this gap. The genomics revolution holds tremendous potential to improve health in developing countries and, if harnessed appropriately, could help to reduce the development divide.
Second, genomics and related biotechnologies can help to achieve the UN MDGs. Fast, accurate molecular diagnostic devices, safer recombinant vaccines, female-controlled vaginal microbicides, and low-cost bioremediation tools are some examples of biotechnologies that can have an impact.
Third, genomics knowledge has the characteristics of a global public good. In order to harness the benefits of genomics for development, the developing world needs, above all, access to genomics knowledge. Fourth, the promotion of the science of genomics as a global public good and the encouragement of global knowledge flows could best be achieved through international partnerships. A GGI involving an international partnership of public and private entities from both developed and developing countries could catalyze genomics knowledge and learning worldwide.
Fifth, countries that have genomics capacity are best positioned to take advantage of the genomics revolution to meet their health needs. For the transfer of technologies to be effective and sustainable, it must be accompanied by transfer of science and knowledge. As well, receiving countries must have the capacity to absorb and use the technology.
And sixth, learning is important for building genomics capacity, and is central to the creation of NSIs in biotechnology in developing countries. These countries can strengthen the building blocks of the NSI framework by doing the following: re-energizing academic institutions and public-sector research to strengthen their science base; training people and building human capital to use, adapt, and innovate biotechnologies; encouraging regional and international cooperation to create new channels for knowledge exchange and trade; improving the policy environment (including intellectual property laws and regulation) to encourage the building of capacity; and fostering the growth of the private sector, encouraging it to address local health needs, and strengthening linkages between public and private sectors to create new biotechnology goods and services.
The Canadian Program on Genomics and Global Health is primarily supported by Genome Canada through the Ontario Genomics Institute and the Ontario Research and Development Challenge Fund. Matching partners are listed at http://www.geneticsethics.net. ASD is supported by the McLaughlin Centre for Molecular Medicine. PAS is supported by a Canadian Institutes of Health Research Distinguished Investigator award.
Citation: Acharya T, Daar AS, Thorsteinsdóttir H, Dowdeswell E, Singer PA (2004) Strengthening the role of genomics in global health. PLoS Med 1(3): e40.
Abbreviations
GGIGlobal Genomics Initiative
MDGMillennium Development Goal
NSInational system of innovation
UNUnited Nations
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United Nations Development Programme Human development report. 2001: Making new technologies work for human development 2001 Oxford Oxford University Press 278
InterAcademy Council Inventing a better future: A strategy for building worldwide capacities in science and technology 2004 Available: http://www.interacademycouncil.net/reportasp?id=6258 . Accessed 27 September 2004
Annan K Science for all nations Science 2004 303 925 14963291
United Nations United Nations millennium development goals 2004 Available: http://www.un.org/millenniumgoals/ . Accessed 27 September 2004
University of Toronto Joint Centre for Bioethics, Genomics Working Group of the Science and Technology Innovation Task Force of the United Nations Millennium Project Genomics and global health Toronto: Canadian Program on Genomics and Global Health 2004 In press
World Health Organization Genomics and world health: Report of the Advisory Committee on Health Research 2002 Geneva World Health Organization Available: http://www3.who.int/whosis/genomics/pdf/genomics_report.pdf . Accessed 27 September 2004
Daar AS Thorsteinsdóttir H Martin DK Smith AC Nast S Top ten biotechnologies for improving health in developing countries Nat Genet 2002 32 229 232 12355081
Santini JM Sly LI Schnagl RD Macy JM A new chemolithoautotrophic arsenite-oxidizing bacterium isolated from a gold mine: Phylogenetic, physiological and preliminary biochemical studies Appl Environ Micriobiol 2000 66 92 97
Acharya T Daar AS Singer PA Biotechnology and the UN's Millennium Development Goals Nat Biotechnol 2003 21 1434 1436 14647321
Thorsteinsdóttir H Daar AS Smith RD Singer PA Genomics—A global public good? Lancet 2003 361 891 892 12648966
Joint United Nations Programme on HIV/ AIDS 2004 report on the global AIDS epidemic 2004 Available: http://www.unaids.org/bangkok2004/GAR2004_html/GAR2004_00_en.htm . Geneva: Joint United Nations Programme on HIV/AIDS. Accessed 28 September 2004
World Health Organization Unfinished business: Global push to save 11 million children 2002 Available: http://www.who.int/inf/en/pr-2002-18.html . Accessed 28 September 2004
World Health Organization Fact sheet No. 112: Water and sanitation 1996 Available: http://www.lifewater.org/fact112.htm . Accessed 27 September 2004
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046310.1371/journal.pmed.0010043Health in ActionEpidemiology/Public HealthHIV/AIDSMedical EducationSexual HealthHIV Infection/AIDSMedicine in Developing CountriesHealth education (including prevention and promotion)Sexual HealthSafe sexPicturing AIDS: Using Images to Raise Community Awareness Health in ActionMapara Edwin *Morley David David Morley is the founder and president of Teaching-aids At Low Cost (TALC), St Alban's, United Kingdom. Edwin Mapara is a postgraduate student studying for an MSc in infectious diseases at the London School of Hygiene and Tropical Medicine, United Kingdom.
*To whom correspondence should be addressed. E-mail: [email protected]
Competing Interests: The authors declare that they have no competing interests.
12 2004 28 12 2004 1 3 e43Copyright: © 2004 Mapara and Morley.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 Botswana, explicit color photos of people with AIDS have been used to spread knowledge, with the aim of saving lives
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Southern Africa poses special problems for AIDS educators and health care workers. Because there is a strong tradition of oral communication in the region, written educational materials often do not have as much impact as the spoken word. We have found that using colour images of HIV/AIDS in a workshop setting to provoke discussion can be a useful alternative to more conventional, written materials. In this article, we discuss our experience of using such images to raise community awareness about the AIDS epidemic in Botswana.
Who We Are
Teaching-aids At Low Cost (TALC) is a nongovernmental organisation that supplies cheap teaching aids and books to raise standards of health care and standards of living—especially in poverty-stricken areas—worldwide (http://www.talcuk.org/). The organisation has traditionally focused on developing countries, particularly sub-Saharan Africa and Asia. In recent years, TALC has become more global; it now distributes materials to more than 200 countries and sends educational materials on CD-ROM at no cost to health workers in developing nations.
In 1964, TALC was founded at the London School of Hygiene and Tropical Medicine as a way of providing low-cost colour transparencies to help students from resource-poor countries to teach after they returned home. By the early 1980s, nearly half a million transparencies were being sold at cost each year. Those who used them came to appreciate how important colour images could be, particularly amongst people who have grown up in societies where knowledge is spread primarily through oral communication and less use has been made of the written word.
Early on in the HIV/AIDS epidemic, we decided that our experience of distributing visual teaching materials could be used to spread information about this new pandemic, which was hitting African societies particularly hard. We produced four sets of 24 colour transparencies on HIV/AIDS, with a detailed accompanying text.
Edwin Mapara: The Botswana Experience
Today, there are an estimated 260,000 people in Botswana living with HIV. This—in a country with a total population of 1.6 million—gives Botswana a prevalence rate of 36.5%, the second highest in the world after Swaziland [1].
As a medical student in Zambia in 1985, I studied patients with Kaposi's sarcoma. The consultant in charge appreciated that this was due to HIV infection, but when she started to acknowledge this publicly, she was strongly censored by the existing authorities and was almost forced to leave the country. I realised that if this kind of denial persisted, the epidemic would spread more widely and would become an even greater disaster. I wanted to try to bring home to both the authorities and the African people the truth about the spread of the disease and the need for fundamental changes in sexual behaviour.
In 1990, on qualifying, I took up a post at the Athlone Hospital, a 175-bed district hospital in the Lobatse region of Southern Botswana. I joined other health workers who shared my concerns. We started the Athlone Anti-AIDS Project to address HIV prevention and care both in the hospital and in the wider community. We began to have organised discussions with local people about HIV/AIDS. The response we heard was often, “You talk about this terrible disease, which may affect us, but show us a patient”. This is how we came to use a set of slides from TALC, in a teaching programme that the Ministry of Health in Botswana called “radical and insensitive”.
We emphasised the essential messages about AIDS prevention by using coloured pictures of black Africans. These pictures included explicit images of ulcers on a penis and a vagina. The slides included clinical manifestations of HIV/AIDS (such as herpes zoster and Kaposi's sarcoma) and other sexually transmitted diseases, images that explained the basic virology and transmission of HIV (Figure 1), and images about HIV prevention (such as condoms) and care (such as caring for orphans infected by HIV/ AIDS).
Figure 1 Don't Worry—Only a Few Sticks
This slide is used in workshops to show that while we only see a handful of patients with symptomatic HIV, many more of us are HIV positive and are infecting others; we do not know our HIV status, since we have not been tested and we have no symptoms. (Illustration: TALC)
Showing these pictures to local people was hugely controversial. For example, some elderly participants walked out when they saw the explicit pictures. Some community members approached local political counsellors to voice their concerns about a “decay of culture” and a “lack of respect”. Some parents did not want their children to see the images at all, because “they would corrupt their morals and young minds” and would encourage children to “experiment with sex”. As the team leader, I was fined chickens on several occasions by local chiefs and elders for the “crime” of showing these explicit TALC slides.
The government gave our teaching project very little support in the early days; we were even cautioned by the highest authorities at the Ministry of Health. The Church, too, wanted nothing to do with our programme of “loose morals”.
Despite these obstacles, over the next ten years we held over a hundred workshops, which eventually involved all government departments and levels of society in Botswana. Today, we are still using the same pictures. In 2000, the United Nations Development Programme declared Athlone Hospital's initiative as one of the “best practices” in Botswana [2], and it is being replicated nationwide. TALC slides have been shown from the pulpits of churches, and community members will ask for the colour pictures specifically when the team is invited to lead a workshop.
Given the terrible impact that AIDS has had on the community, the same community members who once resisted our teaching project ask us angrily why doctors were not sufficiently aggressive in using pictures in the early days of AIDS. One telling statement made in a workshop was: “you doctors are to blame for what has happened to Africa, and particularly to our children. You should have done this ten years ago before one quarter of the population became infected. The blood of our children, who have died, rests on your heads”.
Making the Best Use of Pictures
In Botswana, I used a slide projector and occasionally a mobile electrical generator, but such equipment is not widely available in most African countries. As an alternative to using colour slides, TALC has developed a folded A4 (210 mm × 279 mm) sheet with 12 colour images as a way of presenting the important messages about HIV/AIDS. This leaflet is available on request; E-mail: [email protected] (or mail TALC, P. O. Box 49, St. Albans, AL1 5TX, United Kingdom).
In our experience, the slides or leaflet work best if you can get the participants to sit in small groups for discussion. Each group should have at least one set of pictures. In your introduction, mention that to talk about sex or death is not taboo in a world of AIDS. Encourage active participation by all.
Show one picture at a time—”let the picture talk”—and do not initially look at the accompanying text. Ask the participants to describe what they see in English or in the vernacular. Encourage participants to work out for themselves what the message is in the picture. Discuss all the possible answers. Then look at the text that accompanies the picture. Provide an answer built from the participants' words. If appropriate, ask people about their own relevant experiences. Finally, ask the participants to pin the pictures to the wall, making sure that each picture is put up by a different participant. Revise the lessons learned at the end of the session. Revise again, weeks later, if possible. At the end of the day, the participants should be able to say, “we did it by ourselves”.
The pictures show examples of how HIV/AIDS can affect people. They must not be thought of, or used, as a way to diagnose HIV/AIDS in participants or their relatives or friends. Emphasise to all participants that if they have any reason to suspect that they (or anyone they know) have HIV/ AIDS, they should attend a clinic where trained health workers can help them.
Conclusion
For people in Botswana, “seeing is believing”. Written descriptions are often not enough; showing pictures of herpes zoster, syphilis ulcers, or tuberculosis lymphadenopathy can be a powerful teaching tool. Once the initial shock is overcome, these colour pictures offer a straightforward way to demonstrate the realities of the disease far and wide.
Citation: Mapara E, Morley D (2004) Picturing AIDS: Using photos to raise community awareness. PLoS Med 1(3): e43.
Abbreviation
TALCTeaching-aids At Low Cost
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AVERT HIV and AIDS in Botswana 2004 Available: http://www.avert.org/aidsbotswana.htm Accessed 7 October 2004
Botswana Press Agency Athlone resource centre is number one Daily News Online 2000 12 6 Available: http://www.gov.bw/cgi-bin/news.cgi?d=20001206 . Accessed 7 October 2004
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046410.1371/journal.pmed.0010052The PLoS Medicine DebateBioethicsEthicsResource Allocation and RationingIs It Ethical to Use Enhancement Technologies to Make Us Better than Well? The PLoS Medicine DebateCaplan Arthur Elliott Carl Arthur Caplan is chair of the Department of Medical Ethics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America. E-mail: [email protected]
Carl Elliott is associate professor at the Center for Bioethics at the University of Minnesota, Minneapolis, Minnesota, United States of America, and the author of Better Than Well: American Medicine Meets the American Dream. E-mail: [email protected]
Competing Interests: AC was a member of Dupont's biotechnology advisory panel, advising on genetically modified organisms. He previously served on the scientific advisory board of Celera genomics. From 1997–1999 he served as a consultant to Pfizer on the launch of sildenafil (Viagra) as part of the company's scientific/ethics advisory board. Subsequently Pfizer sponsored a course on research ethics presented by the Center for Bioethics at Pfizer headquarters in which he was one of the lecturers. CE declares that he has no competing interests.
12 2004 28 12 2004 1 3 e52Copyright: © 2004 Caplan and Elliott.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.Background to the debate: A variety of biomedical technologies are being developed that can be used for purposes other than treating disease. Such “enhancement technologies” can be used to improve our appearance and regulate our emotions, with the goal of feeling “better than well.” While these technologies can help people adapt to their rapidly changing lifestyles, their use raises important ethical issues.
Biomedical "enhancement technologies" are being developed that can be used for purposes other than treating disease, such as improving our appearance and regulating our emotions
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Arthur Caplan's Viewpoint: Nobody Is Perfect—But Why Not Try to Be Better?
Perfection has come in for a lot of bad press recently. A torrent of books and articles has recently appeared [1,2,3,4,5,6,7,8,9], all raising serious ethical questions about the wisdom and morality of trying to use biomedical knowledge to perfect ourselves or our offspring.
Biomedical scientists and physicians might be inclined to ignore this literature as just so much abstract philosophical handwringing. After all, it is almost impossible to find mainstream scientists arrogant enough to proclaim their interest in perfecting anything, much less themselves or their fellow human beings.
Beating up on the pursuit of perfection is silly. As Salvadore Dali famously pointed out, “Have no fear of perfection—you'll never reach it.” Critics of those who allegedly seek to perfect human beings know this. While often couching their critiques in language that assails the pursuit of perfection, what they really are attacking is the far more oft-expressed—albeit far less lofty—desire to improve or enhance a particular behavior or trait by the application of emerging biomedical knowledge in genetics, neuroscience, pharmacology, and physiology. Those who might accurately be termed “anti-meliorists” wonder how we will ever resist the obvious temptation to put this knowledge to use to alter ourselves. They are quick to note that we have already given in to such temptation—we augment our breasts, smooth our wrinkles, and pump ourselves full of antidepressants.
It is in our nature as humans to strive for self-improvement
(Illustration: Margaret Shear)
Putting the brakes on biologically driven human betterment would have real consequences for science. Some lines of research would be slowed or restricted [3,5,8]. Their application would be declared off-limits or at least tightly regulated [1,2,3,4,5,7,8,9].
Why is the drive to improve ourselves so disturbing to the anti-meliorists? Their arguments cluster around three key worries: that the pursuit of perfection by biomedical means is vain, selfish, and unrewarding [1,2,3,6,7], that improving ourselves is unfair [1,3,4], and that enhancement or improvement violates human nature [2,4,5,7,8,9] and may actually destroy it [2,5,7,9]. It is the last of these arguments that is at the core of anti-meliorist concerns.
It cannot simply be the pursuit of improvement that is making anti-meliorists nervous. Many religious traditions and spiritual movements seek perfection [10,11,12,13], but these evoke no negative commentary from the anti-meliorists. Nor do efforts to improve animals and plants set this crowd aflutter. Rather, it is biomedical knowledge being applied to you and me that is the crux of their concern. They fear that in applying new biomedical knowledge to improve human beings, something essential about humanity will be lost. If biomedical tinkering is allowed, we will destroy the very thing that makes us human—our nature.
Anti-meliorism rests, however, on a very shaky foundation. To support their position, the anti-meliorists must state what human nature is. They do not. They must also be very clear about why they see human nature as static. They are not. And they must advance an argument about why human nature, which has presumably evolved in response to an enormous array of random forces, tells us anything about what is good or desirable in terms of the traits humans should possess. They cannot.
The fight over whether there is any such thing as human nature is a long-standing one [14]. But one can concede that we are shaped by a causally powerful set of genetic influences and still remain skeptical as to whether these produce a single “nature” that all members of humanity possess. Is there a single trait or fixed set of traits that defines the nature of who we are and have been throughout our entire existence on this planet? Unless they can articulate this Platonic essence, anti-meliorists do not have a foundation for their argument that change, improvement, and betterment are grave threats to humanity.
Worse still for anti-meliorists, we are clearly creatures who have long tinkered with ourselves, using all manner of technologies from clothing to telescopes to computers to airplanes. Our view of our “nature” is closely linked to the technologies that we have invented and to which we have adapted [15]. We are already technological creatures.
Nor is there any normative guidance offered by our evolutionary history that shows why we should not try to improve upon the biological design with which we are endowed. Augmenting breasts or prolonging erections may be vain and even a waste of scarce resources, but seeking to use our knowledge to enhance our vision, memory, learning skills, immunity, or metabolism is not obviously either.
Ultimately, anti-meliorism posits a static vision of human nature to which the anti-meliorists mandate we reconcile ourselves. If anything is clear about human nature, it is that this is not an accurate view of who we have been or what we are now, or a view that should determine what we become.
Carl Elliott's Viewpoint: Pharma's Gain May Be Our Loss
Those of us who worry about medical enhancement are usually less worried about the technologies themselves than about the larger social effects of embracing them too enthusiastically. Just as you do not need to object to cars to worry about urban sprawl, you do not need to object to enhancement technologies to question where these technologies may be taking us. It is not just technophobes who wonder whether a society that consumes 90% of the world's supply of methylphenidate (Ritalin), where the most profitable class of drugs is antidepressants, and where cosmetic surgeons perform liposuction on prime-time television is a society that has somehow lost its way.
Let's look at three of the most commercially successful medical enhancements of recent years: selective serotonin reuptake inhibitors, hormone replacement therapy, and the diet drug fenfluramine-phentermine (Fen-Phen). What can we learn from these interventions?
First, the manufacturers of enhancement technologies will usually exploit the blurry line between enhancement and treatment in order to sell drugs. Because enhancement technologies must be prescribed by physicians, drug manufacturers typically market the technologies not as enhancements, but as treatments for newly discovered or under-recognized disorders. Selective serotonin reuptake inhibitors were marketed not as personality enhancers, or even only as treatments for clinical depression, but as treatments for questionable illnesses like “premenstrual dysphoric disorder” [16]. Fen-Phen was sold not as a mere diet drug but as a treatment for obesity, which Wyeth, the manufacturer, portrayed as a dangerous public health problem [17]. Estrogen replacement therapy was initially marketed as a risk-free way for women to extend their youthfulness. But when a 1974 study found that estrogen replacement therapy was associated with an increased risk of endometrial cancer, the manufacturers added progesterone, renamed the combination “hormone” replacement therapy, and recast it as a treatment for medical problems associated with menopause such as osteoporosis [6].
Where is the pursuit of the perfect face, body, and mind taking us?
(Illustration: Margaret Shear)
Second, an alarming number of supposedly risk-free enhancements have later been associated with unanticipated side effects, some of them deadly. Wyeth has set aside over $16 billion to compensate the thousands of patients who have developed valvular heart disease and pulmonary hypertension after taking Fen-Phen [18]. A 2002 National Institutes of Health study found that hormone replacement therapy was associated with such an elevated risk of heart disease, stroke, pulmonary emboli, and breast cancer that the study was stopped prematurely [19]. Selective serotonin reuptake inhibitors are currently embroiled in controversy over whether they are associated with an elevated risk of suicide [20].
Third, the most successful enhancement technologies have been backed by tremendously influential public relations campaigns. These campaigns have included ghostwritten journal articles, industry-funded front groups, and lucrative payments to academics, professional societies, and university centers [21]. For example, GlaxoSmithKline marketed paroxetine (Paxil) by promoting the previously obscure diagnosis of “social anxiety disorder” through phony support groups, celebrity spokespeople, a direct-to-consumer illness awareness campaign, and generous payments to key opinion leaders [22]. The manufacturers of estrogen replacement therapy marketed the hormone in the 1960s by funding a “research foundation” for Robert Wilson, the gynecologist and author of the best-selling book Feminine Forever
[6]. Wyeth marketed Fen-Phen by funding obesity research centers, launching public fitness campaigns, contracting with a medical education company to produce a series of ghostwritten journal articles, and making generous payments to academic physicians who then published extensively and testified for the drug's safety to the Food and Drug Administration [17].
The traditional worry about enhancement technologies is that users of the technologies are buying individual well-being at the expense of some larger social good. I may improve my own athletic ability by taking steroids, but I set off a steroid arms race that destroys my sport. I may get cosmetic surgery for my “Asian eyes” or use skin lighteners for my dark skin, but I reinforce the implicitly racist social norms that say that Asian eyes or dark skin are traits to be ashamed of. The worry is that some aspect of the way we live together, collectively, is going to be damaged by actions that we take individually [4].
A market-driven health-care system brings this worry much closer to home. The pharmaceutical industry is now the most profitable and politically powerful industry in the United States [23]. It also has a huge financial interest in creating a demand for enhancement technologies. The pharmaceutical industry can buy politicians to pass industry-friendly legislation; it can buy academic scientists to publish favorable journals articles; it can buy professional societies and patient support groups to spread the word on the newly medicalized disorders that its interventions are developed to treat [24]. It can even buy bioethicists to dispense with any moral concerns [25]. In this kind of political and economic climate, how likely is it that dissenting voices will have any effect before it is too late?
Caplan's Response to Elliott's Viewpoint
Elliott professes to be unhappy about enhancement. What arguments does he present to support his unhappiness? Not many, and the arguments that he does offer miss the point completely.
If people want to feel better, sleep less, have fewer hot flashes, better vision, or fewer wrinkles, then they may want to use enhancement technologies to achieve these things. Technology in itself isn't driving us in any particular direction—I believe that we decide where it should go. Elliott, however, gravely warns us that you and I do not really decide a direction when it comes to matters of enhancement. It is—listen carefully for the Darth Vader–esque hissing—drug companies!
The rest of Elliott's viewpoint amounts to what is his increasingly familiar harangue against the pharmaceutical industry. The drug companies sucker us into buying enhancement by getting us hooked on pseudotherapies. The drug companies rob us of our will to fend off their siren-like messages of better living through their chemistry. And the drug companies get us feeling so bad about ourselves that we empty our wallets on their latest overpriced geegaws.
Pharmaceutical companies may be evil incarnate. And we may be putty in their pecuniary little hands. But that has nothing at all to do with the question of whether there is anything wrong with pursuing enhancement. When Elliott eagerly dons his hair shirt to bemoan Big Pharma, he finds so much sin to revel in that he forgets to give a reason, any reason, why enhancement is, in itself, immoral.
At most he presents an argument for keeping the pharmaceutical industry out of enhancement. Okay, so let's take Big Pharma out of the picture. If we left the encouragement of enhancement to the government, the military, schools, foundations, doctors, or parents, would this now be morally acceptable? I think sometimes it would be. And nothing that Elliott says provides any reason to think otherwise.
Elliott's Response to Caplan's Viewpoint
Caplan does not defend medical enhancement so much as attack its critics. Or rather, he attacks a small group of conservative critics who want to preserve “human nature.” He dispatches those critics with admirable precision, but I am not sure why he believes that group of critics includes me. My worry about enhancement technologies has little to do with human nature. My worry is that we will ignore important human needs at the expense of frivolous human desires; that dominant social norms will crowd out those of the minority; that the self-improvement agenda will be set not by individuals, but by powerful corporate interests; and that in the pursuit of betterment, we will actually make ourselves worse off.
It's no secret that many Americans are deeply ashamed of their personal shortcomings and inadequacies. Nor is it any secret that these shortcomings and inadequacies can be exploited for commercial profit. But do we really want to submit our health-care system to the same forces that have made millionaires out of motivational speakers and diet book authors?
Skepticism about enhancement technologies is not equivalent to a wish to set back medical research and declare some applications off-limits. This is a debate about enhancing human traits, not curing human illness. To say that our medical research agenda will be set back if we restrict enhancement technologies makes no more sense than saying that cancer surgery will be set back if the American Broadcasting Corporation cancels its cosmetic surgery reality TV show Extreme Makeover.
We live in a country where 46 million uninsured people cannot get basic medical care, while the rest of us spend a billion dollars a year on baldness remedies. It is not just the inequity here that is so impressive. It is the fact that we have gotten so accustomed to the inequity that we do not see it as obscene.
Citation: Caplan A, Elliott C (2004) Is it ethical to use enhancement technologies to make us better than well? PLoS Med 1(3): e52.
Abbreviation
Fen-Phenfenfluramine-phentermine
==== Refs
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Elliott C Better than well: American medicine meets the American dream 2003 New York W. W. Norton 357
Fukuyama F Our posthuman future: Consequences of the biotechnology revolution 2003 New York Picador 272
Rothman S Rothman D The pursuit of perfection: The promise and perils of medical enhancement 2003 New York Pantheon Books 292
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Elliott C Pharma buys a conscience Am Prospect 2004 12 17 16 20
| 15630464 | PMC539045 | CC BY | 2021-01-05 10:38:03 | no | PLoS Med. 2004 Dec 28; 1(3):e52 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010052 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046510.1371/journal.pmed.0010055Policy ForumOtherEpidemiology/Public HealthHIV/AIDSPublic HealthMedical consequences of war/conflictHuman Carrying Capacity and Human Health Policy ForumButler Colin D Colin D. Butler is a research fellow at the National Centre for Epidemiology and Population Health at the Australian National University, Canberra, Australia. E-mail: [email protected]
Competing Interests: The author declares that he has no competing interests.
12 2004 28 12 2004 1 3 e55Copyright: © 2004 Colin D. Butler.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 issue of overpopulation has fallen out of favor among most contemporary demographers, economists, and epidemiologists. Discussing population control has become taboo. This taboo could be hazardous to public health
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The issue of human overpopulation has fallen out of favor among most contemporary demographers, economists, and epidemiologists. Discussing population control has become a taboo topic. Yet, this taboo has major implications for public health.
The silence around overpopulation prevents the global health community from making the necessary link between the planet's limited ability to support its people (its carrying capacity—see sidebar on following page) and health and development crises. In this article, I describe how popular thinking on population control has been shaped over the last 200 years, and how our failure to address the population explosion may be one cause of recent epidemics and social unrest.
Overpopulation Concerns Peak, Then Decline
The question of human overpopulation and its relationship to human carrying capacity has been controversial for over two centuries. In 1798 the Reverend Thomas Malthus put forward the hypothesis that population growth would exceed the growth of resources, leading to the periodic reduction of human numbers by either “positive checks”, such as disease, famine, and war, or “preventive checks”, by which (in the absence of contraception) Malthus meant restrictions on marriage. This “Malthusian view” was rapidly accepted by most politicians, demographers, and the general public, and remained popular until fairly recently.
Malthus's worst fears were not borne out through the century following his death in 1834—food production largely kept pace with the slowly growing global population. However, soon after 1934, the global population began to rise steeply as antibiotics, vaccines, and technology increased life expectancy. By the 1960s, concerns of a mismatch between global population and global food supply peaked—expressed in books such as Paul Ehrlich's 1968 The Population Bomb
[1]. This book predicted a future scarred by increasing famine, epidemic, and war—the three main Malthusian positive checks.
In 1966, United States President Lyndon Johnson shipped wheat to India to avert a famine on the condition that the country accelerate its already vigorous family planning campaign [2]. Johnson was part of an unbroken series of US presidents concerned with the harmful effects of rapid population growth in developing countries. This line extended (at least) from John F. Kennedy to Jimmy Carter. George H. W. Bush was also sympathetic to this view, prior to becoming vice president in 1981.
But the 1970s surprised population watchers. Instead of being a period shadowed by calamitous famine, the new crop strains introduced by the “Green Revolution” (especially grains such as rice, wheat, and maize) caused a dramatic increase in the global production of cereals, the main source of energy in the global diet. Among the development community, despair turned into cautious optimism. By the end of the decade, the public health community felt sufficiently empowered to proclaim “Health for All by the Year 2000”. Average life expectancy continued to zoom upwards almost everywhere—even in sub-Saharan Africa.
The introduction of safe contraception contributed to a rapid fertility decline in many countries. But while the rate of global population growth declined from its peak in the late 1960s, the absolute increment of increase in annual global population continued to grow. Most population-related scientists, including food scientists and demographers, as well as US President Jimmy Carter, continued to be very concerned about global overpopulation. In 1970, the father of the Green Revolution, the agricultural scientist Norman Borlaug, was awarded the Nobel Peace Prize. In his Nobel lecture, Borlaug warned that the success of the Green Revolution would buy a breathing space for humankind of three decades, unless equivalent action was taken to reduce fertility rates [3]. China tightened its fertility policy in this decade, introducing its one-child policy in 1979.
We are failing to confront the population explosion
(Illustration: Sapna Khandwala)
Concern for the Third World Fades
With hindsight, the 1970s can be seen as the decade when widespread concern about overpopulation started to fade. The social and economic milieu of many developed countries, especially in the US, started to change. US foreign aid, as a percentage of the gross national product, declined from the late 1960s, perhaps in part because of the competing needs of the Vietnam War but also perhaps because of the apparent success of development in the Third World. The economic policies known as Keynesianism, which had been dominant since the end of World War II in many developed nations, came under sustained attack. These policies had placed a high value on full employment and social security. Keynesian policies restrained domestic inequality through high taxation and the promotion of social norms that censured conspicuous consumption (such as company executives exercising restraint in their personal salaries and people buying small houses). Shortly before his death, J. M. Keynes had also been crucially involved in the establishment of the World Bank. Keynes appears to have been personally committed to the advance of global justice, and to the reduction of inequality both within and between nations [4].
The world oil shock in 1973 contributed both to “stagflation”—a combination of rising unemployment with higher prices—and to increased economic power for the oil-producing countries of the Third World. Indeed, the term “Third World” came to be considered pejorative and was replaced by the “South”. Stagflation was interpreted as a failure of Keynesian policy. The demise of Keynesianism was accompanied by a further decline in concern for Third World development among elite economists and the general public.
It is unlikely that the issue of global population policy figured into the election that put US President Ronald Reagan into office in 1980. Nevertheless, Reagan's policies were to cement a new orthodoxy about global overpopulation and development strategies. Unlike his republican predecessor, Richard Nixon, Reagan considered concerns about global population size to be “vastly exaggerated” [5]. In the same year, the US surprised the family planning world by abdicating its previous leadership in the effort to promote global family planning, at the International Conference on Population, held in Mexico City in 1984. The US took this position against the strenuous opposition of the Population Association of America, which represented many US demographers [5].
As foreign aid budgets fell, the “Health for All” targets began to slip from reach. Instead, international agencies promoted structural adjustment programs, health charges for patients (“user fees”), and the “trickle down” effect as the best ways to promote development. It is plausible that a fraction of the public who remained concerned about Third World development thought that these new economic policies deserved a chance. Less charitably, the new economic policies also appeared to allow people already financially comfortable to abdicate concern for Third World development because the new orthodoxy asserted that market deregulation, rather than aid, was the royal road to development. The increased domestic inequality of recent decades in developed countries [6] probably also contributed to a reduction in concern for the Third World, as working people have had to struggle harder to keep their position in their own society.
It is now clear that market deregulation and generally high birth rates have proven disastrous in many Third World countries. “Health for All”, if recalled at all, is now seen as absurdly optimistic. The failure of development is most obvious in many sub-Saharan countries, where life expectancy has fallen substantially. But life expectancy has also fallen in Haiti, Russia, North Korea, and a handful of other nations [7]. The causes for this decline in life expectancy are multiple and complex. Causes that are usually listed include HIV/AIDS (Zimbabwe and Haiti) [8,9], ethnic hatred (Rwanda) [10], crop failure (North Korea) [11], poor governance and poverty (several parts of Africa) [12], and alcoholism (Russia) [13].
Causal theory is complex. Every cause has a cause, and, increasingly, causes are being considered as a part of causal chains, causal webs, and causal snowballs. Some theorists distinguish between identifiable “proximal” causes and deeper, underlying, or “distal”, causes [14]. Yet, among the multitude of causes that can be identified for declines in either total population or life expectancy, overpopulation is hardly considered, except by dissident public health workers such as Maurice King [15]. Demography, the discipline that would appear to be the most likely holder of the Malthusian baton, is now almost entirely silent about overpopulation in developing countries [16]. Instead, most mainstream demographers appear to consider population ageing and European underpopulation as the most important demographic issues for this century. On the other hand, the role of the rapid demographic transition in China (from large to small families, with an average of two or fewer children) is rarely credited as central to the Chinese economic miracle.
Overpopulation: A Cause of Crises in Africa?
Often, the carrying capacity of one region at one point in time is boosted by the appropriation of the carrying capacity from other people and even other generations. Such resources include oil, deep sea fish, and the stability of the global climate and ecological systems. But in Rwanda, the most densely populated country in Africa, the importation of such resources has long been limited. Unlike other densely populated countries such as Hong Kong and Holland, Rwanda's economy at the time of its most infamous genocide, in 1994, depended almost exclusively on its primary production [17]. The country had little industry, few exports, and little tourism. The price of its most important export, coffee, had declined steeply just before the genocide [18]. Unlike many Asian countries, Rwanda also received few remittances from Rwandans working as guest workers abroad [17].
Among the many different explanations for the horrific 1994 Rwandan genocide, the possibility of a Malthusian check (also called “demographic entrapment”) is scarcely mentioned [17,19]. A Malthusian check in Rwanda was plausible not only because the total population was too large, but perhaps more importantly because the rate of population growth in Rwanda was faster than the capacity of Rwandan society to process the additional people. As a result, many indicators of development went backwards. The limited agricultural capacity forced many young men into Kigali, causing a concentration of young men with few prospects other than what they might gain from violence.
There is even less scientific discussion that entertains the possibility that the sub-Saharan epidemic of HIV/ AIDS may also be a Malthusian check [19]. This is plausible if one applies a conceptual framework that combines the erosion of human carrying capacity through the same rapid population growth seen in Rwanda, with a consequent decline in per capita income and food supply. Furthermore, slowly operating feedbacks occurring as a result of the epidemic further undermined development, including the loss of human capital as teachers died [20], the loss of agricultural expertise as farmers died [21], and a deepening debt and loss of productivity from the countless funerals. And leaders in the developed world and many within Africa itself failed to devote the resources and provide the leadership required to quell the epidemic.
Conclusion
Maurice King refers to the silence on overpopulation as the “Hardinian Taboo”, named after the American ecologist Garett Hardin, who described the taboos that humans use to avoid confronting the need for population control [22]. Daniel Orenstein, at the Center for Environmental Studies at Brown University, has argued that powerful social norms inhibit debate about overpopulation in one of the world's most intractable trouble spots, Israel and Palestine [23].
Whatever the cause of the scarcity of modern academic analysis, the related issues of human carrying capacity and overpopulation deserve fresh consideration. The entrapment model has an explanatory power that is lacking in more superficial causal explanations. Of course, solving entrapment is very difficult, but as with most medical problems, a proper diagnosis will help identify the proper treatment.
Human Carrying Capacity
Human carrying capacity is the maximum population that can be supported at a given living standard by the interaction of any given human-ecological system. This apparently simple concept has many nuances and is rarely used by population scientists. However, in rejecting this term, purists risk making a terrible conceptual flaw, that of thinking that environmental and human resources are largely irrelevant to human population size.
It is irrefutable that human ingenuity and cooperation can increase human carrying capacity [24]. But even so, human welfare will continue to depend on the external world, including for resources such as food and water. Humans are neither computer ciphers nor caged mice. That is to say, while a given area might tolerate a theoretically higher density of human population than it does, the reality of human evolution in distinct groups, separated by culture, religion, and language, means that this theoretical maximum will rarely be attained. A degree of underused carrying capacity can be viewed as a desirable buffer around disparate groups, vital for reducing tension and preventing conflict.
Even culturally homogenous groups can outgrow their carrying capacity, as in the case of the Great Hunger in Ireland in the 1840s, when the population crashed because of famine, disease, and emigration. Indeed, Malthusian theory was used, in part, to justify the scanty aid provided to the Irish from Britain, a country that did not identify closely with the Irish.
Citation: Butler CD (2004) Human carrying capacity and human health. PLoS Med 1(3): e55.
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Caufield C Masters of illusion. The World Bank and the poverty of nations 1996 London Pan Books 432
Finkle JL Crane B Ideology and politics at Mexico City: The United States at the 1984 International Conference on Population Popul Dev Rev 1994 11 1 28
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McMichael AJ McKee M Shkolnikov V Valkonen T Global trends in life expectancy: Convergence, divergence—or local setbacks? Lancet 2004 363 1155 1159 15064037
United Nations Population Division Department of Economic and Social Affairs World Population Prospects: The 1998 Revision The Demographic impact of HIV/ AIDS 1999 New York United Nations
Farmer P Fawzi MCS Nevil P Unjust embargo of aid for Haiti Lancet 2003 361 420 422 12573389
Schwab P Africa: A continent self-destructs 2001 New York Palgrave 224
Goodkind D West L The North Korean famine and its demographic impact Popul Dev Rev 2001 27 219 238 18581641
Butler CD HIV and AIDS, poverty, and causation Lancet 2000 356 1445 1446
Leon DA Chenet L Shkolnikov VM Zakharov S Shapiro J Huge variation in Russian mortality rates 1984–1994. Artefact or alcohol? Lancet 1997 350 383 388 9259651
McMichael AJ Prisoners of the proximate. Loosening the constraints on epidemiology in an age of change Am J Epidemiol 1999 149 887 897 10342797
Abbasi K King in a maverick style BMJ 1999 319 942 10514154
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| 15630465 | PMC539046 | CC BY | 2021-01-05 10:38:04 | no | PLoS Med. 2004 Dec 28; 1(3):e55 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010055 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046610.1371/journal.pmed.0010056Neglected DiseasesInfectious DiseasesInfectious DiseasesMedicine in Developing CountriesMicrobiologyDrugs and adverse drug reactionsFinding Cures for Tropical Diseases: Is Open Source an Answer? Neglected DiseasesMaurer Stephen M *Rai Arti Sali Andrej Stephen M. Maurer is in the Goldman School of Public Policy, University of California, Berkeley, California, United States of America. Arti Rai is in the School of Law, Duke University, Durham, North Carolina, United States of America. Andrej Sali is in the Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry and the California Institute for Quantitative Biomedical Research, University of California, San Francisco, California, United States of America.
Competing Interests: The authors declare that they have no competing interests.
*To whom correspondence should be addressed. E-mail: [email protected] 2004 28 12 2004 1 3 e56Copyright: © 2004 Maurer 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.The Tropical Disease Initiative will be a Web-based, community- wide effort where scientists from the public and private sectors join together to discover new treatments
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Only about 1% of newly developed drugs are for tropical diseases, such as African sleeping sickness, dengue fever, and leishmaniasis [1]. While patent incentives and commercial pharmaceutical houses have made Western health care the envy of the world, the commercial model only works if companies can sell enough patented products to cover their research and development (R&D) costs. The model fails in the developing world, where few patients can afford to pay patented prices for drugs.
It is easy and correct to say that Western governments could solve this problem by paying existing institutions to focus on cures for tropical diseases. But sadly, there does not appear to be enough political will for this to happen. In any case, grants and patent incentives were never designed with tropical diseases in mind.
Two main kinds of proposals have been suggested for tackling the problem. The first is to ask sponsors—governments and charities—to subsidize developing-country purchases at a guaranteed price [2,3,4]. The second involves charities creating nonprofit venture-capital firms (“Virtual Pharmas”), which look for promising drug candidates and then push drug development through contracts with corporate partners. In this article, we discuss the limitations of these two approaches and suggest a third, “open source,” approach to drug development, called the Tropical Diseases Initiative (TDI). We envisage TDI as a decentralized, Web-based, community-wide effort where scientists from laboratories, universities, institutes, and corporations could work together for a common cause (see www.tropicaldisease.org).
Why Open Source?
The idea behind asking sponsors to subsidize developing country purchases at a guaranteed price is that this will prop up drug prices and restore incentives for developing new drugs [2,3,4]. In other words, it is a way of fixing the patent problem. However, subsidies have an important weakness: it is almost impossible to determine correctly how large the subsidy should be. In principle, the most cost-effective solution is to set a subsidy that just covers expected R&D costs. But how large is that? R&D costs are very poorly known, with the published estimates quoting uncertainties exceeding $100 to $500 million per drug. If the subsidy is set too low, companies cannot cover their R&D costs and nothing will happen. Set the subsidy too high, and the sponsor's costs skyrocket. To date, no sponsor has tried to implement these proposals.
In the “Virtual Pharma” approach, governments and philanthropies fund organizations that identify and help support the most promising private and academic research. Examples include the Institute for One World Health (www.iowh.org), a not-for-profit pharmaceutical company funded mainly through private sources and the Gates Foundation, and the Drugs for Neglected Diseases Initiative (www.dndi.org), a public sector not-for-profit organization designed to mobilize resources for R&D on new drugs for neglected diseases.
Virtual Pharmas have clearly started to bear fruit, and are responsible for most candidate treatments for tropical diseases currently under development. For example, the Drugs for Neglected Diseases Initiative has a portfolio of nine projects spread out across the drug development pipeline for the treatment of leishmaniasis, sleeping sickness, Chagas disease, and malaria [6]. But Virtual Pharmas face three important problems. The first is similar to the problem faced by subsidy proposals: guessing private-sector R&D costs. One needs to understand what a product costs in order to negotiate the best possible price—and guessing wrong is likely to be expensive. Second, Virtual Pharma's development pipelines will run dry without more upstream research. Research has been particularly weak in exploiting genomic insights [7]. Third, tropical disease research is badly underfunded. For this reason, Virtual Pharma cannot succeed without rigid cost containment.
We believe that a new, community-wide consortium, the Tropical Disease Initiative, can help solve these problems. Its success would help keep Virtual Pharma's R&D pipeline full. Furthermore, it would use open-source licenses to keep its discoveries freely available to researchers and—eventually—manufacturers. As we explain below, well-designed open-source licenses are the key to containing Virtual Pharmas' R&D costs.
While we expect the final choice of license to be made by TDI's members, the guiding principle should be to pick whatever license lets developing country patients derive the most benefit from TDI's work. Possible choices are shown in Box 1.
Box 1. Possible Licenses for TDI Discoveries
A public-domain license that permits anyone to use the information for any purpose.
Licenses similar to the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0) that permit anyone to use the information for any purpose, provided proper attribution is given.
Licenses such as the General Public License (www.opensource.org/licenses/gpl-license.php) that prohibit users from seeking intellectual property rights.
Licenses that permit commercial companies to obtain and exploit patents outside the developing world. These would allow Virtual Pharma to stretch its own R&D funds by letting corporate partners sell patented products to ecotourists, governments, and other consumers living in the industrialized world.
How It Works
To date, open-source methods have made little headway beyond software [8]. However, computing and computational biology are converging. In the same way that programmers find bugs and write patches, biologists look for proteins (“targets”) and select chemicals (“drug candidates”) that bind to them and affect their behavior in desirable ways. In both cases, research consists of finding and fixing tiny problems hidden in an ocean of code.
What would open-source drug discovery look like? As with current software collaborations, we propose a Web site where volunteers use a variety of computer programs, databases, and computing hardware (Figure 1). Individual pages would host tasks like searching for new protein targets, finding chemicals to attack known targets, and posting data from related chemistry and biology experiments. Volunteers could use chat rooms and bulletin boards to announce discoveries and debate future research directions. Over time, the most dedicated and proficient volunteers would become leaders.
Figure 1 The TDI Model of Online Collaboration
Ten years ago, TDI would not have been feasible. The difference today is the vastly greater size and variety of chemical, biological, and medical databases; new software; and more powerful computers. Researchers can now identify promising protein targets and small sets of chemicals, including good lead compounds, using computation alone. For example, a SARS protein similar to mRNA cap-1 methyltransferases—a class of proteins with available inhibitors—was recently identified by scanning proteins encoded by the SARS genome against proteins of known structure [9]. This discovery provides an important new target for future experimental validation and iterative lead optimization. More generally, existing projects such as the University of California at San Francisco's Tropical Disease Research Unit (San Francisco, California, United States) show that even relatively modest computing, chemistry, and biology resources can deliver compounds suitable for clinical trials [10]. Increases in computing power and improved computational tools will make these methods even more powerful in the future.
Just as they do today, Virtual Pharmas would choose the best candidates. The difference is that open-source drugs could not be patented in developing countries. This would not stop Virtual Pharma from developing promising discoveries. (S. Nwaka, V. Hale, personal communications). Importantly, TDI would be a great boost to the efforts of Virtual Pharmas, because it would help to contain the costs of discovering, developing, and manufacturing drugs.
Cost Containment
TDI would contain costs in three important ways. First, TDI would ask volunteers to donate their time (and any patentable discoveries) to the collaboration. Instead of financial incentives, TDI would offer volunteers non-monetary rewards, such as ideological satisfaction, the acquisition of new skills, enhancement of professional reputation, and the ability to advertise one's skills to potential employers. Software collaborations have demonstrated that these incentives are a good way to attract and motivate programmers [11]. Similar incentives should work equally well for biologists, chemists, and other scientists.
Second, we have already pointed out that existing proposals have difficulty containing costs. The root cause is patents. Normally, society relies on competition to keep prices low. Patents—by design—short-circuit competition by giving the owners the legal right to prevent others from using (or even developing) their invention. TDI, on the other hand, would restore competition by making drug candidates available to anyone who wanted to develop them. We expect sponsors to exploit this advantage by signing development contracts with whichever company offers the lowest bid. Such competitive bidding is a powerful way to contain costs, and is also a good way to develop drugs. Virtual Pharma has extensive experience supervising contract research.
Third, the absence of patents would continue to keep prices low once drugs reached the market. The generic drug industry shows what happens once drug makers are allowed to compete. US drugs frequently fall to about one-third their original price when patents expire [12].
Intellectual Property Rights
Would universities and corporations really let their people volunteer? Won't they insist on intellectual property rights? The practical answer is that sensible managers do not care about intellectual property rights unless they expect to earn a profit. This explains why sophisticated university licensing offices seldom bother to interfere with open-source software projects that are not commercially valuable [13]. The same logic would apply to open-source drug discovery. We would hope that life sciences companies would make a similar calculation. But permitting employees to participate is only the beginning. We think that universities and companies will also donate the data, research tools, and other resources needed to make TDI even stronger. The reason, once again, is that they have little to lose. The value of their intellectual property depends almost entirely on US and European diseases. For this reason, it costs very little to share their information with tropical disease researchers. In fact, drug companies already do this [14]. TDI's main challenge will be to show donors that an open-source project can keep members from diverting donated information back into the commercially lucrative diseases that affect patients in the West.
Finally, there are precedents for private companies developing drugs off patent. During the 1950s, March of Dimes (see www.marchofdimes.com) developed polio vaccines without any patents at all [15]. It then signed guaranteed purchase contracts with any drug maker willing to develop commercial-scale production methods. The incentive may not have been conventional, but it worked. And why not? The contracts made good business sense: contract profits may have been small compared to the profits on patented drugs, but so was the risk. Fifty years later, contract research still makes sense. Generic drug companies, developing world drug manufacturers, contract research organizations, and biotech firms have all said that they would consider contracts to develop open-source drug candidates. (M. Spino, S. Sharma, F. Hijek, and D. Francis, personal communications).
Next Steps
So far, we have described a shoestring operation that exists mainly on the Web. Except for computer time, budgets would be more or less the same as existing software collaborations. Computing would be expensive but manageable. Today's biologists routinely scrounge resources from university machines or borrow time on home computers [16, 17]. This Web-centric approach would be a good start, but not a complete solution. Computational biology works best when it can interact with experimental chemistry and biology. Nevertheless, a low-budget computational approach is probably enough to generate new science, suggest ideas for follow-up experiments, and make new drug candidates available under licenses designed to yield maximum benefit to the developing world.
In practice, an open-source drug discovery effort is likely to include modest experiments. Many academic scientists control discretionary resources and, in some cases, tropical disease grants. Furthermore, good science generates its own funding. We expect experimentalists to turn the collaboration's Web pages into grant proposals.
That said, TDI's volunteers will be most productive if sponsors back them. Charities could support open-source drug discovery by making wet chemistry and biology experiments a top priority. Corporations could also help by donating funds, laboratory time, or previously unpublished results. One low cost/high value option would be for companies that have already tried a particular research direction to warn TDI if the collaboration was about to investigate a known dead end. (R. Altman, personal communication)
Conclusion
Open-source drug discovery is feasible—that is, no known scientific or economic barrier bars the way. But what are the risks? Experience with software collaborations highlights the main social and economic challenges. First, the project will have to find and motivate volunteers. Based on existing software collaborations, we estimate a required minimum “critical mass” of a few dozen active members. Second, modest chemistry and biology experiments will be needed to increase the chances for success. Resources of several hundred thousand dollars per year—mostly in the form of in-kind donations of databases, laboratory access, and computing time—would make open-source drug discovery much more powerful. By most standards, such risks are real but acceptable.
The largest uncertainties are scientific. Can a volunteer effort based on computational biology and modest experiments produce the high-quality drug candidates that Virtual Pharma needs? A successful program must (1) make a significant contribution toward supplying the genomic insights that tropical disease research needs to move forward, and (2) make useful drug candidates available for development and production under open-source licenses. Open-source drug discovery looks feasible. The only way to be sure is to do the experiment—and we invite you to join us.
To learn more about TDI or to volunteer, go to http://www.tropicaldisease.org
Citation: Maurer SM, Rai A, Sali A (2004) Finding cures for tropical diseases: Is open source an answer? PLoS Med 1(3): e56.
Abbreviations
R&Dresearch and development
TDITropical Diseases Initiative
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Rai AK Hahn R Open and collaborative research: A new model for biomedicine Innovation in frontier industries: Biotech and software 2005 Washington, (D.C.) AEI-Brookings Press In press
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046710.1371/journal.pmed.0010058Research in TranslationDiabetes/Endocrinology/MetabolismSurgeryDiabetesTransplantationPancreatic Islet Transplantation Research in TranslationNaftanel Mark A Harlan David M *Mark A. Naftanel is a National Institutes of Health Clinical Research Training Program Scholar. David M. Harlan is Professor of Medicine, Uniformed Services University of the Health Sciences, and Chief, Islet and Autoimmunity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Department of Health and Human Services.
Competing Interests: The authors declare that they have no competing interests.
*To whom correspondence should be addressed. E-mail: [email protected] 2004 28 12 2004 1 3 e58Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.Islet transplantation offers hope to many patients with diabetes, who envision a life free of glucose checks and insulin injections. What are the barriers to its widespread implementation?
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Diabetes: Epidemiology and Complications
Treatment for, and the prognosis of, type-1 diabetes mellitus (T1DM) has progressed dramatically during the last century, but the disease remains a major cause of morbidity and mortality. Although precise figures are not available, over 1 million United States citizens currently live with the disease, with approximately 30,000 new cases diagnosed in the US each year. The total number of people with diabetes worldwide is expected to rise to 366 million in 2030, up from 171 million in 2000 [1].
The exact etiology of the disease remains uncertain, but extensive research suggests an interaction between genetic predisposition and environment. In fact, for unknown reasons, the incidence of T1DM is increasing [2]. Diabetes continues to have a tremendous societal impact; it is both difficult and expensive to treat and is associated with a number of long-term complications, including kidney failure, blindness, nerve damage, and premature mortality (predominately due to cardiovascular problems).
Insulin's Impact
Banting and Best's discovery of insulin in the early 1920s revolutionized diabetes treatment and greatly improved the prognosis for what had previously been a rapidly fatal disease. As shown by the Diabetes Control and Complications Trial and the more recent Epidemiology of Diabetes Interventions and Complications trial, insulin therapy has made such considerable advances (with better insulin formulations and delivery systems) that many patients can maintain their blood sugar levels within a tight range and thereby reduce their risk for the disease's long-term complications [3,4,5]. In addition, improved treatment of other associated conditions such as hypertension and hyperlipidemia have helped reduce, or at least delay, many of the long-term sequelae of diabetes [6]. However, problems with insulin-based treatment regimens persist. For the patient, treatment is expensive and difficult, requiring strict attention to blood glucose monitoring, insulin dosing, diet, and exercise. Further, good glycemia control is not easily achieved by all patients, and even for those able to achieve this goal, the treatment is not always completely effective.
Promising Directions
Just as financial investors balance a portfolio, with some risky investments and others that are more secure, researchers will undoubtedly continue to further refine “secure” insulin-based regimens to help patients achieve even better glycemia control. At the same time, scientists are pursuing more high-risk, high-payoff approaches to revolutionize diabetes care. One such approach is the closed-loop insulin pump (i.e., a pump that continuously monitors blood glucose and concurrently converts that data into appropriate insulin dosing), which offers the potential to serve as a mechanical pancreas. However, such a mechanical system would need be fail-safe in order to avoid devastating effects (e.g., if the monitor were to register a falsely elevated blood glucose and thereby trigger an inappropriately high insulin dose). In other, similar scenarios with no tolerance for error, NASA (for instance) sets up systems in which two independent monitoring systems must come up with similar measurements before an action is taken. Perhaps the engineering obstacles that currently limit the closed-loop insulin pump can be overcome.
Other research groups are investigating whether the insulin-producing cells within the pancreas (so-called ß cells), might be promoted to regenerate (in vitro or in vivo) to replace the pool of insulin-producing cells reduced by autoimmune destruction. Another promising approach for creating cells capable of physiologically regulated insulin secretion is to “coax” stem cells—undifferentiated cells with self-regenerative capacity—to differentiate into ß-like cells. Gene therapy approaches may overcome present obstacles and result in cells capable of physiologically regulated insulin secretion [7]. Lastly, the recent completion of the Human Genome Project suggests that the genetics of diabetes may eventually become clearer and may direct appropriate preventative approaches.
While such potential therapies remain experimental, pancreas transplantation is currently performed in patients with complicated diabetes. However, a recent report that shows benefit for patients with both diabetes and kidney failure who receive a combined pancreas and kidney transplant also found that an isolated pancreas transplant (for patients with preserved kidney function) actually worsened survival [8]. The main point is that as we develop new therapies, we must maintain humility and recognize that newer approaches may have great promise, but they also have the potential for harm.
History of Islet Transplantation
Islet transplantation has recently received considerable interest as a potentially definitive treatment for diabetes. The concept of islet transplantation is not new—investigators as early as the English surgeon Charles Pybus (1882–1975) attempted to graft pancreatic tissue to cure diabetes. Most, however, credit the recent era of islet transplantation research to Paul Lacy's studies dating back more than three decades. In 1967, Lacy's group described a novel collagenase-based method (later modified by Dr. Camillo Ricordi, then working with Dr. Lacy) to isolate islets, paving the way for future in vitro and in vivo islet experiments [9]. Subsequent studies showed that transplanted islets could reverse diabetes in both rodents and non-human primates [10,11] (Figure 1). In a summary of the 1977 Workshop on Pancreatic Islet Cell Transplantation in Diabetes, Lacy commented on the feasibility of “islet cell transplantation as a therapeutic approach [for] the possible prevention of the complications of diabetes in man” [12]. Improvements in isolation techniques and immunosuppressive regimens ushered in the first human islet transplantation clinical trails in the mid-1980s. Yet despite continued procedural improvements, only about 10% of islet recipients in the late 1990s achieved euglycemia (normal blood glucose). In 2000, Dr. James Shapiro and colleagues published a report describing seven consecutive patients who achieved euglycemia following islet transplantation using a steroid-free protocol and large numbers of donor islets, since referred to as the Edmonton protocol [13]. This protocol has been adapted by islet transplant centers around the world and has greatly increased islet transplant success.
Figure 1 Central Concepts Underlying Islet Transplantation
The main idea of islet transplantation is to process the organ donor's pancreas so as to remove the 95% of the gland responsible for its exocrine functions (secretion of digestive enzymes) and isolate the 5% of the gland responsible for the endocrine hormone secretion— the so-called pancreatic islets. Once isolated, the medical team can infuse the insulin-producing islets through a thin tube, placed in the main vein that transports blood from the intestines to the liver. Once infused, the islets are transported by the bloodstream into the liver, where they lodge, take up residence, and begin making the right amount of insulin to regulate the blood sugar.
(Illustration: Giovanni Maki)
Current Limitations of Islet Transplantation
While significant progress has been made in the islet transplantation field [14], many obstacles remain that currently preclude its widespread application. Two of the most important limitations are the currently inadequate means for preventing islet rejection, and the limited supply of islets for transplantation. Current immunosuppressive regimens are capable of preventing islet failure for months to years, but the agents used in these treatments are expensive and may increase the risk for specific malignancies and opportunistic infections. In addition, and somewhat ironically, the most commonly used agents (like steroids, calcineurin inhibitors, and rapamycin) are also known to impair normal islet function and/or insulin action. Further, like all medications, the agents have other associated toxicities, with side effects such as oral ulcers, peripheral edema, anemia, weight loss, hypertension, hyperlipidemia, diarrhea, and fatigue [15]. Perhaps of greatest concern to the patient and physician is the harmful effect of certain widely employed immunosuppressive agents on renal function. For the patient with diabetes, renal function is a crucial factor in determining long-term outcome, and calcineurin inhibitors (tacrolimus and cyclosporin) are significantly nephrotoxic. Thus, while some patients with a pancreas transplant tolerate the immunosuppressive agents well, and for such patients diabetic nephropathy can gradually improve, in other patients the net effect (decreased risk due to the improved blood glucose control, increased risk from the immunosuppressive agents) may worsen kidney function. Indeed, Ojo et al. have published an analysis indicating that among patients receiving other-than-kidney allografts, 7%–21% end up with renal failure as a result of the transplant and/or subsequent immunosuppression [16].
Looked at another way, patients with heart, liver, lung, or kidney failure have a dismal prognosis for survival, so the toxicity associated with immunosuppression is warranted (the benefits of graft survival outweigh the risks associated with the medications). But for the subset of patients with diabetes and preserved kidney function, even those with long-standing and difficult-to-control disease, the prognosis for survival is comparatively much better. In addition to the immunosuppressive toxicities, other risks are associated with the islet transplant procedure itself, including intra-abdominal hemorrhage following the transplant, and portal vein thromboses. The fact that there is already a good alternative to islet transplantation (i.e., the modern intensive insulin regimen) forces us to regard any newer, riskier interventions with a critical eye.
Like all transplantation therapies, islet transplantation is also handicapped by the limited donor pool. The numbers are striking; at least 1 million Americans have T1DM, and only a few thousand donor pancreata are available each year. To circumvent this organ shortage problem, researchers continue to look for ways to grow islets—or at least cells capable of physiologically regulated insulin secretion—in vitro, but currently only islets from cadaveric donors can be used to restore euglycemia. Further exacerbating the problem (and unlike kidney, liver, and heart transplants, where only one donor is needed for each recipient) most islet transplant patients require islets from two or more donors to achieve euglycemia. Lastly, the current methods for islet isolation need improvement, since only about half of attempted isolations produce transplant-ready islets.
While islet transplantation research has made important progress and the success stories are encouraging, the long-term safety and efficacy of the procedure remain unclear. Other concerns relating to the field include questions about the impact of having insulin-producing foreign cells within the hepatic parenchyma, the long-term consequences of elevated portal pressures resulting from the islet infusion, and the fact that islet recipients can be sensitized against donor tissue types, making it more difficult to find a suitable donor should another life-saving transplant be required in the future. Also, very few islet transplant recipients have remained euglycemic without the use of any exogenous insulin beyond four years post-transplant. Thus, while most islet recipients achieve better glycemia control and suffer less serious hypoglycemia, islet transplantation continues to fall short of the definitive diabetes cure.
Is Islet Transplantation Ready for Widespread Use?
While no one suggests that the therapy is ready for widespread clinical application, another way of highlighting current problems is to focus on cost. Assuming present hurdles were cleared, islet transplantation costs approximately $150,000 per patient per transplant. With over 1 million Americans dealing with T1DM, it would cost over $100 billion to give each patient a single islet transplant, with little assurance as yet of any long-term benefit. In contrast, the annual direct cost of a proven therapy like intensive insulin treatment is about $3,500 per patient [17].
The limitations of islet transplantation force us to recognize that the therapy remains experimental, and that many questions must be answered before it is incorporated into general clinical practice. At the present time, we urge a focus on the selection of only those patients for whom this procedure offers the greatest likelihood of benefit. Most people with diabetes can, with diligence and perseverance, implement an insulin regimen that maintains tight glucose control while avoiding dangerous hypoglycemia. However, there are some patients who continue to have tremendous difficulty managing their disease despite optimal care and effort. Even the statement “despite optimal care and effort” is difficult to define, and we advocate that all patients being considered for an islet transplant first be referred for several months to specialty teams that are committed to diabetes care. Since such patients whose diabetes is the most difficult to control have a poor quality of life, islet transplantation offers potential benefit. Even a low baseline level of insulin production by the transplanted islets may lower the amount of insulin required, while reducing the number and severity of hypoglycemic events. We also believe the islet transplant risk-benefit ratio is favorable for those with both T1DM and kidney failure who are listed for a life-preserving kidney transplant; such patients will have to take immunosuppressive agents after transplant to preserve the kidney allograft function, so the islets can be added without too much additional risk.
Where do we go from here? Just as early studies showed islet transplantation's promise, research must now overcome the hurdles revealed by the recent islet transplant experience. New immunomodulatory agents offer the greatest hope of revolutionizing the field. New drug regimens capable of inducing tolerance to the transplanted islets would allow recipients to maintain their grafts without general immunosuppression and its associated toxicities. While many targets are currently under investigation, none are ready for clinical use. We advocate that such immunomodulatory approaches be tested first in controlled models where the results can be appropriately attributed to the agent itself.
Conclusion
Less than a century ago, T1DM was invariably a fatal disease. With the advent of insulin, the prognosis changed overnight, and we have continued to witness improvements in diabetes care and outcomes. Pancreatic islet transplant has offered renewed hope to many patients with diabetes, who envision a life free of glucose checks and insulin injections. Some transplanted patients have enjoyed “success” and are pleased with their decisions; unfortunately these results are not universal. Researchers must continue to look for ways to improve the procedure while protecting the welfare of each individual patient. The field has come a long way, but we must remain cautious, as we are treating a non-fatal disease for which there is a very effective standard therapy.
Citation: Naftanel MA, Harlan DM (2004) Pancreatic islet transplantation. PLoS Med 1(3): e58.
Abbreviation
T1DMtype-1 diabetes mellitus
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Onkamo P Vaananen S Karvonen M Tuomilehto J Worldwide increase in incidence of type I diabetes—The analysis of the data on published incidence trends Diabetologia 1999 42 1395 1403 10651256
The Diabetes Control and Complications Trial Research Group The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus N Engl J Med 1993 329 977 986 8366922
[Anonymous] Epidemiology of Diabetes Interventions and Complications [EDIC]. Design, implementation, and preliminary results of a long-term follow-up of the Diabetes Control and Complications Trial cohort Diabetes Care 1999 22 99 111 10333910
Writing Team for the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group Sustained effect of intensive treatment of type 1 diabetes mellitus on development and progression of diabetic nephropathy: The Epidemiology of Diabetes Interventions and Complications (EDIC) study JAMA 2003 290 2159 2167 14570951
Nishimura R LaPorte RE Dorman JS Tajima N Becker D Mortality trends in type 1 diabetes. The Allegheny County (Pennsylvania) Registry 1965–1999 Diabetes Care 2001 24 823 827 11347737
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Shapiro AM Lakey JR Ryan EA Korbutt GS Toth E Islet transplantation in seven patients with type 1 diabetes mellitus using a glucocorticoid-free immunosuppressive regimen N Engl J Med 2000 343 230 238 10911004
Robertson RP Islet transplantation as a treatment for diabetes—A work in progress N Engl J Med 2004 350 694 705 14960745
Hirshberg B Rother KI Digon BJ Lee J Gaglia JL Benefits and risks of solitary islet transplantation for type 1 diabetes using steroid-sparing immunosuppression: The National Institutes of Health experience Diabetes Care 2003 26 3288 3295 14633816
Ojo AO Held PJ Port FK Wolfe RA Leichtman AB Chronic renal failure after transplantation of a nonrenal organ N Engl J Med 2003 349 931 940 12954741
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046810.1371/journal.pmed.0010059PerspectivesInfectious DiseasesInfectious DiseasesMicrobiologyMedicine in Developing CountriesPublic HealthContaining the Threat—Don't Forget Ebola PerspectivesCohen Jonathan Jonathan Cohen is dean and professor of infectious diseases at Brighton and Sussex Medical School, Brighton, United Kingdom. E-mail: [email protected]
Competing Interests: The author is on the editorial board of PLoS Medicine.
12 2004 28 12 2004 1 3 e59Copyright: © 2004 Jonathan Cohen.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 2000, Uganda experienced the largest outbreak of Ebola fever ever described. What can we learn from the Ugandan experience to help us prepare for future outbreaks?
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On 8 October 2000, the acting district director of health services for the Gulu district in northwestern Uganda received two concurrent reports of an unusual illness with high mortality, occurring in the community and at a local hospital. One report attributed the illness to poisoning at a funeral at a remote village in the far north of the district. At the same time, the medical superintendent of the hospital also reported to the health authorities that he was experiencing a cluster of cases of critically ill patients, and that there had been several deaths, including some nurses.
These events heralded what was to become the largest outbreak of Ebola fever so far described, involving 425 cases, of whom 224 died. The development of the epidemic and the measures taken to try and control it have recently been reported by Lamunu and her colleagues [1]. Their report underlines the challenges faced when dealing with such highly contagious and highly virulent infections. (At the request of PLoS Medicine, Lamunu et al. have made a full-text version of their report available on the World Health Organization Web site [2].)
Ebola Virus
Ebola virus is a member of the family Filoviridae, which consists of two distinctive species, Marburg and Ebola, both of which cause severe and often fatal haemorrhagic disease in humans and monkeys. The viruses have a distinctive filamentous morphology under the electron microscope and a genome that consists of a nonsegmented, negative-stranded RNA approximately 19 kb in length.
Ebola testing
(Photo: Public Health Image Library, Centers for Disease Control and Prevention)
Three distinct subtypes (genotypes) of Ebola have been described that are pathogenic for humans: Ebola-Zaire, Ebola-Sudan, and Ebola–Côte d'Ivoire. A fourth type, Ebola-Reston, affects only primates but has been identified in animal facilities in the United States, Italy, and the Philippines.
The Illness
Ebola is transmitted person to person by direct contact with infected body fluids, or by direct inoculation via contaminated instruments such as needles or razors. The incubation period of Ebola haemorrhagic fever is usually between four and 21 days. The illness is characterised by an acute onset of fever, malaise, myalgia, severe frontal headache, and pharyngitis. One of the great difficulties in making the diagnosis is that these symptoms are typical of many acute infective syndromes that occur in Ebola-endemic areas.
As the disease progresses patients develop a maculopapular rash, typically at about six days, followed by vomiting and bloody diarrhoea, with uncontrollable haemorrhage from needle sites and body orifices. Death is from shock secondary to blood loss. Treatment is largely supportive, although a recent study has reported promising results with an inhibitor of tissue factor, which may help control the bleeding diathesis [3].
The Ugandan Outbreak
Lamunu et al. describe how initial identification of the outbreak was delayed: six weeks elapsed before the Ugandan Ministry of Health was notified. There were several reasons for this delay. In part it could be explained by a weak surveillance system, especially at the local and regional levels. But also, the nonspecific nature of the symptoms meant that the initial, sporadic cases were frequently attributed to malaria or typhoid, and patients turned to local healers for help. Important, too, was the fact that this was the first outbreak of viral haemorrhagic fever in Uganda, and lack of familiarity with the disease caused further delays.
It was only when clusters of cases became apparent that wider public health measures were instituted, and the outbreak started to come under control. In this phase, too, there were important lessons to learn. The initial identification of the disease as due to Ebola virus was made in the World Health Organization laboratories in South Africa, but soon thereafter a field laboratory was established, and this proved invaluable in guiding both case management and surveillance activities. Early involvement of specialised agencies, including the Global Outbreak and Response Network of the World Health Organization, was essential. Disseminating up-to-date information through the media, and the local communities, was important in getting the population “on side”.
Lessons from the Outbreak
The 2000 outbreak in Uganda was the last large outbreak, but other, smaller outbreaks continue to occur. During 2004 alone there have been two further epidemics: in January there were 35 cases in the Congo, with 29 deaths, and in August a smaller outbreak in the Sudan infected 17 patients, of whom seven died [4,5]. In each of these cases the epidemic was brought under control relatively quickly, and the infection was largely localised to the immediately surrounding area. However, the lessons of the Uganda outbreak have obvious resonance with many of the recent concerns that have been raised about the global spread of infectious diseases, be they naturally acquired or related to potential biowarfare.
By and large, once an outbreak has been recognised by the public health authorities there are well-tried processes and procedures that come into play that serve to contain further spread of the infection and limit additional cases of the disease. This was shown spectacularly in the case of the SARS outbreak, in which not only was the disease controlled but the novel causative agent was identified, both within a few months. But as Lamunu and colleagues make clear, the most difficult aspect of the outbreak control is the initial recognition of the disease: diagnosis depends on the astute health-care worker who notices an unusual clinical picture, or more usually, an unexpected cluster of cases. Although the viral haemorrhagic fevers have until now been largely confined to their epidemic foci in Africa, cases will continue to occur from time to time in travellers, in whom diagnosis may be delayed.
The key lessons from the Gulu outbreak are the extremely high case mortality of Ebola and the importance of instituting rigorous procedures to control cross-infection. These lessons are crucial both for communities in Africa, where public health infrastructures are often suboptimal, and in developed countries, where the infrastructure is sophisticated but can only be deployed once the disease is recognised.
Citation: Cohen J (2004) Containing the threat—Don't forget Ebola. PLoS Med 1(3): e59.
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References
Lamunu M Lutwama JJ Kamugisha J Opio A Nambooze J Containing a haemorrhagic fever epidemic: The Ebola experience in Uganda (October 2000–January 2001) Int J Infect Dis 2004 8 27 37 14690778
Lamunu M Lutwama JJ Kamugisha J Opio A Nambooze J Containing hemorrhagic fever epidemic, the Ebola experience in Uganda (October 2000–January 2001): A paper presented at the 10th International Congress on Infectious Disease, Singapore, March 2002 2002 Available: http://www.who.int/csr/disease/ebola/en/lamunu.pdf . Accessed 18 October 2004
Geisbert TW Hensley LE Jahrling PB Larsen T Geisbert JB Treatment of Ebola virus infection with a recombinant inhibitor of factor VIIa/tissue factor: A study in rhesus monkeys Lancet 2003 362 1953 1958 14683653
[Anonymous] Ebola hemorrhagic fever—Congo rep 2004 1 ProMED-mail, archive number 20040106.0060. Available: http://www.promedmail.org/pls/promed/f?p=2400:1001:::NO::F2400_P1001_BACK_PAGE,F2400_P1001_PUB_MAIL_ID:1000%2C23946 . Accessed 18 October 2004
[Anonymous] Ebola hemorrhagic fever—Sudan (W Equatoria) 2004 8 ProMED-mail, archive number 20040807.2180. Available: http://www.promedmail.org/pls/promed/f?p=2400:1001:::NO::F2400_P1001_BACK_PAGE,F2400_P1001_PUB_MAIL_ID:1000%2C26312 . Accessed 18 October 2004
| 15630468 | PMC539049 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e59 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010059 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563046910.1371/journal.pmed.0010064Research ArticleImmunologyInfectious DiseasesHIV/AIDSInfectious DiseasesHIV Infection/AIDSClinical trialsRandomized, Controlled Trial of Therapy Interruption in Chronic HIV-1 Infection Therapy Interruption in Chronic HIVPapasavvas Emmanouil
1
Kostman Jay R
2
Mounzer Karam
3
Grant Robert M
4
Gross Robert
5
Gallo Cele
3
Azzoni Livio
1
Foulkes Andrea
6
Thiel Brian
1
Pistilli Maxwell
1
Mackiewicz Agnieszka
1
Shull Jane
3
Montaner Luis J
1
*1The Wistar Institute, PhiladelphiaPennsylvaniaUnited States of America2Philadelphia Field Initiating Group for HIV-1 Trials and the Division of Infectious Diseases, University of PennsylvaniaPhiladelphia, PennsylvaniaUnited States of America3Philadelphia Field Initiating Group for HIV-1 Trials, PhiladelphiaPennsylvaniaUnited States of America4The Gladstone Institute of Virology and Immunology, University of CaliforniaSan Francisco, CaliforniaUnited States of America5Center for Clinical Epidemiology and Biostatistics and the Division of Infectious Diseases, University of PennsylvaniaPhiladelphia, PennsylvaniaUnited States of America6Department of Biostatistics, University of PennsylvaniaPhiladelphia, PennsylvaniaUnited States of AmericaKlenerman Paul Academic EditorUniversity of OxfordUnited Kingdom
Competing Interests:
RMG is a paid consultant for the Bayer Guidelines Project, which develops algorithms for interpretation of drug resistance genotyping assays; received honoraria and research support from ViroLogic and Visible Genetics; received honoraria for speaking at educational programs supported by ViroLogic, Visible Genetics, GlaxoSmithKline, Bristol-Myers Squibb, Roche Pharmaceuticals, and Agouron Pharmaceuticals; and directs a nonprofit academic laboratory at the Gladstone Institute of Virology and Immunology that has provided services for clinical research supported by grants to the University of California from Merck, GlaxoSmithKline, Bristol-Myers Squibb, Boehringer Ingelheim, Roche, Abbott, Agouron Pharmaceuticals, Gilead, Visible Genetics, and Chiron.
RG receives support for his HIV research from Agouron Pharmaceuticals, GlaxoSmithKline, and Bristol-Myers Squibb and serves as a consultant to GlaxoSmithKline; all relationships have been disclosed to the University of Pennsylvania, which deemed them not to constitute a conflict of interest.
Author Contributions: EP, JRK, KM, RMG, JS, and LJM designed the study. EP, JRK, RMG, LA, AF, BT, MP, AM, and LJM analyzed the data. EP, JRK, KM, RMG, RG, CG, LA, AF, BT, MP, AM, JS, and LJM contributed to writing the paper.
*To whom correspondence should be addressed. E-mail: [email protected] 2004 28 12 2004 1 3 e6413 7 2004 24 10 2004 Copyright: © 2004 Papasavvas 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.
Treatment Interruptions in Chronic HIV Infection
Background
Approaches to limiting exposure to antiretroviral therapy (ART) drugs are an active area of HIV therapy research. Here we present longitudinal follow-up of a randomized, open-label, single-center study of the immune, viral, and safety outcomes of structured therapy interruptions (TIs) in patients with chronically suppressed HIV-1 infection as compared to equal follow-up of patients on continuous therapy and including a final therapy interruption in both arms.
Methods and Findings
Forty-two chronically HIV-infected patients on suppressive ART with CD4 counts higher than 400 were randomized 1:1 to either (1) three successive fixed TIs of 2, 4, and 6 wk, with intervening resumption of therapy with resuppression for 4 wk before subsequent interruption, or (2) 40 wk of continuous therapy, with a final open-ended TI in both treatment groups. Main outcome was analysis of the time to viral rebound (>5,000 copies/ml) during the open-ended TI. Secondary outcomes included study-defined safety criteria, viral resistance, therapy failure, and retention of immune reconstitution.
There was no difference between the groups in time to viral rebound during the open-ended TI (continuous therapy/single TI, median [interquartile range] = 4 [1–8] wk, n = 21; repeated TI, median [interquartile range] = 5 [4–8] wk, n = 21; p = 0.36). No differences in study-related adverse events, viral set point at 12 or 20 wk of open-ended interruption, viral resistance or therapy failure, retention of CD4 T cell numbers on ART, or retention of lymphoproliferative recall antigen responses were noted between groups. Importantly, resistance detected shortly after initial viremia following the open-ended TI did not result in a lack of resuppression to less than 50 copies/ml after reinitiation of the same drug regimen.
Conclusion
Cycles of 2- to 6-wk time-fixed TIs in patients with suppressed HIV infection failed to confer a clinically significant benefit with regard to viral suppression off ART. Also, secondary analysis showed no difference between the two strategies in terms of safety, retention of immune reconstitution, and clinical therapy failure. Based on these findings, we suggest that further clinical research on the long-term consequences of TI strategies to decrease drug exposure is warranted.
Structured treatment interruptions, or "drug holidays", in HIV patients yield no clinical benefits but don't lead to short- term treatment failure either
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Introduction
Antiretroviral therapy (ART) has been a milestone in the treatment of HIV infection. Current treatment guidelines for HIV-1 infection in the United States recommend the initiation of ART in patients with CD4 T cell counts of less than 350 cells/μl [1]. In implementing these guidelines, health-care providers face the ongoing challenge of developing treatment strategies that minimize drug-related toxicity and adverse effects while retaining effective control of viral replication. Furthermore, treatment-associated costs (particularly in resource-poor areas), difficulty in maintaining long-term optimal adherence [2], and the emergence of viral resistance [3,4,5] have limited the feasibility of life-long ART-mediated viral suppression, increasing the need for alternative treatment strategies. Intermittent therapy strategies, consisting of alternating cycles on and off ART, have increasingly emerged as a potential intervention to address limitations of continuous ART [6,7,8,9]. Therapy interruption (TI) studies in ART-treated patients with suppressed HIV infection [10] have addressed the general questions as to whether such strategies can achieve greater viral control through increased antiviral responses (autoimmunization hypothesis) or simply serve as a strategy to reduce cost of long-term therapy and drug-associated toxicity. While pilot studies and uncontrolled (or incomplete) trials in patients with chronic HIV infection have addressed viral and immune outcomes of fixed-length TI and fixed on-drug cycles [11,12,13,14,15,16], no completed randomized, controlled trial has yet addressed by intent-to-treat analysis the outcome during an open-ended TI of sequential TIs versus continuous treatment in patients with confirmed suppression. The largest study to date in this area is the prospective single-arm Swiss–Spanish Intermittent Trial (SSITT) conducted in 133 recruited patients undergoing sequential 2-wk TIs and showing a lack of impact of this strategy on achieving sustained viral loads of less than 5,000 copies/ml off therapy in those that completed the study [11]. However, the lack of a control arm in this study has left unanswered questions about the impact of multiple TIs on time to rebound, immune reconstitution, therapy failure, and viral resistance when analyzed against a randomized control arm of continuous treatment followed for equal time before a single open-ended interruption.
We completed a randomized, controlled trial on the outcome of repeated 2- to 6-wk TIs in patients with chronic infection in which the comparator group maintained continuous therapy and then an open-ended interruption period was applied in both treatment groups. The study addressed the potential for repeated interruptions of therapy to delay time to viral rebound as a primary outcome and analyzed secondary outcomes regarding study-defined safety criteria, viral suppression and resistance, and retention of immune reconstitution.
Methods
Participants
Between August 2000 and December 2003, we enrolled 42 patients infected with HIV who were older than 18 y and on ART; eligibility criteria included CD4 counts of greater than 400 cells/μl on ART with a nadir of no less than 100 cells/μl, ART-mediated suppression (< 500 copies/ml) for more than 6 mo and less than 50 copies/ml at recruitment on any antiretroviral regimen. Approval of the study protocol was obtained from the institutional review board (IRB) of the Philadelphia Field Initiating Group for HIV Trials (Philadelphia, Pennsylvania, United States). Written informed consent was obtained from all patients. Human experimentation guidelines of the United States Department of Health and Human Services and of the authors' institutions were followed. The study protocol, including the patient consent form, the CONSORT form, and the IRB approval, can be found in Protocols S1–S4.
Randomization and Study Design
Forty-two eligible patients from the Jonathan Lax Immune Disorder Clinic in Philadelphia, Pennsylvania, were randomized via sealed envelopes in a 1:1 fashion to a first phase (phase I) of either (1) three successive TIs of 2, 4, and 6 wk, respectively, or (2) maintenance of ART for 40 wk before a final interruption of therapy in both arms (phase II) subject to therapy reinitiation criteria as described below. Phase II consisted of an open-ended interruption to allow for virological and immunological comparisons between the groups off therapy. Study visits were every 2 wk for the repeated interruptions group and every 4 wk for the continuous ART group during phase I. Both groups were followed every 2 wk during phase II. We followed a study design with step-wise increases in the length of TI cycles to address potential safety concerns (resuppression was confirmed after shorter TIs before longer interruptions were initiated) and the hypothesis that sequential viral replication intervals would stimulate viral control and a delay in time to viral rebound.
Phase I procedures for the repeated interruptions group included the following. (1) Interruption of therapy was individually timed to occur after two HIV RNA measurements of less than 50 copies/ml without any viral load measurements greater than 400 copies/ml in between; these interruptions increased from 2 to 4 to 6 wk sequentially. (2) If a 0.5-log or greater reduction in viral load did not occur by 6 wk of reinitiated therapy or less than 50 copies/ml was not achieved within 20 wk of reinitiated therapy, patients were withdrawn as therapy failures and a resistance test was performed. (3) Patients were also withdrawn as therapy failures if (a) the CD4 cell number declined by more than 45% of the baseline CD4 count, (b) participants developed an opportunistic infection, even if retaining required CD4 count levels, or (c) a viral load of greater than 500,000 copies/ml occurred once, with or without development of acute retroviral syndrome as defined by fever, skin lesions, and pharyngitis.
Phase I procedures for the continuous therapy arm included the following: (1) patient monitoring if detected viremia was between 50 and 999 copies/ml, with the patient withdrawn if their viral load did not return to less than 50 copies/ml immediately prior to phase II, and (2) patient study withdrawal as therapy failure if during the 40-wk ART period viral load rebounded to more than 1,000 copies/ml at two consecutive time points.
Phase II procedures for both arms included the following: (1) monitoring for patient study withdrawal criteria as described in phase I, (2) determining time to primary end point of a viral load greater than 5,000 copies/ml, (3) monitoring until the time of therapy reinitiation at a viral load greater than 30,000 copies/ml for three consecutive time points, and (4) after reinitiation of therapy, follow-up on therapy to confirm resuppression to less than 50 copies/ml at 6, 10, and 14 wk on therapy. Clinical and laboratory parameters (CD4 count and viral load) were monitored at each visit, and venous blood was collected for additional secondary outcomes during selected study visits.
In both phase I and II, participants taking non-nucleoside reverse-transcriptase inhibitors (NNRTIs) were instructed to stop them a day earlier than the remaining drugs in the regimen.
Primary and Secondary Outcomes
The primary outcome was time to confirmed virological rebound during phase II. Rebound was defined as first time point with greater than 5,000 copies/ml. Viral replication magnitude as defined by mean HIV-1 plasma RNA area under the curve (AUCHIV RNA) was measured as a secondary outcome at weeks 12 and 20 of phase II based on reinitiation-of-therapy criteria outlined above.
Additional secondary outcomes included (1) safety outcomes (serious adverse events [SAEs] and patient withdrawal based on criteria defined above), (2) retention of ART-mediated immune reconstitution, and (3) detection of viral resistance. Retention of immune reconstitution was analyzed by (1) same-day whole blood flow-cytometry-based analysis of CD4 and CD8 T cells, including total and naïve (CD62 l/CD45RA) and memory (CD45RO) subsets as described [17], and (2) same-day recall response analysis of peripheral blood mononuclear cell lymphoproliferative responses to Candida albicans as described [17]. Viral resistance mutations were retrospectively analyzed on cryopreserved plasma samples by genotyping of first available sample with viral load greater than 100 copies/ml following each interruption using the TruGene Assay (Visible Genetics, Toronto, Canada) at the Gladstone Institute of Virology and Immunology (San Francisco, California, United States) as previously described [18,19].
Sample Size
The sample size required was calculated using PS [20] software, and based on a type I error of 0.05, with 90% power, to detect a difference of 4 wk or more in time to viral rebound between arms. Eighteen patients per group resulted in sufficient power (18 for 90%, 13 for 80%) to determine a difference of 4 wk or greater between groups in time to rebound of virus during the open-ended interruption. Assuming a loss to follow-up of 15%, we targeted 21 patients per group, or 42 total.
Statistical Analysis
The primary analysis was an intent-to-treat analysis in which dropouts were assigned a week 0 rebound time (e.g., maximum failure to delay rebound). In secondary analyses, these dropouts were excluded. The log-rank test was used to test the null hypothesis of no difference between arms in the number of weeks from initiation of the open-ended TI to reaching viral rebound as defined. Patients not reaching end point at 26 wk after the beginning of the open-ended TI were censored. Wilcoxon rank sum tests were used to compare baseline and week 0 of the open-ended interruption between groups. Wilcoxon signed rank tests were used to test for no change from baseline to week 0 of phase II. Finally, Wilcoxon rank sum tests were employed to test between groups for equality of the mean AUCHIV RNA up to 12 and 20 wk. In all cases, a two-sided alpha level of 0.05 was used to define statistical significance. Unless otherwise stated, results are presented as median (interquartile range) in text and tables.
Results
Patient Flow and Discontinuations
Trial patient flow is summarized in Figure 1. Between August 2000 and December 2003, 42 patients at the Jonathan Lax Immune Disorder Clinic at the Philadelphia Field Initiating Group for HIV Trials were enrolled, randomized, and followed as shown in Figure 2. In the continuous therapy/single interruption arm, 16 of 21 patients reached the open-ended interruption. Reasons for study discontinuation in this arm were loss to follow-up (n = 1; patient moved away) and virological failure during continuous therapy (n = 4; further discussed below). In the repeated interruptions arm, 18 of 21 patients reached the open-ended interruption following three TIs of 2, 4, and 6 wk duration, with median peak rises in viral loads of 136 (50–2,590), 13,651 (180–222,589), and 18,887 (3,893–96,101) copies/ml, respectively. Median time to less than 50 copies/ml after resumption of therapy was 2 (0–4), 3 (1.8–12), and 9.5 (2–12) wk, respectively, with 9, 18, and 20 wk as the maximum time needed to achieve suppression in 100% of patients before reaching the open-ended interruption. Study discontinuation in the repeated interruptions arm was due to protocol violation (n = 1; patient restarted therapy during interruption out of protocol), loss to follow-up (n = 1; patient imprisoned), and virological failure during on-therapy period (n = 1; further discussed below).
Figure 1 Study Flow
Figure 2 Study Design (Phases I and II)
Baseline Criteria and Follow-Up
The demographic and clinical characteristics of the two groups at baseline are summarized in Table 1. Seventy-five percent of participants were on their second to fourth regimen while 25% were in their first regimen. No significant difference was found in baseline parameters between arms, with 33%–47% of patients on protease-inhibitor-containing and 61%–71% on NNRTI-containing regimens. Owing to the high participation of patients on NNRTI-based regimens and concerns about TI and safety in general, patient outcomes and treatment failure were reviewed monthly by the IRB of this study during the first 8 mo of study, quarterly for the following 4 mo, and semi-annually thereafter. Figure 2 shows study design for both arms, with a median follow-up of 41 (41–42) wk during phase I for the continuous therapy/single interruption arm and 42 (30–51) wk for the repeated interruptions arm. Follow-up during phase II had a median duration of 27 wk in both arms (continuous therapy/single interruption arm, 27 [8.75–47]; repeated interruptions arm, 27 [16.5–35]). Following reinitiation of therapy after phase II, patients suppressed viral replication to less than 50 copies/ml by a median time of 10 (6–12) wk in both arms, excluding for two patients in the continuous therapy/single interruption arm who elected to stay off ART indefinitely and one patient from the repeated interruptions arm who reported nonadherence following regimen reinitiation yet reached 52 copies/ml before withdrawing from additional follow-up.
Table 1 Baseline Demographic and Clinical Characteristics per Study Arm
a Numbers include cases of PI/NNRTI combined use at study entry
AA, African American; C, Caucasian; H, Hispanic, IV, intravenous drug usage; PI, protease inhibitor; S, sexual transmission
Primary Outcome
An intent-to-treat analysis of the time to viral rebound (>5,000 copies/ml) in the open-ended interruption showed no difference between groups (continuous therapy/single TI, median = 4 [1–8] wk, n = 21; repeated TI, median = 5 [4–8] wk, n = 21; p = 0.36). Figure 3 (top panel) shows the probability of plasma HIV-1 RNA remaining less than 5,000 copies/ml for the two groups (n = 21 per group). Exclusion of drop-outs in an as-treated analysis did not alter conclusions (single TI, median = 5 [4–9] wk, n = 18; repeated TI, median = 6 [4–8] wk, n = 16; p > 0.05). Additional secondary analysis of the magnitude of viral load as shown in Figure 3 (second panel) showed similar viral replication as determined by mean AUCHIV RNA analysis at week 12 (single TI, median = 124,621 [23,326–262,348] AUCHIV RNA; repeated TI, median = 100,400 [47,221–365,731] AUCHIV RNA; p > 0.05) or week 20 (single TI, median = 114,550 [31,829–362,628] AUCHIV RNA; repeated TI, median = 153,097 [67,427–515,421] AUCHIV RNA; p > 0.05).
Figure 3 Lack of a Difference between Groups in Plasma HIV-1 RNA during Phase II
Top panel shows Kaplan-Meyer plot summarizing time to a viral load of more than 5,000 copies/ml in both arms. Second panel shows viral load (mean ± standard error) per arm during 27 wk of TI (median time of phase II). Bottom table shows number of patients at time points shown for viral load in the second panel; the decrease in viral load over time is due to the reinitiation of therapy in patients with higher viral loads.
Secondary Outcomes SAEs and patient discontinuation
No patient discontinuation in either group was due to study-defined changes in CD4 cell count (reviewed further below) or due to study-associated SAEs (disease progression or acute retroviral syndrome). However, four non-study-related SAEs occurred: two patients from the continuous therapy/single interruption arm were hospitalized, one for a cholecystectomy and one for acute rectal bleeding, during the 40-wk ART period; a patient from the repeated interruptions arm died of liver cancer during week 26 of the open-ended interruption after previously reaching a viral load greater than 5,000 copies/ml yet electing to stay off ART; and a patient from the repeated interruptions arm developed a transient ileitis.
Immune reconstitution
No significant difference was observed between groups in CD4 T cell counts at the start of phase II, as illustrated in Figure 4. In addition, no difference in the percentage of naïve CD4 cells or decrease of recall response to C. albicans was observed, confirming the absence of significant differences in the retention of baseline immune reconstitution correlates between arms. However, a significant decrease in the abundance of CD4 cells relative to other T cell types as summarized in CD4% (but not in absolute CD4 count ) was present in the repeated TI arm, corresponding to a significant increase in CD8 T cell count. In spite of fluctuations in CD4 T cell count levels between the start and end of each monitored TI, a recovery of CD4 count levels was achieved upon resuppression following each TI in conjunction with a retention of lymphoproliferative responses against C. albicans before, during, and after each TI, as illustrated in Figure 5.
Figure 4 T Cell Subsets and Recall Lymphoproliferative Response at the End of Phase I
End of phase I values for each arm are summarized (median and first and third quartiles) in the stacked figures showing from top to bottom: CD4 T cells/μl, CD4%, CD4−CD45RA+CD62L+% (naïve phenotype), CD8 T cells/μl, CD8%, and C. albicans lymphoproliferative response (shown as stimulation Index, SI). Unpaired p values for each variable are shown above corresponding bracket.
Figure 5 CD4 T Cells/μl and T Cell Recall Lymphoproliferative Response during Sequential TIs in Phase I
Shown are data from the repeated interruptions arm. Panels show the TI initiation visit and TI end visit of each sequential TI inclusive of the initiation visit for phase II (open-ended TI).
Viral resistance mutations and therapy failure
An intent-to-treat analysis of the combined number of patients per arm with detected resistance mutations irrespective of therapy failure in phase I and during the final TI in phase II showed no significant difference between arms (continuous therapy/single TI, 7/21; repeated TI, 10/21; p > 0.05).
Study-defined criteria for therapy failure of a previously suppressive regimen were met by 4/21 patients in the continuous therapy/single interruption arm (patients S37, S47, S52, and S59) in association with self-reported nonadherence to therapy and detection of resistance mutations in phase I, as listed in Table 2. One patient in the repeated interruptions arm (1/21; patient S56) failed therapy after 20 wk following the third TI by maintaining a viral load between 50 and 999 copies/ml in the presence of previously undetected resistance mutations.
Table 2 Therapy Failures with Plasma HIV-1 Protease and Reverse Transcriptase Inhibitor–Associated Resistance Patterns during on Therapy Periods (Study Phase I)
Bold identifies drugs for which mutations were detected in plasma
3TC, lamivudine; ABV, abacavir; ddI, didanosine; d4T, stavudine; EFZ, efavirenz; NVP, nevirapine; RT, reverse transcriptase
a Mutations associated with patient's regimen
In patients who reached phase II in the absence of therapy failure, a total of 12 patients were identified to have resistance mutations at the first viremic time point (continuous therapy/single TI, 3/16; repeated TI, 9/18; p = 0.06). A greater number of resistance mutations was detected in the repeated interruption arm, as summarized in Table 3. In ten out of these 12 patients, a change in resistance patterns was observed when comparing the first viremic time point to the last. All 11 of 12 patients in Table 3 who reinitiated therapy retained suppressive ability of their respective regimens, as did all other patients who did not show resistance mutations in phase II. In the repeated interruptions arm, analysis of newly detected resistance mutations in phase II, as defined by a lack of detection during viremic time points in phase I, identified 3/18 patients (patients S4, S22, and S43) with this pattern (see notations in Table 3).
Table 3 Non-Therapy Failures with Resistance Detected off ART at First and Last Viremic Time Point in Comparator Open-Ended TI (Phase II)
Bold identifies drugs against which mutations were detected
a Mutations associated with patient's regimen
b Patient/physician changed regimen after open-ended interruption for reasons not related to suppression activity on previous regimen: patient S7 changed to 3TC, TNV, EFZ, NVP; patient S40 changed to LOP, RTV, ddI, TNV; and patient S35 changed to LOP, RTV, ABV, TNV
c Mutations not detected at the first plasma HIV-1 RNA tested during prior TIs
d Resistance shown for patient S35 is last available, at week 2 of the third TI
e Patient S45 was lost to follow-up after the end of the third TI. Resistance shown is last available, at week 6 of the third TI. Resuppression noted after completion of the third TI
3TC, Lamivudine; ABV, Abacavir; d4T, Stavudine; ddI, Didanosine; EFZ, Efavirenz; LOP, Lopinavir; NLF, Nelfinavir; NVP, Nevirapine; TNV, Tenofovir; ZDV, Zidovudine
Discussion
Earlier reports on TI strategies in patients with chronic HIV infection include multiple pilot or single-arm study designs centered on the effects on viral control by comparison with pre-therapy periods, detection of resistance mutations without parallel follow-up of a continuously treated arm, and inclusion of variable criteria regarding viral resuppression before proceeding with repeated TIs [11,12,14,16]. In contrast, our strategy mandated resuppression of viral replication to less than 50 copies/ml before each TI and presents the first comparison of viral replication during a final open-ended interruption of therapy between patients randomized to complete three sequential TIs or stay under continuous therapy. Our data, based on intent-to-treat analysis, did not show that repeated TIs resulted in a clinically significant virological benefit as measured by the time to viral rebound to more than 5,000 copies/ml (see Figure 3). Secondary as-treated analysis on viral replication magnitude also indicated a lack of difference between arms. Consistent with the findings of SSITT [11], analysis of our data by the categorical classification of a “responder” as a patient with viral load less than 5,000 copies/ml at week 12 off therapy showed no significant difference in this frequency between arms (single TI, 5/18; repeated TI, 5/16), suggesting the presence of “responders” irrespective of previous protocol-mandated TIs.
Based on secondary outcome measures, the incidence of adverse events (SAEs, therapy failure, and patient discontinuation) or clinical disease progression (as indicated by CD4 count on therapy or opportunistic infections) was not observed to be different between arms. Prospective safety outcomes in our study are in accordance with reports from a retrospective analysis of 1,290 patients who interrupted treatment at least once (< 3 mo) without an increased risk of HIV-associated morbidity or mortality (with the exception of patients in Center for Disease Control and Prevention stage C during first interruption only) [21]. In regards to immunological outcomes, a concern associated with interruption of suppressive therapy is the potential for irreversible, viral-mediated CD4 T cell loss leading to disease progression [6,22]. We did not observe a decrease in CD4 cell numbers or lymphoproliferative responses against C. albicans when measured between arms before the open-ended TI (see Figure 4), nor following resuppression after monitored TI reinitiation cycles in the repeated interruptions arm (see Figure 5). The latter is consistent with observations by others and does not support an immediate immunological “cost” to short-term TIs [12,14,15,16,23]. However, we do show that monitoring CD4 cell numbers by percentage could lead to misinterpreting a significant loss of CD4 cells as a result of a significant increase in CD8 count following TIs, even though absolute CD4 count numbers remained unchanged (see Figure 4). Interestingly, the increase in CD8 T cell number also corresponded with an increase in HIV-specific responses as measured by interferon-gamma expression (data not shown), which in light of an absence of effect on viral load between arms further supports that TI strategies alone may not significantly alter the pre-existing balance between viral replication and host antiviral responses [14,16,23,24].
Importantly, no evidence for an increase of viral resistance in association with therapy failure was present in the repeated interruptions arm (See Table 2). We did not observe a greater clinical failure of NNRTI-based regimens in the repeated interruption arm due to “single drug” periods as predicted by recently redefined drug half-life estimates and the presence of viral replication during each interruption [25,26,27]. However, the percentage of patients with resistance mutations detected in this study in the repeated interruption arm (47%) is higher than the 17% observed in the SSITT cohort [11], in which patients with prior treatment failures were excluded [28]. We interpret this difference to mean that the resistance detected off drug in both our and their cohorts is likely associated with the greater number of drug-experienced patients in our cohort (75%) and the detection of prior archived resistance mutations as supported by Metzner et al. [29], who documented in 14/25 (56%) SSITT patients the presence of minor populations of M184V occurring at least once off drug during interruption of therapy.
In spite of the lack of difference in the total number of patients with resistant mutations detected on therapy during phase I and off therapy in phase II (7/21 [33%] versus 10/21 [47%], respectively) in both arms, we do report in similarity to others a greater detection of resistance mutations in the TI arm when restricting analysis to the last off-drug period only [29,30] as three of 16 (18%) had mutations detected off drug in the continuous therapy/single interruption arm compared to nine of 18 (50%) in the repeated interruption arm. However, based on the lack of association between viral resistance detected off-drug shortly after TI and resuppression by the same regimen in all patients, it remains undetermined to what extent TIs favor the detection of archived mutations in chronically suppressed patients and to what extent these mutations are a signal for a future therapy failure. The latter is best exemplified by the data we collected on patients on NNRTI-based regimens in the repeated interruptions arm where two patients (S19 and S43) showed K103N detection (only during the off-drug periods) in the absence of therapy failure while maintaining the same regimen after each TI, including post-study follow-up (Table S1). On the other hand, virological failure in the continued presence of an NNRTI-based regimen in phase I was associated with detection of K103N, as observed in one patient (S56) in the repeated interruption arm and three patients (S37, S52, and S59) in the continuous therapy arm with self-reported non-adherence.
Drug resistance that occurs during virological drug failure predicts virological responses to salvage treatment [31,32,33]. In contrast, the clinical implications of drug resistance mutations that appear shortly after TI in chronically suppressed patients are not clear. Case reports in this cohort of patients have demonstrated that drug-resistant variants that appeared during TIs may not persist in subsequent time points even after repeated use of the same antiretroviral regimen [19,34]. We now observe that drug resistance appearing during TIs can be transient since 50% and 33% of patients listed in Table 3 showed complete and partial reversion to wild type, respectively, when comparing to resistance at the last available viremic time point in phase II (See Table 3). Further, we observed durable resuppression of plasma viral RNA level in many patients who had drug-resistance mutations off therapy that would otherwise be expected to affect part of their treatment regimen when reinitiated (see Table S1). Virus populations that expand shortly after TI may lack all of the adaptations required to achieve high levels of plasma viremia in the presence of drug during continuous treatment. These adaptations may include the resistance-associated mutations, which were detected, as well as secondary mutations that may increase the viral replication capacity [35,36] or envelope adaptations required to escape concurrent humoral immune responses [37,38]. It is of interest to note that despite the large amount of research activity on TIs in patients with suppressed chronic infection and the hundreds of monitored interruptions studied to date, only limited cases of development of clinical resistance (as evidenced by a lack of viral resuppression following therapy reinitiation) have emerged, in contrast to the multiple reports of detection of viral sequences off ART associated with resistance as shown in this study and others [11,19,29,30,39,40].
Taken together, while our data show no clinically significant benefit for repeated TIs of less than 1.5 mo in patients with CD4 counts greater than 400 on therapy with regard to viral control as defined by time to rebound, secondary outcomes document no significant difference in levels of retention of immune reconstitution between arms and no increased incidence of virological failure as a consequence of TIs. While our data indicate that this TI strategy should not be pursued outside of a clinical trial setting, we argue that it will be important to collect additional data on the potential benefits of drug-sparing regimens (such as reduced long-term toxicity and reduced cost) and to define long-term outcomes in comparison with continuous therapy.
Supporting Information
Registration of randomized trial at clinicaltrials.gov under identifier NCT00051818.
Protocol S1 Protocol Text: Effects of Sequential TI
(614 KB DOC).
Click here for additional data file.
Protocol S2 Study IRB Approval
Current IRB approval for study at clinical site.
(179 KB PDF).
Click here for additional data file.
Protocol S3 Wistar IRB Approval
IRB approval to receive study biological material at the Wistar Institute for research.
(201 KB PDF).
Click here for additional data file.
Protocol S4 CONSORT Checklist
(50 KB DOC).
Click here for additional data file.
Table S1 Patients with Detected Resistance during Phase II: Regimen at Initiation of Phase II and Subsequent Post-Study Follow-Up to August 2004
(36 KB DOC).
Click here for additional data file.
Patient Summary
Why Was This Study Done?
Highly active antiretroviral therapy has revolutionized HIV treatment for patients who have access to the medications. But the drugs are expensive, have side effects, and can become ineffective when the virus develops resistance. Structured treatment interruptions (STIs), also known as “drug holidays” (because patients take a holiday from their drugs), have been suggested as possible alternatives to continuous therapy. Initially, there was fear that patients who went back on therapy after an interruption would not be able to control the virus again, but there was also hope that STIs might actually strengthen the immune system. In addition, STIs might alleviate some side effects, and they would certainly reduce costs. This study uses a particular design to examine the risks and benefits of STIs.
What Did the Researchers Do?
The researchers studied 42 patients who received either continuous therapy for 40 weeks or three successive treatment interruptions of two, four, and six weeks, followed by a final open-ended interruption for both groups. The researchers then recorded how long patients were able to control the virus before their viral load reached a certain threshold and they had to restart therapy. They also examined CD4 counts and therapy failure, and looked for resistant viruses on and off therapy.
What Did They Find?
In terms of being able to control the virus, it made no difference whether patients were on continuous therapy or had three STIs. In other words, when both groups stopped treatment at 40 weeks, the length of time that the patients could control the virus was the same in both groups. Eventually, all patients (except two who elected to stay off antiretroviral therapy) re-initiated therapy because of a rising viral load, and the patients once on therapy all regained control over the virus. Resistant viruses were found in patients from both groups, but during the final interruption they were more common in the group that had received the three STIs.
What Does This Mean?
The study confirms that STIs do not help with viral control, consistent with other studies that found that STIs had no clinical benefit. On the other hand, no short-term adverse events were present, as all patients were able to regain control over the virus after they went back on treatment (without a drop in CD4 count), even after several rounds of interruptions and tests to detect of resistant viruses. There remains concern about whether recurrent cycles of viral replication and suppression might in themselves be harmful, and whether the presence of resistant virus is a signal for future treatment failure. Given these unanswered questions, STIs should only be undertaken within clinical trials.
What Next?
Possible risks and benefits of STIs in the management of therapy remain an active area of research. Evidence so far has not shown clinical benefits. Ongoing studies need to clarify whether there are long-term risks (and what they are), so that we can weigh these against the benefits of reducing costs and side effects.
Additional Online Information
The Body information Web page on STIs: http://www.thebody.com/treat/sti.html
Information on “continuing antriretroviral treatment” from AVERT, an international HIV and AIDS charity based in the United Kingdom: http://www.avert.org/conttrt.htm
Information on STIs from NAM, a United Kingdom registered charity: http://www.aidsmap.com/en/docs/7980314C-97B5–412F-93B1-AD8B64F51F73.asp
Factsheet on HIV treatments from the United States National Institute for Allergy and Infectious Diseases: http://www.niaid.nih.gov/factsheets/treat-hiv.htm
Search results from Clinicaltrials.gov when searching for “HIV” and “treatment interruption” combined terms: http://www.clinicaltrials.gov/search/term=%22Treatment+Interruption%22%5BCONDITION%5D+AND+HIV+%5BCONDITION%5D
We are indebted to the women and men who participated in this study; the members of the Data Safety Monitoring Board (J. Davids, H. Friedman, C. Gallo, R. Goldfein, and R. Pepy) who monitored progress; J. Henry and the National Institute of Allergy and Infectious Diseases (NIAID) staff for administrative support; L. Schmidt and J. Ondercin for clinical oversight; E. C. Moore, T. Liegler, J. Javier, L. Chow, and K. Plamenco for laboratory support; and Philadelphia FIGHT board and staff for study support. We wish to thank Pablo Tebas of the University of Pennsylvania for valuable suggestions on manuscript preparation.
This work was primarily supported by a grant to L. J. Montaner by the National Institute of Allergy and Infectious Diseases NIH AI48398. Additional support was provided by the Philadelphia Foundation (Robert I. Jacobs Fund), The Stengel-Miller family, AIDS funds from the Commonwealth of Pennsylvania and from the Commonwealth Universal Research Enhancement Program, Pennsylvania Department of Health. Resistance data were also supported by the California Universitywide AIDS Research Program and the Gladstone Institute of Virology and Immunology.
The major funding agency (NIAID) reviewed and approved the study protocol, but did not take part and in no way influenced the actual conduct of the study, data collection, data analysis, interpretation of the data, or preparation and approval of the manuscript. NIAID and the National Institutes of Health extramural research staff did confirm compliance with regulatory requirements, Data Safety Monitoring Board meeting schedules, and SAE review throughout the study.
Citation: Papasavvas E, Kostman JR, Mounzer K, Grant RM, Gross R, et al. (2004) Randomized, controlled trial of therapy interruption in chronic HIV-1 infection. PLoS Med 1(3): e64.
Abbreviations
ARTantiretroviral therapy
AUCHIV RNAHIV-1 plasma area under the curve
IRBinstitutional review board
NNRTInon-nucleoside reverse-transcriptase inhibitor
SAEserious adverse event
SSITTSwiss–Spanish Intermittent Trial
TItreatment interruption
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| 15630469 | PMC539050 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e64 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010064 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563047010.1371/journal.pmed.0010065Research ArticleGenetics/Genomics/Gene TherapyOncologyGeneticsOncologyCancer as a Complex Phenotype: Pattern of Cancer Distribution within and beyond the Nuclear Family Familial Clustering of CancersAmundadottir Laufey T
1
*Thorvaldsson Sverrir
1
Gudbjartsson Daniel F
1
Sulem Patrick
1
Kristjansson Kristleifur
1
Arnason Sigurdur
1
Gulcher Jeffrey R
1
Bjornsson Johannes
2
Kong Augustine
1
Thorsteinsdottir Unnur
1
Stefansson Kari
1
1deCODE GeneticsReykjavikIceland2National University HospitalReykjavikIcelandLewis Cathryn Academic EditorGuy's King's and St. Thomas' School of MedicineUnited Kingdom
Competing Interests: JB and SA have declared that no competing interests exist. LTA, ST, DFG, PS, KK, JRG, AK, UT, KS have stock in deCODE Genetics as well as equity interests.
Author Contributions: LTA, ST, DFG, PS, KK, SA, JRG, JB, AK, UT, and KS designed the study. LTA, ST, DFG, PS, AK, and UT analyzed the data. LTA, ST, DFG, PS, KK, SA, JRG, JB, AK, UT, and KS contributed to writing the paper.
*To whom correspondence should be addressed. E-mail: [email protected] (LTA), E-mail: [email protected] (KS)
¤1Current address: Keflavik County Hospital, Keflavik, Iceland
12 2004 28 12 2004 1 3 e6527 8 2004 27 10 2004 Copyright: © 2004 Amundadottir 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.
Cancer in Families
Background
The contribution of low-penetrant susceptibility variants to cancer is not clear. With the aim of searching for genetic factors that contribute to cancer at one or more sites in the body, we have analyzed familial aggregation of cancer in extended families based on all cancer cases diagnosed in Iceland over almost half a century.
Methods and Findings
We have estimated risk ratios (RRs) of cancer for first- and up to fifth-degree relatives both within and between all types of cancers diagnosed in Iceland from 1955 to 2002 by linking patient information from the Icelandic Cancer Registry to an extensive genealogical database, containing all living Icelanders and most of their ancestors since the settlement of Iceland.
We evaluated the significance of the familial clustering for each relationship separately, all relationships combined (first- to fifth-degree relatives) and for close (first- and second-degree) and distant (third- to fifth-degree) relatives. Most cancer sites demonstrate a significantly increased RR for the same cancer, beyond the nuclear family. Significantly increased familial clustering between different cancer sites is also documented in both close and distant relatives. Some of these associations have been suggested previously but others not.
Conclusion
We conclude that genetic factors are involved in the etiology of many cancers and that these factors are in some cases shared by different cancer sites. However, a significantly increased RR conferred upon mates of patients with cancer at some sites indicates that shared environment or nonrandom mating for certain risk factors also play a role in the familial clustering of cancer. Our results indicate that cancer is a complex, often non-site-specific disease for which increased risk extends beyond the nuclear family.
It's not often that an entire nation's genealogy and cancer records are available. But they are in Iceland, and have been used to determine how often cancers occur in families
==== Body
Introduction
Highly penetrant susceptibility variants explain only a small fraction of the genetics of all cancer cases. As an example, mutations in the BRCA1 and BRCA2 genes account for around 2%–3% of all breast cancer cases [1,2], although more prevalent founder mutations in these genes can explain up to about 10% of the disease in some populations [3,4,5,6,7]. However, the role of genetics in the remaining breast cancer cases and the majority of other cancers is not clear.
Family studies have given insight into the contribution of genetic and environmental factors to the etiology of cancer. Case-control, registry- and population-based studies have evaluated familial clustering using either risk ratio (RR) estimations for relatives of cancer patients, or kinship coefficient (KC) estimations for cancer patients. The largest of these studies, utilizing either the Utah Population and Cancer Registry Database or the Swedish Family-Cancer Database, have demonstrated excess familial clustering at practically all cancer sites in the body [8,9,10,11,12]. Most of these studies have been able to evaluate familial clustering only within the nuclear family, thus making it more difficult to separate the roles of shared environmental and genetic factors in the familial aggregation of cancers. However, in one of these studies [12], in which familial clustering was evaluated for more distant relatives, significant clustering outside the nuclear family was demonstrated for a number of cancer sites. Extended familial clustering has also been reported in studies of individual cancers [13,14,15,16,17,18,19,20,21,22].
Twin studies have also evaluated the role of genes versus environment in cancer susceptibility. The largest study involved close to 45,000 twins from Denmark, Sweden, and Finland where the RR of same type of cancer was calculated for individuals with affected twins and compared to those without an affected twin [23]. The authors concluded that for the majority of cancer sites only a limited part of the risk could be explained by heritable factors. Exceptions to this were cancers of the prostate, colon and breast.
In addition to well documented familial clustering for the majority of individual cancers, aggregation of different types of cancers in families has also been observed. Reports have been published on the results of systematic analysis of the aggregation of different cancers using the Utah Population and Cancer Registry Database [24,25]. In addition to demonstrating excess familial clustering for most cancer sites, these studies also indicate that an excess is also shared by different cancer sites. In these studies, cancer clustering was evaluated either by calculating the RR for first-degree relatives or KC between different cancer sites. While distant relationships contributed to the overall calculation of KC, their contributions were not evaluated separately in the studies between cancer sites, hence making it more difficult to separate the effects of genetic and environmental factors in these studies.
We have studied a registry of all cancer cases diagnosed in Iceland from 1 January 1955 to 31 December 2002, with the aim of searching for evidence of genetic factors both at individual cancer sites and those shared by different sites. By cross-referencing cancer prevalence in relatives of cases with the aid of a comprehensive nationwide genealogy database, we have estimated RR separately for first- to fifth-degree relatives of all cancer patients diagnosed in Iceland over 48 y. We demonstrate here an increased cancer risk in relatives outside the nuclear family (third- to fifth-degree relatives) for many cancer sites. These relatives share significant genetic makeup but are less likely to share environmental factors beyond those shared by the general population, indicating that genetic factors may be involved. By applying the analysis across different cancer sites we also demonstrate shared familiality between certain cancer sites both in close and distant relatives. These results suggest that cancer can be considered a broad phenotype with shared genetic factors crossing different cancer sites. That is, the difference between cancers at various sites may in part be the consequence of variable expressivity of the same cancer-predisposing genes.
Methods
This study was approved by the National Bioethics Committee of Iceland, the Data Protection Authority of Iceland, and the Icelandic Cancer Society. All names of patients listed in the Icelandic Cancer Registry (ICR) and the genealogic database were encrypted through a process approved by the National Bioethics Committee and the Data Protection Authority before being analyzed [26].
Cancer Registry
The ICR of the Icelandic Cancer Society is a carefully constructed database containing practically complete records of all cancer cases diagnosed in Iceland after 1 January 1955 [27]. Records are received at the ICR from all hospitals in the country that treat cancer patients, and the very few not listed are individuals who are diagnosed while living abroad. Furthermore, the records are verified by a continuous interaction between the ICR and Icelandic hospitals and clinicians. Approximately 95% of cases are histologically verified [28]. In the present study we used International Classification of Disease version 10 codes as the basis for defining phenotypes. A total of 81 unique phenotypes (sites) were analyzed. In this paper we present data from 27 sites with more than 200 cases each (Table 1). For the 48 years (1 January 1955 to 31 December 2002) a total of 32,534 individuals were found in our genealogy database. Cancer incidence in Iceland is comparable to the Nordic countries of Europe and is detailed in [27].
Table 1 RR Estimates of Cancer at the Same Site for Relatives and Mates for Cancer Sites with 200 or More Cases
Shown are the estimated RRs with 90% confidence interval for first- to fifth-degree (1°–5°) relatives and mates of the 27 cancer sites with ≥200 cases, in bold when the 90% CI does not include 1.00, which corresponds to one-sided p < 0.05. Also shown are combined p values to evaluate the significance of the increased RR for all relatives (first- to fifth-degree) and for close (first- and second-degree) and distant relatives (third- to fifth-degree). p values nominally significant at the 0.05 level are shown in bold
a Nominal p values that remained significant after Bonferroni correction for the 27 individual tests (p < 0.00185)
b na, not applicable (sex-specific cancers)
c NHL, non-Hodgkin's lymphoma
d Number of mates with cancer/total number of mates for each cancer site
ICD10, International Classification of Disease version 10
Genealogic Database
deCODE Genetics has built a computerized genealogy database of more than 687,500 individuals [29,30]. The names of all 288,000 Icelanders currently alive and a large proportion of all Icelanders who have ever lived in the country are in the database. The genealogy of the entered individuals is recorded from multiple sources including church records and censuses from previous centuries and, more recently, from published genealogy books. The genealogy database is quite complete from the 18th century on, thus allowing quite distant relationships to be traced accurately.
Mates are defined as individuals of the opposite sex who have one or more children in common, regardless of marital status.
Calculations of RRs
The RR for relatives is a measure of the risk of disease for a relative of an affected person compared to the risk in the population as a whole. More precisely, for a given relationship the RR for disease B in the relatives of probands with disease A is defined as
where PA denotes the event that the proband is affected with disease A, and RB denotes the event that the relative is affected with disease B. Note that disease A and disease B can be the same in this definition which applies when estimating RR at individual cancer sites. Using Bayes' rule it can be shown that for symmetric relationships, RR is the same if the roles of A and B are switched, i.e., the RR for disease A in the relatives of probands with disease B is the same as the described above. In this study we always chose the less common phenotype as the proband when estimating RR.
A basic underlying assumption in our estimation of RR is that of conditional independence of ascertainment, or censoring, (ORB and OPA are the events that the relative and proband are observed with diseases A and B, respectively):
P(ORB, OPA | PA, RB) = P(ORB | RB) P(OPA | PA).
Some form of this assumption is used by most methods estimating RR [31].
Obtaining valid estimates of the RR is not always straightforward, since the method of ascertainment of affected cases critically affects the estimation, and inappropriate estimators can lead to bias or inflated estimates [32]. The use of a nationwide registry of patients covering close to five decades decreases much of the potential sampling bias. However, the ascertainment of the ICR depends on the year of birth of individuals. This dependence needs to be addressed when estimating the RR.
The approach chosen here is to estimate the RR for a number of subpopulations, where prevalence is reasonably constant, and combine them into a single estimate of RR for the full population. Let r be the number of relatives of probands, counting multiple times individuals who are relatives of multiple probands [33], let a be the number of relatives of probands that are affected (again possibly counting the same individual more than once), let n be the size of the population, and finally let x be the number of affected individuals in the population. If P(RB) and P(RB | PA) can reasonably be assumed to be constant in the population, then x/n and a/r, respectively, are estimates of these probabilities. Given these estimates, RR is consistently estimated by
Assuming the population can be split into N subpopulations, such that within each subpopulation P(RB) and P(RB | PA) can be assumed to be constant, although they may vary between subpopulations, and assuming furthermore that RR is the same in all the subpopulations, then the RR is consistently estimated by a convex combination of the estimates for the subpopulations. We selected weights for the combination such that the efficiency of the estimator was at maximum for RR equal to one. Making the simplifying assumption that the relatives are independent (while this assumption is not entirely correct, it affects only efficiency, not validity), the optimal weight for group j is
(this is the inverse of the variance of the estimate for RR in subpopulation j), where a, r, x, and n are defined as above, restricted to the subpopulation j. Note that probands are not restricted to the subpopulation. Given these weights, our estimate of RR is
In this study, the most relevant variations in P(RB) and P(RB | PA) stem from time-dependent censoring of affected status and sex-specific differences. Hence, we have stratified the population so that j runs over groups of people of the same sex and born in the same 5-y periods. For a fixed year-of-birth stratum, there is censoring of affected status (missing data) based on year of onset because of the fact that records cover only the period 1955–2002. Our approach is designed to address this type of missing data. As an example of the stratification, the breast cancer patients in our analysis were born in the years 1865 to 1970 (5-y strata), yielding 35 subpopulations, 22 for female patients, but only 13 for male, as this cancer is rare for males.
To assess the significance of the RR obtained for a given group of patients, we compared their observed values with the RR computed for up to 100,000 independently drawn and matched groups of control individuals. Each patient was matched to a single control individual in each control group. The control individuals were drawn at random from the genealogic database with the conditions that they had the same year of birth, the same sex, and the same number of ancestors recorded in the database at five generations back as the matched patients. Empirical p values can be calculated using the control groups; thus, a p value of 0.05 for the RR would indicate that 5% of the matched control groups had values as large as or larger than that for the patient's relatives or mates. The number of control groups required to obtain a fixed accuracy of the empirical p values is inversely proportional to the p value. We therefore selected the number of control groups generated adaptively up to a maximum of 100,000. When none of the values computed for the maximum number of control groups were larger than the observed value for the patient's relatives and mates, we report the p value as being less than 0.00001. Using a variance-stabilizing square-root transform, an approximate confidence interval may be constructed based on the distribution of RR for control groups [33].
As another test for significance of RR between cancer sites, we used combined estimators for risk in relatives of degree 1 and 2 together, degrees 3, 4, and 5 together, and degrees 1 through 5 together. If RRd is the RR for relatives of degree d, then RRd – 1 is known to decrease proportional to 2-d as d increases for a monogenetic single variant or additive disease models, and faster for more complex disease models [34]. With the estimate of RRd denoted by , we then chose a test statistic of the form
with d summed over the relevant degrees. For RRd close to one, the variance of the estimate
is inversely proportional to the number of relatives of degree d for the proband. Based on the Icelandic genealogy for the cancers being studied here, the number of relatives is proportional to γd, where the value of γ quantifies how the number of relatives grows with each degree of relatedness to the proband. This factor γ varies only slightly between cancers and is on average 2.46. Minimizing the variance of the test statistic in equation 6 with respect to the weights yields the statistic
As above, the choice of weights and the form of the statistic affects only power, not validity. To assess significance, the observed value of the statistic was compared to its value for multiple matched control groups as described above.
Although our evaluations of familial clustering, for both close and distant relatives, are based on RR, an alternative approach based on comparing KCs among patients and among controls exists [12,24,25]. The two approaches are closely related, and our choice was made in part because relative risk is a less technical concept and its application to genetic counseling more direct. Also, the relationship between relative risk and the power to map disease genes by linkage analysis has been thoroughly investigated [34,35].
Results
We have studied the familial clustering of cancer by estimating RR for first- and up to fifth-degree relatives both within and between all cancer sites. Here we present results for 27 sites that contain 200 or more cancer cases each, based on International Classification of Disease version 10 codes. These 27 sites represent 89% of all cancer cases in the ICR.
Risk Estimations for Cancer at Same Site
A significantly increased RR to first-degree relatives of patients with cancer was seen for 22 of the 27 cancer sites (Table 1). Among the statistically significant RRs, the highest estimates were for lymphoid leukemia, Hodgkin's disease, and cancer of the thyroid, meninges, lip, testis, and larynx (RR above three). These cancers, except for thyroid cancer, were among the least prevalent sites (200–400 cases), as reflected in the large standard deviation of the RR estimates (Table 1). First-degree relatives of individuals with breast, lung, kidney, pancreatic, ovarian, and esophageal cancer and multiple myeloma, had between 2- and 3-fold increased risk of developing the same cancer.
The medians of the estimated RR values for the 27 sites in first- to fifth-degree relatives were 2.00, 1.32, 1.21, 1.10, and 1.04, respectively.
Combined p-values incorporating the increased risk for first- to fifth-degree relatives identified 21 sites being significant at a nominal level of 0.05. Sixteen of those sites remained significant after Bonferroni adjustment for the 27 individual tests (p value < 0.00185) (Table 1). To discriminate between familial clustering in close and distant relatives, combined p values were also calculated for first- and second-degree relatives on one hand and for third- to fifth-degree relatives on the other hand (Table 1). Fourteen sites were nominally significant for the distant relationships (third- to fifth-degree relatives) of which eight were significant after Bonferroni adjustment. These eight sites were all within the group of 16 sites demonstrating significant familial clustering in all relationships.
The RR for developing cancer at the same site was also estimated for mates of cancer patients at 22 out of the 27 individual sites. The remaining five sites are sex-specific and calculations thus not applicable. For seven rare cancer sites, affected mates were not observed, corresponding to a RR of zero. Only lung, stomach, and colon cancer were characterized by significantly increased RR values in mates (Table 1).
Risk Estimations between Cancer Sites
We calculated RR between all cancer sites for first- and up to fifth-degree relatives and mates (results for the 27 largest sites are shown in Table S1). As done for the individual cancer sites, p values were calculated for all (first- to fifth-degree), close (first- and second-degree), and distant (third- to fifth-degree) relationships. Figure 1 shows a diagram representing 20 pairs of cancer sites that associate with a combined p value, significant at a level of 1 × 10-4, for first- to fifth-degree relationships. This level was significant at the 0.05 level after Bonferroni adjustment for the 351 tests (number of unique pairs of cancers). The strength of the distant familiality (i.e., the p value for third- to fifth-degree relatives) between these pairs of cancers is represented by the thickness of the lines joining sites in Figure 1.
Figure 1 A Schematic Representation of Cancer Pairs Demonstrating Significant Familial Aggregation
Cancer pairs that demonstrate significant familial co-clustering (first- to fifth-degree relatives) at the 0.05 level after adjustment for multiple testing (nominal p value < 1 × 10-4) are joined by lines. The thickness of the lines joining the pairs are based on nominal p values corresponding to the significance of the familiality in distant relatives (third to fifth degree): bold, p ≤ 0.001; solid, p ≤ 0.01; and dashed, p ≤ 0.05. The number on the lines joining each pair indicates the cross-cancer RR in first-degree relatives. Shaded ovals correspond to individual cancer sites that were significant for the combined group of first- to fifth-degree relatives at the 0.05 level after Bonferroni adjustment (see Table 1).
In total, 17 cancer sites were involved in 20 significant pairs of sites (Figure 1). Stomach and prostate cancer were involved in most pairs, seven and six pairs, respectively, followed by colon, ovarian, and cervical cancer, each involved in three pairs. The estimated RRs for the 20 pairs are between 1.1 and 1.7 for first-degree relatives and between 1.1 and 1.5 for second-degree relatives (Figure 1; Table S1). The highest RRs in first-degree relatives between cancer sites were seen for esophagus–cervix, with a RR of 1.74, pancreas–ovary, with a RR of 1.66, and colon–rectum, with a RR of 1.64.
All of the 20 pairs shown in Figure 1 were nominally significant (p value < 0.05) for distant relationships, of which nine were significant at the 0.001 level. In the latter group, prostate, rectum, stomach, and cervical cancers each appeared in two pairs, and colon cancer in three.
Discussion
In this study we have comprehensively analyzed familial aggregation of cancer cases in a whole nation, both within and between pairs of cancer sites. The completeness of our genealogy database allows us to accurately trace distant relationships, which we believe is unique to this study. Linking the ICR to our nationwide genealogy database thus has made it possible to uncover distant familial connections between cancer cases, and reach beyond shared environmental factors to identify individual and combined cancer sites with the strongest genetic influences. Furthermore, even though the genetic effect decreases with more distant relationships, the sample sizes used to estimate familiality are dramatically larger for the distant relationships than for the closer ones. This compensates to some extent for the lower effect and adds considerable statistical power to the study.
In this paper we restrict the presentation and discussion to the most significant findings. However, we provide results for all pairs of 27 cancer sites in Table S1, as a resource for other researchers interested in the familiality of specific cancers.
The largest population-based studies reported to date, evaluating familial clustering within the same cancer site, are from Utah and Sweden [8,9,10]. These studies report RR values for first-degree relatives [36] that are comparable to those presented here for first-degree relatives. For example, the median RRs for the occurrence of the same cancer in first-degree relatives were 2.15, 1.86, and 2.00 for the Utah, the Sweden, and our study, respectively. Also, RR values in first-degree relatives ranged between 1.5 and 3.0 for the majority of sites, i.e., 69%, 82%, and 60%, in Utah, Sweden, and this study, respectively.
As seen in Utah and Sweden, high RR values were found in this study for multiple myeloma, lymphoid leukemia, and thyroid, testicular, and laryngeal cancer. The RR for thyroid cancer in first-degree relatives was much higher in Utah and Sweden (8.48 and 9.51) than in Iceland (3.02). One possible explanation of the lower RR may be the high incidence of thyroid cancer in Iceland, due to an excess of the papillary subtype [18,37], which is not a part of the multiple endocrine neoplasia syndromes.
The cancer sites showing the highest RR for first-degree relatives tend to be among the rarer sites. There are two potential reasons why rare tumors tend to show higher RRs than common cancers. Being common, the baseline frequency is not low and that creates a bound on how large the RR can be. Also, common cancers are expected to be genetically complex, whereas it is more likely for a rare tumor to be closer to a Mendelian trait, caused by rare alleles with high penetrances.
Most individual cancer sites, or 16 out of the 27 studied here, showed familiality as evidenced by significant p values (after adjustment for multiple testing) for the combined group of first- to fifth-degree relatives. Furthermore, eight of these 16 sites remained significant even after exclusion of the first- and second-degree relatives (after adjustment for multiple testing). The majority of the 16 significant cancer sites are among the sites of the most prevalent cancers, indicating that we may lack power to detect extended familiality for the less prevalent cancer sites. Indeed the median number of cases per cancer site was 943 for the 16 significant sites compared to 342 for the non-significant sites. Nevertheless, significant familial clustering (first- to fifth-degree relatives) is seen for some of the less prevalent sites, i.e., lymphoid leukemia and esophagus and meningeal cancer.
The largest cancer twin study reported to date [23] documented significant heritability of prostate (42%), colorectal (35%), and breast cancer (27%) and provided suggestive evidence for limited heritability of leukemia and stomach, lung, pancreas, ovarian, and bladder cancer. All of these cancer sites showed significant familial clustering in our study. However, when the analysis was restricted to distant relatives, lymphoid leukemia, pancreatic, and ovarian cancer were no longer significant. Although close to 45,000 pairs of twins were included in the study (of which 10,803 had been diagnosed with cancer), the study clearly lacked statistical power to detect the effects of heritable factors for the less prevalent cancer sites.
A significantly increased risk of the same cancer was seen in mates only for individuals diagnosed with stomach, lung, or colon cancer. These results are in accordance with previous reports, including Swedish population-based studies, except for colon cancer [38,39,40,41]. Environmental factors in adult life (including lifestyle and infections) or nonrandom mating could explain the higher risk of these cancer types in mates. The RR was not significant or not observed in mates for other sites.
We also assessed the significance of familial clustering between cancer sites by calculating combined p values corresponding to the increased risk for first- to fifth-degree relationships. With this method, we detected 17 cancers that linked into 20 pairs of sites that were significant after adjustment for multiple testing. Stomach and prostate cancer appeared more frequently in the pairs than other cancer types, followed by colon, ovarian, and cervical cancer. We emphasize again, as with the same-cancer calculations, that we might lack power to connect rare cancers to other cancer sites. This possibility is highlighted by the fact that the 17 cancers in the significant pairs are the most prevalent cancer sites in Iceland.
Some connections seen here between cancer sites may be partly explained by known high-risk genes involved in heritable syndromes. Thus, mutations in genes associated with hereditary nonpolyposis colorectal cancers could explain a part of the risk shared between stomach, colon, rectal, and endometrial cancer, and possibly brain and ovarian cancer [42,43]. In a similar manner, mutations in BRCA1 and BRCA2 may explain in part the cluster seen between prostate, breast, ovarian, and possibly pancreatic cancer [20,44,45,46]. Other known but even rarer cancer syndromes are likely to explain only a handful of cases.
Undiscovered genetic factors could contribute to some connections seen here to a much greater extent than the known susceptibility factors. Although these could include unknown high-risk susceptibility genes, they are more likely multiple genetic variants, each conferring small to moderate risk.
Familial clusters were identified between cancer sites, both in close and distant relatives, that do not correspond to known cancer syndromes. These include lung, esophageal, cervical, and stomach cancer, which, interestingly, have been associated with environmental rather than genetic factors. One explanation for this excess familiality between these cancer sites is an interaction of genetic susceptibility factors with environmental carcinogens (e.g., tobacco and diet) or infectious agents. Thus, the same environmental factor could interact with the same genetic susceptibility factor or factors to induce different cancers (i.e., smoking in lung and cervical cancer). Alternatively, different environmental factors could interact with the same genetic susceptibility factor or factors to increase the risk for different cancers (i.e., smoking in lung cancer and human papilloma virus in cervical cancer).
Hormone-related cancers form another risk cluster. Thus, shared genetic susceptibility factors could directly influence the hormonal metabolism to induce breast, prostate, thyroid, or ovarian cancer in carriers. Alternatively, shared genetic factors could interact with dietary factors to induce aggregation of cancers at these sites in related individuals. A significantly increased risk of breast, prostate, cervical, and non-melanoma skin cancer was recently reported in first-degree relatives of early-onset breast cancer patients from Sweden that tested negative for BRCA1 and BRCA2 mutations [47]. Our data support the notion that unknown susceptibility variants that increase the risk of breast and prostate cancer and melanoma remain to be characterized.
Two more groups of cancers with shared risk were identified that each include sites that share the same developmental progenitors: the prostate, kidney, and bladder are sites derived from the nephrogenic ridge while colon, rectum, and stomach are derived from the primitive gut tube. Therefore, the sites in each group may share risk alleles that regulate embryonic development, which can later play a role in oncogenesis.
Interestingly, three cancer sites/types, non-melanoma skin, brain, and melanoma, that do not have significant same-cancer familial clustering demonstrate significant cross-cancer familial clustering with more prevalent cancer sites, i.e., rectum, stomach, and kidney cancers, respectively.
Previous reports systematically evaluating the significance of co-clustering of cancer pairs in families have utilized the Utah Population Database. In these studies lip and prostate cancers appear to associate most frequently with other cancer sites. The same is true for prostate cancer in our study, whereas lip cancer does not significantly associate with any other cancer sites. This can at least in part be explained by the difference in age-standardized incidence rates for lip cancer in Iceland and Utah (Iceland 1.1 and Utah 2.4) [48]. In contrast, stomach cancer associates with seven other cancer sites out of the 20 significant pairs in our study, but only three other sites in the Utah study. Of the 20 cancer pairs that significantly associate in our study, eight concur with the Utah studies.
Because the increased cross-site RR extends beyond the nuclear family, shared genetic factors may contribute to the risk of more than one cancer type. This suggests that cancer could be considered a broad phenotype with shared genetic factors across cancer sites. Therefore cancer should in certain cases be studied in a broader context than previously done. Combining multiple cancers that show increased cross-site RR may serve to increase the power of linkage and case-control studies. Our results also have implications for genetic counseling and imply that the focus of attention should broaden to the history of multiple cancer types in relatives within and outside the nuclear family. These results also suggest the utility of comparing expression profiles and in vitro biological processes across the cancers that we have identified as sharing genetic risk. The isolation of cancer predisposition genes with broad effects may define new rate-limiting pathways that can be used to search for drug targets for a more focused treatment with fewer side effects but with utility across multiple cancers.
Supporting Information
Table S1 Cross-Site RR Estimates for Relatives and Mates of Patients Diagnosed with Cancers at 27 Sites with 200 Cases or More
(1.9 MB DOC).
Click here for additional data file.
Accession Numbers
The LocusLink (http://www.ncbi.nlm.nih.gov/projects/LocusLink/) accession numbers for the genes discussed in this paper are BRCA1(LocusLink ID 672) and BRCA2 (LocusLink ID 675).
Patient Summary
Background
Although a few cancers have a fairly simple genetic cause, most, especially the most common cancers, do not, and what makes one person rather than another develop cancer is not clear. One way of trying to work out how much genes rather than environment contribute to disease is to study large populations. One such population is the Icelandic nation: not only is detailed health information about individuals available, including information on cancer, but also very good genealogical information and a substantial amount of genetic data.
What Did the Study Find?
Researchers examined all cancer records dating back to 1955 and then analyzed the chances of relatives and mates of these patients having cancer. They found that some cancers, especially rare ones, had a higher than baseline chance of occurring in relatives, but so did many common cancers, and for some cancers, the higher chances extended to quite distant relatives. In addition, the risk sometimes involved different cancer types.
What Does the Study Mean for Patients?
Even for the highest risk cancers, the absolute increased risk for relatives remains very small. In addition, despite the large numbers of patients studied, the numbers of cancer cases are still not large enough to be completely certain of the results, apart from very common cancers, which had the lowest chance of occurring in relatives. So these results will not help doctors much at the present time in telling an individual patient what their risk is of getting cancer if a relative has it—but they will be useful for other researchers in knowing how to plan future studies to look at the underlying causes of cancer.
Where Can I Get More Information?
Icelandic Cancer Society: http://www.krabb.is/cancer/
The United States National Cancer Institute's Cancer Information Service: http://cis.nci.nih.gov/
CancerHelp UK, a free information service about cancer and cancer care: http://www.cancerhelp.org.uk/
deCODE Genetics:http://www.decode.com
We thank the ICR for providing lists of cancer patients. We also thank Hersir Sigurgeirsson and Snæbjorn Gunnsteinsson for assistance with data processing, and Jon Thor Bergthorsson, Simon N. Stacey, and Julius Gudmundsson for their critical reading of the manuscript and helpful comments. The study was funded by deCODE Genetics.
Citation: Amundadottir LT, Thorvaldsson S, Gudbjartsson DF, Sulem P, Kristjansson K, et al. (2004) Cancer as a complex phenotype: Pattern of cancer distribution within and beyond the nuclear family. PLoS Med 1(3): e65.
Abbreviations
ICRIcelandic Cancer Registry
KCkinship coefficient
RRrisk ratio
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| 15630470 | PMC539051 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e65 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010065 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563047110.1371/journal.pmed.0010066Research ArticleMedical ImagingOncologyMedical ImagingRadiological diagnosisOncologySensitive, Noninvasive Detection of Lymph Node Metastases Noninvasive Cancer StagingHarisinghani Mukesh G
1
Weissleder Ralph
1
*1Massachusetts General Hospital and Harvard Medical School, BostonMassachusettsUnited States of AmericaSchwaiger Markus Academic EditorTechnical University MunichGermany
Competing Interests: The authors have declared that no competing interests exist. RW is a member of the editorial board of PLoS Medicine.
Author Contributions: MGH conducted the study, analyzed the data, and enrolled patients. RW designed and conducted the study and analyzed the data. RW and MGH contributed to writing the paper.
*To whom correspondence should be addressed. E-mail: [email protected] 2004 28 12 2004 1 3 e6617 8 2004 20 10 2004 Copyright: © 2004 Harisinghani and Weissleder.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.
Imaging Lymph Nodes with Nanoparticles
Background
Many primary malignancies spread via lymphatic dissemination, and accurate staging therefore still relies on surgical exploration. The purpose of this study was to explore the possibility of semiautomated noninvasive nodal cancer staging using a nanoparticle-enhanced lymphotropic magnetic resonance imaging (LMRI) technique.
Methods and Findings
We measured magnetic tissue parameters of cancer metastases and normal unmatched lymph nodes by noninvasive LMRI using a learning dataset consisting of 97 histologically proven nodes. We then prospectively tested the accuracy of these parameters against 216 histologically validated lymph nodes from 34 patients with primary cancers, in semiautomated fashion. We found unique magnetic tissue parameters that accurately distinguished metastatic from normal nodes with an overall sensitivity of 98% and specificity of 92%. The parameters could be applied to datasets in a semiautomated fashion and be used for three-dimensional reconstruction of complete nodal anatomy for different primary cancers.
Conclusion
These results suggest for the first time the feasibility of semiautomated nodal cancer staging by noninvasive imaging.
Injection of targeted nanoparticles, combined with magnetic resonance imaging, allows identification and detailed three dimensional placement of malignant lymph nodes
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Introduction
Most primary malignancies spread systemically via lymphatic dissemination [1]. For example, the finding of axillary nodal metastases predicts a much shorter disease-free survival in breast cancer [2]. The total nodal tumor burden (number of affected nodes and metastatic tumor volume) affects prognosis even more severely [3]. Accurate lymph node staging also remains a cornerstone in choosing the most appropriate therapy for a given stage. Therapeutic intervention of metastatic lymph nodes [4], prophylactic radiation of frequently affected drainage routes [5], and systemic therapies [6] all have been shown to improve survival. Genetic profiles identifying metastatic tumors [7], serum biomarkers, and proteomic profiles are currently being developed to identify patients at risk [8,9]. No direct genetic profile, however, has been demonstrated to date to accurately predict the presence of human nodal metastases in a given patient. Rather, surgical approaches, such as sentinel lymph node biopsy or lymph node dissection, are still commonly used. Careful histological analysis includes mapping, bisectioning, and rapid staining in the frozen tissue laboratory. Higher diagnostic accuracies can be achieved by serial sectioning (50 μm) and by immunohistochemical staining [10,11].
Noninvasive imaging studies are commonly used during the workup of primary malignancies. Typically, lymph nodes are diagnosed by tomographic techniques (computed tomography [CT], magnetic resonance imaging [MRI]) as malignant when their short axis is >10 mm in size [12]. Such size criteria, however, have been shown to be unreliable [13]. Similarly, the detection of cancer in nonenlarged (occult) nodes is often quite low by positron-emission tomography (PET) and single photon emission computed tomography imaging. For example, small nodal metastases (< 5 mm) are often missed by PET imaging in patients with breast cancer [14]. More recently, it has become possible to image anatomic regions at submillimeter resolutions by MRI, with excellent spatial coverage and reduced motion artifacts. The development [15,16] and clinical introduction of lymphotropic magnetic nanoparticles has been shown to significantly improve diagnostic accuracies of MRI for nodal staging (LMRI) in prostate cancer [17]. These nanoparticles serve as probes for lymphatic anatomy and function and enhance tumor detection through abnormal distribution patterns in malignant nodes [17,18].
Despite the advances of LMRI for cancer staging, image analysis has been challenging and occasionally controversial. Traditional analysis has been based on a reader's identification of certain structural abnormalities that can be variable, given differences in acquisition parameters and interpretation criteria [19,20,21]. Furthermore, it has been challenging to quickly and accurately analyze large datasets generated by LMRI.
The goal of the current study was to develop and test technologies that would vastly improve the accuracy of current LMRI nodal staging. Specifically we set out to (a) determine whether unique magnetic parameters existed and could be used for semiautomated image analysis and (b) whether the technique could be applied to different primary cancers. Here we provide the first comprehensive analysis of tissue parameters validated against histopathology as an end point.
Methods
Study Design
The Institutional Review Board approved the current study and all patients signed informed consent. The study was divided into a learning (n = 97 lymph nodes with known histopathology) and a test dataset (n = 216 lymph nodes with known histopathology; Table 1). Assignment into datasets was done in temporal fashion. The learning dataset represented retrospective cases at outset of the study, and the test dataset represented prospective cases collected during a 1-y interval. In the learning set, 55% of the nodes were benign, and 45% of the nodes were malignant. The learning dataset was obtained from 36 patients (24 male, 12 female, age 28–85 y, mean 59.7 y) with histologically proven primary genitourinary malignancies (prostate, 21; bladder, 9; testes, 5; ureter, 1). All patients completed the MRI study and then underwent surgical resection (n = 26) and/or nodal biopsy (n = 10). The investigated nodes had a mean short axis diameter of 10.5 mm (range 3–39 mm).
Table 1 Overview of Patient Datasets
The test dataset was obtained from 34 patients (25 male, nine female, age 30–82 y, mean 58.9 y) with histologically proven malignancies from different primaries (Table 1), including prostate (n = 18), breast (n = 7), penile (n = 4), bladder (n = 2), testes (n = 2), and colon (n = 1). Seventy-nine percent of the nodes were benign and 21% of the nodes were malignant. The nodes in the test dataset had a mean short axis diameter of 10.0 mm (range 3–39 mm). Both datasets included the full spectrum of normal nodes to completely replaced nodes.
MRI
MRI was performed at 1.5 T (System 9X, General Electric Medical Systems, Milwaukee, Wisconsin, United States) using phased-array coils. All images were archived on DICOM PACS servers (MIPortal, CMIR and Siemens Medical Systems, Erlangen, Germany; and Impax RS 3000, AGFA Technical Imaging Systems, Richfield Park, New Jersey, United States) for subsequent analysis. Images of the pelvis (n = 56) extended from the pubic symphysis to just above the level of aortic bifurcation. In patients with primary testicular cancers (n = 7) imaging was extended superiorly to include the renal hilum and retroperitoneum. In patients with breast cancer (n = 7) we obtained MR images of the bilateral axillae, including the internal mammary and supraclavicular regions. All patients were imaged with identical pulse sequences and timing parameters. Imaging was performed before and 24 h after intravenous ferumoxtran-10 administration (Combidex, Advanced Magnetics, Cambridge, Massachusetts, United States; 2.6 mg Fe/kg diluted in normal saline and infused over a 20-min period using a 5-μm filter).
The acquired pulse sequences included (a) axial T2-weighted fast spin-echo (TR/TE, 4500/80; flip angle, 90°; field of view, 24–28 cm; slice thickness, 3 mm; matrix, 256 × 256; number of excitations, 2; in-plane resolution, 1.2 mm); (b) a T1-weighted two-dimensional gradient-echo sequence obtained in different anatomical planes (TR/TE 175/1.8; flip angle, 80°; field of view, 22–30 cm; slice thickness, 4 mm; matrix, 128 × 256; in-plane resolution, 2.0 mm); (c) an axial T2-weighted dual TE gradient-echo (TR/TE 2100/14–24; flip angle, 70°; field of view, 26–28 cm; slice thickness, 3 mm; matrix, 160 × 256; in-plane resolution, 1.7 mm); and (d) a three-dimensional (3D) T1-weighted gradient echo sequence; TR/TE 4.5–5.5/1.4; flip angle, 15°; field of view, 24–28 cm; slice thickness, 1.4 mm; matrix, 256 × 256; in-plane resolution, 1.0 mm).
The above listed imaging sequences and parameters had previously been optimized to reduce motion artifacts, maximize signal-to-noise ratio (SNR), and provide diagnostically useful images of the pelvis, abdomen, and chest within clinically acceptable time limits. The T2-weighted fast spin-echo sequence, in (a) above, was primarily used for qualitative nodal detection, and hence a square pixel with more than one acquisition was obtained. The two-dimensional axial T1-weighted gradient-echo sequence, in (b) above, was chosen to achieve adequate anatomical coverage within a short imaging time. The axial dual-echo gradient-echo sequence, in (c) above, was developed specifically for this project to provide artifact-free datasets for quantitative image analysis. A matrix size of 160 × 256 was chosen for this sequence to achieve a balance between the upper limits for imaging time while reducing image noise. Finally, a 3D T1-weighted sequence was obtained, in (d) above to provide a dataset for vascular maximum intensity projection (MIP) reconstructions.
Quantitative Image Analysis
All image analysis was performed on archived DICOM images using different software packages (e.g., custom-built software such as CMIR-Image, MGH, Boston, Massachusetts, United States; Syngo, Siemens Medical Systems; Advantage Windows, General Electric Medical Systems). Lymph nodes were identified by readers who manually placed kernels onto each node for automated boundary detection and calculation of nodal dimensions and volumes. The thus identified regions of interest (ROIs) encompassed the entire lymph node (not only portions of it) and were used for quantitative signal-intensity (SI) measurements (see Table 2). Serial measurements of nodal dimensions on different pulse sequences or time points varied less than 2%.
Table 2 Frequency of Imaging Parameters in Learning Dataset
A number of quantitative tissue parameters were calculated either as differences between pre- and postcontrast scans (δ) or as single-value analysis on postcontrast scans (see Table 2). The lymph node/muscle (LNM) ratio was calculated by dividing signal intensities of an entire lymph node by that of adjacent muscle using a similar-sized ROI, drawn manually. The nodal SI change was calculated by obtaining SI before and after contrast administration. The nodal SNR was calculated by obtaining SD/SDnoise. The T2* was calculated in nodal ROIs on dual TE images using CMIR-Image. T2* maps were constructed by performing fits of a standard exponential relaxation model (S = Ke–TE/T2*) to the data on a pixel-by-pixel basis. Only pixels with intensity greater than a threshold level (2X of noise) were considered during the fitting process. Pixel variance was obtained from post-MR images. Comparative visual analysis included short axis measurements, and identification of heterogeneity, large focal defects, and central hyperintensity, according to criteria previously established [12,17].
To determine the diagnostic accuracy of the different tissue parameters in the learning dataset, we determined sensitivity, specificity, and predictive values for each parameter alone and in combination (Table 3). The most discriminatory parameters were then applied to the test dataset (Table 4).
Table 3 Discriminatory Power of Imaging Parameters in Learning Dataset
PPV, positive predictive value; NPV, negative predictive value
Table 4 Application of Quantitative Parameters to Test Dataset (n = 216)
a Includes short axis > 10 mm or round > 8 mm
PPV, positive predictive value; NPV, negative predictive value
In the final set of semiautomated image analysis, 3D reconstructions were obtained for nodal mapping onto vascular anatomy using MIP projections. While the MIP projections do not aid in the differentiation between malignant and benign lymph nodes, they are invaluable in providing anatomic content to the dozens of lymph nodes identified. In particular, MIP images were generated interactively from postcontrast, fat-saturated, volumetric interpolated breath-hold images to outline vascular anatomy. The evaluated lymph nodes characterized as benign or malignant (by T2*/variance analysis) were then superimposed on the volumetric 3D images, using customized software (Advantage Windows, General Electric Medical Systems).
Statistical Analysis
Data were expressed as mean ± standard deviations (SD) and medians. All statistical testing was performed using GraphPad Prism (GraphPad Software, San Diego, California, United States). The significance between two individual groups was determined using the nonpaired Student's t-test (e.g., benign and malignant datasets in Figure 1). For the more discriminatory datasets alternative-free-response receiver operating characteristic curves were plotted. Ratios for cut-off single-value parameters were defined to yield highest sensitivity and specificity. Accuracy for a given parameter was expressed as the area under the curve (Az), and values are summarized in Table 4.
Figure 1 Tissue Parameters in Learning Dataset
Nodal tissue parameters for benign and malignant nodes are shown before (A and B) and after (C–E) intravenous administration of magnetic nanoparticles. Note the insensitivity of conventional MRI (A and B), better separation using single-value analysis (C and D) and excellent separation using two-value analysis (E).
Histology
All lymph nodes were sampled histologically within 2 wk of the MRI (mean: 6 d; range: 2–14 d). The analysis was done in surgically resected lymph nodes (n = 55; both benign and malignant nodes) or in fine needle aspirates and core biopsies (n = 15; malignant nodes only), implementing careful mapping procedures to correlate nodes. Surgically excised nodes were sectioned at 10–20 μm intervals after bihalving and were stained with hematoxylin-eosin.
Results
Learning Dataset
The learning dataset consisted of 97 histologically validated lymph nodes from 36 patients with different primary malignancies (see Table 1). The mean short axis diameter was 10.5 mm (range 3–39 mm) with 56 of the 97 nodes (58.3%) measuring less than 10 mm, that is, below the traditional imaging cutoff for malignancy (“occult nodes”). Table 2 summarizes the incidence of different visual, comparative (before and after contrast administration), and semiautomated (postcontrast administration only) parameters in the two different groups. Figure 1 is a graphical representation of overlaps between malignant and benign groups for different parameters listed in Table 2. Table 3 summarizes sensitivities, specificities, and predictive values for the different quantitative imaging parameters. Sensitivities of metastasis detection by visual image analysis ranged from 50%–94%, however, often with lower specificities. Volumetric measurements, in particular, were insensitive markers of malignancy in nonenlarged nodes (see Table 3).
In contradistinction, image analysis of pre- and postcontrast image sequences resulted in higher specificities and sensitivities (see Table 3). Comparative differences between benign and malignant nodal groups were highest for T2* and pixel variance measurements (see Table 3). Of all the semiautomated parameters tested alone, T2* measurements showed the highest sensitivity (93%; 95% confidence interval: 82%–98%) and specificity (94%; 95% confidence interval: 84%–99%) in the learning dataset (see Figure 1 and Table 3).
Of all the semiautomated parameters tested in combination, T2* measurements combined with pixel variance analyses postcontrast showed the highest sensitivity (98%; 95% confidence interval: 88%–99%) and specificity (94%; 95% confidence interval: 82%–98%) in the learning dataset (Figure 1E). Using the dual-value analysis, there was one malignant outlier in the benign dataset (the lymph node was 3 mm in overall size, with few malignant cells seen on histology, and probably too small for analysis) and two benign outliers in the malignant dataset (both these nodes showed hyalinosis replacing more than 50% of the nodal architecture).
Test Dataset
To determine whether feature extraction would be accurate for prospective nodal staging, we utilized the above criteria against a larger test dataset encompassing 216 validated lymph nodes from 34 patients, including different primaries (see Table 1). The sensitivity, specificity, and predictive values of the most discriminatory parameters of this prospective analysis are summarized in Table 4. We primarily focused on semiautomated image analysis of postcontrast scans because of the high sensitivity and specificity determined in the learning dataset. T2* measurements showed a sensitivity of (93%; 95% confidence interval: 82%–99%) and a specificity of (91%; 95% confidence interval: 85%–96%). Combined T2* and pixel variance analysis achieved a sensitivity of 98% (95% confidence interval: 88%–99%) and a specificity of 92% (95% confidence interval: 87%–96%) comparable to that of the learning set and much superior to currently used size criteria.
Using the dual-value analysis, there were two malignant outliers in the benign dataset (both of these nodes were less than 3 mm in overall size and probably too small for analysis—similar to the learning dataset) and three benign outliers in the malignant dataset (two of these nodes had hyalinosis replacing more than 50% of the nodal architecture and one had macrocalcifications). More important, all the misclassified nodes occurred in individual patients rather than in the same patient and, hence, did not affect the overall nodal staging on a patient-by-patient basis in this dataset.
Image Reconstruction
Video 1 Automated 3D Reconstruction of Pelvic Nodal Anatomy
Utilizing semiautomated feature extraction to identify lymph nodes and image analysis (based on T2* and pixel variance), we subsequently proceeded to map individual lymph nodes onto vascular anatomy in the different anatomic drainage patterns. Figure 2 summarizes the different steps in image analysis. Figure 3 and Video 1 shows an example of a 45-y-old patient with colorectal cancer undergoing semiautomated nodal staging. In this particular patient, MRI identified six positive lymph nodes (< 10 mm each), reconstructed as a 3D dataset, whereas all positive lymph nodes were missed by PET scans. Figure 4 and Video 2 show reconstructions and analyses from a patient with a breast cancer primary with bilateral nodal metastases. Note the high spatial resolution allowing the detection of a 3-mm nodal metastasis.
Figure 2 Steps in Semiautomated Image Analysis
Semiautomated image analysis involves recognition and automated segmentation of each lymph node (A), quantitation of magnetic tissue parameters (T2*, variance of pixel values; [B]), comparison of extracted tissue parameter to a database (C), and 3D reconstruction of nodal anatomy onto vascular anatomy (D).
Figure 3 Pelvic Nodal Staging
Nodal staging in patient with colorectal cancer. A PET scan using 18FDG as a tracer (A) and a CT scan (B) were interpreted as negative for nodal metastases. LMRI identified six small pelvic lymph nodes ([C] and [D]; red arrowheads), which had magnetic parameters of malignancy. Semiautomated reconstruction (E) identifies multisegmental metastases, subsequently proven histologically (F). For 3D reconstruction of pelvic nodal anatomy see Video 1.
Figure 4 Breast Cancer Mapping
Patient with breast cancer prior to sentinel lymph node biopsy.
(A) Conventional axillary MRI shows nonenlarged lymph nodes that do not meet the size criteria of malignancy (white bar = 5 mm).
(B) Following intravenous administration of nanoparticles, a single 3-mm intranodal metastasis was correctly identified.
(C) Ex vivo MRI of sentinel node specimen confirms metastasis.
(D) Semiautomated nodal analysis and reconstruction correctly juxtaposed solitary lymph node metastases adjacent to two normal lymph nodes.
(E) Correlative histopathology confirms the diagnosis. For 3D reconstruction of axillary nodal anatomy see Video 2.
Video 2 Automated 3D Reconstruction of Axillary Nodal Anatomy
Discussion
We show that it is feasible to extract various quantitative tissue parameters to predict the likelihood of nodal metastases in vivo. These results are highly relevant in cancer staging because they provide evidence that (a) quantitative tissue parameters enable diagnosis of lymph node metastases while reducing interobserver variability and (b) that semiautomated reconstructions allow spatially more extensive mapping than is currently possible.
Metastases to lymph nodes occur during growth of most primary malignancies, and their presence mandates the need for more extensive and systemic therapy. Nodal cancer staging currently relies on invasive procedures (surgical lymph node dissection, sentinel lymph node resection, biopsy) with significant morbidity and cost [22,23], or insensitive tomographic imaging methods [24]. For example, detection sensitivities using size criteria with state-of-the-art multislice CT are as low as 50%, whereas PET imaging of nonenlarged nodes has equally low sensitivities [14]. Based on the observation that nanoparticulate solutions accumulate in nodal macrophages upon systemic injections [25,26], lymphotropic superparamagnetic preparations have been developed [16]. In earlier clinical trials (using lower spatial resolution sequences), metastases of 1–2 mm have been detected [17], whereas as few as 1,000 tumor cells have been detected in nodes in experimental mouse models [18]. Despite these advances, it has been difficult to acquire images of sufficiently high resolution and to derive parameters to automate diagnosis. The data presented here indicate that unique magnetic parameters allow identification of nodal metastases and accurate 3D reconstructions, including surgically inaccessible lymph nodes.
The significance of the above findings is 3-fold. First, the ability to directly and noninvasively monitor nodal tumor involvement represents a powerful diagnostic tool for cancer. Accurate staging represents the cornerstone for triaging patients to either localized or to more aggressive and systemic therapies. Second, the method described here was sensitive for the limited subsets of primary cancers tested. It is reasonable to hypothesize that such analysis could be applied to staging of other common primaries. In particular, lung, colorectal, genitourinary, and head and neck cancers could benefit from this staging procedure. In addition to nodal staging, the nanoparticle-enhanced MRI can also be used to measure microvascularity in primary tumors [27] and to improve the detection of liver metastases [28]. Third, our results are significant because the semiautomated staging method is highly accurate and reduces variability in visual image analyses between different observers.
The LMRI staging technique is believed to be clinically relevant in several key areas. First, LMRI may play a significant role in avoiding unnecessary surgeries, that is, those in node-positive patients. Second, since LMRI can detect lymph nodes outside traditional surgical fields, this information may influence surgical approaches. In colorectal cancer, LMRI may provide a “sentinel-node-like” guide to staging. Third, it is likely that LMRI would be useful to identify appropriate patients to receive neoadjuvant chemotherapy prior to surgery. Currently, neoadjuvant therapy is often reserved for postoperative patients, once the nodal status has been determined. Fourth, LMRI may be particularly useful to guide radiation therapy by mapping the complete nodal status onto bony and vascular landmarks. Finally, LMRI could be used to avoid invasive diagnostic procedures, which are not part of therapy. For example, LMRI could replace lymphangiography, mediastinoscopy, or endoscopic ultrasound for nodal staging.
Our findings have a number of direct implications for technology development and in clinical care. Accurate measurements of T2* relies on motion artifact-free multiecho pulse sequences that are not routinely available on clinical scanners at spatial resolutions required for nodal staging. Such sequences will have to be implemented and combined with postprocessing tools to simplify and semiautomate analysis. Similar software approaches are already used routinely in lung nodule characterization [29] or screening for breast cancers [30]. We predict that in the case of LMRI, such automation routines will be highly specific, given the unique mechanism of image contrast. As a proof-of-principle, we implemented approaches to identify, segment, analyze, and display nodal information. While the current technology is already highly accurate, we anticipate further improvements with hardware and software advances. We hope that this will ultimately translate into clinical practice and replace unnecessary intervention.
Patient Summary
Background
When deciding on treatment for patients with cancer, it is very important to assess whether the cancer has spread to lymph nodes—both to help decide what treatment a patient should have and what the eventual outcome might be. Previous ways of finding involved lymph nodes included taking out the nodes by surgery, ultrasound, and CT and MRI scans.
What Does This Study Show?
A solution of magnetic nanoparticles that tend to go to lymphoid organs was injected and then tracked by MRI. The pattern of the particles was abnormal when there was metastasis in the nodes, and it was possible to train a computer to recognize this abnormality. The authors developed the program in one group of patients and then tested it in another group, in which they were able to correctly predict whether the nodes were involved in about nine of ten nodes. In addition, they could use the information to display a virtual picture of the involved nodes.
What Does This Study Mean for Patients?
The technique will need to be validated in a larger group of patients, and by other investigators. However, it means that it is potentially possible to work out much more precisely, and with less chance of error, whether lymph nodes are involved in cancer. Hence, treatment can be better planned, and if surgery is needed to remove nodes for analysis, then this technique could ensure that the surgery is as minimal as possible.
Where Can I Get More Information?
RadiologyInfo, a public information site developed by the American College of Radiology and the Radiological Society of North America: http://www.radiologyinfo.org/
Medline Plus, which has health information from the National Library of Medicine: http://www.nlm.nih.gov/medlineplus/cancer.html
The authors would like to acknowledge Drs. M. Saksena and M. Torabi for help with data collection, Dr. Elkan Halpern for statistical advice, Drs. Guimaraes and Ross for critical review and Benjamin King for assistance in early data analysis. We would also like to acknoweledge Drs. P. F. Hahn, S. Gazelle, and R. Seethamraju for many helpful discussions. Parts of the study were funded by National Institutes of Health grants P50 CA86355 and NO1-CM037008, a Center for Molecular Imaging Research development grant, a grant to Massachusetts General Hospital from Siemens Medical Systems, and a General Electric Medical Systems–Association of University Radiologists fellowship grant (MH). Combidex was provided by Advanced Magnetics. The funding sources had no role in study design, collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the paper for publication.
Citation: Harisinghani MG, Weissleder R (2004) Sensitive, noninvasive detection of lymph node metastases. PLoS Med 1(3): e66.
Abbreviations
CTcomputed tomography
LNMlymph node to muscle ratio
LMRIlymphotropic nanoparticle enhanced magnetic resonance imaging
MIPmaximum intensity projection
MRImagnetic resonance imaging
PETpositron-emission tomography
ROIregion of interest
SIsignal intensity
SNRsignal-to-noise ratio
3Dthree-dimensional
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| 15630471 | PMC539052 | CC BY | 2021-01-05 10:38:00 | no | PLoS Med. 2004 Dec 28; 1(3):e66 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010066 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0010067SynopsisMedical ImagingOncologyMedical ImagingRadiological diagnosisOncologyImaging Lymph Nodes with Nanoparticles Synopsis12 2004 28 12 2004 1 3 e67Copyright: © 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.
Sensitive, Noninvasive Detection of Lymph Node Metastases
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Accurate staging of cancers is one of the most important parts of the work up of patients for both prediction of prognosis and determination of the most appropriate treatment. And an essential part of this work up is assessing whether or not there has been lymphatic spread. Current methods include surgical removal of nodes for examination and various types of imaging, ranging from ultrasound to newer technologies such as magnetic resonance imaging (MRI). All these methods have problems; some are very invasive, others are very time consuming, and none are completely reliable.
3-D image of lymph node after automated analysis
However in one of the more exciting crossovers from chemistry into medicine, researchers have developed nanoparticles to improve the diagnostic accuracy of MRI. The nanoparticles contain a central superparamagnetic iron oxide core and are covered by dextran, imparting long circulation times and biocompatibility. When injected intravenously, the nanoparticles localize to lymphoid tissue, and are internalized into macrophages. There is then a decrease in signal intensity on T2- and T2*-weighted images, and when metastases are present there is a recognizably abnormal pattern on MRI scans.
In a previous paper published in the New England Journal of Medicine, Ralph Weissleder and colleagues described using these nanoparticles to assess lymphoid spread in patients with prostate cancer. Now, in a paper published in this month's PLoS Medicine, they have gone further by extending the analysis to patients with different types of cancer, and producing an algorithm that allows semiautomation of the procedure.
The authors developed the algorithm in a training group of 36 patients and then validated it in a group of 34 patients. The results are encouraging: the analysis showed a sensitivity of 98% (95% confidence interval, 88%–99%) and a specificity of 92% (95% confidence interval, 87%–96%). The advantages of automating this procedure are substantial, not least because it can remove the problem of different observers assessing data differently.
And what is more, once the data have been collected and assessed it is possible to reconstruct a virtual picture of the patient's lymph nodes, thus potentially allowing accurate surgical removal of the nodes.
| 0 | PMC539053 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e67 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010067 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0010069SynopsisCancer BiologyGenetics/Genomics/Gene TherapyOncologyGeneticsOncologyCancer in Families Synopsis12 2004 28 12 2004 1 3 e69Copyright: © 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.
Cancer as a Complex Phenotype: Pattern of Cancer Distribution within and beyond the Nuclear Family
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The Icelandic population is now a part of a unique epidemiological study, which has involved investigating the genetic heritage of many of them. The reason that this experiment can be done is because of the remarkable records that exist in Iceland. Not only is there almost complete genealogical information dating back to the 18th century on all current (288,000) and many previous Icelanders (more than 600,000 in total), but in addition the country has an almost complete cancer registry dating from 1955. A company, deCODE Genetics, was set up to mine health-care data in Iceland, and to use it to assess the effect of genetics on health. Initially, the company attracted criticism, with some questioning the ethics of providing access to health-care data for many disease projects to a for-profit company. But the company has been supported by many Icelanders themselves, demonstrated by Icelanders donating blood samples with informed consent for research on multiple diseases, and now the project's scientific value is becoming apparent.
One such analysis is the subject of a paper by Laufey Amundadottir and colleagues in this month's PLoS Medicine that assesses how much genetic factors contribute to cancer risk across the whole Icelandic population.
The paper looked at 27 different types of cancers (all those with more than 200 cases) that had been registered between 1955 and 2002 and analysed the frequency of close and distant relatives also having that cancer, or another kind of cancer. Of the 27 cancers, 16 showed significant “familiality,” and for some this risk even extended to distant (that is, third- to fifth-degree) relatives. The seven cancers with the highest increased familial occurrence both in close and distant relatives were breast, prostate, stomach, lung, colon, kidney, and bladder cancers. And, interestingly, three cancers—stomach, lung, and colon cancer—were also seen more frequently in mates of patients, indicating a shared environmental risk factor. And for some cancers there was a familial association with other cancers, for example, relatives of individuals with stomach, colon, rectal, or endometrial cancer were more likely to have any of these cancers.
Icelandic genetics and genealogy
Cathryn Lewis, the academic editor for the paper comments on the study's strengths. “This level of family relationship and clinical diagnosis is rarely available from interviewing patients and family members. The size of the study (over 600,000 individuals, with 32,000 cancer cases) and the high quality of data enables the authors to detect subtle effects across distant relationships.”
How robust are these data, and what do they mean for the biological understanding of cancer? As Lewis says, “Although the current study is impressive in its size and scope, even here, the sample size becomes an issue, with the most convincing results seen in the most common cancers.” Certainly not all the findings are surprising; some rare cancers are already known to be associated with particular genetic defects, and syndromes that predispose to multiple cancers have been described, for example, that of Hereditary Nonpolyposis Colorectal Cancer. Other associations are more intriguing—the cluster of related cancers that include prostate, kidney, and bladder could possibly have a developmental origin, since all arise from the same part of the embryo.
So, by highlighting these subtle links, the study's particular value may become apparent: deciding future avenues of investigation in the complex interrelationships that interact to produce cancer.
| 0 | PMC539054 | CC BY | 2021-01-05 10:38:00 | no | PLoS Med. 2004 Dec 28; 1(3):e69 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010069 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 10.1371/journal.pmed.0010070SynopsisImmunologyInfectious DiseasesHIV/AIDSInfectious DiseasesHIV Infection/AIDSDrugs and adverse drug reactionsTreatment Interruptions in Chronic HIV Infection Synopsis12 2004 28 12 2004 1 3 e70Copyright: © 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.
Randomized, Controlled Trial of Therapy Interruption in Chronic HIV-1 Infection
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Adverse side effects, viral resistance, and the high cost of antiretroviral therapies remain obstacles in the way of turning HIV/AIDS into a manageable chronic disease. Structured treatment interruptions (STIs) in individuals who have good viral control on therapy have been proposed as a strategy for overcoming these obstacles. The initial hope that STIs would help patients achieve greater viral control has so far not been supported by data from clinical trials, but interrupting treatment has also been proposed as a strategy to reduce the cost of long-term therapy and drug-associated toxicity.
Luis Montaner and colleagues now report results from a randomized trial of 42 participants (75% on their second to fourth regimen, 66% on regimens containing non-nucleoside reverse-transcriptase inhibitors) who received either continuous therapy for 40 weeks or three successive treatment interruptions of two, four, and six weeks, followed by a final open-ended interruption for both groups.
The study was designed to be able to detect a difference of four weeks or greater between the two groups for the time to viral rebound during the open-ended interruption—the primary outcome. No difference between the two groups was seen (median time for the group on continuous treatment was four weeks, and for the STI group was five weeks).
Secondary outcomes included serious adverse events (disease progression, acute retroviral syndrome, therapy failure, or opportunistic infections at any point in the study), changes in CD4 count on therapy, immune reconstitution changes (CD4 recall responses and CD4 naïve/memory T cell distribution), and detection of viral mutations There were no study-related serious adverse events in either group and no increase of therapy failure in the STI arm. CD4 counts fluctuated between the start and end of each monitored treatment interruption, but levels recovered after resuppression of virus, with retention of recall responses throughout. Viral resistance was detected in both groups (in seven of 21 patients in the continuous treatment group and ten of 21 patients in the STI group), but it was more commonly detected (50% versus 18%) in the STI group during the open-ended final interruption, even though all subjects suppressed virus upon reinitiating the same therapy.
Possible risks and benefits of STIs remain controversial, but data from this and other published trials do not support short-term clinical benefits of treatment interruptions. However, because they do not see increased therapy failure and find preservation of immune function in the STI group, the authors conclude that, in light of the possibility of reducing costs and drug-related toxicity, additional trials of STIs are warranted.
Particularly important in the debate over the safety of STIs is whether the detection of resistant mutants should be of concern. The authors point out that all participants were able to resuppress the mutant virus when they resumed their previous drug regimens but state that it remains undetermined to what extent resistant mutations are a signal for future therapy failure. Moreover, viral replication and rebound—which eventually occurred in all participants—is seen by some researchers as inherently detrimental, and these experts argue that treatment interruptions are unsafe and their use should be discontinued.
What seems clear is that STIs have no place outside controlled clinical trials and that questions regarding long-term safety remain unanswered. At least a dozen additional trials that examine STIs are currently recruiting patients and will help answer these questions.
| 0 | PMC539055 | CC BY | 2021-01-05 10:38:00 | no | PLoS Med. 2004 Dec 28; 1(3):e70 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010070 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563047210.1371/journal.pmed.0010071EditorialInfectious DiseasesOtherHIV Infection/AIDSTuberculosisMalariaMedicine in Developing CountriesInternational healthA New Vision for Clinical Trials in Africa EditorialThe PLoS Medicine Editors 12 2004 28 12 2004 1 3 e71Copyright: © 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.A new funding body (the EDCTP) will fund clinical trials in developing countries, particularly in Africa, that help to develop affordable interventions against HIV, TB, and malaria. How is it doing so far?
A promising European funding body is stumbling over the details
==== Body
Last year the European Parliament and Council formed the european and Developing Countries Clinical Trials Partnership (EDCTP). The aim of this new funding body, which has a budget of €400 million spread over five years, is a noble one: to fund research in developing countries, particularly in Africa, that contributes to the development of affordable prophylactics and drugs for HIV/AIDS, tuberculosis, and malaria.
Unfortunately, the organization has not got off to an auspicious start. Its executive director, Piero Olliaro, was ousted from power at the first EDCTP annual forum at the end of September. There have been rumblings of discontent among grant applicants who say that the first round of grant assessments was administered poorly. And not-for-profit organizations that would like to partner with EDCTP have been left in the dark regarding whom to speak to at the organization.
This omission is significant because partnership is one of the key tenets of the EDCTP. European research agencies are slowly beginning to realize that they need to cooperate with each other if they are to be competitive with the United States. The history of many European countries is such that Europe has much stronger ties with Africa than does the United States, so it makes political sense for the European Union to fund research that provides a springboard for European researchers to compete effectively with US scientists.
Crucially, the EDCTP was also set up to enable European and African scientists to work together as equal partners. There is increasing recognition that the paternalistic, colonial attitude that pervaded “tropical medicine” in the past just will not do. The EDCTP hoped to change that by having a Partnership Board that contains equal numbers of African and European representatives. However, the EDCTP Assembly, which contains a representative from each of 14 EU member states but none from African countries, has the power to veto the decisions of the Partnership Board, which is supposedly the scientific decision-making authority.
Doing clinical trials in Africa is far from easy. There are too few adequately resourced research centers, and those that do consistently perform well are oversubscribed. Therefore, there is a clear need for "capacity building"—development of a research infrastructure, in terms of both equipment and personnel, that is capable of coping with the challenges of clinical trials. The EDCTP hopes to contribute to this essential endeavor by funding clinical trials that are sustainable in the long term. In particular, it believes that the best way to train a new generation of African scientists is by teaching them on the job, that is, involving them fully in the planning and execution of the trial, rather than flying in European experts who leave as soon as the trial is finished.
A commitment from European researchers to be engaged for the long term is essential for the success of these projects. In addition, partnerships need to be brokered with national programs in Africa to ensure that the new capacity can be sustained over time. The end goal is to produce centers of excellence that are run by Africans doing internationally recognized research that conforms to Good Clinical Practice guidelines. But this will only happen if African researchers are treated as equal partners and are allowed to be fully engaged in the projects that are taking place in their countries.
So can the EDCTP work, or is it doomed to failure? In many ways the organization has a great deal going for it. Although the budget of 3400 million spread over five years is tiny considering the combined burden of HIV/AIDS, tuberculosis, and malaria, it is important to remember that it is the biggest single European project for clinical trials in Africa. In many ways the EDCTP is a demonstration project: if some success can be achieved it is very likely that additional funds will follow. The project is certainly strengthened by the involvement of Pascoal Mocumbi, the former prime minister of Mozambique, as High Representative of the project. Mocumbi is highly respected by the global-health community and carries considerable weight with African politicians. Mobilization of political will within Africa will be essential if research capacity is to be sustained for the long term.
On the downside, it seems clear to most insiders that the management structure needs to be radically changed and partnership with other organizations needs to be improved. The EDCTP Assembly met on October 28 and 29 to discuss these issues and to elect a new leader. At the time this editorial went to press, there was still no public announcement of the outcome of this meeting. In addition, the political infighting that pervades European politics at all levels needs to be controlled, or at least managed effectively. This might be a tall order, but it is essential if this worthwhile and high-profile project is to succeed.
| 15630472 | PMC539056 | CC BY | 2021-01-05 10:38:06 | no | PLoS Med. 2004 Dec 28; 1(3):e71 | utf-8 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010071 | oa_comm |
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PLoS MedPLoS MedpmedplosmedPLoS Medicine1549-12771549-1676Public Library of Science San Francisco, USA 1563047210.1371/journal.pmed.0010071EditorialInfectious DiseasesOtherHIV Infection/AIDSTuberculosisMalariaMedicine in Developing CountriesInternational healthA New Vision for Clinical Trials in Africa EditorialThe PLoS Medicine Editors 12 2004 28 12 2004 1 3 e71Copyright: © 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.A new funding body (the EDCTP) will fund clinical trials in developing countries, particularly in Africa, that help to develop affordable interventions against HIV, TB, and malaria. How is it doing so far?
A promising European funding body is stumbling over the details
==== Body
Last year the European Parliament and Council formed the european and Developing Countries Clinical Trials Partnership (EDCTP). The aim of this new funding body, which has a budget of €400 million spread over five years, is a noble one: to fund research in developing countries, particularly in Africa, that contributes to the development of affordable prophylactics and drugs for HIV/AIDS, tuberculosis, and malaria.
Unfortunately, the organization has not got off to an auspicious start. Its executive director, Piero Olliaro, was ousted from power at the first EDCTP annual forum at the end of September. There have been rumblings of discontent among grant applicants who say that the first round of grant assessments was administered poorly. And not-for-profit organizations that would like to partner with EDCTP have been left in the dark regarding whom to speak to at the organization.
This omission is significant because partnership is one of the key tenets of the EDCTP. European research agencies are slowly beginning to realize that they need to cooperate with each other if they are to be competitive with the United States. The history of many European countries is such that Europe has much stronger ties with Africa than does the United States, so it makes political sense for the European Union to fund research that provides a springboard for European researchers to compete effectively with US scientists.
Crucially, the EDCTP was also set up to enable European and African scientists to work together as equal partners. There is increasing recognition that the paternalistic, colonial attitude that pervaded “tropical medicine” in the past just will not do. The EDCTP hoped to change that by having a Partnership Board that contains equal numbers of African and European representatives. However, the EDCTP Assembly, which contains a representative from each of 14 EU member states but none from African countries, has the power to veto the decisions of the Partnership Board, which is supposedly the scientific decision-making authority.
Doing clinical trials in Africa is far from easy. There are too few adequately resourced research centers, and those that do consistently perform well are oversubscribed. Therefore, there is a clear need for "capacity building"—development of a research infrastructure, in terms of both equipment and personnel, that is capable of coping with the challenges of clinical trials. The EDCTP hopes to contribute to this essential endeavor by funding clinical trials that are sustainable in the long term. In particular, it believes that the best way to train a new generation of African scientists is by teaching them on the job, that is, involving them fully in the planning and execution of the trial, rather than flying in European experts who leave as soon as the trial is finished.
A commitment from European researchers to be engaged for the long term is essential for the success of these projects. In addition, partnerships need to be brokered with national programs in Africa to ensure that the new capacity can be sustained over time. The end goal is to produce centers of excellence that are run by Africans doing internationally recognized research that conforms to Good Clinical Practice guidelines. But this will only happen if African researchers are treated as equal partners and are allowed to be fully engaged in the projects that are taking place in their countries.
So can the EDCTP work, or is it doomed to failure? In many ways the organization has a great deal going for it. Although the budget of 3400 million spread over five years is tiny considering the combined burden of HIV/AIDS, tuberculosis, and malaria, it is important to remember that it is the biggest single European project for clinical trials in Africa. In many ways the EDCTP is a demonstration project: if some success can be achieved it is very likely that additional funds will follow. The project is certainly strengthened by the involvement of Pascoal Mocumbi, the former prime minister of Mozambique, as High Representative of the project. Mocumbi is highly respected by the global-health community and carries considerable weight with African politicians. Mobilization of political will within Africa will be essential if research capacity is to be sustained for the long term.
On the downside, it seems clear to most insiders that the management structure needs to be radically changed and partnership with other organizations needs to be improved. The EDCTP Assembly met on October 28 and 29 to discuss these issues and to elect a new leader. At the time this editorial went to press, there was still no public announcement of the outcome of this meeting. In addition, the political infighting that pervades European politics at all levels needs to be controlled, or at least managed effectively. This might be a tall order, but it is essential if this worthwhile and high-profile project is to succeed.
| 0 | PMC539057 | CC BY | 2021-01-05 10:38:01 | no | PLoS Med. 2004 Dec 28; 1(3):e73 | latin-1 | PLoS Med | 2,004 | 10.1371/journal.pmed.0010073 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047410.1371/journal.pbio.0030002Research ArticleBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyImmunologyHomo (Human)Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods Prediction of the Response to IFNβBaranzini Sergio E [email protected]
1
Mousavi Parvin
2
Rio Jordi
3
Caillier Stacy J
1
Stillman Althea
1
Villoslada Pablo
4
Wyatt Matthew M
1
Comabella Manuel
3
Greller Larry D
5
Somogyi Roland
5
Montalban Xavier
3
Oksenberg Jorge R
1
1Department of Neurology, School of MedicineUniversity of California, San Francisco, CaliforniaUnited States of America2School of Computing, Queen's UniversityKingston, OntarioCanada3Department of Neuroimmunology, Hospital Vall d'HebronBarcelonaSpain4Department of Neurology, Clinica Universitaria de Navarra, University of NavarraSpain5Biosystemix, SydenhamOntarioCanadaBrazma Alvis Academic EditorEuropean Bioinformatics InstituteUnited Kingdom1 2005 28 12 2004 28 12 2004 3 1 e24 2 2004 8 10 2004 Copyright: © 2004 Baranzini 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.
Gene Signatures Predict Interferon Response for MS Patients
Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.
By studying gene expression in patients with multiple sclerosis before and after therapy with beta interferon, it is possible to identify gene expression signatures that are associated with therapeutic effects
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Introduction
Interferons are small, inducible proteins secreted by nucleated cells in response to viral infection and other stimuli. They act in a paracrine fashion on other cells in their immediate vicinity, triggering a state of growth arrest, so that infected cells cannot be forced to produce viral proteins, and activating the process of programmed cell death, so that infected cells can be removed [1]. Interferons are important not only in the defense against a wide range of viruses but also in the regulation of immune responses and hematopoietic cell development [2,3]. Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in relapsing-remitting multiple sclerosis (MS) [4,5]. Although effective in reducing the number of exacerbations and brain disease activity in some patients, rIFNβ produces no benefit in almost one-half of these patients [6,7]. Furthermore, it is not at all certain how significant its long-term effects on disease progression are. Therapy has been associated with a number of adverse reactions, including flu-like symptoms, transient laboratory abnormalities, menstrual disorders, increased spasticity, and dermal reactions [8].
We generated and analyzed longitudinal patterns of gene expression from interferon beta (IFNβ)–treated patients suffering from MS with the aim of identifying preexisting and drug-induced signatures that would predict or explain the clinical response to the drug.
Results/Discussion
Fifty-two patients with relapsing-remitting MS were followed for at least 2 y after initiation of therapy with IFNβ. Clinical follow-up included a neurological examination every 3 mo and at the time of relapse. At each visit, a blood sample was obtained by venipuncture. After the 2-y endpoint, patients were classified as either good or poor responders based on strict criteria, as described in Materials and Methods. We measured the expression profile of 70 carefully selected genes from peripheral blood mononuclear cells isolated from each patient at each time point, using one-step kinetic reverse-transcription PCR (Dataset S1). This process offers remarkable sensitivity and specificity and a dynamic range of several orders of magnitude, allowing the comparison of expressed transcripts from many different genes without compromising accuracy. Targets for analysis were selected on the basis of their presumed biological action and included genes coding for type I and II IFN-responsive molecules, cytokine receptors, members of the interferon (IFN) signaling and apoptosis pathways, and several transcription factors involved in immune regulation (Table S1). Altogether, more than 70,000 reactions were carried out. A common inherent prediction performance limitation of most high-throughput gene-expression profiling projects arises from the largely asymmetric expression data matrix obtained as a result of measuring far more genes than samples [9]. Such ill-conditioned data matrices inevitably lead to overfitting of predictive models (among other difficulties), some effects of which can be mitigated by judicious application of various established inverse and regularization schemes [10]. The undesirable properties (i.e., overfitting) of such massively under-determined datasets are largely avoided in this study design because the number of genes measured is commensurable with the numbers of samples.
Using linear discriminant analysis–based integrated Bayesian inference system (IBIS), we were able to detect the gene MX1 as the single best discriminating variable between samples obtained at baseline (T = 0) and at 3 mo after initiation of therapy (T = 3) with a classification accuracy of 79% (data not shown). Given that MX1 is a known marker of IFN bioavailability [11], this result validates our experimental approach as well as our sample handling and processing.
To search for expression signatures associated with therapeutic outcome (good or poor responder), we conducted clustering of samples using normalized data for all 70 genes at each time point [12]. Despite applying several different similarity measures and clustering algorithms [13], we did not observe concomitant segregation of samples according to their responder status, with the exception of a few local clusters of small size (Figure 1). This result may suggest that overall differences in gene expression in the two groups of patients, as assessed by conventional similarity measures, are small or negligible. The clustering null results with respect to concomitant class segregation, however, do not rule out the possibility of discovering outcome-predictive combinatorial and nonlinear relationships. To investigate this possibility further, we used quadratic discriminant analysis–based IBIS, implemented for three-dimensional (3D) searches, in the search for highly predictive sets of three genes whose expression at T = 0 correlated with a good or poor outcome of therapy at the 2-y endpoint. This process exhaustively carried out sample classification by searching through all 54,740 possible three-gene combinations of 70 genes. Higher-order combinatorial searches beyond combinations of three genes are possible using IBIS and are currently under investigation. However, higher-order predictive variable combinations do require the support of many more samples to prevent overfitting of the model.
Figure 1 Nonsupervised Two-Way Hierarchical Clustering of Samples at T = 0
A clear aggregation of samples cannot be seen by this technique. The first column indicates the type of responder to which each sample belongs (red, good; blue, poor).
We implemented a stringent method for examining the statistical validity of our classification results that consisted in testing the obtained classifier in an independent set of samples not previously “seen” by the program. All of the following statistical analyses were thus performed on split datasets, namely, training (75% of the samples) and test (25%), each reflecting the same relative proportion of classes (63% good and 37% poor responders). We started by conducting 3D IBIS searches using expression data from only the training set and selected the top nine scoring triplets (on the basis of their high prediction accuracy and low mean squared error [MSE] values). For each gene triplet, and using the training data only, a committee of classifiers was built based on an internal cross-validation scheme. Subsequently, the classifiers were used to predict the outcome of an independent test set of samples. Gene triplets were ranked on the basis of the prediction accuracies of the classifiers on this independent test set. We identified nine gene triplets with a predictive accuracy of at least 80% (Table 1). We considered it essential to empirically rule out the chances of fortuitous data splits in the accuracy results obtained from the top-scoring gene triplets.
Table 1 Best-Scoring Predictors of Response to IFNβ at T = 0
For each triplet, the mean prediction accuracy for 100 and 500 splits of the data are shown. Prediction was recalculated after the addition of Gaussian noise to each expression value
a The percentage drop in prediction accuracy after the addition of noise
Consequently, for the nine top-scoring gene triplets and their corresponding classifiers, we generated 100 random splits and built classifiers for each new resulting training set. Next, we tested how well the classifiers predicted therapeutic outcome in the corresponding test datasets. Figure 2 illustrates the distribution in the prediction accuracies obtained for the triplet composed of Caspase 2, Caspase 10, and FLIP, which yielded a predictive accuracy of 86% in the original split. The bell-shaped distribution resulting from 100 tests for this triplet displayed a mean accuracy of 87.8% and a tenth percentile of 78.6%, meaning that if the prediction were performed multiple times, in 90% of these instances an accuracy of almost 79% or better would be obtained. This histogram only reflects the range of accuracies obtained, should the initial data split be different. Notably, the genes in the top-scoring triplet were Caspase 2, Caspase 10, and FLIP—three apoptosis-related molecules. The second-highest-scoring triplet was that of Caspase 2, Caspase 3, and IRF4 (86.8% mean accuracy after 100 splits). Other high-scoring triplets included IL4Ra and MAP3K1, in addition to other apoptotic molecules (Table 1). When we repeated this experiment with the top three scoring genes, using F-test, the obtained mean accuracy was 64% (tenth percentile at 50%) (Figure 3).
Figure 2 Accuracy Ranges of the Three-Gene Predictive Model of IFNβ Response
After the initial data split into training and test sets, using IBIS on the training set only, nine best-performing triplets were identified. The triplet of Caspase 2, Caspase 10, and FLIP resulted in an accuracy rate of 86% correct prediction on the blind test set resulting from the original split. To minimize the effect of fortuitous initial data division in the accuracy outcome, an extra 100 data splits were performed as a coarse approximation of the possible ranges of accuracies in which this gene triplet could result. A histogram of prediction accuracy over the 100 trials for the gene triplet composed of Caspase 2, Caspase 10, and FLIP is shown as an example of classification and prediction of response to IFNβ at T = 0. A red Gaussian curve encompasses the distribution, where the mean prediction accuracy was 87.9%, with a maximum of 100% (in 11 cases) and a minimum of 64.3% (in two cases). The broken blue line indicates the tenth percentile (78.6%). No major differences were found when we performed the same classification/prediction strategy in 500 random splits of the data.
Figure 3 Best-Scoring Gene Triplet by F-Test Analysis
Notably, as observed with IBIS, Caspase 10 was also the single best discriminant (p = 1.87 × 10−4) variable, but the second and third best scoring genes by F-test (IL12Rb2, IL4Ra) did not seem to add any significant predictive power. The mean prediction accuracy for the test set of samples was 65.6% (tenth percentile, 57.1%), well below that observed for the triplet derived from IBIS (Caspase 2, Caspase 10, and FLIP) shown in Figure 2. This suggests that F-test could efficiently capture individual linear separators but cannot identify and prioritize the nonlinear combinations of genes discovered by IBIS that ultimately provide the most predictive accuracy and robustness.
In Figure 4, the predictive capability of the best-scoring triplet (Caspase 2, Caspase 10, and FLIP; 3D model) was compared with those obtained with the single-gene (1D) and gene-pair (2D) models. We observed that the classification accuracy improves as more genes are added to the classifier. We next plotted the samples of a test dataset (25% of samples) on the predictive probability model shown in Figure 4G and compared the performance of the 3D IBIS model to those of the individual 2D models (Figure 5). Overall, the 2D projections of the 3D predictive model show that the Caspase 2/Caspase 10 and Caspase 10/FLIP gene pairs show significant predictive capability, but that all three genes are required to provide the highest level of model accuracy and robustness.
Figure 4 Training Dataset Performance of the Three Genes from the Top Predictive Model of IFNβ Response
One-, two-, and three-dimensional IBIS searches were conducted independently on the same training dataset. Each chart shows a two-colored background, corresponding to regions predictive of good response (red) and poor response (blue). Each colored dot corresponds to an individual sample (red, good responder; blue, poor responder).
(A–C) One-dimensional IBIS predictive models. High values of Caspase 10 are associated with poor response according to a linear relationship. In contrast, Caspase 2 levels are associated with poor response at intermediate values, suggesting a nonlinear relationship. FLIP expression is associated with good responders at low values, again depicting a linear relationship. The highest cross-validation accuracy score for a single gene predictor was 73% (Caspase 10).
(D–F) Two-dimensional IBIS predictive models. Each of the three possible pairs of this classifier was tested. Linear and nonlinear combinatorial predictive relationships were revealed, specifically, a nonlinear predictive relationship associating poor response with high values of Caspase 10 and intermediate values of Caspase 2, a nonlinear relationship associating good response with high values of FLIP and either low or high (but notintermediate) values of Caspase 2, and a linear relationship associating poor response with low values of FLIP and high values of Caspase 10. The highest cross-validation score was obtained for the Caspase 2/Caspase 10 pair according to a nonlinear, quadratic distribution (85% accuracy).
(G) Three-dimensional IBIS predictive model. The shapes identified in the 1D and 2D distributions were optimized by the 3D model, providing a better separation of good and poor responders.
Figure 5 Test Dataset Performance of the Top Three-Gene Predictive Model of IFNβ Response
The same probability model generated from the training dataset (see Figure 4G) provides the background shading of volumes predictive of good response (red) and poor response (blue). Three samples are identified with arrows and followed along different graphical representations.
(A and B) The two rotations of the full 3D model show that all good responder samples are correctly classified.
(C) Projection of full model onto one of the possible 2D surfaces is provided as an aid to visualization.
(D–F) Two-dimensional IBIS predictive models. Three samples are identified with arrows and followed along different graphical representations. If prediction was performed in only two dimensions, a higher number of misclassifications would have occurred. For example, the 2D model built using only Caspase 2/FLIP (D), could not resolve the good responding sample identified by a cyan arrow, whereas it correctly resolves the good responding sample shown by the orange arrow. The model built using Caspase 10/FLIP (E), in contrast, acts oppositely and can resolve the good responding sample shown by the cyan arrow and not the sample shown by the orange arrow. Both these sample are correctly resolved the 2D model built using Caspase 2/Caspase 10 (F); however, this model is unable to resolve the poor responding sample identified by the yellow arrow, whereas one of the previous models (E) was able to do this. As demonstrated in the full 3D model view from (A) and (B), as well as the projection of model (C), all the labeled poor and good responding patients are correctly classified. Although 2D models show high predictive capabilities, all three genes are needed to increase the classification accuracy of the IBIS model.
To validate the specificity and predictive capability of the top-scoring gene triplet (for the good and poor responding classes) and its associated classifiers, we examined the model exhibiting the best performance on a “default” expression dataset. This null dataset was built keeping the original gene expression data and randomly permuting the class labels of the outcomes 1,000 times (keeping the same counts of good and poor responding patients as were in the original dataset). The prediction accuracies for all the gene triplets obtained with this dataset dropped dramatically as the means ranged from 49.2% to 53.6% (data not shown), emphasizing the specificity of the classifiers. In addition, for the top-scoring gene triplet (for good and poor responder classes), we calculated the probability of achieving, under the null hypothesis, an equal or better accuracy than that obtained in the original prediction (86%), as previously described [14]. This achieved significance level was 0.009, suggesting that it is very unlikely that the prediction accuracies observed for this classifier are caused by chance.
Finally, we tested the robustness of each of these gene sets as predictors of IFNβ response by simulating experimental measurement error. To accomplish this, we first calculated the standard deviation of the expression measurements for all genes as an estimation of the overall experimental error. We then added a fixed amount of Gaussian noise corresponding to one standard deviation (taken from 20 random deviations) to each expression value and repeated the classification/prediction in 30 different splits of the data (a total of 600 tests). Notably, the mean drop in predictive accuracy after the addition of noise was less than 10%, denoting a significant tolerance to reasonable measurement errors (Table 1).
Because all the patients in this study were systematically followed up for a period of 2 y, we were able to perform a longitudinal analysis. Using a repeated-measures analysis of variance (ANOVA), we searched for genes with significantly different expression patterns based on models that tested for responder effect, time effect, and interaction effect (time × response).
Significant responder effect for 20 genes (Figure 6) and significant time effect for 13 genes were detected (Figure 7). Interestingly, six of the genes that showed statistically significant differences between good and poor responders, IRF4 (p = 0.03), IL4Ra (p = 0.01), Caspase 10 (p = 0.0008), Caspase 7 (p = 0.01), IRF2 (p = 0.02), and IRF6 (p = 0.03) are among the 12 genes that best predict response at T = 0 (shown in bold in Figure 7B). A pattern consistent with increased apoptosis (five members of the Caspase family of proteins, TRADD, and BAX) was observed for the poor responders.
Figure 6 Characteristic Gene Expression Profiles of Good and Poor Responders to IFNβ over Time
(A) An unsupervised hierarchical clustering representation of the weighted difference between the average expression of good and poor responders. For each gene, the obtained differences were log normalized and multiplied by the F-statistic from an ANOVA (responder effect) run previously (shown in [B]). The “heat” colored bar represents the absolute value of this difference. With the exception of MX1 (indicated by an arrow), all genes showing a significant difference in expression between the two groups of patients were automatically arranged in only two clusters (framed in blue).
(B) List of all genes showing a significant responder effect along with their F-statistic and p-values. Genes that were part of any triplet showing more than 80% prediction accuracy at T = 0 are shown in bold.
(C) A continuous representation of the longitudinal average expression of two representative genes for good (^) and poor (•) responders. TRADD shows two widely parallel curves, indicative of a significant difference in the expression averages, correlating with its profile (#) observed in the clustering shown in (A). In contrast,
GATA
3 displays two almost overlapping curves, consistent with its shading (*) in the clustering in (A).
Figure 7 IFNβ-Induced Changes in Gene Expression over Time
(A) An unsupervised hierarchical clustering representation of the weighted difference in gene expression at each time point versus baseline. For each gene, the obtained differences were log normalized and multiplied by the F-statistic from an ANOVA (time effect) run previously (shown in [B]). The “heat” colored bar represents the absolute value of this difference. With the exception of IFNAR1 (arrow), all genes showing a significantly different expression in at least one time point with respect to baseline were arranged in the same cluster (framed in blue).
(B) List of all genes showing a significant time effect along with their F-statistic and p-values. Genes that were part of any triplet showing more than 80% prediction accuracy at T = 0 are in bold.
(C) A continuous representation of the longitudinal average expression of two representative genes over all samples. MX1 (^) shows a marked departure from T = 0 and remains elevated for the rest of the observed period. This correlates well with the shading (#) displayed in the clustering shown in (A). In contrast, IRF6 (•) displays an almost flat curve, consistent with its color in the clustering (*).
Although we successfully identified informative, combinatorial relationships, establishing the causality of the association between gene expression and outcome to therapy is beyond of the scope of this work, and these genes are therefore considered surrogate markers. Moreover, although extensive in vivo and in vitro experiments have been conducted, the full mechanism of action of IFNβ in MS remains unknown.
Transcription profiling experiments have involved IFNβ in the regulation of apoptosis in both cancer and MS [15,16,17,18]. Induction of programmed cell death could lead to a reduction in the number of activated lymphocytes, macrophages, and monocyte-derived dendritic cells—all key components of the pathogenic process leading to tissue damage in MS [19,20,21]. However, increased levels of some anti-apoptotic molecules have also been observed in IFNβ-treated MS patients, possibly reflecting a compensatory mechanism [16]. Furthermore, even the inhibition of activated T cell apoptosis in response to IFNα and IFNβ has been reported [22]. Our finding of increased apoptosis in poor responders does not support the hypothesis of programmed cell death as a primary therapeutic mechanism for IFNβ. We hypothesize that a net increase in pro-apoptotic transcripts in peripheral blood mononuclear cells from poor responders could be reflecting undesired elimination of certain regulatory cell populations that are much needed to maintain a homeostatic balance.
Other differentially expressed transcripts included IRF4, a gene essential for mature T and B lymphocyte function and homeostasis, and a transcription factor with dual function (activator/repressor) that regulates transcription of IL4 through physical interaction with NFATc2 [23]. Remarkably, IRF4 is a repressor of other IFN-induced genes [24], an observation consistent with the elevated expression of IRF4 observed in the poor responders before initiation of therapy.
As expected, the gene MX1 showed a significant time effect independent of clinical response (p = 0.01). This result is in agreement with previous findings indicating substantial MX1 upregulation in response to type I IFNs [25]. Interestingly, upregulation of MX1, which occurs minutes after IFN stimulation [26], is sustained over at least 2 y, spanning several orders of magnitude of time units. This also correlates well with our results identifying MX1 as the best single classifier for samples from patients before (T = 0) and after (T = 3) initiation of therapy. In fact, as Figure 7 illustrates, most of the significance for the time effect in MX1 comes from the difference between T = 0 and T = 3.
Also of interest, a significant time effect (but not responder effect) was observed for IFNAR1 and STAT2 (Figure 7B). Because IFNAR1 is a subunit of the heterodimeric type I IFN receptor and STAT2 is a critical component of the DNA binding complex ISGF3a (which regulates the expression of IFN-responsive genes), their upregulation on administration of rIFNβ is likely related to mechanistic aspects of IFN signaling. Our results suggest that poor response is associated with downstream signaling events rather than deficient recognition or metabolism of the drug. Our previous finding that IFN receptor polymorphisms do not affect therapeutic response in this same set of patients partially supports this hypothesis [27]. Only two genes with significant time effects, Caspase 10 (p = 0.01) and MAP3K1 (p = 0.01) were part of any predictor set (Figure 7B). In addition, MAP3K1 also showed a significant interaction effect (p = 0.05; data not shown). These results highlight the involvement of these genes in the response to IFN both at T = 0 and once therapy has started.
Here we combined large-scale, function-oriented gene expression with advanced data mining to identify a set of markers that accurately and robustly predict the response to rIFNβ therapy. Although larger, prospective studies are needed to confirm these findings, our results suggest that the underlying gene activity profile of an individual at the verge of therapy harbors sufficient information to allow investigators to estimate the chances of experiencing satisfactory therapeutic effects. As analytical tools to predict clinical outcomes based on molecular evidence evolve, these types of studies are likely to become a substantial aid to the physician, taking the paradigm of personalized medicine one step further.
Materials and Methods
Patients and samples
All studies were approved by the respective Committees of Human Research at Hospital Vall d'Hebron, Barcelona, Spain, and the University of California, San Francisco, United States. Informed consent was obtained for all study participants. All patients were examined by a trained neurologist at the CNI Unit, Vall d'Hebron Hospital. Inclusion criteria for this study were clinically definite MS (Poser's criteria), disease in relapsing-remitting phase, age between 18 and 65 y, recorded history of at least two clearly identified relapses within the preceding 24 mo, and expanded disability status scale between zero and 5.5 (inclusive). Detailed information about clinical aspects of these patients has been recently reported elsewhere [6].
Patients were categorized as good responders (n = 33) if they experienced a total suppression of relapses and no increase in the expanded disability status scale after a 2-y follow-up period. Poor responders (n = 19) were defined as having suffered two or more relapses or having a confirmed increase of one point in the expanded disability status scale score. Patients with intermediate phenotypes were excluded from this study. Blood specimens were taken following institutional guidelines at approximately the same time of the day just before the administration of the first dose of rIFNβ and every 3 mo thereafter during the neurological examination, with the exception of T = 15 and T = 21 mo. Altogether, 336 samples were tested (an average of 6.5 time points along 2 y for each individual). Immediately after being drawn, all blood samples were spun over Ficoll-Paque (Amersham Biosciences, Piscataway, New Jersey, United States) gradients to enrich the sample for mononuclear cells. After three washes with PBS, aliquots of 5 × 106 cells were frozen in RPMI1640 containing 20% DMSO and 20% fetal calf serum.
RNA purification, quantitation, and handling
Peripheral blood mononuclear cells were thawed at 37 °C for 1 min, and RNA lysis buffer was added immediately. RNA was purified with the Strataprep kit (Stratagene, La Jolla, California, United States) and finally resuspended in nuclease-free water (Promega, Madison, Wisconsin, United States). One-microliter RNA aliquots were subjected to fluorescence-based quantitation (in duplicate) using the Ribogreen reagent (Molecular Probes, Eugene, Oregon, United States) in a Spectra Max Gemini fluorometer (Molecular Devices, Sunnyvale, California, United States). Samples were diluted to 1 ng μl−1 using nuclease-free water, and 5 μl was aliquoted in triplicates into 384-well plates using a Multiprobe II liquid-handling instrument (PerkinElmer Life and Analytical Sciences, Boston, Massachusetts, United States). Plates were kept frozen at −70 °C until needed.
One-step kinetic reverse-transcription PCR
A master mix was prepared essentially as described previously [28], with the addition of 200 μM ROX (Sigma, St. Louis, Missouri, United States), and overlaid on top of each well of a freshly thawed 384-well plate containing 5 ng of RNA in each well. Reactions were performed in triplicate using an ABI 7900 Sequence Detection System (Applied Biosystems, Foster City, California, United States). Positive and negative controls, as well as calibration curves, all in triplicate, were also included in each reaction plate. Total reaction volume was 10 μl. All expression values were calculated by interpolation in a calibration curve spanning five orders of magnitude constructed with an in vitro transcribed clone of GAPDH. The average of each expression measurement was then divided by that of one of the positive controls (thymus RNA) to account for plate-to-plate variability. On the basis of reports addressing the limited utility of normalization [29,30] and of our unpublished observations, we avoided housekeeping gene normalization and used instead RNA content, thus relying on repeated precise fluorescence-based quantitation and highly accurate liquid-handling procedures.
Data collection and analysis
A custom Microsoft Excel (Microsoft, Redmond, Washington, United States) worksheet was prepared for handling reaction data import and performing initial statistics. Normalized data were imported as a .csv file into GeneLinker Platinum (Predictive Patterns Software, Kingston, Ontario, Canada) for preprocessing and clustering analysis. Quadratic discriminant analysis–based IBIS implemented for 3D searches was carried out at the School of Computing, Queen's University, Kingston, Ontario, Canada, and at Biosystemix, Sydenham, Ontario, Canada. IBIS is a data-mining algorithm that searches through the gene space for a single gene (or group of genes) that can predict the outcome class (in this case, good and poor response to rIFNβ therapy). This algorithm incorporates a complete 10-fold cross-validation method with several independently trained classifiers to predict an outcome on the basis of a voting scheme (see below). We used MSE and classification accuracy to assess how well the classification predictions matched the true response of the patients to therapy. The top-performing gene triplets were selected on the basis of a mixed threshold for low MSE levels and high accuracy rates.
The algorithm
IBIS identifies genes (or gene pairs or groups of genes) that are highly predictive of the outcome based on probability distributions of those genes in different outcome classes. For example, for a given gene gi, two Gaussian functions are fitted to the distributions of the observed expression levels in good responding and poor responding patients (let us call these fitted distributions D
g and D
p for good and poor responding patients, respectively). Our fitted distribution, D
g(x), denotes the probability of a patient having an expression level of gi = x, given that this patient is a good responder. The fundamental question we are aiming to answer using data-mining methods (here using IBIS particularly) is as follows: what is the probability of a patient being a good responder given the observed expression level of a gene for that patient? Taking advantage of the fitted distributions, a classifier applies Bayes' formula to answer the fundamental question. According to this formula:
where P
1
= P((gi = x)|Pa
good_responder)P(Pa
good_responder). In fact, P((gi = x)|Pa
good_responder) is the distribution function fitted to the observed gene expression values of gi above (D
g
), and P(Pa
good_responder), the probability of a patient being a good responder, can be easily calculated using the total and good responding patient counts. The term P
2 is strictly analogous to P
1 but applies to poor responders. Therefore, according to equation 1, for a given gene, a comprehensive model is built that predicts the probability of a patient being a good responder for different values of observed expressions of that gene. IBIS searches through all the genes and calculates such models for a single gene or combination of genes, resulting in singular or combinatorial mining of most relevant genes. The probability of a patient being a poor responder can also be calculated in a similar fashion.
To obtain a reliable classifier that is generalizable to all patient samples obtained under similar conditions, the Gaussian distributions and the classifier were only trained on a subsample of the patient data (training set). The results of the classification (i.e., probability of a patient being a good or poor responder), however, were tested on patient samples never seen by the classifier before (test set). This ensures limitation of the classifier overfitting. Further, a complete 10-fold cross-validation scheme was built into the training phase. In IBIS, linear and quadratic classifiers correspond to classifiers built using Gaussian distributions with equal or different covariance matrices, respectively. The prediction results of IBIS are visualized graphically within the observed gene expression space by presenting the probabilities of a patient being a good or poor responder as a background color (see Figures 4 and 5). The red background in the gene space represents a high probability of a patient sample being a good responder if the observed gene expression values are in that range in the gene space. The blue background, similarly, represents a high probability of a patient sample being a poor responder if the observed gene expression values are in that range in the gene space.
Several measures were used to assess how well the calculated probabilities matched the true patient responses to therapy. Two of these measures were MSE and classification accuracy. MSE was calculated as the sum of (responsei
observed − responsei
expected)2 averaged over all patients. For a given patient, the clinical response determined by the end of the 2-y monitoring period is denoted by responsei
observed and responsei
expected and represents the probability of that patient being a good responder to rIFNβ therapy, using the Bayes' formula above. Classification accuracy simply expresses the percentage of patients that were correctly predicted as being good or poor responders.
Classification and prediction procedure
The initial dataset of patients was divided into two parts; namely, a training set with 75% of the samples and a test set with 25% of the samples, each reflecting the same proportion of classes (63% good and 37% poor responders). Only the training set was used for identifying the best predictive gene triplets with the IBIS method, as well as for building the classifier. A committee of classifiers was then generated using a 10-fold cross-validation scheme during training. The training data themselves were divided into ten parts, and each time, a classifier was built using only nine parts of the data. That classifier's predictive capability was determined by its accuracy over the one-tenth of the data withheld. A committee of ten classifiers was assembled from the results of this training stage; this committee was then applied to the test data (which have thus far been hidden from the classifiers). For a patient sample in the test data, each classifier in the committee made a prediction. A majority voting scheme then decided as to which class the sample would be assigned.
Given the initial data split into training and test sets, it was important to rule out the role of fortuitous idiosyncrasies in this split and the resulting accuracy rates. To address this point, we created 100 random splits of the data into training and test subgroups. A committee of classifiers was trained on the training set for each data split, and the accuracies were calculated over the blind test set. A histogram of the test set accuracies was then built, representing the expected ranges of accuracies had the initial data split been different. This histogram is not representative of the estimated or idealized distribution of the accuracies for a gene triplet in a machine learning sense. Rather, it is a coarse approximation of the possible range of gene triplet outcome–prediction accuracies that could be expected.
Controlling for false discoveries
To assess the significance and specificity of the top-scoring gene triplets and their corresponding trained committee of classifiers, a null dataset was created by keeping the same expression levels of genes in the dataset and randomly permuting the class labels of the patients 1,000 times (the total count of poor and good responding patients was unchanged). Classifiers were built using the training null data, and accuracies were calculated on the corresponding test sets. The mean of these accuracies for all the top-performing gene triplets was around 50%. The achieved significance level, which represents the probability of achieving accuracy levels better than or equal to that of the nonpermuted classification, was calculated to be 0.009. This value can be considered a significance level, or p-value, and indicates the number of times in 1,000 trials for which accuracies of 86% or higher can be achieved under the “no predictive capability” null hypothesis.
Time-series analysis was performed using SAS version 8.0 (SAS Institute, Cary, North Carolina, United States). Permutation analysis and histogram graphic outputs were produced with Matlab (The Mathworks, Natick, Massachusetts, United States).
Supporting Information
Dataset S1 Raw Expression Dataset
Gene expression values for all samples at all time points. This is the raw file from which all analyses were performed.
(491 KB XLS).
Click here for additional data file.
Table S1 Target Information
Gene names, symbols, and LocusLink and GenBank accession numbers, as well as primer sequences, are listed for all targets.
(160 KB DOC).
Click here for additional data file.
Accession Numbers
The LocusLink (http://www.ncbi.nlm.nih.gov/projects/LocusLink/) accession numbers for the genes discussed in this paper are BAX (LLID 581), Caspase 10 (LLID 843), Caspase 2 (LLID 835), Caspase 3 (LLID 836), Caspase 7 (LLID 840), FLIP (LLID 8837), IFNAR1 (LLID 3454), IL4 (LLID 3565), IL4Ra (LLID 3566), IRF2 (LLID 3660), IRF4 (LLID 3662), IRF6 (LLID 3664), MAP3K1 (LLID 4214), MX1 (LLID 4599), NFATc2 (LLID 4773), STAT2 (LLID 6773), and TRADD (LLID 8717).
We are grateful to the MS patients who participated in this study. This work was funded by National Institutes of Health grant 1RO1 AI42911, the National Multiple Sclerosis Society, and the Wadsworth Foundation.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. SB, JR, XM, and JRO conceived and designed the experiments. SB, SJC, and AS performed the experiments. SB, PM, MMW, LDG, and RS analyzed the data. JR, PV, MC, and XM contributed reagents/materials/analysis tools. SB and JRO wrote the paper.
Citation: Baranzini SE, Mousavi P, Rio J, Caillier SJ, Stillman A, et al. (2004) Transcription-based prediction of response to IFNβ using supervised computational methods. PLoS Biol 3(1): e2.
Abbreviations
[number]D[number] dimensional
ANOVAanalysis of variance
IBISintegrated Bayesian inference system
IFNinterferon
IFNβinterferon beta
MSmultiple sclerosis
MSEmean squared error
rIFNβrecombinant human interferon beta
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| 15630474 | PMC539058 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 28; 3(1):e2 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030002 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047710.1371/journal.pbio.0030005Research ArticleInfectious DiseasesMolecular Biology/Structural BiologyVirologyVirusesThe Structure of a Rigorously Conserved RNA Element within the SARS Virus Genome Crystal Structure of SARS s2m RNARobertson Michael P
1
2
Igel Haller
1
3
Baertsch Robert
1
4
Haussler David
1
4
Ares Manuel Jr
1
3
Scott William G [email protected]
1
2
1The Center for the Molecular Biology of RNA, University of CaliforniaSanta Cruz, CaliforniaUnited States of America2Department of Chemistry and Biochemistry, University of CaliforniaSanta Cruz, CaliforniaUnited States of America3Department of Molecular, Celland Developmental Biology, University of California, Santa Cruz, CaliforniaUnited States of America4Howard Hughes Medical Institute and Department of Biomolecular Engineering, University of CaliforniaSanta Cruz, CaliforniaUnited States of AmericaWickens Marv Academic EditorUniversity of WisconsinUnited States of America1 2005 28 12 2004 28 12 2004 3 1 e511 8 2004 13 10 2004 Copyright: © 2004 Robertson 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.
Structure of a Conserved RNA Element in the SARS Virus Genome Determined
We have solved the three-dimensional crystal structure of the stem-loop II motif (s2m) RNA element of the SARS virus genome to 2.7-Å resolution. SARS and related coronaviruses and astroviruses all possess a motif at the 3′ end of their RNA genomes, called the s2m, whose pathogenic importance is inferred from its rigorous sequence conservation in an otherwise rapidly mutable RNA genome. We find that this extreme conservation is clearly explained by the requirement to form a highly structured RNA whose unique tertiary structure includes a sharp 90° kink of the helix axis and several novel longer-range tertiary interactions. The tertiary base interactions create a tunnel that runs perpendicular to the main helical axis whose interior is negatively charged and binds two magnesium ions. These unusual features likely form interaction surfaces with conserved host cell components or other reactive sites required for virus function. Based on its conservation in viral pathogen genomes and its absence in the human genome, we suggest that these unusual structural features in the s2m RNA element are attractive targets for the design of anti-viral therapeutic agents. Structural genomics has sought to deduce protein function based on three-dimensional homology. Here we have extended this approach to RNA by proposing potential functions for a rigorously conserved set of RNA tertiary structural interactions that occur within the SARS RNA genome itself. Based on tertiary structural comparisons, we propose the s2m RNA binds one or more proteins possessing an oligomer-binding-like fold, and we suggest a possible mechanism for SARS viral RNA hijacking of host protein synthesis, both based upon observed s2m RNA macromolecular mimicry of a relevant ribosomal RNA fold.
The SARS RNA genome contains a unique structure that resembles a portion of ribosomal RNA; this may allow the virus to hijack its hosts protein synthesis machinery
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Introduction
The virus that causes SARS, like other pathogenic coronaviruses and astroviruses, possesses a linear plus-sense strand RNA genome that has a 5′ methylated cap and 3′ poly-A tail. The viral replicase is translated directly from the genomic sense-strand RNA, and it then creates a full-length complementary (minus-sense strand) copy of the genomic RNA, as well as a nested set of shorter, subgenomic mRNAs having common 3′ UTRs. These 3′ UTRs all share with the genomic SARS RNA a 32-nucleotide element, immediately upstream of the 3′ poly-A tail (residues 29,590–29,621) [1], originally termed the stem-loop II motif (s2m) in human astroviruses [2]. The s2m element is the most highly conserved RNA element within the coronaviruses and astroviruses that contain it (Figure 1).
Figure 1 The Primary, Secondary, and Tertiary Structures of the SARS s2m RNA
(A) Phylogenetic comparisons of s2m RNA sequences from various coronavirus and astrovirus species. The SARS RNA sequence is color-coded to match the color scheme used throughout. Conserved sequences are highlighted as bold letters, and co-varying sequences involved in conventional RNA helical base-pairing are indicated in italics. Sequence complements are indicated using color-coded brackets.
(B) The 2.7-Å experimental SIRAS platinum-phased and solvent-flattened electron density map contoured at 1.25 root mean square deviation. The map allowed unambiguous tracing of the RNA molecule because the density was unambiguous for all backbone atoms and all nucleotide bases except U(25), U(30), and U(48).
(C) A corresponding ribbon diagram highlighting the unusual fold.
(D) Schematic representation of the s2m RNA secondary structure, with tertiary structural interactions indicated as long-range contacts. The schematic diagram is designed to approximate the representation of the fold. The GNRA-like pentaloop structure is shown in yellow, A-form RNA helices are shown in blue and purple, the three-purine asymmetric bulge is in red, and the seven-nucleotide bubble is in green. Long-range tertiary contacts are indicated by thin red and yellow lines.
Standard structural genomics analyses focus upon obtaining the three-dimensional structures of proteins encoded within a genome, and on identifying unknown protein function based on three-dimensional homology to protein structures of known function [3]. However, it is also imperative to identify and to elucidate the three-dimensional structures of non-protein gene products, including the various RNAs required for mRNA processing, protein synthesis, and other cellular functions [4]. In the case of viruses that possess an RNA genome, including such pathogens as HIV and SARS, it becomes critical to expand the scope of structural genomics analyses even further to include biologically relevant RNA tertiary interactions that occur within the RNA genome itself. Those genomic RNA elements having the greatest degree of conservation are the most likely to be crucial to the evolution, growth, and replication of these viruses, and therefore demand the most attention from those seeking to understand RNA viral pathogenesis and to design appropriate anti-viral drugs.
Using X-ray crystallography, we have solved the three-dimensional structure of the SARS virus s2m RNA to 2.7-Å resolution. The structure reveals a dramatic 90° bend and several additional novel tertiary interactions. Although the sequence and three-dimensional structure of the s2m RNA are both unique, comparison of the global fold of the SARS s2m RNA to known RNA tertiary structures reveals that the backbone fold of the s2m RNA mimics that of the 530 loop of 16S rRNA, permitting us to hypothesize that the biological function of s2m in SARS and related viruses is based upon macromolecular mimicry of this region of ribosomal RNA. The ribosomal RNA 530 loop and the proteins that bind to it are involved in translational initiation, suggesting that the role of the s2m in SARS may also involve translation initiation. Specifically, we propose, based on structural homology arguments, that the SARS s2m RNA might bind to the host's eukaryotic translation initiation factor 1A (eIF-1A) to hijack the host's translational machinery for use by the virus, or to bind other translational regulation proteins having similar folds for similar purposes.
Results
Sequence Analysis of the Conserved s2m Element
We aligned the most recent available genomic sequences of coronaviruses and astroviruses and analyzed conservation patterns within the s2m element (Figure 1). Remarkably, about 75% of this sequence is absolutely invariant between viral species (nucleotides shown in boldface in Figure 1A) and much of the variation that does occur preserves secondary structural elements (nucleotides shown in italics in Figure 1A). In addition, we analyzed 38 sequenced SARS variants and found that the motif is absolutely conserved within all of them. No insertions or deletions appear to be tolerated, indicating that this region forms a highly conserved RNA tertiary structure that is universally required for viral function [1,2,5].
The Crystal Structure of the s2m RNA Element of SARS
Using in vitro transcription, we prepared and crystallized a 48-nucleotide construct containing the 45-nucleotide s2m element. We solved the crystal structure to 2.7-Å resolution using a single platinum isomorphous/anomalous derivative and obtained a readily interpretable solvent-flattened electron density map (Figures 1B–1D and 2A). The quality of the electron density enabled us to fit the s2m RNA sequence unambiguously to the map and to build a model of the unusual tertiary structure. The initial map was virtually indistinguishable from the final 3Fo–2Fc map calculated using phases from the refined RNA structure, indicating that the single isomorphous replacement with anomalous scattering (SIRAS) experimental phases initially obtained were quite accurate (Tables 1 and 2). Two well-ordered hydrated Mg2+ complexes bound to the phosphate backbone of the RNA are also readily observable in the initial electron density map (Figure 2B).
Figure 2 Stereo Representations of the SARS s2m RNA Structure
(A) The overall SARS s2m RNA three-dimensional structure and (B) a detailed view of tertiary contacts the and [Mg(H2O)5]2+ binding sites in the context of the experimentally phased electron density map (dark blue). The [Mg(H2O)5]2+ complex ions, depicted as white octahedra, bind to the pro-R and pro-S phosphate oxygen atoms of A(12). An extensive network of potential hydrogen bonds between the metal-coordinated water molecules and the RNA is shown as yellow dotted lines.
Table 1 Crystallographic Data Collection
All X ray intensity data to 2.7 Å were processed without imposing a cutoff, and all amplitude data for which F ≥ 0.0 were used for model refinement and electron density map calculations
Table 2 Phasing and Refinement
Data for which F ≤ 2σ, for which isomorphous differences were greater than five times the root mean square isomorphous difference, or for which anomalous differences were greater than 3.5 times the root mean square anomalous differences were excluded from the initial phase estimation only. The crystallographic spacegroup is P6522 and the cell dimensions are a = b = 93.2184 Å and c = 128.109 Å. There is one RNA molecule per asymmetric unit, consistent with a 73% solvent content. The number of non-hydrogen atoms in the refined structure is 1,037, the number of Mg[(H2O)5]2+ complex ions is two, and one well-ordered water molecule that interacts with the metal complexes was explicitly modeled
rms, root mean square
The crystal structure of the s2m domain of the SARS RNA reveals several novel tertiary structural elements (Figure 3). Three regions of canonical A-form RNA are indicated in various shades of blue, and three regions of unusual structure, including tertiary interactions, are represented in green, red, and yellow. The actual three-dimensional fold of the RNA is illustrated in Figure 1C, with Figure 1D designed to represent this fold schematically as well as the secondary and tertiary structural contacts that stabilize it. Figure 2A shows a corresponding stereo diagram in which all non-hydrogen atoms are present.
Figure 3 Tertiary Structural Interactions in the SARS s2m RNA
(A) Close-up of the pentaloop structure together with the augmenting helix, shown in yellow, and the perpendicular junction formed with the A-form stem, shown in cyan. The pink hydrogen bonds indicate base-quartet hydrogen bonding, as shown in (B). The 90° kink thus formed is facilitated by a very sharp bend in the backbone involving unpaired residues 29 and 30.
(B) Formation of the junction of two perpendicular helices is facilitated by a base quartet composed of two G–C pairs.
(C) The unusual pairing between A(17) and G(34) facilitates formation of a long-range tertiary contact between A(33) of the three-purine asymmetric bulge and G(11) and A(12) of the seven-nucleotide asymmetric bubble. A(38) forms a base triple with C(39) and G(13), forcing G(11) and A(12) out of the main helix.
(D) Space-filling representation of the region shown in (C), but rotated approximately 180°. A tunnel is created by the tertiary contacts between A(33) of the purine asymmetric bulge (red), G(11) and A(12) of the seven-nucleotide bubble (green), and the helical region between them (purple). The non-bridging phosphate oxygens of G(11) and A(12) line the surface of the cavity, creating a negatively charged region into which Mg2+ ions are observed to bind.
The Fold of the s2m RNA, the Pentaloop, and a Nucleotide Quartet
The overall structure of the s2m SARS RNA consists of two regions that are defined by two perpendicular RNA helix axes (see Figures 1 and 2). The larger region contains several non-helical motifs involved in long-range tertiary contacts (see Figure 3). The smaller region (residues 20–30, shown in yellow in Figure 3) forms a stem-loop structure in which a pentaloop (residues 22–26) is structured similar to a conventional GNRA tetraloop motif but has an extra residue (U[25]) bulged out of the stack formed by A(23), G(24), A(26), and the augmenting helical stem (residues 20–21 and 27–28). This is similar to what is observed in a spliceosomal stem-loop structure [6]. The base of U(25) is disordered in the structure, and little side-chain density is apparent in an otherwise well-defined electron density map. Residues 29 and 30 are unpaired and are involved in forming a rather severe backbone reversal that accompanies the 90° kink in the helix axis. The phylogenetic comparisons shown in Figure 1A reveal that the pentaloop sequence is highly conserved. Although the structure of the pentaloop is very similar to the standard GNRA tetraloop structure [7,8], the “extra” U(25) insertion between R and A is always present. The unusual perpendicular helical junction is stabilized by the formation of an RNA base quartet involving two adjacent G–C pairs wherein the G(19)/C(31) pair shares four hydrogen bonds with the C(20)/G(28) pair (shown as pink dotted lines in Figure 3A and 3B). The RNA sequences required to preserve these G–C pair interactions are present in all but one of the viral sequences analyzed (avian nephritis virus), implying that the base quartet serves a significant structural role in SARS and most related viruses. All previously characterized RNA base quartets are purine tetrads [9,10,11,12,13,14,15] and do not occur within double-helical structures; the G–C quartet thus appears to be another novel structural feature present within the s2m element of SARS and related viruses.
A Three-Purine Asymmetric Bulge
An asymmetric bulge in the s2m SARS RNA secondary structure containing A(17), A(33), and G(34) (highlighted in red in Figure 3C) is absolutely conserved in SARS and all other related viruses analyzed (as shown in Figure 1). A(17) pairs with G(34), involving the Watson–Crick base-pairing faces of both purines. This mode of interaction is rather distinct from the more usual “sheared” G–A pairings involving the Hoogsteen faces of these purines, and has the effect of significantly widening the RNA helix from the standard A-form geometry. As a consequence, A(33) is able to adopt a very unusual conformation in which it becomes completely excluded from the helical stack, and instead forms long-range tertiary interactions with G(11) and A(12). G(34), in addition to forming a Watson–Crick-like base pair with A(17), hydrogen bonds to C(18) as well as to G(21), thereby stabilizing the unusual pentaloop-stem conformation and 90° helical kink.
A Seven-Nucleotide Asymmetric Bubble Interacts with the Purine Bulge
The remaining non-canonically base-paired region of secondary structure (residues 10–13 and 38–40), highlighted in green in Figure 3C, contains mostly conserved nucleotides including an absolutely conserved pair between C(10) and A(40), and a Watson–Crick pair within an otherwise highly distorted helical region between conserved residues G(13) and C(39). A base triple forms between A(38) and this G–C pair, a variant of the adenosine platform motif [16], and consequently G(11) and A(12) are rotated out of the helical structure completely. A(33) forms long-range tertiary interactions with G(11) and A(12) by hydrogen bonding to the N3 of G(11) and the ribose of A(12). Substitutions at position 12 are thus tolerated, as is a single instance of purine substitution at position 11 (which will preserve the N3 hydrogen-bonding interaction with A[12]). Together, these interactions superficially resemble those observed in domain IV of 4.5S RNA of the signal recognition particle [17,18], but the structural details are completely different.
G(11), A(12), and A(33), despite their extrusion from the helical base-pair stack, form a well-defined structure that is highly ordered, judging by electron density in the initial map as well as the comparatively low temperature factors these residues have in the refined structure. They conspire with the remaining residues in the asymmetric bubble and the helical region above it to form a rather wide tunnel whose channel runs approximately perpendicular to the main helical axis. The phosphates of G(11) and A(12) are turned inward, creating a negatively charged environment within the tunnel cavity. Consequently, the tunnel forms a binding site for two [Mg(H2O)6]2+ ions in the native structure (see Figure 2B), and the tunnel is also the binding site for cis-[(NH3)2Cl2Pt(IV)]2+ and [Ru(NH3)6]3+ metal complexes that were introduced for heavy atom isomorphous replacement phasing. These highly structured and rigorously conserved features allow us to suggest that SARS pathogenesis might be inhibited by a drug designed to bind to s2m and disrupt one of these structures.
Chemical Probing of the Solution Structure
To compare the crystal structure with the solution structure of s2m, we performed chemical modification experiments. The results are consistent with the crystal structure, and in some cases enable us to verify that long-range tertiary interactions observed in the crystal structure also occur in solution. Dimethyl sulfate (DMS) modification patterns (Figure 4A) of the N1 atomic position of A and the N3 of C residues are consistent with the observed fold in the crystal structure (Figure 4B). A and C residues that are solvent-exposed in the tertiary structure, such as A(12), A(23), and C(27), are among the most heavily modified by DMS (along with A[44] and A[45] near the helical terminus). These modification sites are shown as red spheres in Figure 4B. Although A(33) is quite exposed in the tertiary structure, the N1 is protected from modification by DMS (shown as a green sphere in Figure 4B), consistent with the involvement of the N1 of A(33) in a 2.8-Å hydrogen bond with the exocyclic N2 of G(11) (white atom and dotted line in Figure 4B) in the crystal structure. We therefore conclude that this tertiary structural interaction observed in crystals of s2m RNA is likely to be quite similar to what occurs in solution. G(11) is the only G residue of the s2m RNA detectably modified by kethoxal (data not shown), which reacts with nitrogens at the N1 and N2 positions. The N1 modification site is highlighted as an orange sphere and is consistent with the observed tertiary structure formed by G(11), A(12), and A(33) that exposes G(11) to the solvent. U(30) is solvent-exposed in the crystal structure and is reactive to 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate (CMCT; magenta spheres in Figure 4B; data not shown), as are the non-conserved 3′-terminal uridines (probably due to helix fraying in solution). U(25), which is not well ordered in the crystal but which we expect is also solvent-exposed, appears not to be reactive.
Figure 4 Chemical Probing of the SARS s2m RNA in Solution
(A) An autoradiogram of DMS modification of the s2m RNA in solution.
(B) Mapping the results of DMS, kethoxal, and CMCT modifications onto a stereo representation of the RNA structure. Red spheres represent strongly reactive N1 positions of adenosines and N3 positions of cytidine residues in the presence of DMS, and yellow spheres represent weaker reaction. Green spheres represent positions that appear to be protected from DMS. The orange sphere represents reaction with kethoxal at the N1 position of G(11), and magenta spheres represent CMCT reactions with uridines.
(C) The most extensive crystal packing interaction involves stacking of G(11) upon its symmetry mate, G(11)′.
(D) Temperature factors mapped onto all non-hydrogen atoms (left) and the phosphate backbone (right) of the s2m RNA crystal structure. U(25) is the most disordered residue in the structure and has the highest temperature factor. Density of the base of U(25) is not apparent even after refinement. Most of the rest of the structure is rather well ordered.
Discussion
The intricate three-dimensional structure of the SARS s2m RNA, along with its rigorous sequence conservation, is compelling prima facie evidence for its biological importance in coronaviruses and astroviruses. The structure by itself, however, does not indicate what the function of this motif must be. Hence, comparison of this unique fold with those of known RNA structures is of particular value for formulating testable hypotheses regarding potential biological functions of the s2m RNA. In addition, identification of novel and rigorously conserved tertiary structures that are unique to the viral RNA is of critical importance for future rational design of anti-viral therapeutic agents that specifically target SARS and other coronaviruses and astroviruses.
Biological Relevance of the s2m Sequence and Crystal Structure
The s2m RNA sequence we crystallized was originally identified from the genomic sense strand within a rigorously conserved region of the 3′ UTR of the RNA. However, because RNA replication and transcription take place via a full-length negative-strand RNA intermediate, it is formally possible that the conserved sequence instead corresponds to a conserved structure at the 5′ end of the anti-sense RNA. We believe this to be improbable because of the energetically unfavorable tertiary structures that would be required to form from the sequence complement. For example, the variant of the energetically stable and rather common GNRA loop structure (GAGUA) would have to be replaced with an energetically unstable and rare CUCAU loop. Similar arguments apply to the other non-Watson–Crick regions of the structure.
Crystal packing interactions may potentially distort RNA structures. This effect is sometimes observed for small stem-loop sequences, which often crystallize as duplex dimers rather than as monomeric hairpins. The s2m RNA structure is sufficiently large, and apparently contains enough stabilizing secondary and tertiary interactions, to offset any energetic advantage that might come from crystallizing as a duplex. In addition, the 73% solvent content of the s2m RNA crystals ensures that most of the crystallized RNA is solvent-exposed, rather than involved in extensive packing interactions. At least three inter-molecular contacts are required to form a crystal. The most extensive contact is the base of residue G(11); it stacks upon that of its 2-fold symmetry mate (Figure 4C). It is likely that these nucleotide bases become oriented in such a way as to optimize this stacking interaction. The nonessential nucleotide G(1) forms a weak (3.4-Å) hydrogen-bonding interaction with A(29) of an adjacent molecule, but most of this packing interaction appears to be due to shape complementarity and is thus expected to have little distorting effect. The remaining interaction is a nonspecific, presumably cation-mediated backbone parallel helical interaction, again unlikely to result in significant distortions.
Crystallographic temperature factors provide direct physical evidence for the relative flexibility or mobility of various regions of a macromolecule. Figure 4D shows relative temperature factors color-coded on all non-hydrogen atoms (left) and on the RNA phosphate backbone atoms (right). Blue atoms have the lowest relative temperature factors and red atoms have the highest. Consistent with the observed electron density map, by far the most flexible region of the RNA is U(25). U(30) and the 5′-terminal triphosphate are also moderately disordered. Much of the rest of the structure appears to be rather rigid and well defined, including the three-purine asymmetric bulge and the seven-nucleotide asymmetric bubble, along with the hydrated magnesium complex ions that bind to the non-bridging phosphate oxygens of A(12). The phosphate backbone atoms of these non-Watson–Crick regions are among the most ordered in the structure.
Therefore, based on our chemical probing data, analysis of crystal packing interactions, and consideration of the crystallographic temperature factors, along with the ability to rationalize the sequence conservation pattern and intolerance for nucleotide insertions or deletions based on the structure, we conclude that the crystal structure of s2m is likely to be a close representation of the structure that forms in solution and in the context of the SARS virus RNA genome.
Functional Implications of the s2m Three-Dimensional Structure
The several unique features and unanticipated tertiary contacts we identified in the SARS s2m RNA crystal structure allowed us to reexamine genomic sequences and previously determined RNA tertiary structures for similar motifs with additional constraints imposed by knowledge of the tertiary structure. Our analysis of the human genome, other animal and viral genomes, and the currently available database of RNA three-dimensional structures revealed that the s2m element is found only in astroviruses and coronaviruses; no cellular homologs are immediately apparent. The G(11) to A(33) tertiary contact in the s2m RNA is homologous to the G(1,452) to A(1,486) contact in Domain III of the 23S ribosomal RNA, but the context of the interaction in the ribosome is completely different, and the sequence is not conserved between Escherichia coli and Thermus thermophilus. However, if we relax the sequence constraints and focus attention upon the conformation of the RNA backbone, we find that the phosphodiester backbone fold accompanying the 90° kink in s2m RNA mimics that found in the 530 stem-loop of 16S ribosomal RNA [19] (Figure 5A). The latter binds to the S12 protein found at the interface between the small and large ribosomal subunits. The 530 stem-loop, and the S12 protein that binds to it, have been implicated in EF–G-independent ribosomal translocation [20]. Remarkably, superposition of the s2m RNA upon the 530 stem-loop within the 30S ribosome in which prokaryotic initiation factor 1 (IF-1) has been added [21] reveals plausible modes of s2m RNA binding to both the S12 protein and to IF-1 (Figure 5B). Both S12 and IF-1 have eukaryotic homologs; the structure of IF-1 and its eukaryotic analog, eIF-1A, possess almost identical RNA oligomer binding (OB) folds [22,23]. Based upon these structural homology arguments, we propose that the SARS s2m RNA is a functional macromolecular mimic of the 530 loop of the small subunit ribosomal RNA (which is conserved in eukaryotes). Mechanisms of translation and protein synthesis regulation via macromolecular mimicry are in fact well established [24,25]. We propose, on the basis of the similarity between the 530-loop fold and the s2m fold, that the s2m RNA of SARS may be capable of binding one or more eukaryotic proteins whose structures resemble S12 or the OB folds typical of these ribosomal proteins, and that each would do so in a manner similar to that shown in Figure 5B. This proposal leads us to formulate two separate, testable hypotheses regarding the function of the s2m RNA in SARS.
Figure 5 SARS Virus RNA Macromolecular Mimicry
(A) The SARS s2m RNA structure (red) is superimposed upon the 530 loop of 16S rRNA (cyan), revealing the similar stem-loop folds.
(B) The IF-1 (magenta) and S12 protein (blue) that bind to the 16S rRNA 530 loop (now hidden) are shown relative to the same s2m RNA superposition, suggesting that their eukaryotic homologs might plausibly bind to the s2m RNA.
Does s2m Macromolecular Mimicry Facilitate Viral Hijacking of Protein Synthesis?
eIF-1A, like IF-1, possesses an OB fold. Our first hypothesis is that eIF-1A may bind to the 90° bend of the SARS s2m RNA. In addition, we suggest that the function of the s2m RNA of SARS and related viruses might involve viral hijacking [26] of the cell's protein synthesis machinery, either facilitating mRNA circularization and ribosome re-initiation, in gross analogy to viral internal ribosomal entry site–mediated mechanisms [27,28], or perhaps even more simply by titrating eIF-1A away from the host initiation complexes and thus inhibiting host cell protein synthesis in favor of viral protein synthesis by sequestering a factor required by the host.
Does s2m Bind to the nsp9 SARS Protein to Facilitate Virus Transcription?
Recently, two protein structural genomics investigations of SARS revealed the structure of a so-called nonstructural protein, nsp9, that is believed to be involved in viral RNA synthesis and to interact with the viral polymerase in an unspecified manner [29,30,31]. The crystal structure of nsp9 reveals it to be a variant of the OB fold, a protein structural motif not previously recognized to be involved in viral replication. The authors demonstrate nonspecific single-strand RNA binding affinity for nsp9. We propose that nsp9, by virtue of its OB fold, may bind specifically to s2m in a manner similar to that illustrated in Figure 5B, and may thus facilitate viral polymerase RNA transcription, translation, or replication.
From Structure to Functional Predictions
Our structural genomics analysis of the SARS RNA has thus enabled us to formulate specific, experimentally testable hypotheses regarding the function of a highly conserved RNA motif whose importance has been evident [2] but whose biological activity hitherto was completely unknown. The possibility that the 90° bend of the s2m RNA binds to an OB-like protein permits us to propose two potential mechanisms of interaction relevant to the two main functions of the SARS virus (protein synthesis and viral replication). The possibility of additional interactions with proteins at the S12-like site and in the highly structured and rigorously conserved tunnel region formed by the three-purine bulge and the seven-nucleotide bubble should also not be overlooked, as these both are likely sites for RNA–protein or RNA–RNA interactions that are crucial to the function of the SARS virus, and therefore also merit further attention.
The s2m RNA Tunnel Is an Attractive Target for the Design of Anti-SARS Drugs
Figure 3C and 3D dramatically illustrates the most striking and unique structural feature within the SARS s2m RNA. A tunnel is created by the tertiary contacts between A(33) of the purine asymmetric bulge (red), G(11) and A(12) of the seven-nucleotide bubble (green), and the helical region between them (purple). The non-bridging phosphate oxygens of G(11) and A(12) line the surface of the cavity, creating a negatively charged region into which Mg2+ ions are observed to bind. It is likely that in the context of the virus, this invariant feature of the s2m structure is involved in binding interactions with highly conserved proteins or other components of the host cell that interact specifically with the negatively charged cavity. Because this tunnel structure is unique to coronaviruses and astroviruses and because the sequence comprising this structure is invariant, it is reasonable to propose that by designing a drug that specifically targets this structural feature and binds tightly to it, an anti-SARS therapeutic might be obtained that avoids the pitfall of being toxic to uninfected host cells while escaping the usual problem of drug resistance that develops in rapidly mutating RNA viruses.
Materials and Methods
Crystals of a 48-nucleotide T7 RNA transcript containing the conserved s2m RNA element were obtained via hanging-drop vapor diffusion by equilibrating a solution containing equal volumes of the RNA sample and the reservoir solution against 1-ml of the reservoir solution. The RNA sample solution contained 4.5 mg/ml s2m RNA dissolved in 30 mM Tris (pH 7.6), 100 mM NaCl, and 60 mM MgCl2. The reservoir solution contained 50 mM MES (pH 5.6), 100 mM Mg(OAc)2, and 20% MPD. Data from a native crystal diffracting to 2.7-Å resolution, and 3.0-Å cis-(NH3)2(Cl)2Pt(IV)–derivative single-wavelength anomalous dispersion data, were collected at Beamline 9.1 at Stanford Synchrotron Radiation Laboratory on a 3 × 3 CCD detector using 0.98-Å wavelength X rays and crystals that were cryoprotected in the reservoir solution spiked with 12% glycerol and maintained at 100 K. The native and platinum derivative data were processed using CCP4's MOSFLM and reduced and scaled within CCP4 version 5.0 [32,33]. A single platinum heavy atom site was found in both isomorphous- and anomalous-differences Patterson-map Harker sections calculated using data from 10- to 5-Å resolution. Phase calculation, solvent flattening, phase extension, and simulated annealing refinement were carried out within CNS version 1.1 [34]. The initial SIRAS map was uninterruptible in spacegroup P6122 but was unambiguous in P6522, permitting the hand of the space group to be determined. A 47-nucleotide poly-C model was built into the SIRAS map using O, the actual nucleotide-sequence register was then confirmed by inspecting the electron density, and residues 1–47 were built in using O [35]. The phosphate for residue 48 is clearly present in the electron density map, but the density for the remainder of U(48), as well as that for the bases of U(25) and U(30), was rather disordered. The final refinement was performed using CCP4's refmac [36], and the figures were produced using MacPymol [37]. All crystallographic computations were performed on the Mac OS X platform. Details of data processing, phasing, and refinement are provided in Tables 1 and 2. The crystal structure of the SARS s2m RNA was compared to others in the RCSB Protein Data Bank using the program MC-Annotate [38,39] and by visual inspection. Sequence comparisons prior to obtaining the s2m tertiary structure were performed using the UCSC Genome Browser [40], and were subsequently supplemented with tertiary constraints imposed by the crystal structure using the programs PatScan [41] and RNABOB [42,43]. Transcripts containing s2m for solution structure analysis were prepared using plasmid templates cleaved downstream, so that the s2m element was present at the 5′ end of the transcript and contained an RNA tail consisting of plasmid sequences. Chemical probing experiments were carried out according to established methods [44]. Primer extension was performed as described previously [45] using a primer complementary to sequences 3′ of the s2m element.
Supporting Information
Coordinates, native and derivative amplitudes, and experimental phases have been deposited in the RCSB Protein Data Bank (http://www.rcsb.org/pdb/) under accession number 1XJR and are also available with other supplementary materials at http://www.chemistry.ucsc.edu/%7Ewgscott/sars.
Accession Numbers
The RCSB Protein Data Bank accession number for the SARS s2m RNA structure reported here is 1XJR. The RCSB Protein Data Bank accession numbers for the other protein and RNA structures discussed in this paper are as follows: the 30S ribosome (1J5E), the 30S ribosome in which prokaryotic IF-1 has been added (1HR0), the eukaryotic analog of prokaryotic IF-1 (1D7Q), and the crystal structure of nsp9 (1QZ8 and 1UW7).
We thank Harry Noller for pointing out the similar fold found in the 16S rRNA 530 loop, Luca Jovine for the NUCCYL perl script used to generate the ribbon diagram in Figure 2A, Jay Nix for generous assistance with data collection, and Abraham Szöke, Sara O'Rourke, Harry Noller, and other members of the Center for the Molecular Biology of RNA at the University of California at Santa Cruz for helpful discussions. This project was supported by National Science Foundation and National Institutes of Health grants to WGS, MA, and DH, and the RNA Center is supported by a grant from the William Keck Foundation. Portions of this research were carried out at the Stanford Synchrotron Radiation Laboratory (SSRL), a national user facility operated by Stanford University on behalf of the United States Department of Energy, Office of Basic Energy Sciences. The SSRL Structural Molecular Biology Program is supported by the Department of Energy, Office of Biological and Environmental Research, and by the National Institutes of Health, National Center for Research Resources, Biomedical Technology Program, and the National Institute of General Medical Sciences.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. The experiments were conceived, designed, and interpreted in a cooperative effort among all of the authors. MPR and WGS determined the crystal structure and investigated possible structural homologies. HI and MA designed the transcription templates for the SARS s2m RNA and performed the biochemical analyses. RB and DH performed the viral and cellular genomic sequence analyses and called our attention to the striking conservation and biological importance of the SARS s2m RNA.
Note Added in Proof: The crystal structure of a 5′ UTR guanine-binding RNA of the xpt-pbuX operon of B. subtilis complexed to hypoxanthine was recently reported, revealing two base quartet interactions that stabilize a loop-loop interaction [46].
Citation: Robertson MP, Igel H, Baertsch R, Haussler D, Ares M, et al. (2004) The structure of a rigorously conserved RNA element within the SARS virus genome. PLoS Biol 3(1): e5.
Abbreviations
CMCT1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate
DMSdimethyl sulfate
eIF-1Aeukaryotic initiation factor 1A
IF-1initiation factor 1
OBoligomer binding
s2mstem-loop II motif
SIRASsingle isomorphous replacement with anomalous scattering
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| 15630477 | PMC539059 | CC BY | 2021-01-05 08:21:23 | no | PLoS Biol. 2005 Jan 28; 3(1):e5 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030005 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047810.1371/journal.pbio.0030006Research ArticleDevelopmentEvolutionGenetics/Genomics/Gene TherapyCaenorhabditis
fog-2 and the Evolution of Self-Fertile Hermaphroditism in Caenorhabditis
Evolution of Self-Fertile HermaphroditismNayak Sudhir
1
Goree Johnathan
1
Schedl Tim [email protected]
1
1Department of Genetics, Washington University School of MedicineSt. Louis, MissouriUnited States of AmericaMeyer Barbara Academic EditorUniversity of California at BerkeleyUnited States of America1 2005 28 12 2004 28 12 2004 3 1 e623 7 2004 16 10 2004 Copyright: © 2004 Nayak 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.
The Evolution of Self-Fertile Hermaphroditism: The Fog Is Clearing
Somatic and germline sex determination pathways have diverged significantly in animals, making comparisons between taxa difficult. To overcome this difficulty, we compared the genes in the germline sex determination pathways of Caenorhabditis elegans and C. briggsae, two Caenorhabditis species with similar reproductive systems and sequenced genomes. We demonstrate that C. briggsae has orthologs of all known C. elegans sex determination genes with one exception: fog-2. Hermaphroditic nematodes are essentially females that produce sperm early in life, which they use for self fertilization. In C. elegans, this brief period of spermatogenesis requires FOG-2 and the RNA-binding protein GLD-1, which together repress translation of the tra-2 mRNA. FOG-2 is part of a large C. elegans FOG-2-related protein family defined by the presence of an F-box and Duf38/FOG-2 homogy domain. A fog-2-related gene family is also present in C. briggsae, however, the branch containing fog-2 appears to have arisen relatively recently in C. elegans, post-speciation. The C-terminus of FOG-2 is rapidly evolving, is required for GLD-1 interaction, and is likely critical for the role of FOG-2 in sex determination. In addition, C. briggsae gld-1 appears to play the opposite role in sex determination (promoting the female fate) while maintaining conserved roles in meiotic progression during oogenesis. Our data indicate that the regulation of the hermaphrodite germline sex determination pathway at the level of FOG-2/GLD-1/tra-2 mRNA is fundamentally different between C. elegans and C. briggsae, providing functional evidence in support of the independent evolution of self-fertile hermaphroditism. We speculate on the convergent evolution of hermaphroditism in Caenorhabditis based on the plasticity of the C. elegans germline sex determination cascade, in which multiple mutant paths yield self fertility.
A comparison of sex determination genes in C. elegans and C. briggsae provides evidence in support of the convergent evolution of self-fertile hermaphroditism in the Caenorhabditis clade
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Introduction
Sex determination is an ancient and universal feature in metazoans. In spite of this, comparison of distantly related species such as Caenorhabditis elegans and Drosophila melanogaster has revealed little about the evolution of the complex pathways that mediate the sexual fate decision in the soma and germline [1,2,3]. This is likely due to the combination of gross morphological, functional, and behavioral dissimilarity and extensive sequence divergence. Thus, if we wish to clarify the etiology of diverged sex determination pathways, an alternative approach is required.
One approach is to perform comparative analysis of sex determination genes in species separated by sufficient evolutionary time to allow for changes in pathway components yet retain comparable somatic and germline morphology and function. The clade containing C. elegans and C. briggsae represents an ideal case for this type of study, as the sex determination pathway has been well studied in C. elegans and an abundance of sequence information is available for both species [4,5].
C. elegans and C. briggsae, while sharing very similar germline and somatic morphology, are separated by approximately 100 million years and are members of a clade that employs multiple mating systems [5,6,7,8,9,10]. C. elegans and C. briggsae are self-fertile hermaphrodites that maintain males at a low frequency (androdioecious), whereas the morphologically similar C. remanei and C. sp. CB5161 are obligate female/male (gonochoristic) species [6,7,10]. Phylogenetic analysis of the four closely related Caenorhabditis species suggests that self-fertile hermaphroditism has evolved independently in C. elegans and C. briggsae from an ancestral male/female state [10,11]. Importantly, a transition in mating system from female/male to hermaphroditic (or hermaphroditic to male/female) requires that one or more changes in the sex determination pathway have occurred.
C. elegans and C. briggsae, like many other animals, have two sexes specified by the ratio of X chromosomes to sets of autosomes [8,12,13]. In both species, XX animals are somatically female while the germline is hermaphroditic. Self fertility is achieved by a transient period of spermatogenesis beginning in the third larval (L3) stage before the organism switches to the production of oocytes in the L4 stage, which continues throughout adulthood [14,15]. In both species, XO males begin sperm production in the L3 stage and continue spermatogenesis throughout their reproductive lives [14,16,17].
A major determinant of germline sexual fate in C. elegans is the relative activity of two key regulators: tra-2, which promotes the female fate (oocyte), and fem-3, which promotes the male fate (sperm) [18,19] (Figure 1A). The activities of tra-2 and fem-3 must be regulated in both males and hermaphrodites to allow spermatogenesis to occur, however the mechanisms by which this regulation occurs differs between the two sexes. In males, her-1 represses tra-2 feminizing activity and raises the relative level of fem-3 activity so that spermatogenesis is continuous [20,21]. Since null mutations in her-1 have no effect on hermaphrodites and her-1 is not expressed in XX animals, a different mechanism is used to allow for the transient production of sperm [22,23].
Figure 1 The C. elegans XX Hermaphrodite Germline Sex Determination Pathway
(A) Genetic pathway for gene activity, where arrows represent positive regulation and bars represent negative regulation. The key genes tra-2 and fem-3 and the upstream regulators of tra-2 that are the focus of this work, fog-2 and gld-1, are in large bold font. The upstream genes fog-2 and gld-1, which are key regulators of tra-2 and addressed in this work, are also in large bold font. The gene activities at each level in the hierarchy are indicated below as “ACTIVE” in bold or “inactive” in grey. In L3 and L4 hermaphrodites the activities of fog-2 and gld-1 are high, leading to repression of tra-2 activity (also see [B]) and the de-repression of fem-3, resulting in the onset of spermatogenesis. In L4 and adult hermaphrodites the activity of fog-2 and gld-1 are low, leading to high tra-2 activity and the repression of fem-3, resulting in oogenesis. The shift in tra-2/fem-3 balance allows for the switch from spermatogenesis to oogenesis in an otherwise female somatic gonad in the hermaphrodite.
(B) C. elegans FOG-2/GLD-1/tra-2 mRNA ternary complex. Current data indicates that FOG-2 and GLD-1 are required for the translational repression of the tra-2 mRNA [25]. GLD-1 binds as a dimer to the tra-2 mRNA 3′UTR at two 28 nucleotide direct repeat elements (TGE/DRE, blocks) and FOG-2 makes contact with GLD-1 [32,34]. All three components are required for the proper specification of hermaphrodite spermatogenesis.
Self fertility in C. elegans hermaphrodites is achieved by an early period of spermatogenesis followed by a later period of oogenesis (Figure 1A). The promotion of spermatogenesis during the L3 stage (early) is achieved by translational repression of the tra-2 mRNA mediated by gld-1 (“defective in germline development”) and fog-2 (“feminization of germline”)[24,25] (Figure 1A and 1B). The transient reduction in the level of tra-2 feminizing activity raises the relative level of fem-3 masculinizing activity to promote spermatogenesis (Figure 1A). Later in L4 and adult animals, oogenesis is promoted by relieving the fog-2/gld-1-mediated repression of tra-2 feminizing activity combined with repression of fem-3 masculinizing activity by mog-1 to mog-6, fbf-1 and fbf-2, and nos-1 to nos-3 [18,19,26].
Central to this work are the genes fog-2 and gld-1. fog-2 is required for hermaphrodite, but not male, spermatogenesis in C. elegans, as XX animals that lack fog-2 produce only oocytes, resulting in functional females, whereas XO males are unaffected [27]. Similarly, loss-of-function mutations in gld-1 result in the feminization of the hermaphrodite germline without affecting males [28,29]. Both fog-2 and gld-1 are germline-specific regulators of sexual fate, since they do not appear to be expressed in the soma, and null mutations in either gene do not affect somatic sexual fate [25,27,28,29,30].
C. elegans gld-1 is a germline-specific tumor suppressor that is indispensable for oogenesis [28,29] and encodes a conserved KH-type RNA-binding protein [30]. GLD-1 is a translational repressor that binds to multiple mRNA targets [31], including tra-2, where it binds as a dimer to each of two tra-2 and GLI elements (TGEs) present on the 3′ untranslated region (UTR) of the tra-2 mRNA [24,32] (Figure 1B). Deletion of the tra-2 TGEs results in a loss of GLD-1-mediated translational control and feminization of the germline, such that only oocytes are produced [20,25,33,34].
C. elegans FOG-2 was identified as a GLD-1-interacting protein with a structure similar to canonical F-box proteins; it has an N-terminal F-box and a C-terminal protein–protein interaction domain. In the case of FOG-2 the putative protein–protein interaction domain is referred to as Duf38 (Pfam in [35]) or FOG-2 homology domain (FTH) [25]. F-box proteins are often core components of the Skp1/Cullin/F-box-type E3 ubiquitin ligase complexes, and they serve to link specific substrates to the ubiquitin ligase machinery for subsequent proteolysis [36]. However, FOG-2 cannot target GLD-1 for degradation since both function to promote hermaphrodite spermatogenesis [25] (Figure 1A). Current data suggest that the formation of a FOG-2/GLD-1/tra-2 mRNA ternary complex mediates translational repression of tra-2 and a corresponding reduction in feminizing activity to allow hermaphrodite spermatogenesis [24,25] (Figure 1B).
The completion of the C. elegans genome sequence [4] and the 10X sequence (representing more than 98% coverage) of the closely related species C. briggsae [5] permits studies of the evolution of sex determination and the inception of hermaphrodite spermatogenesis in morphologically comparable species. Here, we pose the question, do C. elegans and C. briggsae specify male sexual fate in the hermaphrodite germline similarly?
We find that 30 of 31 C. elegans sex determination genes have C. briggsae orthologs, indicating that there is extensive conservation of sex determination pathway components; the lone exception is fog-2. We provide evidence that the essential role of FOG-2 in C. elegans hermaphrodite spermatogenesis evolved from post-speciation duplication and divergence of the fog-2-related (FTR) gene family and that a fog-2 gene is not present in C. briggsae. Furthermore, double-stranded-RNA-mediated interference (RNAi) of the gld-1 ortholog in C. briggsae results in masculinization of the germline instead of the feminization of the germline phenotype observed in C. elegans. The lack of a potential C. briggsae fog-2 combined with the opposite sex determination function of GLD-1 in C. briggsae indicate that the control of hermaphrodite spermatogenesis, while using most of the same gene products, is fundamentally different between the species and is likely to have evolved independently.
Results
Components of Sex Determination Pathway Are Conserved between C. elegans and C. briggsae
To survey conservation in the sex determination pathway between C. elegans and C. briggsae we used reciprocal best BLAST [37,38,39] to identify potential C. briggsae orthologs of 31 known C. elegans sex determination genes, some of which have been previously identified. The 31 genes included 16 that function only in germline sex determination, seven that function in both somatic and germline sex determination, two that function only in somatic sex determination, and six that coordinate sex determination and dosage compensation. We found that 30 of 31 genes have C. elegans–to–C. briggsae reciprocal best BLAST hits and alignments consistent with a high level of conservation (Table 1). Using this method, putative orthologs of all known sex determination genes, including less conserved members, and previously identified genes were recovered [17,26,40,41,42,43,44], with the notable exception of fog-2.
Table 1 Comparative Analysis of Sex Determination Genes in C. elegans and C. briggsae
WormPep (C. elegans) and BriggPep (C. briggsae) entries are protein identification numbers from Wormbase (http://www.wormbase.org). ID entries are C. elegans gene identifiers from Wormbase
Reciprocal best BLAST hits are indicated by “yes” or “no,” and e-values are presented using WormPep release 112 and C. briggsae protein predictions (Wormbase). “Percent Length” is the extent of alignable sequence. All proteins with the exception of fog-2 returned reciprocal best BLAST hits in C. elegans and C. briggsae. Proteins that contain RNA-binding motifs or that function in RNA regulation are the following: ATX-2, FOX-1, FOG-1, FOG-2, GLD-1, GLD-3, NOS-1, NOS-2, NOS-3, FBF-1, FBF-2, MOG-1, MOG-4, MOG-5, and MOG-6
a
C. elegans FBF-1 and FBF-2 share 90% amino acid identity and 95% amino acid similarity. BLAST searches using C. elegans FBF-1 or FBF-2 result in the same C. briggsae best hit (CBP14598). A partial FBF family phylogeny suggests recent duplications of a common FBF ancestor have occurred in both C. elegans and C. briggsae (data not shown)
N/A, not applicable
The functions of seven C. briggsae sex determination genes have been tested, and current data indicate that these genes exhibit similar and possibly identical functions in C. elegans and C. briggsae (her-1 [43], tra-2 [21], fem-1 [A. Spence, personal communication], fem-2 [45], fem-3 [41], fog-3 [42], and tra-1 [17]). Importantly, the epistatic relationship and function of two key regulators of sex determination, tra-2 and fem-3, are essentially intact between the sister species in somatic sex determination [21,41] (Figure 1A). At first glance, given the conservation of 30/31 sex determination genes, similar or identical functions for 7/7 genes tested, and maintenance of a key epistatic relationship, it would appear that the sex determination pathway is generally conserved between C. elegans and C. briggsae. However, genetic and molecular studies will be required to determine whether the C. briggsae orthologs are functionally equivalent to their C. elegans counterparts.
A single FOG-2 ortholog could not be resolved by reciprocal best BLAST or by using the reciprocal smallest distance algorithm [46], which uses global sequence alignment and maximum likelihood estimation of evolutionary distances, to infer putative orthologs (data not shown). This indicates that fog-2 is either highly diverged, present in an unsequenced portion (<2%) of the C. briggsae genome, or potentially a C. elegans–specific adaptation not present in C. briggsae.
fog-2 Is a C. elegans–Specific Adaptation
FOG-2 is part of a large, highly diverged F-box- and DUF38/FTH-containing protein family in C. elegans with more than 100 members referred to as FTR proteins [25,36]. The FTR family is also expanded in C. briggsae, making the identification of a single functionally equivalent ortholog from a large number of paralogs difficult. Therefore, to discern the relationships among C. elegans and C. briggsae FTR family members, 30 C. elegans and C. briggsae FTR proteins or protein predictions closely related to FOG-2 were used to generate a neighbor-joining phylogeny. The remaining, more diverged FTR members from either species were not included in the phylogeny to avoid long branch attraction [47].
The C. elegans and C. briggsae FTR phylogeny reveals that all of the C. elegans FOG-2 relatives form a single clade and all of the C. briggsae relatives a distinct clade. An unrooted radial phylogram illustrating C. elegans and C. briggsae FTR relationships is presented in Figure 2, and a rectangular representation of the same phylogeny with bootstrap support information is shown in Figure S1. If a closely related homolog of C. elegans FOG-2 were present in C. briggsae the expectation is that it would have clustered with the C. elegans proteins. Contrary to this, the phylogenetic separation of C. elegans and C. briggsae FTR family members into distinct lineages indicates that extensive expansion in the FTR family occurred post-speciation and that C. elegans and C. briggsae FTR genes do not have one-to-one orthologous relationships.
Figure 2 The FTR Gene Family in C. elegans and C. briggsae
A radial phylogram showing the relationships of 30 C. elegans and C. briggsae FTR genes closely related to FOG-2 was generated using neighbor-joining. C. elegans and C. briggsae protein predictions with complete F-box and Duf38/FTH (FTR proteins) were identified using BLAST and HMMs, aligned using CLUSTALW, trimmed, de-gapped, and realigned (see Materials and Methods). A clear separation of C. elegans (below dashed line) and C. briggsae (above dashed line) FTR proteins is indicated by the phylogeny. The branch containing FOG-2 and FTR-1 is in bold. Tree is unrooted, and branch lengths are proportional to divergence (also see Figure S1). Bar represents 0.1 substitutions per site. FOG-2 and FTR-1, across their entire length, are more similar to each other than to any other gene in C. elegans. Comparison of the diverged approximately 40aa C-terminal region from both proteins to the closely related FTR genes in the FOG-2 cluster reveals 48% average pairwise identity between these FTRs and FTR-1 and 22% average pairwise identify between these FTRs and FOG-2 (Figure S2). One interpretation of this greater similarity is that FTR-1 may be ancestral; however, it is not clear whether the slight increase in similarity over about 40aa is significant or whether selection rather than evolutionary history produced the sequence similarity observed.
The above results could be misleading if a closely related C. briggsae fog-2 homolog were present in the less than 2% of the genome sequence that is not present in the final assembly or if the fog-2 ortholog diverged sufficiently such that the computational methods were not able to distinguish between orthologous and paralogous relationships. To address these possibilities we used low-stringency cross-species Southern blotting in an effort to identify closely related fog-2-like sequences in unsequenced portions of the C. briggsae genome, and we used conserved synteny in an attempt to identify a diverged fog-2 ortholog that might reside in the same genomic location. Both approaches were used to effectively identify other diverged sex determination genes from C. briggsae (tra-2, her-1, and fem-2) prior to the release of the C. briggsae genome sequence [40,43,44].
For low-stringency Southern blotting we used a C. elegans fog-2 probe and a fem-2 positive control probe against C. briggsae genomic DNA. Under conditions that detected cross-species hybridization with the C. elegans fem-2 probe against C. briggsae genomic DNA [40], no C. briggsae signal was observed with the C. elegans fog-2 probe (Figure 3A). This suggests either that a close fog-2 relative is not present in the less than 2% of the C. briggsae genome that is unsequenced or that it has diverged significantly beyond the level of fem-2.
Figure 3
fog-2 Is Likely Absent in C. briggsae
Low-stringency Southern blotting (A) and conservation of synteny (B and C) were used in an attempt to identify a potential fog-2 gene in C. briggsae.
(A) A total of 2–20 ug of digested genomic DNA was used in low-stringency Southern blotting. C. elegans fem-2 probe (Ce_fem-2) was able to detect fem-2 on both same-species and cross-species blots (first two panels). The C. elegans fog-2 probe (Ce_fog-2), which detects both fog-2 and ftr-1 on the 5.8-kb XhoI fragment, produced a signal with C. elegans but not C. briggsae genomic DNA (next two panels). fog-2 cross-species blot integrity was verified by stripping and reprobing with same-species C. briggsae fem-2 (final panel). Same-species exposures were 4 h and cross-species were 4 d. The C. elegans fem-2 probe is 70% identical to the C. briggsae genomic sequence.
(B) Scale diagram of the C. elegans Chromosome 5 region containing fog-2. A 82.6-kb enlargement below, indicated by the dashed lines, shows the fog-2 cluster containing five canonical FTR genes, one FTR gene with divergent structure, and 16 non-FTR genes (also see Table S1).
(C) C. briggsae contig from the genome assembly containing flanking regions with conserved synteny. A 194.4-kb enlargement below, indicated by the dashed lines, covers the C. briggsae region that is predicted to contain a putative fog-2 ortholog. The conserved genes used to identify the C. briggsae contig are indicated by the arrowheads, with the genes flanking fog-2 indicated by the large arrowheads.
Each gene from the C. briggsae contig with an ortholog defined as a reciprocal best BLAST hit is present on both maps (B and C), and blocks of synteny defined by the C. elegans organization are in the same color. Only one (Y113G7B.11) of the 22 genes from the 82.6-kb fog-2 cluster was found to have a reciprocal best BLAST hit in C. briggsae (contig cb25.fpc0129, corresponding to the predicted gene CBG05618; Table S1). No FTR genes or genes related to those in the fog-2 cluster were found within 50-kb on either side of CBG05618, indicating that this region does not share conserved synteny with the fog-2 cluster. Instead, the potential C. briggsae ortholog of Y113G7B.11 is located on a C. briggsae contig region that shows extensive conserved synteny with a different portion of C. elegans Chromosome 5 not involving the fog-2 cluster (Table S2).
For analysis of conserved syntenic relationships, five conserved C. elegans genes surrounding fog-2 (srg-34, sec-23, psa-1, Y113G7A.14, and Y113G7B.15) were used to query C. briggsae contigs. The genes srg-34, sec-23, and psa-1 are highly conserved across metazoans and have reciprocal best BLAST hits in C. briggsae (Figure 3B and 3C, small arrow heads). The genes Y113G7A.14 and Y113G7B.15 flank the gene-dense C. elegans fog-2 region and also have reciprocal best BLAST hits in C. briggsae (Figure 3B and 3C, large arrow heads). All five genes were found to be represented on a single C. briggsae contig, suggesting that the global syntenic relationships are conserved, but with detailed analysis revealing a number of differences in gene order (Figure 3B and 3C). However, fog-2, its four adjacent close FTR relatives, and 16 surrounding genes in an 82.6-kb region were absent from this C. briggsae contig, while the conserved genes on either side were present (Table S1 and S2).
The closest relative of fog-2 is the gene ftr-1, which is part of a group of five closely related ftr genes that are colinear in C. elegans and not present in C. briggsae [25] (Figures 2 and 3). If fog-2 and ftr-1 are the result of a “recent” post-speciation duplication within the C. elegans lineage, as suggested by the phylogeny, then we would expect that fewer synonymous substitutions (Ks) have occurred between fog-2 and ftr-1 relative to other C. elegans/C. briggsae best BLAST orthologs. Consistent with a recent duplication, the Ks for fog-2/ftr-1 is not saturated (Ks = 0.36) whereas the average Ks for reciprocal best BLAST hits between C. elegans and C. briggse is saturated (Ks = 1.72) [5].
The finding that fog-2 and ftr-1 arose from a relatively recent local duplication within C. elegans strongly supports the contention that fog-2 is not present in C. briggsae. These results imply that C. briggsae must regulate hermaphrodite spermatogenesis differently than C. elegans.
The Diverged C-Terminal of FOG-2 Is Necessary for GLD-1 Binding
Previous work has shown that FOG-2 is an integral part of the tra-2 3′ UTR translational repression complex. The RNA-binding protein GLD-1 makes direct contact with the tra-2 3′ UTR, and FOG-2 is recruited to the complex via its interaction with GLD-1 [24,25]. In spite of the high similarity between fog-2 and ftr-1 (Figure 4), ftr-1 cannot compensate for fog-2 in the promotion of hermaphrodite spermatogenesis [25]. This indicates that fog-2 must contain unique sequences that allow it to function in sex determination.
Figure 4 The Highly Diverged FOG-2 C-Terminal Region Is Responsible for GLD-1 Interaction in C. elegans
(A) Dot plot of FOG-2/FTR-1, with the black diagonal line delimiting regions of greater than 70% identity based on a 10-aa sliding window. The dashed horizontal line at the C-terminus indicates a region of low identity. The arrow indicates the final exon 4 boundary.
(B) Protein sequence alignment of FOG-2 and FTR-1 encoded by exon 4. Differences are shaded in black and illustrate the abrupt breakdown in sequence conservation. The dashed line marks the region required for GLD-1 interaction.
(C) Nucleotide alignment of fog-2 and ftr-1 EST coding regions expanded from a portion of the protein sequence alignment, with vertical lines delimiting the reading frame relative to fog-2. Amino acid sequence for FOG-2 (above) and changes in FTR-1 (below) are below the alignment. Frame-shifting indels are indicated by the large open arrowheads.
(D) The C-terminal FOG-2 region is required for GLD-1 interaction in the yeast two-hybrid system. Full-length FOG-2 (black) and FTR-1 (grey) constructs were tested for interaction with GLD-1. FOG-2 interacts with GLD-1 (++++) whereas FTR-1 does not (−). Progressive C-terminal deletions (black) in FOG-2 were generated to identify FOG-2 requirements for GLD-1 interaction. Binding to GLD-1 was completely eliminated with the removal of the C-terminal 64 aa of FOG-2 exon 4. Transfer of exon 4 to FTR-1 (grey/black chimera) resulted in the transfer of GLD-1 binding to FTR-1. Control interactions to test for the production of functional proteins were performed with the Skp1 homolog SKR-1, which binds to the F-box region (see Materials and Methods). Searches for C. elegans and C. briggsae proteins with homology to the 64-aa FOG-2 region required for GLD-1 interaction (or FOG-2 exon 4) failed to identify any predicted proteins with significant homology (>35% or e-value = 0.01) other than FTR-1, which cannot bind GLD-1 and does not compensate for FOG-2 in sex determination.
(E) Sliding-window (100-nt window, 25-nt shift) estimation of Ka/Ks ratio for fog-2/ftr-1 using full-length average Ks. The Ka/Ks ratio is highest at the C-terminal end of the Duf38/FTH domain, reaching a peak of 2.2 in window 37. The position of the F-box and Duf38/FTH domain are indicated by grey shading. The bold horizontal line is at the Ka/Ks = 1 threshold. The dashed vertical line indicates the boundary between exon 3 and exon 4.
Pairwise comparisons between FOG-2 and FTR-1 reveal a highly diverged C-terminal region encoded by the final exon (exon 4) (Figure 4A–4C). Before the C-terminal region of low similarity, the relative reading frames of fog-2 and ftr-1 are conserved with all insertions and deletions in three nucleotide multiples and an overall amino acid identity of 70%. Within the final exon, multiple amino acid substitutions, insertions, and deletions have occurred, resulting in a region of low nucleotide and amino acid identity (Figure 4B and 4C). For example, an indel (deletion relative to fog-2) at nucleotide 805 shifts the reading frame of FOG-2 relative to FTR-1 and results in a region of low similarity between the proteins (Figure 4B). A second indel at position 819 restores the reading frame but additional substitutions result in a diverged amino acid sequence (Figure 4C).
The dramatic differences between the FOG-2 and FTR-1 C-terminal regions suggested a connection between the unique functionality of FOG-2 in sex determination and the highly diverged C-terminal region. Since FOG-2 interacts with GLD-1 and both are required for the promotion of the male germ cell fate in the hermaphrodite, we determined whether the diverged FOG-2 C-terminal region was necessary for its interaction with GLD-1 (Figure 4). Progressive C-terminal deletions of FOG-2 were tested for their ability to interact with GLD-1 in the yeast two-hybrid system (Figure 4D). Full-length FOG-2 interacts with GLD-1 [25]; however, C-terminal deletions of nine and 28 aa in FOG-2 reduced the interaction, and deletion of 64 and 76 aa (essentially all of exon 4) eliminated the interaction (Figure 4D), indicating that the highly divergent C-terminal region is necessary for GLD-1 binding. All full-length and deletion constructs were tested against the Skp1 homolog SKR-1 as a positive control for functionality in the two-hybrid system (see Materials and Methods).
To determine whether the C-terminal region of FOG-2 is sufficient to confer GLD-1 interaction, an FTR-1/FOG-2 exon 4 chimera was generated and assayed for its ability to interact with GLD-1. Normally FTR-1 lacks the ability to interact with GLD-1 [25] (Figure 4D). The replacement of exon 4 from ftr-1 with exon 4 from fog-2 allowed the chimera to interact with GLD-1 (Figure 4D). Thus, the C-terminal 74aa region of FOG-2, when in the context of the FTR-1 F-box and Duf38/FTH sequences, is sufficient to confer GLD-1 binding.
FOG-2/GLD-1 Interaction Evolved Rapidly in C. elegans
Gene duplication provides the raw material for the evolution of novel adaptations, having been implicated in the diversity of the host–pathogen immune response, rapid onset of insecticide resistance, and diversity of vertebrate body plans [48]. Rapidly evolving genes, or portions of genes, under positive selection can be identified by comparison of nucleotide alterations that result in amino acid changes (non-synonymous substitutions [Ka]) to alterations that do not change the amino acid (Ks) [49,50]. Ka/Ks ratios that are equal to or less than one are indicative of neutral or purifying selection, where substitutions that change amino acids offer no fitness advantage or result in lowered fitness. In contrast, Ka/Ks ratios greater than one, common in rapidly evolving genes, are indicative of positive selection, where non-synonymous changes offer some fitness advantage and are fixed at a higher rate than synonymous substitutions [51].
To determine the selection acting on the fog-2/ftr-1 duplication we compared Ka/Ks ratios between fog-2, ftr-1, and the five FTR genes closest to fog-2 in C. elegans. Pairwise comparisons of codon-delimited full-length coding sequences closely related to fog-2 suggest that purifying selection dominates along the fog-2 branch, as all comparisons produced Ka/Ks ratios less than one (mean = 0.46). However, while the overall Ka/Ks ratio for fog-2/ftr-1 is not indicative of positive selection (mean = 0.58), sliding-window Ka/Ks ratio estimates [52] for fog-2 and ftr-1 indicate that the highly diverged C-terminal region of FOG-2/FTR-1 contains residues under positive selection (Ka/Ks = 1.98 for nucleotides 777–987, windows 33–37) (Figure 4). An alternate method using maximum likelihood estimation of Ka/Ks (PAML and codeml [53]) confirmed the presence of residues under positive selection within the C-terminal region (see Materials and Methods). Thus, the primary differences between FOG-2 and FTR-1 are localized to the rapidly evolving C-terminus of FOG-2 that is required for GLD-1 binding and is under positive selection.
The yeast two-hybrid data, together with the genetics of fog-2 [25], indicate that FOG-2 is unique among C. elegans FTR genes in functioning with GLD-1 in germline sex determination. Given the specificity of the FOG-2/GLD-1 interaction in C. elegans, phylogenetic analysis of FTR proteins (see Figure 2), and additional experiments (see Figures 3 and 4) that indicate that there are no close relatives of fog-2 among C. briggsae FTR genes, it is unlikely that any C. briggsae FTR protein functions with C. briggsae GLD-1 in sex determination.
In contrast with FOG-2, a highly conserved GLD-1 ortholog is present in C. briggsae (Table 1) and has a germline expression pattern essentially identical to that of C. elegans (Figure 5A, top right and middle right). In fact, C. elegans GLD-1 and C. briggsae GLD-1 share 81% amino acid identity overall and more than 90% in the maxi-KH RNA-binding region. Since FOG-2 and GLD-1 function together to promote the male germ cell fate in C. elegans hermaphrodites, this raised the question of what role, if any, C. briggsae GLD-1 plays in C. briggsae germline sex determination.
Figure 5 GLD-1 Has the Opposite Sex Determination Function in C. elegans and C. briggsae
For (A) and (B) the distal end of the gonad arm is indicated by the asterisk, and regions of the germline are delimited by dashed vertical lines as follows: M, mitotic zone; TZ, transition zone; P, pachytene; Pa, abnormal pachytene; and S, spermatocytes. For both (A) and (B) staining indicated is as follows: DAPI, blue, nuclear DNA; GLD-1, green; and MSP, red.
(A) RNAi of C. briggsae gld-1 results in masculinization of the germline. Paired DAPI-stained (left) and GLD-1- and MSP-stained (right) images of dissected young adult hermphrodite germlines. Top four panels illustrate the similarity between C. elegans and C. briggsae germline morphology and polarity (DAPI, blue; GLD-1, green; MSP, red). In both species, sperm (“sperm” arrow) are produced first before switching to oogenesis (“oocytes” arrow), and the pattern of cytoplasmic GLD-1 accumulation (green) is identical. GFP-injected controls were identical to wild-type animals. C. briggsae gld-1 RNAi animals exhibit masculinization of the germline (lower panels). A vast excess of sperm extends to the loop region (“sperm” arrows), and spermatogenesis extends further distally (solid line). Masculinization is confirmed by a corresponding extension in MSP staining beyond the loop (compare lower right to controls above).
(B) RNAi of gld-1 and fog-3 in C. elegans and C. briggsae results in a similar tumorous germline phenotype. C. elegans (top) and C. briggsae (bottom) have normal mitotic, transition, and entry into pachytene, but abnormal progression through pachytene, based on DAPI morphology. Both MSP and GLD-1 staining were below the level of detection in both cases.
GLD-1 Has Distinct Functions in C. elegans and C. briggsae Germline Sex Determination
To examine C. briggsae GLD-1 function in sex determination we performed RNAi [54] by injecting double-stranded C. briggsae gld-1 RNA into C. briggsae adult hermaphrodites followed by phenotypic analysis of F1 self progeny (see Materials and Methods). From genetic analysis of C. elegans gld-1 [28,29] there are two functions relevant to this study. First, C. elegans GLD-1 has an essential function in meiotic prophase progression during oogenesis. In null mutant hermaphrodites oogenic germ cells progress to pachytene and then return to the mitotic cell cycle, giving rise to ectopic proliferation and a germline tumor [28]. For this function C. elegans GLD-1 acts to spatially restrict the translation of multiple target mRNAs during oogenesis. GLD-1 oogenic target mRNAs are repressed during early meiotic prophase, but then are translated during late meiotic prophase following the loss of GLD-1 at the end of pachytene [30,31,55]. Second, C. elegans GLD-1 is necessary for the specification of the male sexual fate in the hermaphrodite germline. This function is most simply revealed as a haplo-insufficient feminization of the hermaphrodite germline [28,29]. C. elegans gld-1 has no known essential functions in male meiotic prophase progression or in XO male germline sex determination as C. elegans null males are wild-type [28,29].
C. briggsae GLD-1 may still function as a translational repressor of C. briggsae tra-2 mRNA even in the absence of a FOG-2 ortholog. This is a possibility because FOG-2 is not required for C. elegans GLD-1 binding to the C. elegans tra-2 mRNA in vitro [25], and some conservation is preserved between the C. elegans and C. briggsae tra-2 3′ UTRs [34]. In this case, RNAi of GLD-1 in C. briggsae might feminize the germline given that C. briggsae tra-2 promotes female development in both the germline and soma [21]. Alternatively, C. briggsae GLD-1 might have no role in germline sex determination, in which case RNAi would not result in a sex determination phenotype.
Surprisingly, C. briggsae gld-1 RNAi resulted in a masculinized germline (Figure 5A, bottom; Table 2), with no effect on the soma. Staining with 4′,6′-diamidino-2-phenylindole hydrochloride (DAPI) and anti–major sperm protein (MSP) (see Materials and Methods) revealed continuous spermatogenesis leading to a vast excess of sperm at the expense of oogenesis. Anti-GLD-1 antibody staining of gld-1 RNAi F1 gonad arms indicated that the level of GLD-1 protein was reduced to below detectable limits (Figure 5A, bottom right). C. briggsae control hermaphrodites injected with double-stranded RNA for green fluorescent protein (GFP) had gonad morphology identical to wild-type (Figure 5A, top left and middle left). The masculinized phenotype of gld-1 RNAi in C. briggsae indicates that the wild-type function of GLD-1 in C. briggsae is to promote the female germ cell fate, likely by the translational repression of an mRNA that encodes a masculinizing gene product. This function is in direct contrast to that of C. elegans GLD-1, which promotes the male germ cell fate by translational repression of the feminizing tra-2 mRNA.
Table 2 Summary of GLD-1 RNAi Germline Phenotype in C. elegans and C. briggsae
a Results are from a single group of experiments. Similar results were obtained in other experiments
b “Other” refers to masculinized arms with proximal proliferation
GLD-1 Function in Meiotic Prophase Progression during Oogenesis Is Conserved
Given the difference in sex determination function, it is possible that C. elegans and C. briggsae GLD-1 have few conserved functions in germline development. To investigate this we took advantage of well-defined activities of gld-1 in C. elegans such as its essential function in female meiotic prophase progression and in the translational repression of the evolutionarily conserved yolk receptor mRNA encoded by the rme-2 locus [28,31].
The gld-1-null tumorous phenotype results from aberrant oogenic prophase progression and a return to mitosis [28,29]. This phenotype is dependent on germline sex because a tumor only occurs when germ cell fate is set to female [28,29]. The masculinized phenotype caused by gld-1 RNAi in C. briggsae is likely to preclude the detection of this function as the C. elegans gld-1-null tumorous phenotype is suppressed by mutations that cause masculinization of the germline [29]. To overcome the masculinization we combined fog-3 RNAi with gld-1 RNAi in C. briggsae. Since C. elegans fog-3 functions near the end of the sex determination pathway and in C. briggsae fog-3 RNAi results in feminization of the germline [42], we predicted that C. briggsae fog-3 RNAi would be epistatic to the masculinization of the germline of C. briggsae gld-1 RNAi.
Similar to the C. elegans gld-1-null, RNAi of gld-1 or gld-1 and fog-3 in C. elegans and double RNAi of gld-1 and fog-3 in C. briggsae resulted in a robust proximal germline tumor (Figure 5B; Table 2). Control RNAi with fog-3 alone resulted in feminized germlines in both species [42]. Both the mitotic zone and transition zone appear to have roughly normal nuclear morphology, with more proximal nuclei having abnormal pachytene morphology (Figure 5B), suggesting that germ cells are entering meiosis but progressing aberrantly before returning to mitosis. The return-to-mitosis tumorous phenotype in each species was confirmed using phosphohistone H3 staining, a mitotic proliferation marker [56]. We cannot rule out the possibility that the C. briggsae phenotypes observed, masculinization of the germline with gld-1 RNAi alone and tumorous germline with gld-1 and fog-3 RNAi, are the result of incomplete knockdown leading to partial gld-1 loss of function.
The rme-2 yolk receptor mRNA is a known target of GLD-1-mediated translational repression in C. elegans [31]. In C. elegans, GLD-1 and RME-2 have mutually exclusive expression patterns because rme-2 mRNA is translationally repressed in the transition zone and pachytene region, where GLD-1 levels are high, and translated in oocytes, where GLD-1 levels are low [31]. In C. elegans gld-1-null germlines RME-2 is ectopically expressed in the transition zone and pachytene region owing to loss of GLD-1-mediated translational repression of the rme-2 mRNA [31].
A similar, mutually exclusive accumulation pattern in C. briggsae suggests that C. briggsae GLD-1 is a translational repressor of C. briggsae rme-2 mRNA (Figure 6). To determine whether C. briggsae GLD-1 represses the rme-2 mRNA, double RNAi of gld-1 and fog-3 was performed in both species, and gonad arms were stained for RME-2 protein [57]. Reduction of GLD-1 and FOG-3 by RNAi results in the ectopic accumulation of RME-2 protein in both C. elegans and C. briggsae (Figure 6), indicating that the role of GLD-1 in the translational repression of the rme-2 mRNA is conserved. Thus, despite the opposite roles of GLD-1 in sex determination, essential functions of GLD-1 in oogenesis are conserved between the species.
Figure 6 GLD-1-Mediated Translational Repression of rme-2 mRNA in C. elegans and C. briggsae
In both C. elegans and C. briggsae wild-type (WT) animals (left panels), GLD-1 (green) and RME-2 (red) have mutually exclusive accumulation patterns. In C. elegans (upper right), gld-1 and fog-3 RNAi results in a germline tumor with ectopic RME-2 accumulation (red expanded). In C. briggsae (lower right), RNAi of gld-1 and fog-3 also results in germline tumor with ectopic RME-2 accumulation (red expanded). The germline tumor and expansion of RME-2 expression due to ectopic translation are similar between the two species (compare right top and bottom, DAPI [blue]). The distal end of the gonad arm is indicated by the asterisk, and regions of the germline are delimited by dashed vertical lines. DAPI, blue, nuclear DNA; GLD-1, green; RME-2, red; M, mitotic zone; TZ, transition zone; P, pachytene; Pa, abnormal pachytene.
Discussion
Our results indicate that the control of hermaphrodite spermatogenesis is fundamentally different between the sister species C. elegans and C. briggsae at the level of FOG-2/GLD-1/tra-2 mRNA regulation. While FOG-2 is essential for self-fertile hermaphroditism in C. elegans, a closely related homolog of FOG-2 could not be recovered in C. briggsae by reciprocal best BLAST, phylogenetic inference, low-stringency hybridization, or analysis of conserved synteny. Comparison of synonymous changes between fog-2 and its closest relative, ftr-1, indicates that fog-2 is the product of a recent expansion “specific” to C. elegans in the FTR gene family and implies that the evolution of FOG-2 and its incorporation into the sex determination pathway occurred post-speciation. Consistent with this, the C-terminal region of FOG-2 required for binding to GLD-1 was found to be highly diverged and “unique” to FOG-2 in C. elegans. Interestingly, GLD-1 was found to have a sex determination function in C. briggsae opposite that in C. elegans while retaining similar functions in female meiotic prophase progression and oogenesis. The absence of FOG-2, and the opposite sex determination function of GLD-1, provides evidence for the independent evolution of hermaphroditism in C. elegans and C. briggsae.
General Conservation of the Sex Determination Pathway
Reciprocal best BLAST indicates that C. elegans and C. briggsae have orthologs of 30 of 31 known sex determination pathway genes. Conserved functions for C. briggsae her-1, tra-2, fem-1, fem-2, fem-3, fog-3, and tra-1 have been demonstrated by transgene rescue of C. elegans mutations or similarity of RNAi loss-of-function phenotype [17,21,26,41,42,43,45]. The general conservation of genes that govern sex determination suggests that the underlying pathway remains largely intact between the species.
RNAi and transgenic experiments have suggested that while fem-2 and fem-3 have conserved roles in the somatic sex determination of both species, they may play diminished roles in C. briggsae germline sex determination [41,45]. There are two possibilities that could explain these results. One is that there are inherent species-specific differences in susceptibility to RNAi or in the ability to reconstitute complete gene function by transgene rescue. The other is that differences in C. elegans and C. briggsae phenotypes reveal functional divergence in sex determination pathway components. Analysis of null mutations in C. briggsae orthologs of C. elegans sex determination genes will help to distinguish between these possibilities. While some functional differences may turn out to be valid, tra-2 (feminizing) and fem-3 (masculinizing) apparently play the same somatic roles in both species, and their epistatic relationship appears to be conserved [41].
fog-2 Is Unique to C. elegans
Within the context of general conservation of sex determination pathway components and conserved key epistatic relationships, the absence of fog-2 in C. briggsae is intriguing. fog-2 arose as a consequence of recent C. elegans–specific gene duplication events, and none of the closely related C. elegans fog-2 paralogs can compensate for loss of fog-2 in sex determination [25]. Thus, it is unlikely that more distantly related C. briggsae FTRs are involved in GLD-1/tra-2-mRNA-mediated promotion of hermaphrodite spermatogenesis. Since fog-2 is essential for the promotion of spermatogenesis in C. elegans hermaphrodites and is not present in C. briggsae, the direct implication is that specification of the male germ cell fate in C. briggsae hermaphrodites is fundamentally different from that in C. elegans and that it evolved independently.
The highly diverged C-terminus of FOG-2 is under positive selection and is necessary and sufficient for GLD-1 binding within the context of an F-box and FTH domain (see Figure 4). Acquiring the diverged C-terminus was crucial in FOG-2 becoming incorporated into the sex determination pathway. With respect to the C. elegans lineage, it is unclear whether fog-2 retains an ancestral function in sex determination and ftr-1 has changed/drifted away or, alternatively, whether ftr-1 represents the ancestral function and fog-2 has recently evolved a role in sex determination (also see Figure S2). The ftr-1 gene is expressed, though its function is currently unknown. RNAi of ftr-1 into the fog-2 null did not reveal any obvious phenotypes beyond feminization of the germline [25].
Conserved GLD-1 Functions in C. elegans and C. briggsae Meiotic Prophase during Oogenesis
GLD-1 function in meiotic prophase progression and oogenesis shows substantial conservation between the species (see Figures 5 and 6), which is not surprising given the high level of sequence conservation between C. elegans and C. briggsae GLD-1. This is illustrated by the rme-2 yolk receptor mRNA being regulated similarly between the species (Figure 6). Current data indicate that C. elegans GLD-1 binds to, and likely represses translation of, more than 100 mRNA targets [31,55] (M.-H. Lee, V. Reinke, and T. Schedl, unpublished data). The C. elegans gld-1-null tumorous phenotype likely results from misregulation of multiple mRNA targets [31]. While the identity of the misregulated mRNA targets causing the gld-1-null tumorous phenotype are currently unknown, the fact that C. briggsae gld-1 and fog-3 RNAi results in a similar tumorous phenotype suggests that a similar, if not identical, set of C. briggsae GLD-1 mRNA targets are misregulated. The absence of a FOG-2 ortholog in C. briggsae is unlikely to have a major effect on GLD-1-mediated translational control since FOG-2 appears to be required only as a cofactor for tra-2 repression [25,27,31,55,58]. Thus, it is possible that the majority of GLD-1 mRNA targets involved in prophase progression and oogenesis are regulated similarly between species.
Divergent GLD-1 Function in C. elegans and C. briggsae Sex Determination
Genetic analysis reveals that C. elegans and C. briggsae GLD-1 have opposite functions in germline sex determination; C. elegans GLD-1 promotes spermatogenesis while C. briggsae GLD-1 promotes oogenesis. This indicates that the major sex determination function of C. briggsae GLD-1 is not translational repression of tra-2 feminizing activity. C. elegans GLD-1 binds two 28 nucleotide direct repeat elements on the C. elegans tra-2 mRNA 3′ UTR to mediate translational repression [24]. Somatic reporter gene assays in C. elegans and C. briggsae have suggested that the tra-2 3′ UTRs of both species are able to function in translational repression [34], with the implication being that the C. elegans and C. briggsae 3′ UTRs are regulated similarly. However, these data are difficult to interpret in the context of germline sex determination, as GLD-1 and FOG-2 are not natively expressed in the soma and neither GLD-1 nor FOG-2 have essential functions in somatic sex determination [25,27,28,29,30].
One hypothesis to explain our results is that C. briggsae GLD-1 binds to the C. briggsae tra-2 mRNA but is necessary for translational activation instead of translational repression as in C. elegans. However, for all characterized C. elegans GLD-1 targets, and C. briggsae rme-2 mRNA, GLD-1 acts as a translational repressor [2,31,55,58,59]. We currently do not understand how FOG-2 acts with GLD-1 in translational repression of C. elegans tra-2 mRNA. In C. elegans, GLD-1 can bind the tra-2 mRNA in the absence of fog-2 in worm extracts but cannot properly repress its translation in vivo [25]. This suggests that the role of FOG-2 may be to recruit additional factors specific to the C. elegans tra-2 mRNA 3′ UTR that allow for efficient GLD-1 translational repression. Assuming C. briggsae GLD-1 binds C. briggsae tra-2 mRNA in vivo, given the absence of a FOG-2 ortholog, there may be no regulatory consequence of this binding.
Another possibility is that C. briggsae GLD-1 binds and translationally represses an mRNA that promotes spermatogenesis. This could occur if a masculinizing sex determination gene, either present in both species or unique to C. briggsae, has come under GLD-1 control in C. briggsae. Given the conservation of GLD-1 and its regulation of at least some common targets (e.g., rme-2) it is unlikely that changes in GLD-1 are responsible for a new mRNA target in C. briggsae. Instead, it is more likely that one or more new target mRNAs have acquired sequences that direct GLD-1 binding and translational repression. The requirements for GLD-1 binding are only just being elucidated, with a hexanucleotide sequence being one important feature amid otherwise diverse GLD-1 binding regions [32,55]. Thus, small numbers of changes in UTRs are likely to be sufficient for new mRNAs to come under GLD-1-mediated regulation.
Evolution of Self-Fertile Hermaphroditism
Current phylogenetic data suggest that hermaphroditism evolved independently in Caenorhabditis and other lineages of Rhabditid nematodes from an ancestral female/male state [5,6,7,10,11,60]. This is consistent with our results showing that control of hermaphrodite spermatogenesis at the level of FOG-2/GLD-1/tra-2 mRNA is fundamentally different between C. elegans and C. briggsae. This raises the question, how might the transition from the ancestral female/male to hermaphrodite/male system of reproduction have occurred multiple times within the Caenorhabditis clade?
The anatomy and reproductive physiology of C. elegans allow both sperm that is introduced by mating and sperm that develops within the female gonad of the hermaphrodite to be effectively used in reproduction [14,61,62]. Either source of sperm generates a MSP-derived signal that is required for full-grown oocytes to undergo meiotic maturation, ovulation, and fertilization in the spermatheca [62,63]. Not only is the anatomy conserved but an MSP-derived sperm signal also appears to be utilized by both C. briggsae and C. remanei (a female/male species) to induce oocyte maturation and ovulation [63,64]. This conservation within Caenorhabditis indicates that major changes in anatomy and reproductive physiology are not necessary in the transition from female/male to hermaphrodite/male reproduction.
The relative ease with which mutants and mutant combinations can alter the sex determination system in C. elegans has suggested that transitions between mating systems may not be difficult and that the overall sex determination pathway reflects selection for a particular mating system rather than a constant regulatory mechanism [65]. The hermaphrodite pattern of spermatogenesis first then oogenesis is achieved by high masculinizing/low feminizing activity in early larvae followed by low masculinizing/high feminizing activity in late larvae/adults (see Figure 1; reviewed in [18,19,26,66]). Lowering or eliminating germline masculinizing activity in XX animals can convert C. elegans from hermaphrodite/male to female/male reproduction (Table 3, and references therein [20,27,28,29,66,67,68,69]). For example, fog-2-null mutations result in strains that reproduce as XX females and XO males. The mutant female/male strains can be converted back to hermaphrodite reproduction by introducing masculinizing mutations in certain genes (e.g., fog-2-null; fem-3-gf; Table 3). The generality of high masculinizing/low feminizing activity early followed by low masculinizing/high feminizing activity late is borne out by other sets of mutually suppressing feminizing-plus-masculinizing combinations in which the double mutants are self-fertile while each single mutant is usually self-sterile (e.g., tra-1-gf; fem-3-gf; Table 3). Thus, multiple genetic states can yield self-fertile hermaphrodite/male and male/female reproduction in C. elegans.
Table 3
C. elegans Sex Determination Mutants That Yield Female/Male Reproduction and Mutually Suppressed Hermaphrodite Reproduction
a gf, gain of function; Mog, allele(s) show a masculinization of the germline phenotype; Fog, allele(s) show a feminization of the germline phenotype; Fem, allele(s) show a feminization of the soma and germline phenotype; ts, temperature sensitive; lf, loss of function, in these cases non-null
b All of the masculinizing and feminizing mutant combinations that show mutual suppression display the pattern of sperm first then oocytes as in wild-type. The opposite pattern, oocytes first then sperm, would not result in self fertility and thus would not be reproductively successful. The reason that these mutant combinations all display the wild-type pattern, instead of the oocyte then sperm pattern, is unclear and suggests that an additional level of sex determination pathway regulation remains to be uncovered
c Mutually suppressing feminizing and masculinizing double-mutant hermaphrodites often have intersexual germ cells between the sperm and oocytes, unlike wild-type hermaphrodites
d Embryos generated showed developmental arrest
Given the conservation of anatomy and reproductive physiology, an initial conversion from an ancestral Caenorhabditis female/male species to a hermaphrodite/male mode of reproduction may only require a genetic event that results in a transient increase in germline masculinizing activity in early larvae to produce sperm. As long as this change does not interfere with the higher level of feminizing activity (oogenesis) in late larvae/adults, self fertility would be possible. After the establishment of self fertility, there would likely be strong selection for additional genetic events that would optimize self-fertile brood size [70] and result in a clean transition from sperm to oocyte development so that wasteful intersexual gametes are not formed (Table 3). Thus, it is very likely that multiple genetic events now define the differences in the C. elegans and C. briggsae germline sex determination pathways.
In C. elegans, the relative levels of TRA-2 feminizing to FEM-3 masculinizing activity appear to be the major regulatory point for the sperm-then-oocyte pattern. There is no a priori reason for TRA-2 or FEM-3 to be the major focus of regulation to achieve hermaphroditism in C. briggsae; if one of these is the focus, then at least some of the regulation must differ between C. elegans and C. briggsae, given the absence of fog-2 and the changed role of GLD-1. Since the last common ancestor of C. briggsae and C. elegans must have contained orthologs of 30 of 31 C. elegans sex determination genes, a change in the regulation of one or more of these genes might be responsible. Alternatively, since much of the regulation of C. elegans germline sex determination is by translational control, mutations in UTRs of mRNAs may result in new genes coming under the control of GLD-1 or another RNA sex determination gene regulator (Table 1). Additionally, duplication and divergence, analogous to what we have found for FOG-2 in C. elegans, may have resulted in a new gene being incorporated into the germline sex determination pathway. To move beyond speculation, the forward genetic analysis currently in progress (R. Ellis and E. Haag, personal communication) will be important for the identification of C. briggsae–specific genes, analogous to fog-2, that are necessary for self-fertile hermaphroditism.
Materials and Methods
Sex determination pathway conservation
Protein coding sequences of cloned C. elegans sex determination genes were obtained from Wormbase (http://www.wormbase.org; WormPep release 112). C. briggsae genomic sequence was obtained from The Sanger Institute (Cambridge, United Kingdom) or the Genome Sequencing Center (St. Louis, Missouri, United States), and protein sequences were obtained from either Wormbase or Ensemble (http://www.ensembl.org/; version 17.25.1). Best BLAST orthologs of C. briggsae sex determination proteins were obtained using C. elegans sex determination protein sequences as queries against C. briggsae predicted proteins and six-frame translated C. briggsae genomic sequence. C. briggsae proteins obtained at an e-value cutoff of 1 × 10−50 reciprocal best hits were recovered for 26 of 31 C. elegans proteins. NOS-1 and XOL-1 orthologs were identified at an e-value cutoff of 1 × 10−20 and were also reciprocal best BLAST hits between species. In each case a single reciprocal best hit was identified for each component of the sex determination pathway with the exception of FBF-1 and FBF-2, which returned the same best BLAST hit, and FOG-2. Searches of the non-redundant National Center for Biotechnology Information protein database (GenBank CDS+PDB+SwissProt+PIR+WormPep) with full-length FOG-2 as query revealed only weak similarity to the F-box motif for non–C. elegans or –C. briggsae sequences. Using the highly diverged C-terminal end of FOG-2, including a portion of the Duf38/FTH, or the GLD-1 interaction region of FOG-2 as query did not reveal any hits below an e-value of 0.01 in C. elegans or C. briggsae other than FOG-2 and FTR-1.
Identification of FTR family members
FTR family members are defined by the presence of an N-terminal F-box and C-terminal Duf38/FTH domain (FTR) [25]. C. elegans FTR family members were identified using FOG-2 as a query against WormPep release 112. Each potential FTR was scanned for an N-terminal F-box motif and C-terminal Duf38/FTH domain using the hidden Markov models (HMMs) for each domain (HMMER 2.3.2) [35]. Similarly, C. briggsae FTR family members were identified using FOG-2 as a BLAST query and HMMs. In C. elegans, fog-2 (Y113G7B.5), ftr-1 (Y113G7B.4), CE35646 (Y113G7B.1), CE24144 (Y113G7B.3), CE23289 (Y113G7B.6), and CE23288 (Y113G7B.7) are closely related and tightly linked on Chromosome 5. CE35646 was not included in later analysis because of a divergent N-terminal structure.
An FTR family also appears to be present and expanded in the obligate male/female species C. remanei based on the currently sequence assembly (Genome Sequencing Center, Washington University, St. Louis, Missouri, United States; 16 September 2004, BLASTn and tBLASTn; ftp://genome.wustl.edu/pub/seqmgr/remanei/plasmid_assembly). Our preliminary analysis suggests that closest FOG-2 homologs from C. remanei have diverged from C. elegans approximately to the same level as the FTR genes in C. briggsae. A comprehensive phylogenetic analysis to resolve the relationships between C. elegans, C. briggsae, and C. remanei FTR family members will await accurate C. remanei protein predictions and a complete C. remanei assembly.
Sequence alignments and analysis
Alignments were generated using CLUSTALW, and conserved residues were identified with the Lasergene MEGALIGN (DNASTAR, Madison, Wisconsin, United States) package and Dialign [71,72], which was also used to identify conserved regions for subsequent phylogenetic analysis. The best BLAST C. briggsae hit to each C. elegans FTR protein used in the phylogeny was included in order to identify any potential one-to-one orthologous pairs along the FOG-2 branch. Non-homologous N- and C-terminal extensions were trimmed, and extremely distant family members unlikely to be functional FOG-2 orthologs were excluded to avoid long branch attraction [47]. Phylogenetic inference was performed using the neighbor-joining (neighbor) program in the PHYLIP package (Phylogeny Inference Package version 3.5c; Department of Genetics, University of Washington, Seattle, Washington, United States) using the BLOSUM45 distance matrix. Trees with and without gaps were generated, and comparison revealed some differences in branching order, but only within the species. For the tree presented here, positions with gaps were excluded and all non-homologous or highly divergent sequences trimmed. The topology of the tree structure was tested by bootstrapping with 1,000 replicates and by analysis of the alignment using protpars from the PHYLIP package (a maximum parsimony method), which produced a tree with a similar branching order. Trees were processed using TreeView [73].
Codon-restricted alignments for Ka/Ks calculation were generated using Se-Al (a sequence alignment editor by A. Rambaut, version 2; available at http://evolve.zoo.ox.ac.uk/software.html?id=seal) to modify CLUSTALW-aligned cDNA or predicted cDNA sequences, and all gaps and frame-shifted regions were removed. Sliding-window Ka and Ks estimates [74] were generated using DNASP (version 3) [52], and codon-based analysis was performed using PAML (codeml) [53] (HKY substitution model) to confirm the presence of codons under positive selection (95% confidence) within the sliding windows.
Worm culture and RNAi
C. elegans (N2, Bristol, United Kingdom) and C. briggsae (AF16) were obtained from the Caenorhabditis Genetics Center University of Minnesota, Minneapolis, Minnesota, United States. Cultures of both were maintained on Escherichia coli OP50 on NGM plates at 20 °C as previously described [75]. RNAi was performed by injection in C. elegans and C. briggsae essentially as described previously [54]. Double-stranded RNAs for species-specific gld-1 and fog-3 were generated by PCR amplification of cDNA with SP6 (5′) and T7 (3′) linkers, gel purified, sequenced, and used in RNA synthesis reaction using the appropriate Ambion kit (MEGAscript SP6 or T7; Austin, Texas, United States). Double-stranded RNAs were injected at 0.5 mg/ml into young adult N2 animals and F1 progeny collected 12–48 h post injection and matured to 24 h post L4 stage before gonads were dissected, fixed, and stained to score for abnormal phenotypes.
Staining
Dissection, antibody, and DAPI staining of C. elegans and C. briggsae gonads were performed essentially as previously described with fixation in 3% formaldehyde, 80% methanol, and 100 mM dibasic potassium phosphate [29,30]. Affinity purified rabbit polyclonal anti-GLD-1 antibodies were used at 1:50, and MSP mouse monoclonal antibody was used at 1:2,000, both with overnight incubation at room temperature (anti-MSP antibody was the kind gift of M. Kosinski and D. Greenstein, Vanderbilt University School of Medicine, Nashville, Tennessee, United States). Texas Red or Alexa488 secondary antibodies were used to detect staining, and DAPI was used visualize DNA morphology. Epifluorescent images were captured with a Zeiss (Oberkochen, Germany) Axioskop coupled to a Hamamatsu Photonics (Hamamatsu City, Japan) digital CCD camera, and processed with Photoshop 7.0 (Adobe, San Jose, California, United States). All image post-processing (brightness, contrast, pseudo-color, unsharp mask) was performed identically for each image.
Constructs and transformation
GLD-1 and FOG-2 yeast two-hybrid binding assays were performed as previously described [25] with the inclusion of 20 mM 3-amino-triazole. Progressive C-terminal deletions in FOG-2 and FTR-1/FOG-2 chimeric constructs were generated using PCR amplification of the appropriate coding sequences (FOG-2 full-length [327 aa], 318 aa, 299 aa, 263 aa, or exon 4 [251aa], or FTR-1 full-length [318 aa]) and cloned by recombination in yeast. In each case GLD-1 was used as bait in the pAS1 vector (DNA binding) and FOG-2 deletion constructs in the pACTII vector (activation). FOG-2 was found to exhibit low levels of auto-activation in the pAS1 (DNA binding) vector, so binding assays were performed in only one direction to avoid background and using high levels of 3-amino-triazole. The constructs were sequenced, and the Skp1-related F-box-binding protein SKR-1 (in pAS1) was used as a positive control for interaction [76,77].
Supporting Information
Figure S1 Phylogenetic Relationships of 30 C. elegans and C. briggsae FTR Genes Closely Related to FOG-2 Presented as a Rectangular Phylogram
A clear separation of C. elegans and C. briggsae FTR genes (C. briggsae is in grey shade) is suggested by the phylogeny. The branch containing FOG-2 and FTR-1 is in bold. Tree is unrooted, and branch lengths are proportional to divergence. Bar represents 0.1 substitutions per site. Bootstrap support for separation of C. elegans and C. briggsae sequences is indicated at the node (black dot) and at each node for the C. elegans FOG-2 branch.
(34.1 MB TIF).
Click here for additional data file.
Figure S2 Alignments of FTR-1 and FOG-2 C-Terminal Regions to Other Closely related C. elegans FTR Family Members
(A) FTR-1 and FTR family alignment. Residues identical to FTR-1 are shaded black, and residues identical between all FTR family members tested are shaded red. Average pairwise identity to FTR-1 is 48%.
(B) FOG-2 and FTR family alignment. Residues identical to FOG-2 are shaded black, and residues identical between all FTR family members tested are shaded red. Average pairwise identity to FOG-2 is 22%.
(15.6 MB TIF).
Click here for additional data file.
Table S1 Analysis of Genes in the fog-2 Cluster
(59 KB PDF).
Click here for additional data file.
Table S2 Analysis of Genes Surrounding Y113G7B.11 in C. briggsae
(59 KB PDF).
Click here for additional data file.
This work was supported by National Institutes of Health (NIH) grant GM63310 to TS and NIH National Research Service Award GM20864 to SN. JG was supported in part by Howard Hughes Medical Institute grant 52003842 through the Undergraduate Biological Sciences Education Program to Washington University. Some nematode strains used in this work were provided by the Caenorhabditis Genetics Center, which is funded by the NIH National Center for Research Resources. We would like to thank Mary E. Kosinski and David Greenstein for the anti-MSP antibody. We would like to thank Justin Fay for important suggestions and assistance with the work on positive selection. We would like to thank Eric Haag, Ronald Ellis, and members of the Schedl lab for helpful discussions and Dave Hansen, Jim Skeath, Sean Eddy, and Susan Dutcher and the three anonymous referees for comments on the manuscript. Finally, we would like to thank the Consortium at Washington University, St. Louis, and at the Sanger Institute for the high-quality genome sequence of C. elegans and C. briggsae that made this project possible.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. SN and TS conceived and designed the experiments. SN and JG performed the experiments. SN and TS analyzed the data. SN and TS wrote the paper.
Citation: Nayak S, Goree J, Schedl T (2004) fog-2 and the evolution of self-fertile hermaphroditism in Caenorhabditis. PLoS Biol 3(1): e6.
Abbreviations
DAPI4′,6′-diamidino-2-phenylindole hydrochloride
FTHFOG-2 homology domain
FTR
fog-2 related (F-box and FTH)
GFPgreen fluorescent protein
HMMhidden Markov model
Kanon-synonymous substitutions
Kssynonymous substitutions
L[number][number] larval
MSPmajor sperm protein
RNAidouble-stranded-RNA-mediated interference
TGE
tra-2 and GLI element
UTRuntranslated region
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| 15630478 | PMC539060 | CC BY | 2021-01-05 08:28:09 | no | PLoS Biol. 2005 Jan 28; 3(1):e6 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030006 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563047310.1371/journal.pbio.0030011Research ArticleCell BiologyDevelopmentMus (Mouse)A Signaling Pathway Involving TGF-β2 and Snail in Hair Follicle Morphogenesis Mechanisms Underlying Hair Bud FormationJamora Colin
1
Lee Pedro
1
Kocieniewski Pawel
1
Azhar Mohamad
2
Hosokawa Ryoichi
3
Chai Yang
3
Fuchs Elaine [email protected]
1
1Howard Hughes Medical Institute, Laboratory of Mammalian Cell Biology and DevelopmentThe Rockefeller University, New York, New YorkUnited States of America2Department of Molecular Genetics, Biochemistryand Molecular Biology, University of Cincinnati, CincinnatiUnited States of America3Center for Craniofacial Molecular Biology, University of Southern CaliforniaLos Angeles, CaliforniaUnited States of AmericaHogan Brigid L.M. Academic EditorDuke University Medical CenterUnited States of America1 2005 28 12 2004 28 12 2004 3 1 e113 9 2004 2 11 2004 Copyright: © 2004 Jamora 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.
Hirsute or Hairless? Two Proteins May Spell the Difference
In a common theme of organogenesis, certain cells within a multipotent epithelial sheet exchange signals with their neighbors and develop into a bud structure. Using hair bud morphogenesis as a paradigm, we employed mutant mouse models and cultured keratinocytes to dissect the contributions of multiple extracellular cues in orchestrating adhesion dynamics and proliferation to shape the cluster of cells involved. We found that transforming growth factor β2 signaling is necessary to transiently induce the transcription factor Snail and activate the Ras-mitogen-activated protein kinase (MAPK) pathway in the bud. In the epidermis, Snail misexpression leads to hyperproliferation and a reduction in intercellular adhesion. When E-cadherin is transcriptionally down-regulated, associated adhesion proteins with dual functions in signaling are released from cell-cell contacts, a process which we demonstrate leads to Ras-MAPK activation. These studies provide insights into how multipotent cells within a sheet are stimulated to undergo transcriptional changes that result in proliferation, junctional remodeling, and bud formation. This novel signaling pathway further weaves together the web of different morphogens and downstream transcriptional events that guide hair bud formation within the developing skin.
The study of hair follicle morphogenesis provides insights into how cells within a sheet can be triggered to proliferate, remodel, and form buds - a recurring theme in development
==== Body
Introduction
Mammalian development involves the morphogenesis of complex three-dimensional structures from seemingly uniform sheets or masses of cells. A simple bud-like structure initiates the formation of many organs, including lungs, spinal cord, mammary glands, and hair follicles [1]. The multipotent, adhering epithelial cells are typically attached to an underlying basal lamina that polarizes the epithelial sheet and separates it from surrounding mesenchyme. Budding morphogenesis is guided by a reciprocal exchange of signals between epithelium and mesenchyme to specify the identity of the organ that will form and to govern its growth.
At the helm of these molecular communication pathways are Wnts, bone morphogenic proteins (BMPs), transforming growth factor βs (TGF-βs), and fibroblast growth factors (FGFs). Through activation of cell surface transmembrane receptors, these external signaling molecules trigger distinct cascades of intracellular events that culminate in changes in gene expression, growth, and differentiation [2]. How this constellation of signals collaborates in tailoring each budding process so that it executes a distinct morphogenetic program has yet to be comprehensively defined. However, the process appears to be patterned at the initial stages of bud formation, since the relative importance of these pathways and their downstream effectors differ as buds begin to develop and cell fates are specified.
The development of a bud requires a number of coordinated changes in the behavior of the targeted cells within an epithelial sheet. The process must be accompanied by alterations in the proliferation, polarity, shape, and adhesiveness of selected cells, as well as by modifications in their underlying basal lamina. Thus, extracellular epithelial-mesenchymal crosstalk must be intricately orchestrated to couple the determination of distinct cell fates with the contemporaneous remodeling of the physical and structural properties of the cell.
Among the few dispensable organs, hair follicles offer an excellent model system to study epithelial bud formation. Mammalian skin epithelium begins as a single sheet of multipotent ectodermal cells. During development, specialized mesenchymal cells populate the skin in a spatially defined pattern to initiate the complex epithelial-mesenchymal crosstalk that will specify the bud [3]. Once committed, a small cluster of epithelial cells, the placode, instructs a group of underlying mesenchymal cells to condense and form the nascent dermal papilla, which will be a permanent fixture of the hair follicle. Subsequent exchanges between the placode and nascent dermal papilla result in further growth of the follicle into the underlying dermis, or down-growth, and eventual differentiation into the six concentric layers of the mature follicle.
Previously, we delineated how two respective epithelial and mesenchymal signals, Wnts and the BMP-inhibitory factor noggin, function in concert to induce lymphoid enhancer factor-1/β-catenin (LEF-1/β-catenin)-mediated gene transcription within the follicle placode [4]. The downstream changes elicited through convergence of these two early signaling pathways include down-regulation of the gene encoding E-cadherin, the prototypical epithelial cadherin that forms the transmembrane core of intercellular adherens junctions (AJs) [5]. We subsequently showed that when E-cadherin is transgenically elevated in mouse skin, hair follicle morphogenesis is blocked, suggesting that E-cadherin down-regulation is a critical event in governing the adhesion dynamics necessary for budding morphogenesis [4]. Like LEF-1, E-cadherin also binds to β-catenin. At sites of cell-cell contact, however, E-cadherin-β-catenin complexes recruit α-catenin, which in turn coordinates the associated actin polymerization dynamics necessary to stabilize nascent AJs and integrate the cytoskeleton across an epithelial sheet [6,7,8]. α-Catenin also binds to the class III Lin-1, Isl-1, Mec-3 (LIM) protein Ajuba (a member of the zyxin family of proteins), which appears to function dually in both adhesion and in activation of the Ras-mitogen-activated protein kinase (MAPK) pathway [9,10]. Through these links, AJs appear able to couple adhesion with cytoskeletal dynamics as well as with nuclear and cytoplasmic signaling. This provides a framework for conceptualizing why E-cadherin levels appear to impact upon a plethora of developmental processes (reviewed in [11]).
As we probed more deeply into the underlying mechanisms governing E-cadherin promoter activity, we were intrigued by the close proximity of the LEF-1/β-catenin binding site to a site known to bind the Snail/Slug family of zinc finger transcriptional repressor proteins [12,13,14,15]. Both activity of Snail and down-regulation of E-cadherin play pivotal roles in epithelial to mesenchymal transitions (EMTs), typified by the transformation of polarized, adhering epithelial cells into motile mesenchymal cells [16,17]. Bud formation differs from an EMT in that E-cadherin activity needs to be down-regulated but not prevented, so that adhesive junctions are remodeled rather than quantitatively impaired. Supportive of an underlying ability to fine-tune cadherin expression at the transcriptional level, Snail seems to have an additive effect with LEF-1/β-catenin in negatively modulating E-cadherin promoter activity [4].
In the present study, we discovered that Snail is expressed briefly at an early stage of hair bud formation, when E-cadherin down-regulation and activation of proliferation take place. Thereafter, Snail disappears and remains absent during subsequent follicle down-growth and maturation. This exquisite pattern appears to be functionally relevant since altering it in vivo correspondingly affects features associated with hair bud formation, including down-regulation of E-cadherin, increased proliferation, and repressed terminal differentiation. Although the temporal spike of Snail in the hair bud is reflected at the mRNA level and seems to follow Wnt signaling and BMP inhibition, LEF-1/β-catenin activation does not appear to induce Snail gene expression in embryonic skin keratinocytes. In contrast, we provide in vitro, transgenic (Tg), and gene targeting evidence to show that TGF-β2 and small phenotype– and mothers against decapentaplegic–related protein 2 (SMAD2) signaling are upstream inducers of Snail gene expression in skin epithelium. In the absence of TGF-β2 signaling and Snail gene expression, hair placodes can form, but further follicle down-growth is blocked. Our studies point to the view that Snail likely functions downstream of cell fate specification, at a stage where the bud begins to exhibit enhanced proliferation and migration.
Results
Snail mRNA and Protein Are Expressed Transiently at the Hair Bud Stage of Follicle Morphogenesis
Although Snail family members are most frequently associated with EMTs, they also participate in many malignant processes involving a down-regulation but not a quantitative abrogation of intercellular junctions [18]. The range of developmental processes in which Snail family members have been implicated thus includes the type of epithelial remodeling that is observed in hair follicle bud formation. Given our prior observation that exogenously added Snail can participate with LEF-1/β-catenin in down-regulating E-cadherin expression in keratinocytes [4], coupled with the established requirement for LEF-1/β-catenin in hair follicle morphogenesis [4,19], we turned to addressing whether Snail/Slug family members might also participate in the process.
PCR analyses identified transient Snail mRNA expression during a period of skin embryogenesis when waves of hair follicles are forming (unpublished data).To pinpoint specifically where Snail mRNA is expressed in the developing skin, we conducted in situ hybridization using a cRNA probe unique to the Snail 3′ untranslated region (UTR). Embryonic day 17.5 (E17.5) was chosen, since the multiple waves of follicle morphogenesis occurring at this time enabled us to evaluate Snail expression at different stages of the process. As shown in Figure 1A, specific hybridization was detected within the epithelium of nascent hair buds. By contrast, as follicles progressed further through their development (e.g., germ and peg stages), they exhibited no signs of hybridization (Figure 1A). The transient nature of Snail mRNA expression during follicle development was most apparent in hybridized skin sections containing follicles from two different waves of morphogenesis (as shown in Figure 1). Hybridizing hair buds from a later wave appeared juxtaposed with nonhybridizing follicles from an earlier wave.
Figure 1 Snail Is Expressed Exclusively in the Hair Bud during Morphogenesis
Embryos were either frozen in OCT embedding compound (A, F, and H) or embedded in paraffin (C, D, E, and G), and then sectioned (8 μm).
(A) In situ hybridizations with Snail sense or antisense cRNA probes. Black dotted lines demarcate the basement membrane that separates the epidermis (epi) from dermis (der). Arrows point to Snail RNA expression, restricted to the hair bud stage of follicle morphogenesis. It was not seen in later hair germ or peg stages.
(B) Expression of Snail protein coincides with hair development. Protein extracts were prepared from keratinocytes transfected with empty expression vector (K14), containing the K14 promoter or with the vector driving HA-tagged Snail (K14-Snail); or from whole skin from E13.5 to P5 animals, including newborn (nb). Equal amounts of proteins were then resolved by SDS-PAGE through 12% gels and subjected to Western blotting using either an affinity-purified Snail polyclonal antiserum, which we generated, or anti-tubulin (loading control).
(C–E) Immunohistochemistry shows expression of Snail protein in the nuclei of cells within the hair and skin. (C) E13.5 skin with a single layered epidermis (epi) shows no Snail expression. (D) The first morphological sign that cells have adopted a hair follicle fate is a cluster of cells called a placode in E16.5 skin. Snail is not expressed at this stage of development. (E) Snail is expressed in the hair bud of E17.5 skin but not in later stages of development such as the germ or peg.
(F) Immunofluorescence with anti-Ki67 (green) identifies the proliferating cells of the skin, restricted to the basal layer of the epidermis and developing hair follicles. Anti-β4 int labeling reveals the presence of the hemidesmosomal integrin β4, restricted to the base of cells adhering to the underlying basement membrane. The white dotted line marks the outermost surface of the skin.
(G) Immunohistochemistry with pMAPK marks a subset of proliferating cells within the epidermis and hair bud. Anti-pMAPK labeling was consistently robust within the hair bud.
(H) Immunofluorescence with anti-laminin 5 (lam5), which demarcates the basement membrane, and anti-E-cadherin (E-cad), a component of AJs. At the leading edge of the growing bud, cell-cell borders show markedly diminished anti-E-cadherin labeling (arrowheads).
To determine whether this transient nature of Snail mRNA expression is reflected at the protein level, we generated an antibody against the N-terminal sequence that resides upstream of the more conserved zinc finger domains. As judged by Western blot analysis, the antibody did not detect endogenous proteins from cultured keratinocytes, but it did yield a band of the expected size from keratinocytes transiently expressing a hemagglutinin (HA)-tagged Snail protein (Figure 1B). The antibody also recognized a band corresponding to the size of endogenous Snail (approximately 28 kDa) in lysates from embryonic mouse skin, the temporal appearance of which corresponded to the waves of hair follicle morphogenesis from E15.5 to newborn when over 90% of the hair on the mouse is formed (Figure 1B). Consistent with the Western blot data, immunohistochemical analysis did not detect Snail in single-layered E13.5 epidermis (Figure 1C) nor in the placode, which is the earliest morphological sign of the commitment of multipotent cells of the embryonic ectoderm to a hair cell fate (Figure 1D). Consistent with the in situ hybridization results, anti-Snail antibody labeled only hair buds and not follicles at more mature stages of development (Figure 1E). Taken together, the anti-Snail antibody appeared to be specific for its target protein. It did not detect other Snail family members known to be expressed in keratinocytes and/or skin (unpublished data). Furthermore, the immunohistochemical data paralleled our Snail in situ hybridization data revealing transient Snail expression at the hair bud stage (Figure 1A).
As judged by immunohistochemistry, Snail protein was localized to the nuclei of the hair bud cells (Figure 1E). This feature was consistent with Snail's known function as a transcriptional repressor [12,13]. Additionally, anti-Snail labeling was detected in only three of the four major waves of follicle morphogenesis. Snail was not found in the buds of guard hairs that are the earliest of all hairs to form (at E13.5), and which constitute less than 5% of the mouse coat (unpublished data).
As judged by immunofluorescence with antibodies against the proliferating nuclear antigen Ki67, the timing of Snail expression coincided with the stage at which the developing follicle enhanced its proliferation and down-growth (Figure 1F). Immunohistochemistry with antibodies against the active (phosphorylated) form of MAPK (pMAPK) marked a subset of the proliferating (Ki67-positive) cells, and pMAPK-positive cells were enriched in the hair bud (Figure 1G). The timing of Snail induction and Ki67 and pMAPK enrichment in the hair bud appeared to follow closely the induction of LEF-1/β-catenin activity, known to initiate in the hair placode stage [20]. However, like placodes, hair buds exhibited down-regulation in E-cadherin expression (Figure 1H; see also [4]).
Sustained Expression of Snail Results in Epidermal Hyperproliferation and Differentiation Defects in Tg Mouse Skin
The striking spike of Snail expression coincident with hair bud formation and enhanced proliferation prompted us to examine the consequences of ectopically expressing Snail elsewhere in mouse skin epidermis. To distinguish Tg from endogenous Snail, we used the HA-epitope, shown previously not to alter Snail's transcriptional activity [12]. Of 20 K14-Snail[HA] Tg animals generated, three expressed the transgene and all exhibited analogous phenotypes. Mice that integrated the transgene at the single-cell stage died at or shortly after birth. The three surviving full-Tg founder mice harbored transgene integrations that gave stable transmission of mosaic Snail gene expression through the germline. Progressively poor health necessitated our sacrificing most offspring from these lines within a year of birth.
As Snail Tg animals grew, they became distinguished by their small size, short tails, and flaky skin (Figure 2A). Histological analyses of 3-d old (P3) mice revealed mosaic patches marked by epidermal thickening (Figure 2B). The mosaic morphology was reflected at the level of Tg Snail protein, with only the hyperthickened regions expressing nuclear HA-tagged Snail (Figure 2C). These hyperthickened areas were marked by excessive proliferation, as revealed by antibodies against the proliferating nuclear antigen Ki67 (Figure 2D and 2E). Activated, pMAPK-positive cells were also prevalent in these areas (Figure 2F and 2G), as were cells expressing keratin 6, a keratin induced in the suprabasal layers of hyperproliferative skin (Figure 2H and 2I).
Figure 2 Misexpression of Snail in Mouse Skin Epidermis Results in Hyperproliferation
Three different surviving Tg founder mice harbored a K14-Snail transgene that was integrated into a locus that resulted in inheritable, mosaic expression of the transgene in skin epidermis. All displayed similar abnormalities, as did their offspring.
(A) P16 WT and K14-Snail Tg mice. Insets denote magnified tail segments, which displayed a mosaic, flaky appearance in Tg mice. Size differences appeared with age, and are likely due to K14-promoter activity in the tongue and oral epithelium, resulting in progressive defects and reduced food intake. Hence, skin sections from young (P3) mice were analyzed (B–I).
(B) Hematoxylin- and eosin-stained Tg skin section. Double arrows demarcate the border of mosaic histology, with seemingly normal epidermis (epi) and a mature hair follicle (hf) at left and hyperthickened epidermis at right.
(C) Immunofluorescence of Tg skin section labeled with antibodies as color-coded on frame. Double arrows demarcate the border of mosaic anti-Snail (green), revealing Snail expression coincident with regions of hyperthickened epidermis (at left) and absent in regions of normal epidermis (at right).
(D–I) Sections of P3 WT or Tg skin (affected region) subjected to either immunofluorescence (D, E, H, and I) or immunohistochemistry (F and G) with antibodies as indicated on the panel. Anti-keratin 5 indicates K5, normally restricted to the basal layer of the epidermis; anti-keratin 6 detects keratin 6, expressed in postnatal epidermis under conditions such as wounding, in which hyperproliferation occurs. All other antibodies are as in the legend to Figure 2. Comparison of D and E provide representative examples that illustrate that pMAPK is found in only a subset of all proliferating (Ki67-positive) cells. Note also the presence of Ki67- (E) and pMAPK-positive (G) cells in some suprabasal areas; Ki67-positive cells colabeled with anti-Snail (E).
Expression of the Snail transgene did not block terminal differentiation in the hyperproliferative epidermis, but it distorted it markedly (Figure 3A–3H). Typical of most hyperproliferating conditions, Snail expression led to a large expansion in layers with spinous and granular morphology. Additionally, however, was a marked and variable expansion of keratin 5 (K5), normally restricted to the innermost basal layer (see Figure 3). Although the failure of Snail-null mice to develop past gastrulation [21] precluded our ability to study the loss of Snail function in skin development, a good correlation emerged between the expression of Snail protein and the extension of K5, Ki67, and pMAPK suprabasally (compare data in Figures 2 and 3).
Figure 3 Alterations in the Differentiation Program and Basement Membrane Organization in Snail-Expressing Tg Epidermis
(A–H) Immunofluorescence of skin sections from P3 WT and Tg mice. Shown are affected areas of Tg skin; in areas where Snail protein was not expressed, stainings were normal. Sections were labeled with antibodies as indicated and color-coded on each frame. Antibodies are against markers of normal epidermal differentiation, and include K5 (a basally expressed keratin), K1 (a suprabasal keratin, expressed in spinous layer cells), involucrin (Inv; a suprabasally expressed cornified envelope protein found in upper spinous and granular layer cells), loricrin (Lor; a cornified envelope protein expressed in the granular layer), and filaggrin (Fil; a protein that bundles keratin filaments in the granular layer and stratum corneum). Note abnormal extension of anti-K5 suprabasally, often present in anti-K1 positive suprabasal Tg cells.
(I–N) Immunohistochemistry (I and J) or immunofluorescence (K–N) of sections of P30 Wt (I, K, and M) and Tg (J, L, and N) (affected areas) skins using the antibodies indicated. Note that with age, affected areas of the Tg epidermis became increasingly undulating, often exhibiting papilloma-like invaginations (J). Insets in I and J are magnified views of the boxed areas, illustrating the absence (Wt) or presence (Tg) of nuclear anti-cyclin D staining. With age, affected areas of the Tg epidermis also displayed perturbations within the basement membrane, as judged by antibody labeling against either basement membrane (K and L) or hemidesmosomal (M and N) components. Double arrows in L demarcate mosaic zones, revealing that perturbations were restricted to hyperthickened, i.e., Snail-positive zones (to left of double arrows). Other abbreviations are as noted in the legend to Figure 2.
The changes in hyperproliferation and differentiation were not initially accompanied by gross signs of epithelial invaginations. With age, however, epidermal folds and undulations developed in areas where Snail was expressed, and proliferative markers persisted in these regions (Figure 3I and 3J; anti-cyclin D staining). The undulations were accompanied by partial dissolution of the underlying basement membrane (Figure 3K and 3L). Aberrant staining was also observed with antibodies against components of the hemidesmosomes, which provide strong adhesion of basal epidermal cells to the underlying basal lamina (Figure 3M and 3N). Interestingly, similar types of alterations occur in the basement membrane in the hair bud of embryonic and newborn mice when Snail is normally expressed. The fact that the basement membrane separating the epidermis from the dermis is altered only in the adult Tg animals suggests the involvement of intermediary factors not as readily available in the epidermis as they are in the follicle.
Possible Links between Epidermal Hyperproliferation and Down-regulation of AJ Proteins in Snail Tg Mice
Given that the E-cadherin promoter is a direct target for Snail-mediated repression in vitro [4,12,13], and that E-cadherin was down-regulated in Snail-expressing hair buds, we examined the status of E-cadherin and other AJ proteins within regions of hyperproliferative epidermis where Tg Snail was present (Figure 4A). In these regions, immunofluorescence staining of E-cadherin and α-catenin were markedly diminished. In contrast, the intensity of antibody staining for two other AJ proteins, β-catenin and Ajuba, was still strong. Interestingly, however, despite appreciable immunofluorescence, localization of β-catenin and Ajuba appeared to be largely cytoplasmic rather than at cell-cell borders (Figure 4A insets).
Figure 4 Snail-Mediated Remodeling of AJs Contributes to Hyperproliferation
(A) Immunofluorescence of skin sections from P30 Wt and Tg mice. Shown are affected areas of Tg skin; in areas where Snail protein was not expressed, stainings were normal. Antibodies used are against AJ proteins and include E-cadherin (E-cad), the transmembrane core protein; β-catenin (β-cat), which binds E-cadherin at AJs and which can also participate as a transcription cofactor when associated with LEF-1/TCF proteins in the nucleus; α-catenin (α-cat) which binds to both β-catenin and Ajuba, a close relative of zyxin; and Ajuba, which can associate with proteins that bind to the actin cytoskeleton, as well as with Grb-2, a mediator of the GTP nucleotide-exchange protein Sos, involved in activation of the Ras-MAPK signaling cascade. In Snail-expressing Tg regions, there was a reduced staining with anti-E-cad and anti-α-cat and a more diffuse staining with anti-Ajuba. Insets in the panels for β-catenin and Ajuba staining are magnified views of the boxed areas. Arrows mark membrane localization of the protein and asterisks mark cells with elevated levels of cytoplasmic β-catenin or Ajuba.
(B) Western blot analyses of protein extracts from P30 Wt and Tg back and ear skins. Antibodies are as in (A) except anti-P-cad, which detects P-cadherin, whose expression in the hair follicle was not affected, and anti-tubulin, which detects tubulin, a control for equal protein loadings. Note that the reductions seen in E-cadherin and α-catenin are likely to be underestimates of the actual differences in affected regions, since the Tg skin expressed Snail mosaically.
(C) In the presence of elevated Snail, α-catenin levels can be restored by overexpression of E-cadherin. Keratinocytes were transfected with either HA-tagged Snail (Snail[HA]; images on the left) or Snail(HA) and Ecad(HA) (images on the right). 2 d after transfection, cells were switched from low-calcium growth medium to high-calcium medium for 6 h to induce AJ formation. Cells were stained with antibodies as indicated on the panels. Arrowheads point to sites of intercellular contact between a Snail-transfected keratinocyte and its neighboring untransfected cell.
(D) Reintroduction of E-cadherin in keratinocytes expressing Snail returns pMAPK to basal levels. Keratinocytes were transfected with control vector (K14), or Snail(HA), or Snail(HA) + E-cad(HA). After 2 d, cells were serum starved for 4 h and whole cell lysates were made and Western blotted with antibodies to pMAPK, HA to recognize the HA-tagged Snail and E-cadherin protein, 20or tubulin as a loading control.
(E) Ajuba interacts with Grb-2 under conditions where α-catenin levels are reduced. Protein extracts were made from skins of P30 Wt and K14-Snail Tg P30 mice (blots on the left) and of newborn Wt and K14-Cre/α-catenin (fl/fl) conditionally null animals (blots on the right) [7]. Equal amounts of protein extracts were treated with anti-Grb-2 antibody (+) or control isotype antibody (–), and following centrifugation, immunoprecipitates were subjected to SDS-PAGE and Western blot analysis with anti-Ajuba and anti-Grb-2 antibodies. Note the presence of Ajuba only under conditions where levels of α-catenin and other AJ proteins were aberrantly low or absent.
(F) Transgene expression of excess Ajuba or the Grb-2-interacting domain (pre-LIM) of Ajuba in keratinocytes results in the activation of the Ras-MAPK pathway. Primary newborn mouse keratinocytes were transfected with either the empty K14 expression vector (K14), or the expression vector driving Snail, full length Ajuba, or the pre-LIM domain of Ajuba in the absence or presence of a peptide inhibitor (inh) that disrupts the interaction between Grb-2 and Sos. 48 h posttransfection, protein extracts were prepared and subjected to SDS-PAGE and Western blot analyses with antibodies against pMAPK, total MAPK, Ajuba (also recognizing the smaller, pre-LIM domain), and Snail.
Architectural differences in the epidermis made Western blot analyses somewhat difficult to gauge. However, in regions such as ear skin, where the highest levels of Snail protein were expressed, the effects were accentuated. In both back skin and ear skin, overall levels of E-cadherin and α-catenin were reduced, under conditions where β-catenin and Ajuba levels remained unchanged relative to controls (Figure 4B). Taken together, these data were consistent with our results obtained from immunofluorescence microscopy.
A priori, the decrease in α-catenin levels could be due to either direct transcriptional repression by Snail or perturbations in AJ formation caused by the decrease in E-cadherin gene expression. To distinguish between these possibilities, we tested whether α-catenin levels could be restored by exogenous expression of E-cadherin in Snail-expressing keratinocytes. As shown in Figure 4C, transiently transfected keratinocytes expressing HA-tagged Snail displayed a loss of E-cadherin and α-catenin at cell-cell borders. Coexpression of exogenous HA-tagged E-cadherin not only enabled cell-cell border localization of E-cadherin protein, but also rescued the cell-cell border staining of α-catenin (Figure 4C). The ability to restore α-catenin expression and localization under these conditions argues against the notion that Snail transcriptionally represses α-catenin. Rather, the findings are consistent with a previous report that E-cadherin is required for the translation of α-catenin mRNA [22].
Despite the reductions in AJ markers, Tg skin still displayed sealed membranes and intercellular junctions that were largely intact, as judged by ultrastructural analyses (unpublished data). In this respect, the skin epithelium resembled that of the hair bud, where the down-regulation in junction proteins is permissive for cell-cell remodeling without abrogating intercellular adhesion.
The similarities between Snail Tg epidermis and hair buds extended to the hyperproliferative state, leading us to wonder whether the down-regulation of AJ proteins might contribute to this condition. Given the increase in pMAPK staining in Snail Tg epidermis (see Figure 2G), we used pMAPK levels as our assay to test whether the loss of E-cadherin contributed to the Snail-mediated increase in proliferation. Consistent with our in vivo observations, transfected keratinocytes expressing Snail exhibited a substantial increase in pMAPK levels relative to control cells (Figure 4D). Coexpression of E-cadherin with Snail appeared to abrogate this effect. Together, these findings raised the possibility that an AJ-associated protein that is normally sequestered at the plasma membrane may participate in a proliferation signaling pathway when AJs are deconstructed.
Numerous studies have correlated a down-regulation of E-cadherin with a translocation of β-catenin to the nucleus and a transactivation of genes that are regulated by the LEF-1/T cell factor (TCF) family of DNA binding proteins [23,24,25]. The presence of nuclear cyclin D in hyperproliferative Snail Tg epidermis was particularly intriguing since prior studies have reported cyclin D gene as a direct target of TCF/β-catenin transcription [26]. This said, we did not detect nuclear β-catenin in our Tg epidermis, and mating the Snail Tg mice against the TOPGal reporter mouse [20] gave no signs of ectopic LEF-1/Tcf/β-catenin activity (unpublished data).
We next turned to the presence of cytoplasmic Ajuba for a possible mechanistic link to the proliferative increase in our Snail Tg epidermis. In addition to its documented ability to bind α-catenin [10], Ajuba can also associate with growth factor receptor-bound protein-2 (Grb-2)/son of sevenless (Sos), the nucleotide exchange factor for Ras, which is upstream from activation of MAPK [9]. Given the increase in pMAPK staining in Tg skin, we examined the possibility that Ajuba might have changed its binding partner in Snail-expressing epidermis. Interestingly, Ajuba was readily detected in anti-Grb-2 immunoprecipitates of protein lysates from skins of Snail Tg mice but not from the corresponding wild-type (WT) animals (Figure 4E). When these experiments were repeated with α-catenin-null epidermis, a similar Grb-2-Ajuba association was detected, and again, this interaction was not detected in the protein extracts from control littermate skin (Figure 4E). Together, these data demonstrate that the reduction in α-catenin levels, either by Snail-mediated down-regulation of E-cadherin or by α-catenin conditional targeting, allows Ajuba to interact with Grb-2/Sos.
If the competition between Grb-2/Sos and α-catenin for Ajuba is functionally relevant to the hyperproliferative state of a keratinocyte, then overexpression of Ajuba would be expected to bypass the competition and promote activation of the Ras-MAPK pathway in WT keratinocytes. Indeed, when serum-starved keratinocytes were transiently transfected with an Ajuba expression vector, the levels of pMAPK were not only elevated but also comparable to those transfected with the K14-HASnail transgene (Figure 4F). This activation was abolished when cells were treated with a small peptide inhibitor that specifically interrupts the Grb-2/Sos interaction (Figure 4F; see lanes marked “inh”) [27].
Ajuba's pre-LIM domain is the segment that associates with Grb-2's Src-homology 3 domain [9]. When this domain was overexpressed in serum-starved keratinocytes, a comparable elevation in pMAPK was observed (Figure 4F). As expected, the small peptide inhibitor that interrupts the Grb-2/Sos association blocked the effects. These data suggested that by elevating cytosolic Ajuba levels, Ajuba's pre-LIM domain may associate with Grb-2/Sos in a manner that stimulates its nucleotide exchange activity and leads to activation of the Ras-MAPK pathway. Although this pathway provides one mechanism by which Snail expression and proliferation may be coupled in skin epithelium, proliferative circuitries involving AJs are known to be complex and often interwoven. Future studies will be needed to systematically dissect these putative intricacies at a molecular level.
Probing the Regulation of Snail Gene Expression Reveals an Essential Link to TGF-β2 Signaling in the Developing Hair Bud
The temporal spike of Snail mRNA expression in the hair bud prompted us to consider what factor(s) may be regulating the Snail gene. A variety of extracellular signals have an impact on the cell type-specific expression of different Snail family members, and many of them, including Wnts, BMPs, FGFs, and TGF-βs, also affect hair bud development [2,16,28]. Since Snail is not expressed in cultured skin keratinocytes that secrete active BMPs and FGFs (see Figure 1B), we focused our attention on Wnt and TGF-β signaling as more likely candidates for Snail induction in this cell type.
Previously, we showed that effective transmission of a Wnt-3a signal in cultured keratinocytes can be achieved through their exposure to the BMP inhibitor noggin, which induces LEF-1 expression [4]. In vitro, these conditions down-regulated the E-cadherin promoter and induced a LEF-1/β-catenin-sensitive reporter gene, TOPFLASH [4]. In contrast, Snail expression was not induced by these conditions (Figure 5A). Thus, despite essential roles for Wnts and noggin in hair follicle specification [4,29,30], our studies did not support an essential role for these signals in governing Snail expression in keratinocytes.
Figure 5 TGF-β2, but Not Wnt/noggin, Transiently Induces Snail Expression In Vitro
(A) Failure of Wnt and noggin signaling to induce Snail in cultured keratinocytes. Primary mouse keratinocytes were treated with Wnt- and/or noggin-conditioned medium (+) or the corresponding control medium (–). These conditions are known to activate the LEF-1/β-catenin reporter TOPGal and down-regulate the E-cadherin promoter (see [4] for details). Using Western blot analyses, cellular proteins were then analyzed for Snail, LEF-1, β-catenin, and tubulin. Proteins from keratinocytes transfected with K14-Snail were used as a positive control for Snail expression.
(B) TGF-β2 can induce Snail protein. Primary keratinocytes were treated for the indicated times with recombinant TGF-β2 (+) or heat inactivated TGF-β2 (–).Total cellular proteins were then isolated and analyzed by Western blot for Snail, pSMAD2 (reflective of activated TGF- signaling), and tubulin. Note the activation of Snail expression, peaking at 2 h post-TGF-β2 treatment and then disappearing thereafter.
(C) Snail mRNA expression is transiently induced by TGF-β2. The experiment in (B) was repeated, and this time, total RNAs were isolated from keratinocytes treated with TGF-β2 for the indicated times. RT-PCR was then used with (+) or without (–) reverse transcriptase (RT) and with primer sets specific for Snail and GAPDH mRNAs. Note that Snail mRNA expression also peaked at 2 h, paralleling Snail protein.
(D) TGF-β2 treatment results in enhanced activity of a Snail promoter-β-galactosidase reporter. Keratinocytes were transfected with a β-galactosidase reporter driven by a Snail promoter that is either WT (wt prom) or harbors a mutation in a putative pSMAD2/pSMAD4 binding site (mt prom). At 2 d posttransfection, cells were treated with either TGF-β or heat-inactivated TGF-β2 (inact) for the times indicated. β-galactosidase assays were then conducted, and results are reported as fold increase over a basal level of activity of 1. The experiment was repeated three times in triplicate, and error bars reflect variations in the results.
TGF-β1 has been shown to induce Snail family members in hepatocytes and heart [15, 31]. In keratinocytes, however, TGF-β1 inhibits keratinocyte growth and seems to be involved in triggering the destructive phase of the cycling hair follicle [32]. Of the loss-of-function mutations generated in each of the TGF-β genes, only the TGF-β2 null state blocked follicle development at the hair bud stage [32]. Thus, we turned towards addressing whether TGF-β2 might be involved in regulating Snail expression in keratinocytes isolated from the basal layer of the epidermis. Though there is no cell culture system available to specifically study placodal cells, these keratinocytes are their progenitors and are the closest approximation available to study the behavior of epithelial cells of the placode.
Interestingly, treatment of cultured keratinocytes with as little as 5 ng/ml of TGF-β2 caused a rapid and transient induction of Snail (Figure 5B). Following this treatment, Snail protein was detected within 30 min, peaked at 2 h, and then declined thereafter. The induction of Snail appeared to be specific for the active form of the growth factor, as pretreatment of TGF-β2 for 10 min at 100 °C obliterated the response [Figure 5B, lanes marked (–)]. By contrast, although TGF-β receptor activation remained elevated during the duration of the experiment (as measured by the sustained phosphorylation of the downstream effector SMAD2) Snail expression could not be maintained (Figure 5B). Thus, although Snail expression correlated with phosphorylated SMAD2 (pSMAD2) induction, its decline seemed to rely on secondary downstream events.
The rapid kinetics of Snail expression were reflected at the mRNA level, suggesting that Snail promoter activity in keratinocytes might be sensitive to TGF-β2 signaling (Figure 5C). To test this possibility, we engineered a transgene driving the β-galactosidase reporter under the control of approximately 2.2 kb of promoter sequence located 5′ from the transcription initiation site of the mouse Snail gene. At 2 d after transient transfection, keratinocytes were treated with TGF-β2 (t = 0) and then assayed for transgene activity over the same time course in which we had observed Snail protein induction. The results of this experiment are presented in Figure 5D.
Within 0.5 h of TGF-β2 treatment, Snail promoter activity had increased 3-fold, and by 2 h, it peaked to approximately 10-fold over control levels (Figure 5D). Thereafter, Snail promoter activity rapidly returned to the basal levels seen in unstimulated keratinocytes. The kinetics of Snail promoter activity closely paralleled those observed for Snail protein induction. Moreover, the stimulatory effects appeared to be specific to TGF-β2, since they were abrogated either by heat inactivation of the TGF-β2 protein or by mutation of a putative SMAD binding element located about 1.8 kb 5′ from the Snail transcription start site (Figure 5D). Taken together, these results suggested that in keratinocytes, TGF-β2 signaling results in a pSMAD2-dependent transient activation of the Snail gene, and that maintenance of Snail protein relies, in part, upon sustained promoter activity.
The brevity of Snail gene and protein induction in TGF-β2 treated cultured keratinocytes resembled the temporal appearance of Snail mRNA and protein at the initiation of hair follicle morphogenesis in embryonic mouse skin. To test whether TGF-β2 might be required for Snail induction in hair bud formation in vivo, we first analyzed whether TGF-β2 was expressed in or around the hair bud. Consistent with previous observations [33], an anti-TGF-β2 antibody labeled developing hair buds (Figure 6A). This labeling appeared to be specific as judged by the lack of staining in follicle buds from mice homozygous for a TGF-β2 null mutation (Figure 6A; [34]). Moreover, the downstream effector of TGF-β2 signaling, pSMAD2, was also expressed in WT, but not TGF-β2-null, hair buds (Figure 6B). Together, these data underscore the importance of the TGF-β2 isoform despite expression of both TGF-β1 and TGF-β2 in developing hair buds at this stage.
Figure 6 TGF-β2 Is Necessary to Induce Snail Expression and Regulate Proliferation and E-Cadherin in the Hair Bud
(A–D) Skins from TGF-β2 WT or KO E17.5 embryos were analyzed for expression of TGF-β2 protein (A), which is present in the epidermis and dermis as previously described [33] and in the hair bud, pSMAD2 (B), Snail (C), and Snail mRNA (D). Arrows point to the hair buds.
(E) Western blot analyses of Snail expression in the skins of 2-wk-old K14-Smad2 transgenic (SMAD2 TG) and WT littermate (WT) mice. Antibody to tubulin was used as a control for equal protein loadings. The K14-Smad2 Tg mouse was previously shown to possess activated TGF-β signaling [35].
(F–G) Proliferation markers Ki67 (F) and pMAPK (G) are diminished in TGF-β2-null hair relative to its WT counterpart.
(H–J) TGF-β2-null hair fails to down-regulate E-cadherin (H). Wnt and noggin signaling pathways are still intact in the TGF-β2 null hair as nuclear LEF-1 (I) and nuclear β-catenin (J) are still expressed.
To further explore the possible relation between Snail and TGF-β2, we examined the status of Snail expression in TGF-β2-null hair buds. As judged by immunohistochemistry, Snail protein was absent from E17.5 skin of TGF-β2-null embryos but not from that of control littermates (Figure 6C). This effect appeared to be exerted at the transcriptional level, since Snail mRNAs were also not found in TGF-β2 null hair buds under conditions in which the signal was readily detected in the hair buds of littermate skin (Figure 6D).
Conversely, in 2-wk-old K14-Smad2 Tg mice, which display elevated TGF-β signaling in skin [35], Snail protein was readily detected by Western blot analyses, where it was not found in postnatal skin (Figure 6E). Taken together, these results provide compelling evidence that TGF-β2 is functionally important for inducing Snail gene expression in a pSMAD-dependent manner in developing hair buds. Whether pMARK activity also contributes to Snail induction was not addressed in the present study [15].
Although some hair buds still formed in TGF-β2 null skin, their number was reduced by approximately 50% [32]. Thus, although the pathway mediated by TGF-β2 signaling impacts the earliest step of epithelial invagination, it does not appear to be essential for bud morphogenesis. Consistent with this notion, basement membrane remodeling still took place in the TGF-β2-null buds, as judged by immunofluorescence with antibodies against β4 integrin, an integral component of keratinocyte-mediated adhesion to its underlying basement membrane (Figure 6F). In contrast, TGF-β2 signaling appeared to be an important factor for the early proliferation that occurs in the developing hair buds, as judged by anti-Ki67 and anti-pMAPK immunofluorescence (Figure 6F and 6G).
If TGF-β2 stimulates Snail expression in developing buds, loss of this morphogen would be expected to affect the expression of genes that are typically repressed by Snail. Since a major target for Snail-mediated repression is the E-cadherin gene [12,13], we investigated the status of E-cadherin in TGF-β2-null buds. As shown in Figure 6H, hair buds in TGF-β2 null skin displayed elevated immunofluorescence staining relative to their WT counterparts.
Previously we demonstrated that the concerted action of the extracellular signals Wnt and noggin are required for the generation of a LEF-1/β-catenin transcription complex to repress E-cadherin transcription at the onset of hair fate specification. As shown in Figure 6I and 6J, both WT and TGF-β2 null buds exhibited nuclear LEF-1 and β-catenin localization, signs that the Wnt-noggin signaling pathway was intact. These data suggest that during hair follicle morphogenesis, TGF-β2 functions subsequently to Wnt/noggin-mediated determination of hair fate. Moreover, through activation of Snail gene expression, TGF-β2 appears to work in tandem with these other morphogens to down-regulate E-cadherin levels, which contributes to the activation of proliferative circuitries.
Discussion
During budding morphogenesis, intersecting signaling networks from the epithelium and mesenchyme govern transcriptional, adhesive, polarity, and motility programs in these select groups of cells. The dynamic nuclear and cytosolic changes that take place during this time form the cornerstone for organ morphogenesis. Two major challenges in understanding the mechanisms underlying a particular budding process are to order the temporal sequence of external cues involved and then to dissect how the cells of the developing bud translate these signals into the downstream events of cellular remodeling, proliferation, and differentiation. Our studies here provide some insights into how these events are orchestrated during hair bud formation in developing skin.
Signaling during Early Hair Follicle Morphogenesis
Recent studies on hair bud morphogenesis suggest that Wnt signals likely from the epithelium and BMP inhibitory signals from the underlying mesenchyme converge to produce an active transcription factor complex involving β-catenin and LEF-1, which in turn plays a key role in specifying the hair follicle fate [4,29,30,36,37]. Sonic hedgehog (Shh) and TGF-β2 signaling also play essential roles in follicle morphogenesis, but in contrast to β-catenin null skin, in which follicle invaginations are absent [30], some hair buds still form in the absence of LEF-1, Shh, or TGF-β2 [32,38]. These likely reflect the first wave of follicle (i.e., guard hair) morphogenesis, which accounts for a small number (fewer than 5%) of hairs and is under distinct regulatory control. Guard hairs form in the absence of LEF-1 and TGF-β2, and we have found that they also fail to express Snail at the budding stage of development (unpublished data). How E-cadherin is regulated in guard hairs remains to be determined. Several candidates include other Snail family members such as Slug or twist, or alternatively, transcription factors involving β-catenin and a different member of the LEF-1/TCF/Sry-type HMG box (commonly known as SOX) family [39,40]. Further investigation will be required to determine whether the signaling pathway we have elucidated here is a theme with multiple variations.
TGF-βs are known to promote withdrawal of keratinocytes from the cell cycle [41]. Hence, when TGF-β2 protein was detected at the transition between the growing and destructive phases of the adult hair cycle, research initially and naturally focused on a role for this family member in cessation of growth and/or triggering apoptosis ([42] and references therein). However, in contrast to TGF-β1-null skin, which exhibits an extended growing phase of postnatal hair follicles, TGF-β2-null skin displays an embryonic block in follicle bud progression [32]. Although this phenotype is consistent with TGF-β2's embryonic expression patterns [33], about 50% of TGF-β2 null buds appear unable to progress to the down-growth phase, a feature that cannot be explained readily on the basis of previously established effects of TGF-βs.
Our finding that TGF-β2 is upstream from Ki67 expression and MAPK activation lends further support to the notion that hair follicle keratinocytes at this early stage of development react to TGF-β2 signaling in a fashion opposite to that typically expected for TGF-β factors. This said, based upon pSMAD2 immunohistochemistry, the immediate steps of downstream signaling appeared to be intact. Thus, we surmise that the proliferative outcome is likely to be rooted in differences in the repertoire of activated SMAD target genes. In this regard, the positive effects of TGF-β2 on proliferation within the hair bud may be more analogous to what has been seen in progression of squamous cell carcinoma to metastatic carcinoma [43] rather than that typically observed for keratinocytes [44,45,46].
The prior identification of the Snail gene as a potential target of TGF-β signaling [15] was intriguing, given the temporal wave of Snail gene expression that occurs in the developing hair bud. The additional correlation between epithelial hyperproliferation and Snail transgene expression further fostered our interest in a possible link between TGF-β2 and Snail. Our functional studies demonstrate that without TGF-β2, Snail expression is abolished in the mutant hair buds, and conversely, in K14-Smad2 skin, Snail is ectopically activated. Moreover, our in vitro studies indicate that even sustained TGF-β2 exposure may cause only a transient induction of Snail, offering a possible explanation as to why Snail is so briefly expressed during hair follicle morphogenesis. An additional point worth mentioning is that prolonged expression of Tg Snail in postnatal skin resulted in morphological and biochemical signs of epithelial to mesenchymal transitions (unpublished data), underscoring why transient Snail expression may be so important during normal hair follicle morphogenesis [18].
At first glance, the sparsity in hair coat of K14-Snail Tg mice seemed indicative of a defect in follicle formation (see Figure 2A). Closer inspection, however, revealed that not all hairs penetrated the hyperthickened Tg epidermis. Several factors may contribute to the seemingly normal follicle development in these mice. One obvious factor is the K14 promoter, which is elevated in the basal layer of the epidermis and the outer root sheath (ORS) of the hair follicle, but is markedly down-regulated in developing embryonic hair buds as well as in the postnatal hair progenitor cells. The K14 promoter is also less active in the ORS than epidermis and hence this might also account for the lack of apparent response of the ORS to ectopic Snail. Additional contributing factors could be the multiplicity of Snail family members and their differential expression, the saturation, and/or diversity of regulatory mechanisms that govern AJ formation, migration, and proliferation in the follicle ORS. Distinguishing between these possibilities must await the generation of mice harboring skin-specific loss-of-function Snail mutations.
Links between Signaling, Transcriptional Cascades, Epithelial Remodeling, and Proliferation in the Hair Bud
Previously, we discovered that early during hair follicle morphogenesis, E-cadherin gene expression is down-regulated concomitantly with the invagination of developing bud cells into the skin [4]. Because the timing of this event correlated with the activation of a LEF-1/β-catenin transcription factor complex [20], we were intrigued by the presence of a putative LEF-1/TCF binding site in the E-cadherin promoter. This prompted an investigation that subsequently led to our discovery that LEF-1/β-catenin can contribute to repression of E-cadherin gene expression in skin keratinocytes [4]. In the course of these studies, we also noted that Snail can also contribute to this process in keratinocytes in vitro, and our present studies revealed that Snail is expressed at the right place and time to be physiologically relevant in the process.
In noggin-null embryonic skin, LEF-1 expression and subsequent activation of the LEF-1/β-catenin reporter gene is abrogated in the developing placodes. The corresponding failure of E-cadherin down-regulation underscores the importance of Wnt/noggin signaling in regulating this event in follicle morphogenesis [4]. Conditional gene targeting studies will be necessary to establish whether Snail family members also contribute to the down-regulation in E-cadherin gene expression that occurs during follicle formation. However, it is intriguing that K14-Snail Tg epidermis displayed a marked down-regulation in E-cadherin expression, thereby demonstrating its potential to do so in skin. Our prior findings showed that by elevating E-cadherin levels or by conditionally ablating α-catenin, hair follicle morphogenesis can be impaired [4,7]. The marked epidermal hyperproliferation seen in the K14-Snail Tg skin, coupled with the converse suppression of proliferation and Snail in TGF-β2-null hair buds led us to wonder whether the down-regulation of E-cadherin during follicle morphogenesis might have a direct impact on elevating the proliferative state of these cells.
Our Tg studies suggested that, at least in part through its regulation of E-cadherin, Snail is able to influence the subcellular localization of a variety of AJ-associated proteins. One of these appears to be Ajuba, which was previously shown to have the dual capacity to bind Grb-2 as well as α-catenin [9,10]. Our studies revealed that in skin keratinocytes that either harbor a conditional null mutation in α-catenin or that overexpress Snail, Ajuba develops an interaction with Grb-2 that is otherwise not observed in WT keratinocytes. The corresponding abilities of either Snail-transfected or Ajuba-transfected keratinocytes to exhibit elevated activation of the Ras-MAPK pathway suggest that the Grb-2 association of Ajuba under conditions of reduced levels of AJ proteins may be directly relevant to the parallel in hyperproliferation.
In stable epithelial (i.e., Madin-Darby canine kidney, or MDCK) cell lines, Snail has been shown to block cell cycle progression and promote motility and shape changes for invasion [47]. While our in vivo studies are consistent with a role for Snail in motility and epithelial remodeling, they differ with respect to Snail's apparent proliferative effects. A priori, this could be simply due to variations in the response of different cell types to Snail expression. Alternatively, these differences may be relevant to the benefit of using mouse models to reveal functions not always recapitulated in stable cell line models. Future studies should highlight the underlying reasons for these opposing results.
Irrespective of these differences, our in vivo studies do not stand alone, as there are many situations in which a down-regulation in AJ proteins correlate with enhanced proliferation. In fact, a myriad of diverse mechanisms have been implicated in activating epithelial proliferation upon down-regulation of AJ proteins [7,23,24,48]. Sifting through these converging pathways is likely to be a difficult and painstaking process. This said, by identifying the status of different players involved in specific cell types and at specific stages in development, our mechanistic understanding of how intercellular remodeling is linked to proliferation in epithelial morphogenesis should begin to surface in the future. Elucidating the molecular mechanisms through which these networks converge is also a prerequisite for understanding how these processes go awry during tumorigenesis.
Materials and Methods
Reagents
Primary antibodies used were against: E-cadherin (M. Takeichi, Kyoto University, Japan); α-catenin, β-catenin, pMAPK, tubulin (Sigma, St. Louis, Missouri, United States), Ajuba (G. Longmore, Washington University, St. Louis, Missouri, United States); β4 integrin/CD104 (BD Pharmingen, San Diego, California, United States), laminin 5 (R. Burgeson, Harvard University, Cambridge, Massachusetts, United States), K5, K1, loricrin (Fuchs Lab), involucrin, fillagrin (Covance, Berkeley, California, United States), MAPK, pSMAD2 (Cell Signaling, Beverly, Massachusetts, United States); Grb-2 (Santa Cruz Biotech, Santa Cruz, California, United States); P-cadherin (Zymed Laboratories, South San Francisco, California, United States); HA (Roche Biochemicals), vimentin (Chemicon, Temecula, California, United States), Ki67 (Novo Castra, Newcastle Upon Tyne, United Kingdom), keratin 6 (P. Coulombe, John Hopkins University, Baltimore, Maryland, United States), cyclin D (Oncogene, San Diego, California, United States), and TGF-β2 (L. Gold, New York University, New York, New York, United States). FITC-, Texas Red-, or HRP-conjugated secondary antibodies were from Jackson ImmunoResearch (West Grove, Pennsylvania, United States). Biotinylated secondary antibodies were from Vector Labs (Burlingame, California, United States). Dilutions were according to the manufacturer's recommendation. The Snail antibody was generated in Guinea pigs by inoculating them with the N-terminal sequence of murine Snail fused to GST (Covance, Princeton, New Jersey, United States). Recombinant human TGF-β2 was purchased from R&D (Minneapolis, Minnesota, United States). Heat inactivated TGF-β2 was generated by heating the recombinant protein at 100 °C for 10 min.
Mice
The K14-Snail Tg mouse was generated by digesting the pcDNA3-mm Snail-HA plasmid (G. de Herreros, Universitat Pompeu, Fabra, Barcelona, Spain) with BamHI and NotI and subcloned into the K14 vector [49]. The linearized construct was injected into the nucleus of embryos from CD1 mice. The K14-Smad 2 Tg mouse was reported in Ito et al., 2001. The TGF-β2 knockout (KO) mouse was described in [34]. The shh KO mouse [38] and TOPGal mouse [20] have previously been reported.
Western blot and immunoprecipitation
Protein extracts from primary keratinocytes were generated either by lysing cells in lysis buffer (1% NP-40, 1% sodium deoxycholate, 20 mM Tris-Cl [pH 7.4], 140 mM NaCl containing 1 mM sodium vanadate, 2 mM phenylmethylsulfonyl fluoride, and protease inhibitors) or directly in Laemmli bβuffer and boiled. For skin tissue: Frozen tissue was pulverized in a liquid nitrogen-cooled Gevebesmascher and the powder scraped into a chilled microcentrifuge tube. RIPA buffer (1% Triton X-100 in PBS with 10 mM EDTA, 150 mN NaCl, 1% sodium deoxycholate, and 0.1% SDS) and protease inhibitors or Laemmli buffer was added. The cell suspension was sonicated three times for 15 s and centrifuged at 14,000 rpm at 4 °C. The supernatant was separated from the pellet and used in the experiments. Extracts subjected to immunoprecipitation were precleared with Protein G Sepharose (Amersham, Piscataway, New York, United States) and incubated with antibody with rocking overnight at 4 °C. Protein G Sepharose was added and samples were incubated for 1 h at 4 °C with rocking. Samples were washed three times for 5 min each in lysis buffer, and the Protein G Sepharose-antibody-antigen pellet was resuspended in Laemmli buffer and boiled for 10 min. Samples were run on SDS-PAGE and transferred to nitrocellulose membrane (Schleicher and Schuell Bioscience, Keene, New Hampshire, United States). Western blot signals were developed using the enhanced chemiluminescence kit from Amersham
Cell culture
Primary keratinocytes were culture in low-calcium medium as previously described [4]. Transient transfections were carried out with FuGENE6 reagent (Roche, Indianapolis, Indiana, United States) according to the manufacturer's protocol. Measurement of β-galactosidase or luciferase levels in promoter activity studies were carried out with the Galacto-Lite assay kit (TROPIX, Bedford, Massachusetts, United States) and the Dual luciferase (Promega, Madison, Wisconsin, United States), respectively. Runella luciferase was cotransfected into cells to correct for transfection efficiency. Experiments were done in triplicate and repeated at least three times. Measurements were done on a luminometer (MGM Instruments, Hamden, Connecticut, United States). For experiments measuring phosphorylation of MAPK, keratinocytes were serum starved for 3 h prior to harvesting of cells by incubation in medium lacking serum. Treatment of cells with Wnt- and noggin-conditioned medium was previously described [4].
Constructs
The 2.2-kb murine Snail promoter was generated by PCR using a forward primer with an XbaI linker sequence, 5′-
TCTAGAATTGTTTGCTGCTGTATGGTCTTC-3′, along with a reverse primer with a BglII linker sequence, 5′-
AGATCTGTTGGCCAGAGCGACCTAG-
GTAG-3′, and mouse genomic DNA as a template. The PCR product was purified with the Gel Extraction Kit (Qiagen, Valencia, California, United States) and ligated into pCRII-TOPO TA vector (Invitrogen, Carlsbad, California, United States). The promoter was verified by sequencing and digested with XbaI and BglII and subcloned into the pβ-gal BASIC vector (BD Biosciences Clontech, Palo Alto, California, United States). The point mutations in the SMAD binding element was generated with the Quik-Change Kit (Stratagene, La Jolla, California, United States) using the forward primer 5′-
GGGCGGGCTTAGGTGTTTTCATTTACTCTTGAGGAAAAGCTTGGC-3′ and the reverse primer 5′-
GCTTTT-
CCTCAAGAGTAAATGAAAACACCTAAGCCCGCCCTGCCC-3′. The probes for the Snail in situ hybridization were generated against the 3′ UTR by PCR using the forward primer 5′-
ACCTTCTCCCGCATGTCCTTGCTCC-3′ and the reverse primer 5′-
CTGCTGAGGCATGGTTACAGCTGG-3′, and genomic DNA as a template. The PCR product was gel purified and ligated into pCRII-TOPO TA vector. The pre-LIM domain of Ajuba was generated essentially as described [9], but was fused to GFP by subcloning from the pEGFP-N1 20 vector (BD Biosciences Clontech)
In situ hybridization
The pCRII-TOPO TA vector containing a region of the 3′ UTR of Snail was used as a template to generate digoxigenin-labeled sense and antisense riboprobes (Roche). The respective probes were obtained by XhoI and BamH1 digestions. In situ hybridizations were performed on 10-μm thick sections of E17.5 mouse embryos. The sections were fixed with 4% PFA for 10 min at room temperature, prehybridized at room temperature for 4.5 h, hybridized with the probe (2 μg/ml) at 55 °C for 12–14 h, blocked with 10% NGS, and treated with anti-dig Fab-AP antibody (Roche #1093274) at a 1:2,500 dilution for 3 h. The sections were incubated with NBT and BCIP until adequate signal was detected.
Immunofluorescence and immunohistochemistry
Tissue samples for immunofluorescence were frozen in OCT and sectioned 10 μm thick on a cryostat. Sections were fixed in 4% paraformaldehyde for 10 min at room temperature, blocked, and stained with antibodies. Tissue samples for immunohistochemistry were fixed in 4% paraformaldehyde, dehydrated, and embedded in paraffin. Samples were sectioned on a microtome (10 μm thick) and rehydrated prior to staining with antibody. Samples stained with Snail, pMAPK, pSmad2, and cyclin D were antigen unmasked with 10 mM sodium citrate (pH 6) in an Antigen Retriever 2100 (Pickcell Laboratories, Leiden, Netherlands). The DAB substrate kit (Vector Labs) was used according to manufacturer's instructions to develop the signal.
RT-PCR
RNA was extracted from keratinocytes or skin tissue with Trizol (Invitrogen) according to the manufacturer's protocol. cDNA was generated using oligo-dT primers and the Superscript II kit (Invitrogen). The primers used for PCR were Snail forward: 5′-
CAGCTGGCCAGGCTCTCGGT-3′; Snail reverse: 5′-
GCGAGGGCCTCCGGAGCA-3′; GAPDH forward 5′-
CGTAGACAAAATGGTGAAGGTCGG-3′; and GAPDH reverse: 5′-
AAGCAGTTGGTGGTGCAGGATG-3′.
We thank M Takeichi, P Coulombe, G Longmore, and L Gold for sharing their antibodies, and AG de Herreros for the Snail construct. L Degenstein and L Polak from the Fuchs laboratory provided outstanding technical assistance with transgenic work and animal husbandry. T Doetschman and Y Chai are acknowledged for the TGF-β2 KO and K14-Smad2 mice, respectively. We thank additional members of the Fuchs lab for generously sharing their reagents and ideas. CJ was partially supported by a fellowship from the Helen Hay Whitney Foundation. EF is an investigator at the Howard Hughes Medical Institute. This work was supported by the Howard Hughes Medical Institute and a grant from the National Institutes of Health.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CJ and EF conceived and designed the experiments. CJ, PL, and PK performed the experiments. CJ and EF analyzed the data. MA, RH, YC, and EF contributed reagents/materials/analysis tools. CJ and EF wrote the paper.
Note Added in Proof
Our results are particularly interesting in light of the recent implication that GSK-3β controls Snail's stability and subcellular localization [50]. Since Wnts are known to deactivate GSK-3β, Wnt and TGF-β2 signaling may contribute to Snail's transient induction and accumulation. Moreover, since inhibition of GSK-3β results in Snail upregulation and E-cadherin downregulation, Snail and GSK-3β may function at a crossroads in controlling hair bud development.
Citation: Jamora C, Lee P, Kocieniewski P, Azhar M, Hosokawa R, et al. (2004) A signaling pathway involving TGF-β2 and Snail in hair follicle morphogenesis. PLoS Biol 3(1): e11.
Abbreviations
AJadherens junction
BMPbone morphogenic protein
EMTepithelial to mesenchymal transition
E[number]embryonic day [number]
FGFfibroblast growth factor
Grb-2growth factor receptor-bound protein-2
HAhemaglutinin
K1keratin 1
K5keratin 5
KOknockout
LEF-1lymphoid enhancer factor-1
LIMLin-1
MAPKmitogen-activated protein kinase
ORSouter root sheath
pMAPKphosphorylated MAPK
P[number]postnatal day [number]
pSMADphosphorylated SMAD
Shhsonic hedgehog
SMADssmall phenotype– and mothers against decapentaplegic–related proteins
Sosson of sevenless
SOX
Sry-type HMG box
TCFT cell factor
Tgtransgenic
TGF-βtransforming growth factor β
TOPTCF-optimal-promoter
UTRuntranslated region
WTwild-type
==== Refs
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Fowlis DJ Cui W Johnson SA Balmain A Akhurst RJ Altered epidermal cell growth control in vivo by inducible expression of transforming growth factor beta 1 in the skin of transgenic mice Cell Growth Differ 1996 7 679 687 8732677
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Vega S Morales AV Ocana OH Valdes F Fabregat I Snail blocks the cell cycle and confers resistance to cell death Genes Dev 2004 18 1131 1143 15155580
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| 15630473 | PMC539061 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 28; 3(1):e11 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030011 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030018SynopsisBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyImmunologyHomo (Human)Gene Signatures Predict Interferon Response for MS Patients Synopsis1 2005 28 12 2004 28 12 2004 3 1 e18Copyright: © 2005 Public Library of Science.2005This 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.
Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods
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Multiple sclerosis (MS) can be an unpredictable disease. It develops when the body's immune system attacks healthy nerve cells and disrupts normal nerve signaling. Patients experience a wide range of symptoms—including tingling, paralysis, pain, fatigue, and blurred vision—that can appear independently or in combination, sporadically or persistently. Although symptoms appear in no particular order, flare-ups are common in the majority of patients.
MS flare-ups are commonly treated with beta-interferon. Adverse effects are not uncommon, and, more importantly, a sizable proportion of patients show a reduced response, or no response at all. Given the variability of the disease and treatment response, being able to predict how a particular patient is likely to respond to interferon would help doctors decide how close to monitor the patient or even whether to consider alternative treatments. In a new study, Sergio Baranzini et al. describe a computational model that can predict a patient's therapeutic response to interferon based on their gene expression profiles.
Immune cells typically secrete interferons to fend off viruses and other pathogens. Interferons stem viral infection by inhibiting cell division in neighboring cells—thus preventing the virus from reproducing—and triggering pathways that kill the infected cells. It's thought that interferon therapy may relieve symptoms associated with MS by correcting imbalances in the immune system that lead to disease. Interferon therapy produces changes in the gene expression profile of targeted cells—that is, it inhibits or activates certain genes—which in turn alters the cells' activity.
Blood samples were taken from 52 patients with relapsing-remitting MS (marked by acute flare-ups followed by partial or full recovery), and their RNA was isolated from a class of immune cells called peripheral blood mononuclear cells. After patients started interferon therapy, blood was taken at specific time points over the course of two years. Baranzini et al. measured the expression level of 70 genes—including a number involved in interferon interactions and immune regulation—at each time point.
Expression levels of three genes in beta-interferon responders (red) and non-responders (blue)
The authors used statistical analyses to search for gene expression profiles that were associated with patients' therapeutic outcomes. They looked for patterns in analyses of single genes, gene pairs, and gene triplets, and found their model's predictive accuracy increased with gene number. They also looked for genes that showed different expression patterns over the two years based on patient response, time passed, and patient response over time. These analyses identified genes that increased activity independently of clinical response (interferon can activate genes that have no effect on disease), as well as genes that were associated with a good or poor response. Some of these genes were also the best predictors of patient response before therapy was started.
This approach can predict the probability of a good or poor clinical response with up to 86% accuracy. Baranzini et al. offer hypotheses to explain how the observed gene activity might produce the differential responses to therapy—for example, a poor response may stem from downstream signaling events rather than from problems with drug metabolism. But the authors caution that the mechanisms connecting these genetic signatures to specific outcomes—and the mechanisms that produce a positive interferon response—have yet to be established. For now, these patterns should be thought of as markers. Still, these results suggest that doctors could one day tailor MS patients' treatments to their molecular profile, and perhaps take some of the uncertainty out of this capricious disease.
| 0 | PMC539062 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Jan 28; 3(1):e18 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030018 | oa_comm |
==== Front
PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030018SynopsisBioinformatics/Computational BiologyGenetics/Genomics/Gene TherapyImmunologyHomo (Human)Gene Signatures Predict Interferon Response for MS Patients Synopsis1 2005 28 12 2004 28 12 2004 3 1 e18Copyright: © 2005 Public Library of Science.2005This 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.
Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods
==== Body
Multiple sclerosis (MS) can be an unpredictable disease. It develops when the body's immune system attacks healthy nerve cells and disrupts normal nerve signaling. Patients experience a wide range of symptoms—including tingling, paralysis, pain, fatigue, and blurred vision—that can appear independently or in combination, sporadically or persistently. Although symptoms appear in no particular order, flare-ups are common in the majority of patients.
MS flare-ups are commonly treated with beta-interferon. Adverse effects are not uncommon, and, more importantly, a sizable proportion of patients show a reduced response, or no response at all. Given the variability of the disease and treatment response, being able to predict how a particular patient is likely to respond to interferon would help doctors decide how close to monitor the patient or even whether to consider alternative treatments. In a new study, Sergio Baranzini et al. describe a computational model that can predict a patient's therapeutic response to interferon based on their gene expression profiles.
Immune cells typically secrete interferons to fend off viruses and other pathogens. Interferons stem viral infection by inhibiting cell division in neighboring cells—thus preventing the virus from reproducing—and triggering pathways that kill the infected cells. It's thought that interferon therapy may relieve symptoms associated with MS by correcting imbalances in the immune system that lead to disease. Interferon therapy produces changes in the gene expression profile of targeted cells—that is, it inhibits or activates certain genes—which in turn alters the cells' activity.
Blood samples were taken from 52 patients with relapsing-remitting MS (marked by acute flare-ups followed by partial or full recovery), and their RNA was isolated from a class of immune cells called peripheral blood mononuclear cells. After patients started interferon therapy, blood was taken at specific time points over the course of two years. Baranzini et al. measured the expression level of 70 genes—including a number involved in interferon interactions and immune regulation—at each time point.
Expression levels of three genes in beta-interferon responders (red) and non-responders (blue)
The authors used statistical analyses to search for gene expression profiles that were associated with patients' therapeutic outcomes. They looked for patterns in analyses of single genes, gene pairs, and gene triplets, and found their model's predictive accuracy increased with gene number. They also looked for genes that showed different expression patterns over the two years based on patient response, time passed, and patient response over time. These analyses identified genes that increased activity independently of clinical response (interferon can activate genes that have no effect on disease), as well as genes that were associated with a good or poor response. Some of these genes were also the best predictors of patient response before therapy was started.
This approach can predict the probability of a good or poor clinical response with up to 86% accuracy. Baranzini et al. offer hypotheses to explain how the observed gene activity might produce the differential responses to therapy—for example, a poor response may stem from downstream signaling events rather than from problems with drug metabolism. But the authors caution that the mechanisms connecting these genetic signatures to specific outcomes—and the mechanisms that produce a positive interferon response—have yet to be established. For now, these patterns should be thought of as markers. Still, these results suggest that doctors could one day tailor MS patients' treatments to their molecular profile, and perhaps take some of the uncertainty out of this capricious disease.
| 0 | PMC539063 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Jan 28; 3(1):e29 | latin-1 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030029 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030030SynopsisDevelopmentEvolutionGenetics/Genomics/Gene TherapyCaenorhabditisThe Evolution of Self-Fertile Hermaphroditism: The Fog Is Clearing Synopsis1 2005 28 12 2004 28 12 2004 3 1 e30Copyright: © 2005 Public Library of Science.2005This 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.
fog-2 and the Evolution of Self-Fertile Hermaphroditism in Caenorhabditis
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The nematode Caenorhabditis elegans is a little less lonely than the rest of us—it is a self-fertile hermaphrodite, which as a larva makes and stores sperm before switching to egg production for the remainder of its lifespan. (C. elegans also maintains some males at a low frequency, about 1 in 500, and the hermaphrodite's eggs can be fertilized by sperm either from males or themselves.) A sister species, C. briggsae, is also hermaphroditic, but phylogenetic evidence suggests the last common ancestor of the two species had a female/male mode of reproduction. This raises the question of how the sex determination mechanisms, which must have evolved independently, differ between the two species. In this issue, Sudhir Nayak, Johnathan Goree, and Tim Schedl show that a crucial difference lies in the activities of two genes.
In C. elegans, the early period of sperm production is controlled by multiple proteins, two of which are the focus of this study, the RNA-binding protein GLD-1 (encoded by the gene gld-1) and the F-box-containing protein FOG-2 (encoded by the gene fog-2). Together, they repress translation of a gene, tra-2, by binding to its messenger RNA. This allows another gene, fem-3, to transiently masculinize the larval germline to produce sperm.
Wild-type C. elegans hermaphrodite stained to highlight the nuclei of all cells
Comparing the genomes of C. elegans and C. briggsae, Schedl and colleagues found they share 30 out of 31 sex determination genes, but not fog-2. More surprisingly, they found that the role of gld-1 in sex determination is opposite in the two species. When C. elegans is deprived of gld-1, would-be hermaphrodites produce only oocytes. But when C. briggsae is deprived of gld-1, would-be hermaphrodites produce only sperm. Thus, the authors conclude, the control of hermaphrodite spermatogenesis is fundamentally different in the two species.
By further examining the C. elegans genome, the authors showed that fog-2 arose from a gene duplication event after the C. elegans–C. briggsae split, which occurred approximately 100 million years ago. Since then, its final exon, which codes for the C-terminal end of the protein, has undergone rapid evolution. The authors also show that this is the “business end” of the protein for its interaction with GLD-1, suggesting that the divergence of C. elegans and C. briggsae sex determination pathways resulted, in part, from FOG-2's new interaction with GLD-1.
Exactly what the role of fog-2 is in C. elegans is still unclear. The authors speculate that it may recruit additional factors onto the gld-1/tra-2 mRNA complex, increasing efficiency of translation repression. Much remains to be discovered about C. briggsae sex determination as well. The authors suggest that additional genetic differences promoting self-fertility are likely to have accumulated since the two species diverged, which may act to strengthen the male–female germline switching signal. Investigation of this possibility may shed more light on how hermaphroditism operates in these two species, and how a developmental pathway controlling sex determination can evolve.
| 0 | PMC539064 | CC BY | 2021-01-05 08:21:22 | no | PLoS Biol. 2005 Jan 28; 3(1):e30 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030030 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1563048010.1371/journal.pbio.0030031Research ArticleCell BiologyMolecular Biology/Structural BiologyBiochemistryEubacteriaCobalamin-Independent Methionine Synthase (MetE): A Face-to-Face Double Barrel That Evolved by Gene Duplication Crystal Structure of MetEPejchal Robert
1
Ludwig Martha L [email protected]
1
2
1Department of Biological Chemistry, University of MichiganAnn Arbor, MichiganUnited States of America2Biophysics Research Division, University of MichiganAnn Arbor, MichiganUnited States of AmericaStroud Robert M. Academic EditorUniversity of California at San FranciscoUnited States of America2 2005 28 12 2004 28 12 2004 3 2 e3119 8 2004 17 11 2004 Copyright: © 2004 Pejchal and Ludwig.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.
Unique Double-Barreled Enzyme Makes Methionine the Hard Way
Cobalamin-independent methionine synthase (MetE) catalyzes the transfer of a methyl group from methyltetrahydrofolate to L-homocysteine (Hcy) without using an intermediate methyl carrier. Although MetE displays no detectable sequence homology with cobalamin-dependent methionine synthase (MetH), both enzymes require zinc for activation and binding of Hcy. Crystallographic analyses of MetE from T. maritima reveal an unusual dual-barrel structure in which the active site lies between the tops of the two (βα)8 barrels. The fold of the N-terminal barrel confirms that it has evolved from the C-terminal polypeptide by gene duplication; comparisons of the barrels provide an intriguing example of homologous domain evolution in which binding sites are obliterated. The C-terminal barrel incorporates the zinc ion that binds and activates Hcy. The zinc-binding site in MetE is distinguished from the (Cys)3Zn site in the related enzymes, MetH and betaine–homocysteine methyltransferase, by its position in the barrel and by the metal ligands, which are histidine, cysteine, glutamate, and cysteine in the resting form of MetE. Hcy associates at the face of the metal opposite glutamate, which moves away from the zinc in the binary E·Hcy complex. The folate substrate is not intimately associated with the N-terminal barrel; instead, elements from both barrels contribute binding determinants in a binary complex in which the folate substrate is incorrectly oriented for methyl transfer. Atypical locations of the Hcy and folate sites in the C-terminal barrel presumably permit direct interaction of the substrates in a ternary complex. Structures of the binary substrate complexes imply that rearrangement of folate, perhaps accompanied by domain rearrangement, must occur before formation of a ternary complex that is competent for methyl transfer.
By solving the structure of MetE, the authors have shed light on how the chemically difficult transfer of a methyl group from methyltetrahydrofolate to homocysteine can occur
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Introduction
Methionine synthases catalyze the transfer of a methyl group from N5-methyl-5,6,7,8-tetrahydrofolate (CH3-H4folate) to L-homocysteine (Hcy), the terminal step in the biosynthesis of methionine. Two apparently unrelated families of proteins catalyze this reaction: cobalamin-dependent methionine synthase (MetH; EC 2.1.1.13) and cobalamin-independent methionine synthase (MetE; 5-methyltetrahydropteroyltriglutamate–homocysteine methyltransferase; EC 2.1.1.14) Organisms that synthesize or transport B12 encode the cobalamin-dependent enzyme whereas organisms that cannot obtain B12 encode only the cobalamin-independent enzyme. Escherichia coli and many other species of bacteria express both enzymes, but mammals utilize only cobalamin-dependent methionine synthase while plants and yeasts utilize only the cobalamin-independent enzyme.
MetH and MetE both face the same mechanistic challenge. They must catalyze the transfer of a very poor leaving group from the tertiary amine, CH3-H4folate, to a relatively poor nucleophile, the sulfur of Hcy. MetH facilitates this transfer by using cobalamin as an intermediate methyl carrier [1]. Cobalamin accepts a methyl group from CH3-H4folate at one active site and donates it to Hcy at a second site [2]. In contrast, MetE appears to catalyze the direct transfer of the methyl group from CH3-H4folate to Hcy [3]. This latter strategy seems to offer a less satisfactory answer to the mechanistic problems: measured kcat values for MetE are smaller than those for MetH by a factor of approximately 50–100.
MetE and MetH both activate Hcy by binding the thiolate form of the substrate to Zn+2 [4]. A similar strategy for alkylation of thiol groups is employed in protein farnesyltransferase [5], geranylgeranyltransferase [6], methanol:CoM methyltransferase (MtaA) [7], the E. coli DNA repair Ada protein [8], and betaine–Hcy methyltransferase (BHMT) [9]. However, the sets of zinc ligands and the structures that house the zinc-binding sites are not conserved within this functional family. In particular, the metal ligands and their positions in the sequence are not the same in MetH and MetE. Three cysteines bind the essential zinc in MetH; the first cysteine ligand resides at the end of strand 6 of a (βα)8 barrel, and the remaining vicinal cysteine ligands follow strand 8. A histidine and two cysteines have been identified as metal ligands in E. coli MetE by a combination of mutagenesis experiments [10,11] and extended X-ray absorption fine structure (EXAFS) measurements [12]. The relative positions of these residues in the sequence led to the prediction that in a (βα)8 MetE barrel the histidine and cysteine ligands would reside at the ends of strands 5 and 8 [4].
In contrast, the sequences of MetE enzymes give few if any clues to the strategy for binding and activation of folate by MetE. Thus, the mode of folate binding is a key question to be addressed by structure analysis. In both MetE and MetH, activation of the leaving group is thought to involve the protonation of CH3-H4folate in a ternary complex, E·Hcy·CH3-H4folate in MetE, or E·cob(I)alamin·CH3-H4folate in MetH [4]. However, the residues that may facilitate protonation have not been identified for either enzyme.
MetE appears to have evolved through gene duplication of a sequence encoding a domain of approximately 340 residues that binds and activates Hcy. Within the family of MetE enzymes (Figure 1), the N- and C-terminal halves exhibit significant sequence homology. The C-terminal half is more highly conserved than the N-terminal half and has homologs in archae and elsewhere. Among these thiol methyltransferases are several enzymes that are approximately half the size of MetE and utilize corrinoid proteins, rather than folates, as methyl donors. Taken together, these observations suggested that the MetE gene arose as the result of a primordial gene duplication event followed by loss of zinc- and Hcy-binding determinants from the duplicated sequence [11]. If this hypothesis is correct, the two halves of the MetE sequence should display structural homology, and the N-terminal domain should be more closely related to the C-terminal domain than to any other protein in the database.
Figure 1 Multiple Alignment of MetE from T. maritima (METE_THEMA), E. coli (METE_ECOLI), Saccharomyces cerevisiae (METE_YEAST), and A. thaliana (METE_ARATH)
Conservation in the N-terminal domain is indicated in aqua while conservation in the C-terminal domain is shown in yellow. Zinc ligands His618, Cys620, Glu642, and Cys704 are highlighted in green. The conserved repeat at β4 is marked by asterisks. Main barrel elements are designated β(1–8)F and α(1–8)F and β(1–8)H and α(1–8)H for the N- and C-terminal barrels, respectively. Extension elements are labeled alphabetically and numbered based on the β strand that they follow. For example, α1AF follows β1F and precedes α1F.
To determine how MetE has assembled an active site for catalysis of direct methyl transfer from CH3-H4folate to Hcy, we have solved the crystal structure of Thermotoga maritima MetE at 2.0 Å resolution, along with structures of the binary substrate complexes with Hcy and folate. Difficulties in crystallization of the E. coli enzyme were circumvented by analyzing the MetE from T. maritima. This thermophilic bacterium encodes orthologs of E. coli MetH and E. coli MetE. T. maritima MetE (TM1286) is 41% identical to the E. coli enzyme and is only 19 residues shorter than E. coli MetE (Figure 1), making it an excellent prototype for the MetE family. MetE comprises two (βα)8 barrels. To our knowledge, it is the first example of a dual-(βα)8 barrel enzyme in which the active site is located between barrels arranged in a head-to-head orientation. MetE also provides a rare example of a catalytic zinc site in which four residues serve as metal ligands. Repetition of features within the structure supports the idea that MetE evolved through gene duplication of a primordial zinc/Hcy (βα)8 barrel.
Results
Description of the Fold and Its Evolution
We have determined the structures of several forms of MetE from T. maritima (Table 1), including the zinc-replete binary substrate complexes with folate (the substrate in these experiments was N5-methyl-5,6,7,8-tetrahydropteroyl-(tri)-γ-L -glutamate [CH3-H4PteGlu3]) at 2.59 Å resolution and with Hcy at 2.20 Å resolution. The two (βα)8 barrels are formed by residues 1–351 (folate barrel) and 387–734 (Hcy barrel) and joined by an extended inter-domain linker (Figure 2A). An unusual feature is that many of the binding determinants for folate actually reside in the C-terminal barrel that binds Hcy.
Figure 2 The Fold of MetE and Similarities between the Two Barrels
(A) MetE folds into two (βα)8 barrels. The N-terminal barrel (aqua) is joined to the C-terminal barrel (yellow) by a 35-residue inter-domain linker (gray) that spans 65 Å. Except for the α1 helix (at the right), the linker residues are in extended conformations. This view is along the approximate 2-fold axis that relates the two barrels. The drawing is based on coordinates for zinc-replete MetE in complex with CH3-H4folate (Table 1). The zinc ligands, zinc, and CH3-H4folate are shown in ball-and-stick representation. This figure and all subsequent figures were prepared using RIBBONS [43]. See Figure 1 for the nomenclature used to describe secondary structures.
(B) A side-by-side view of the barrels of MetE, arranged to show the similarities of the β–α loop extensions. Major extensions that follow the first four β strands of the barrels are shown in cyan and gold for the N-terminal and C-terminal barrels, respectively. The drawing is based on coordinates for the zinc-replete binary complex with folate (not shown). The active site is located in the C-terminal barrel (on the right) between the extra-barrel β hairpin of the β2–α2 loop and the C-termini of the barrel strands. Zinc is gray and the zinc ligands, His618, Cys620, Glu642, and Cys704, are shown in ball-and-stick mode.
Table 1 Data Collection and Refinement Statistics for TM1286-HIS
a Statistics calculated using the programs XDS and DENZO/SCALEPACK
b Number of unique data assigned as test
c Determined experimentally at APS
n/a, not applicable
In (βα)8 (triose phosphate isomerase [TIM]) barrel enzymes, the active site is usually located near the C-termini of the inner barrel strands, with catalytic residues contributed by the β–α segments (loops) that join these strands to the outer helices. These barrels are topologically polar with their “tops” decorated by insertions that extend the β–α loop segments. MetE is the first example of a dual-(βα)8 barrel in which the decorated tops of the two barrels face each other to form a single active site that lies between the domains. A deep cleft between the barrels permits entry of the substrates (Figure 2A). As a result of the arrangement of the barrel domains, residues 352–386 of the inter-barrel linker must span approximately 65 Å to connect the bottoms of the two barrels (Figure 2A).
The N- and C-terminal barrels share a number of strikingly similar features that provide structural evidence for gene duplication. Both barrels incorporate long extensions in the first four β–α loops but, with the exception of α8AF (Figure 2B), lack insertions in the last four loops. A pseudo 2-fold axis superimposes these similar extensions (Figure 2). The helical extension at the start of β1–α1, α1AH, augments the side of the C-terminal barrel and is repeated in the N-terminal barrel. The β2–α2 loops that appear in both barrels are the longest extensions in the structure, and we refer to them as the “long hairpin loops.” In the C-terminal barrel that binds Hcy and Zn+2, the long β2–α2 loop begins with helix α2AH and then forms an antiparallel excursion that harbors a number of conserved residues, some of which are involved in binding folate. The β3–α3 loops both include a short helix, α3A, that carries folate-binding determinants in the Hcy (C-terminal) barrel. The β4–α4A segments of both barrels incorporate a conserved sequence, identified by asterisks in Figure 1. In E. coli MetE, this sequence, Gln-Ile-Asp-Glu-Pro-Ala, is identical in both barrels.
Despite the pseudosymmetry that relates the β–α loops of the two barrels, there are significant differences in the sequences and conformations of these connecting loops that distinguish the functional roles of the two barrels. The major binding determinants for the folate substrate lie primarily in the second, third, and fourth β–α loops of the C-terminal barrel. The equivalent binding site in the duplicated N-terminal barrel has been obliterated. Although the long β2–α2 hairpin of the N-terminal barrel resembles the corresponding hairpin of the C-terminal (Hcy) barrel, the potential binding groove for the folate tail is closed by extensive hydrophobic contacts between α1AF, α1F, and α2CF and by interaction with the long hairpin of the C-terminal barrel. The positioning the long β2 hairpin significantly closer to the barrel top than in the C-terminal barrel occludes the pterin-binding site (Figure 2B).
Several other adaptations enhance the barrel–barrel interface and appear to influence the relative orientations of the barrels. The β8–α8 loop containing helix α8AF is 16 residues longer than the corresponding loop in the C-terminal domain and makes numerous inter-barrel contacts. Likewise, loop β4–α4 in the C-terminal domain is extended by six residues, increasing contacts with the N-terminal barrel. The single-domain archaeal Hcy methyltransferases that are homologous to the C-terminal domain are missing these six residues, supporting the notion that this insert evolved to enlarge the inter-domain interface.
The Zinc Site
The zinc-binding site of MetE is distinguished from most catalytic zinc sites by the presence of four protein ligands to zinc. Mutagenesis of E. coli MetE had previously shown that zinc is bound by a histidine and two cysteines [11] that are equivalent to residues His618, Cys620, and Cys704 in T. maritima MetE. EXAFS studies of the E. coli enzyme indicated a fourth oxygen or nitrogen coordinated to zinc that seemed likely to be a water oxygen. However, the structure of zinc-replete enzyme with folate bound (Table 1) clearly reveals the presence of a fourth protein ligand to zinc. A carboxylate oxygen of the invariant Glu642, which had not previously been identified as a metal ligand, is coordinated to zinc.
In the zinc-replete complexes of MetE with folate that provide resting-state structures of the zinc site, the metal–ligand cluster adopts tetrahedral geometry. EXAFS measurements on substrate-free E. coli MetE are also consistent with tetrahedral coordination [3,11] with bond lengths of 2.31 Å for two Zn–S bonds and 2.04 Å for two nitrogen or oxygen ligands. The observed Zn–S bond lengths in our structure are 2.30 Å, Zn–N is 2.07 Å, and Zn–O is 2.14 Å, in good agreement with the EXAFS measurements.
The residues that bind zinc are located near the ends of barrel strands 5, 6, and 8 (Figure 3). His618 is the C-terminal residue of β5 and Cys620 is located on the following β–α loop. Glu642 is at the C-terminus of β6, and Cys704 resides on the loop following strand β8. The β–α loops that contain the two cysteine residues are drawn together to form the metal-binding site, distorting the barrel (see Discussion).
Figure 3 The Resting-State Zinc Is Coordinated in a Tetrahedral Fashion by Four Protein Residues
The Binary Complex with Hcy
In the complex of Hcy with zinc-replete MetE (Table 1), Hcy is positioned by numerous interactions with conserved protein residues (Figure 4). The amino group is coordinated by hydrogen bonds to Asp577, Glu462, and the carbonyl of Ile409. The carboxyl group of Hcy is bound by the backbone amide and the side chain hydroxyl of Ser411, and hydrophobic contact to the Hcy sulfur is provided by Met468. These interactions with the Hcy substrate are reminiscent of those observed in MetH [2] (see Discussion).
Figure 4 The Geometry at the Zinc Ion in Complexes with Hcy
(A) Interactions of Hcy in the MetE·Hcy binary complex. The amino group of Hcy is bound by hydrogen bonds to Asp577, Glu462, and the backbone carbonyl of Ile409; the Hcy carboxyl group interacts with the backbone amide and side chain hydroxyl of Ser411. The Hcy sulfur is coordinated to zinc via a long (3.15 Å) bond, which is eclipsed in this view.
(B) Superposition of the MetE resting state (gray) and the Hcy binary complex (yellow). Upon Hcy binding, zinc and His618 move away from Glu642 and closer to Hcy, and the zinc site adopts trigonal bipyramidal geometry with three strong equatorial ligands (Zn–NHis618, 2.07 Å; Zn–SCys620, 2.23 Å; Zn–SCys704, 2.24 Å) and two distant axial ligands (Zn–OGlu642, 2.90 Å; Zn–SHcy, 3.13 Å). Zinc moves 0.75 Å toward the substrate in the Hcy complex. Full inversion at zinc, upon tight binding of Hcy to MetE, would displace the metal ion approximately 1.5 Å.
(C) The MetH·Hcy complex. The zinc configuration in substrate-free MetH is opposite to that found in MetE; binding of Hcy occurs without inversion in MetH and in BHMT.
(D) Difference electron density for the MetE·Hcy complex, showing the geometry at the metal-binding site. The map was computed after simulating annealing and refinement of a model omitting the zinc and its five neighbors. The long Zn–Hcy and Zn–Glu642 interactions are indicated with dashes. Contour levels are 2σ (green) and 6σ (orange).
Hcy binding induces significant changes at the metal site (Figure 4). Binding of the substrate sulfur to zinc does not proceed by a simple dissociative reaction in which sulfur is substituted for the oxygen of Glu642. Instead, Hcy approaches the metal ion from the side opposite the Glu642 ligand. This mode of association is unusual; in most catalytic zinc sites the incoming substrate replaces a dissociable fourth ligand without inversion [13]. In the structure of the binary complex determined at pH 5.2, displacement of Glu642 and inversion at Zn+2 are incomplete. Zinc coordination changes from tetrahedral to distorted trigonal bipyramidal geometry (Figure 4), and the substrate sulfur and glutamate oxygen both exhibit unusually long ligand–metal distances: 3.15 Å and 2.9 Å for Zn–S and Zn–O, respectively. Comparison with the zinc-replete complex with folate (Figure 4B) shows that zinc and His618 have moved 0.76 Å and 0.98 Å toward the substrate while Cys620, Glu642, and Cys704 remain essentially fixed. The Zn–S(Cys) and Zn–N distances are not significantly altered, but the zinc and these three ligands become more nearly coplanar (Figure 4B). Complete inversion would result in geometry resembling the Hcy complex of MetH (Figure 4C).
The geometry at the metal site in the Hcy complex has been confirmed by omit refinement and by tests with restrained models. The density around the zinc atom in omit maps is well resolved from Hcy but continuous with that of the cysteine ligands (Figure 4D), consistent with a Zn–Hcy distance that is significantly longer than the Zn–Cys bond distances. Imposing tetrahedral restraints in refinements with Hcy as the fourth ligand results in large difference Fourier peaks that also indicate a long Zn–Hcy distance.
Cysteinyl tRNA-synthetase incorporates an active-site zinc with the same set of ligands as MetE and provides a precedent for an inversion at the metal center induced by substrate binding. Cysteinyl tRNA-synthetase uses this zinc ion not for catalysis, but to discriminate against serine, exploiting the strong zinc-thiolate interaction with its substrate [14,15]. In the absence of substrate the zinc site displays geometry that is intermediate between tetrahedral and trigonal bipyramidal. As in MetE, the cysteine substrate binds opposite a glutamate residue and does not displace the glutamate ligand directly. Upon cysteine binding, zinc moves away from glutamate and forms a 2.5 Å bond to cysteine.
Folate Binary Complex
Structures of CH3-H4folate bound to MetE from T. maritima have been determined in both the reduced (zinc-replete) enzyme and the oxidized (disulfide-bonded) form (Table 1). CH3-H4folate is bound in identical fashion in both structures. The novel feature of these structures is that the MetE·folate complex fails to comply with the classic picture of substrate binding in a TIM barrel. The folate substrate binds in a deep cleft between the two barrels, with its glutamate tail accommodated by a groove in the enzyme surface (Figure 5). The pterin is displaced from the top of the N-terminal barrel, and simultaneously shifted away from the axis of the C-terminal barrel that binds zinc and Hcy (see Figure 2A). It is thus inappropriate to call the N-terminal barrel the “folate-binding domain.” An animation, in which Figure 5 is rotated about its vertical axis, provides a more complete view of the structure and its bound ligands (Video S1).
Figure 5 Interactions of CH3-H4folate with MetE
(A) A stereoview of T. maritima MetE showing the substrate and metal-binding sites. This is a composite picture in which Hcy from the MetE·Hcy complex has been positioned by superposition on the structure of the MetE·CH3-H4folate binary complex. The substrates and metal ligands are displayed in ball-and-stick mode; Hcy is in green.
(B) A stereoview of the zinc site and bound CH3-H4folate. Folate is bound by conserved residues in the N-terminal barrel (aqua) and the C-terminal barrel (yellow) with the N5-CH3 facing away from the zinc, at a distance of almost 14 Å. The pterin interacts with the long hairpins of both barrels and with the extra-barrel helices α3AH and α4AH. The groove that binds the glutamate tail of the substrate is bordered by α1AF, by the β hairpin (β2BH and β2CH) and α1AH of the C-terminal domain, and by the conserved DMV sequence that begins α2AH.
The pterin ring of CH3-H4folate is positioned by stacking and by hydrogen bonding with conserved residues (Figure 5). Glu583 makes a bidentate interaction with the 2-NH2 and N3 groups of CH3-H4folate, an arrangement found in several other folate-binding sites. Interaction of N3 with an acidic group has been shown to be important for catalytic activity in dihydrofolate reductase [16], thymidylate synthase [17], and MetH [18]. In the binary complexes with MetE that we report here, the pterin ring of the folate is stacked against Trp539, Lys104 hydrogen bonds to O4, and the folate N5 is hydrogen bonded to a water molecule. However the N8 and N1 positions of the pterin are exposed to solvent.
The subsite that binds the glutamate tail is a groove lined by conserved basic residues (Figure 5). Arg15, Lys18, Arg493, and Arg496 interact with the first glutamyl residue, which is the only one of the three γ-linked glutamate residues that is ordered in the binary complex. Weak binding of the other tail residues is surprising for an enzyme that exhibits an absolute requirement for polyglutamylated folate, but glutamate tails have displayed disorder in other structures where they also contribute to strength of binding [19].
The orientation of bound folate in the binary complexes does not allow transfer of the methyl group to Hcy. As can be seen in Figure 5, the N5-methyl carbon faces away from zinc and Hcy; it is 11 Å from the sulfur of Hcy when the binary complexes are superimposed. A rotation about the folate N10–C4′ dihedral angle, with the interactions of the para-amino benzoyl moiety and glutamate acting to anchor the substrate, would position the methyl group correctly with respect to Hcy, decreasing the distance between groups that react to 6.0 Å. This distance is still long, and additional protein or substrate rearrangements would be necessary to close the gap between the sulfur of Hcy and the methyl carbon. An alternative route to a ternary complex that supports methyl transfer would be complete dissociation and reassociation of the CH3-H4folate. However, because so many interactions with conserved residues are observed in this binary complex, it seems likely to represent an initial intermediate rather than a dead-end complex (see Discussion).
The 467Asp-Met-Val Sequence Mediates Interaction between the Substrate-Binding Sites
One of the fascinating features of the binary substrate complexes is the evidence for communication between the substrate-binding sites, mediated by the invariant 467Asp-Met-Val (DMV) sequence, which forms the N-terminal turn of helix α2AH. When Hcy binds, the side chain of Met468 alters its position in concert with backbone displacements of the aspartic acid and methionine residues that start the helix. Comparison of the substrate-free structure with the Hcy binary complex shows how the DMV region moves toward the zinc center when Hcy binds (Figure 6). These changes in turn affect the interactions and orientation of Trp539, favoring the conformation in which Trp539 can stack against the pterin ring. In the absence of substrates, Trp539 can adopt another conformation that would overlap the binding site for the pterin ring.
Figure 6 Superposition of the Hcy Binary Complex (Yellow) and Substrate-Free (Gray) Enzymes Showing Local Changes in the DMV Region
Met468 moves toward the Hcy substrate, rearranging the start of helix α2AH, and a new hydrogen bond is formed between the carbonyls of Met468 and Thr531. This position of Met468 stabilizes a rotamer of Trp539 that favors folate binding.
In the binary complex with CH3-H4folate, the DMV loop is also recruited to the position it occupies when Hcy is bound. The changes that are induced by binding of folate are reproduced in the complex of folate with the oxidized enzyme. Thus, binding of either substrate favors a conformation that would be expected to increase the affinity of MetE for the other substrate. These small but significant conformation changes observed in the binary complexes precede larger rearrangements that must be induced by binding of both substrates to form a competent ternary complex. Cooperativity in substrate binding could increase the concentrations of the ternary complex and thereby increase turnover in a system that is already plagued by slow chemistry [4].
Discussion
Comparisons with MetE from Arabidopsis thaliana
A very recent paper, which appeared after the submission of our manuscript, has described the structure of MetE from A. thaliana [20]. Although the folds of the enzymes from A. thaliana and T. maritima are obviously similar, there are some significant differences in the reported features of the zinc-binding sites. In the complexes of A. thaliana MetE with Hcy or methionine, the distances between zinc and substrate or product sulfur are long, as is the case in the Hcy complex of MetE from T. maritima, but water rather than glutamate has been assigned as the ligand opposite to Hcy or Met. In contrast, the electron density in omit maps of the Hcy complex of T. maritima MetE shows no evidence for a water intervening between glutamate and zinc (see Figure 4D). In substrate-free A. thaliana MetE, the metal–ligand bonds are all very long and the geometry is highly distorted, suggesting some disordering or partial oxidation of the metal site under the conditions used for crystallization.
To study folate binding, PteGlu5 and CH3-H4PteGlu5 were added to crystals of the Hcy or methionine complexes of A. thaliana MetE [20]. In the resulting structures, the pterin ring is flipped relative to its position in the binary folate complex of T. maritima MetE, and adopts an orientation that is similar to what we anticipated from model building. Although the occupancy of the reduced folate appears to be low, it is estimated that the methyl group of the CH3-H4PteGlu5 is about 7 Å from the sulfur of Hcy, too distant for transfer to Hcy. Thus, both structure analyses suggest that additional conformation changes must occur to form a reactive ternary complex.
Comparisons of MetE with MetH
MetE and MetH display no detectable sequence homology and have different sets of zinc ligands. Comparison of the barrels from MetE with the corresponding domains of MetH that bind folate or Hcy reveal that the N-terminal barrel of MetE, which carries some folate-binding determinants, differs in significant ways from the folate barrel of MetH, whereas the Hcy barrels share many similar features.
Two other (βα)8 barrels that bind CH3-H4folate have been described: methyltetrahydrofolate corrinoid/iron-sulfur protein methyltransferase [21] and the folate-binding module of MetH [2]. These homologous barrels both bind the CH3-H4folate substrate at the top of the folate barrel and use similar interactions with residues contributed by the C-termini of the inner barrel strands. In MetE, CH3-H4folate is displaced from the N-terminal barrel and bound primarily by residues in the long extra-barrel β hairpin of the C-terminal Hcy domain (see Figures 2A and 5). Dissimilarities of the decorating loops in the N-terminal barrel of MetE and the folate barrel of MetH are documented by poor statistics for sequence matches and for alignments with the structures of corrinoid/iron-sulfur protein methyltransferase or MetH.
The Hcy barrels of MetE and MetH are compared in Figure 7, which shows how each structure accommodates metal binding. Strand distortions in the Hcy barrels, which have been associated with construction of a metal-binding site [2,9], are related but not identical in MetE and MetH (or BHMT [9]). In MetH, strand β7 is extruded from the barrel and strands β6 and β8 are pinched together to bind the zinc [2]. In MetE, strands β6 and β7 are displaced relative to their positions in MetH, allowing strands β5 and β8 to approach one another. In both enzymes, distortion of β8 is accompanied by a splay in strand β1; only one classic hydrogen bond is made between strands β1 and β2. Conserved residues in MetH, MetE, and BHMT stabilize inter-strand interactions by forming side-chain-to-main-chain hydrogen bonds.
Figure 7 Superposition of MetE and MetH Zn+2/Hcy Barrels
In MetH (gray) the sulfur of Hcy is positioned close to the center of the barrel for interaction with methylcobalamin. In MetE (yellow) the α1–β1 and α8–β8 connectors make large incursions across the top of the barrel, displacing Hcy to the other side of the barrel.
Major differences in the connecting loops β1–α1 and β8–α8 displace the zinc and Hcy sites in MetE relative to MetH by approximately 6 Å so that the sulfur of Hcy is no longer on the barrel axis but is shifted toward one wall of the barrel (Figure 7). The altered positions of the ligands and the binding of zinc by Glu642 lead to inversion of the zinc center relative to its configuration in MetH. Displacement of the Zn+2/Hcy site and the unusual mode of folate binding seem to have evolved to allow methyl transfer in a ternary E·Hcy·CH3-H4folate complex.
Despite the translation and inversion of the metal and its ligands, the orientation and local interactions of Hcy are almost identical in MetE and MetH (Figure 7). Both enzymes use conserved carboxylate residues to interact with the amino group of Hcy, forming salt bridges (see Figure 4). In both MetH and BHMT, the carboxyl group of Hcy is bound by a pair of backbone amides located at the beginning of the β1–α1 extension. In MetE the corresponding interactions of COO− are made by the backbone amide and the side chain hydroxyl of Ser411, again located on the extension following β1.
Curiously, it appears that distortions of the barrel strands and connecting loops need not be undone when metal binding is lost through evolution. Distortions of the barrel strands and their downstream loops are retained in the N-terminal barrel of MetE despite loss of metal and Hcy binding, and similar distortions were first observed in uroporphyrinogen decarboxylase [22], which is also not a metalloenzyme. Despite the lack of detectable sequence homology, uroporphyrinogen decarboxylase is the closest structural relative of the Hcy domain of MetE in the current protein database: alignment using DALI [23] matches the two folds with a similarity score that is higher than that for the Hcy barrels from MetE and MetH. Uroporphyrinogen decarboxylase may have evolved from a Zn+2/Hcy barrel, despite the fact that it is no longer a zinc-dependent thiol alkyltransferase.
Substrate Binding and Activation: Inferences from the Structures
In the structure of the MetE·Hcy complex, the zinc site adopts distorted trigonal bipyramidal geometry with long bonds from the metal ion to glutamate and Hcy (see Figure 4). In contrast, EXAFS measurements on the Se–Hcy complex of E. coli MetE at pH 7.2 are best fit to a tetrahedral ligand environment with two sulfurs (2.33 Å), one nitrogen or oxygen (2.02 Å), and one selenium (2.433 Å), in which the longer Zn–Se distance reflects the increased radius of selenium relative to sulfur [3]. EXAFS studies of the related methyltransferase MT2-A, which has the same set of zinc ligands as MetE, also indicate tetrahedral geometry in the binary complex with coenzyme M (2-mercaptoethanesulfonic acid) [7]. Both of these studies concluded that the geometry at zinc does not change appreciably upon binding of the thiolate substrate.
Because the crystals of the Hcy binary complex of MetE were equilibrated at pH 5.2, it is possible that Hcy may be protonated in the X-ray structure. Thus, the long S–Zn bond and partial inversion at zinc might be explained by the relatively weak interaction between the thiol and the metal ion, as documented for thiol and thioether ligands in model compounds [24,25,26]. X-ray studies of Hcy binding at neutral pH and EXAFS measurements at lower pH will be necessary to determine whether pH is a key parameter that affects the metal–ligand geometry. It is possible that complete inversion of zinc geometry will be observed in the structure of the MetE·Hcy complex at neutral pH.
A long Zn–S− bond may be functionally important. It has been suggested that a long bond and/or distorted geometry would optimize the reactivity of zinc-dependent thiol alkyltranferases [27] by increasing the charge on the thiolate sulfur, and would avoid trapping of a lower-energy tetrahedral species. Since zinc is known to have flexible coordination geometry [28], a five-coordinate state seems plausible, and the observed structure of MetE·Hcy may indeed afford a glimpse of an intermediate or transition-like state that is poised to attack the N5 methyl group of the folate substrate.
The motion of Zn+2 that accompanies Hcy binding to MetE is unique among the Hcy methyltransferases with known structures. There is no evidence for inversion of configuration at zinc in MetH, and inversion is precluded in BHMT, where a leucine occupies the position analogous Glu642 of MetE. Zinc motion provides a novel way to alter the distribution of charge among the zinc and its ligands, thereby modulating thiolate reactivity. The effects on the electronic structure could be larger than those resulting from the changes in bond lengths observed in other zinc-dependent alkyltransferases [29,30]. By analogy with a proposal for the reaction cycle of protein farnesyltransferase [30], reassociation of Glu642 could promote dissociation of the methionine product following methyl transfer.
An unexpected feature of the MetE structures is the binding mode of CH3-H4folate, with the pterin ring incorrectly oriented for methyl transfer. It is possible that folate binds initially in this manner to avoid blocking access to the Hcy binding site, which lies between zinc and CH3-H4folate. Space-filling models show that the Hcy-binding site remains accessible in the MetE·CH3-H4folate binary complex. The observed binding mode thus permits random addition of substrates but does not rule out a kinetically preferred order of binding.
In both MetH and MetE, protonation of methyltetrahydrofolate is thought to occur in the ternary complexes but not in the binary folate complexes [4]. The immediate proton donor has not yet been identified by enzymatic or biochemical studies of E. coli MetE. Formation of the binary Hcy complex results in release of a proton to solvent (Z. S. Zhou and R. G. Matthews, unpublished data). Thus Hcy is not a likely proton donor. It is more likely that an active-site residue may serve as an acid catalyst. Structures of the binary complexes do not implicate a particular residue as a general acid catalyst, but suggest possible candidates. His111 could serve as a proton donor through water if protonation were to occur before folate rearrangement. His672, part of a conserved 670Asp-Ile-His-Ser-Pro sequence, or Asp467, located in the DMV loop, may be positioned to serve as donors if protonation occurs after folate rearrangement. All three of these candidate residues are invariant in multiple sequence alignments.
Gene Duplication of a Sequence Encoding the Hcy Barrel
Earlier comparisons of sequences had suggested that MetE evolved by gene duplication. The two domains of E. coli MetE share a conserved Gln-Ile-Asp-Glu-Pro-Ala repeat [31], and the sequences display 39% identity (50% similarity) within the regions now seen to span β3 to α4A of the two barrels. The pseudosymmetry of the structural features decorating the barrels provides compelling evidence for a close relationship between the two halves of MetE, verifying the inferences based on sequence alignments. The cores of (βα)8 barrels, though they display characteristic distortions and can be classified into subgroups [32], do not provide as strong evidence for relatedness as do the similarities of regions inserted in intervening loops. It is difficult to ascertain whether the regions responsible for folate binding were inserted before or after gene duplication. The long β2–α2 loop in the N-terminal barrel and corresponding sequences in archaeal relatives of MetE favor the notion that precursors of folate-binding segments were present before duplication. In any case, folate-binding determinants have developed or been retained primarily in the C-terminal Hcy barrel but not in its N-terminal replicate.
The idea that the zinc/Hcy barrel is the ancestral fold is based on several lines of evidence. This barrel shows significant structural homology to a broad family of zinc-dependent thiol methyltransferases, including not only MetH and BHMT but also the single-domain archaeal transferase enzymes that react with methylcobalamin. Indeed it seems likely that the three enzymes that convert Hcy to methionine, MetE, MetH, and BHMT, are all descended from a primordial zinc/Hcy barrel. In contrast, DALI searches that assess structural similarities [23] reveal that the N-terminal barrel is more similar to the C-terminal barrel of MetE than to any other known protein, suggesting that its immediate precursor is the Hcy barrel of MetE.
Gene duplication of a Zn+2/Hcy barrel would have replicated the sites that bind Zn+2 and Hcy, but these sites have been disabled in the N-terminal barrel of contemporary MetE. Disruption of zinc and Hcy binding is effected by both residue mutations and backbone conformational changes (Figure 8). Mutation of the equivalent of Cys620 in the Hcy barrel to Tyr232 leads to stacking with the adjacent Tyr233 (phenylalanine in most MetE sequences) and results in a major backbone conformational change. The hydroxyl group of the Tyr232 forms a hydrogen bond to Asn199 of the conserved Leu-Val-Asn-Glu-Pro-Ala sequence at the β4–α4 loop and thus removes a crucial Hcy-binding determinant. Although Cys309, the equivalent of Cys704 of the C-terminal barrel, is retained in most MetE sequences, mutation of the other zinc ligands and many of the Hcy-binding residues leads to a complete overhaul of the binding site for Hcy.
Figure 8 Disruption of Hcy and Zinc Binding in the N-Terminal Barrel
Overhaul of the Zn+2/Hcy site in the N-terminal domain (aqua) following gene duplication is accomplished through mutation and small backbone displacements. The competent Zn/Hcy site from the C-terminal barrel is at the left; the remodeled site from the N-terminal barrel is at the right. Important substitutions that disable Hcy and zinc binding are Trp146, Phe230, Tyr232, and Asp253 (see text).
Why Is a Second Barrel Recruited for a Ternary Complex Mechanism?
Although an entire barrel domain is recruited to elicit direct methyl transfer from folate to Hcy, the structure reveals that the functional groups of this domain are mostly disabled. The binding determinants and potential catalytic residues are located primarily in extensions and excursions of the C-terminal Zn+2/Hcy barrel rather than in the “new” N-terminal domain. Duplication and divergence of an entire barrel is an elaborate strategy to accommodate a second relatively large substrate at a single active site, and MetE provides the first instance of the face-to-face barrel construction that is required to build such an active site. A more common strategy to accommodate a ternary complex is exemplified by the related Hcy methyltransferase, BHMT. In this enzyme the site for the rather small second substrate, betaine, is constructed in part from β–α barrel extensions and in part from a dimerization arm that is appended to the barrel and contributes to the active site of the partner chain of the functional dimer.
The N-terminal domain of MetE may nevertheless be essential for ternary complex formation and catalysis. In the family of thiol alkyltransferases, quenching of opposite charges on Hcy and the alkyl donor is believed to drive the reactions, and a hydrophobic environment [33] and desolvation [34] of reactants may be critical for reactivity. Rearrangement of folate to form a viable ternary complex may be accompanied by rearrangement or closure of domains around the substrate-binding cleft that would reposition key residues and isolate the active site from solvent. Hcy binding is accompanied by a contraction of the top of the C-terminal barrel that alters the relative positions of the N- and C-terminal barrels. This small domain shift clearly gives hints of inter-domain flexibility. On the other hand, the commissioning of the N-terminal domain may reflect an evolutionary strategy in which gene duplication is utilized as the most facile way to recruit additional sequences. Structures of ternary complexes, obtained using mutant enzymes and/or substrate analogs, should provide further insights into the methyl transfer reaction and the functional roles of the N-terminal domain.
Materials and Methods
Cloning and purification
The TM1286 gene was PCR amplified from T. maritima genomic DNA (
ATCC, Manassas, Virginia, United Stats) and cloned into pET-151D/TOPO (Invitrogen, Carlsbad, California, United States). The expression construct contains an N-terminal leader sequence consisting of a 6X histidine tag followed by a V5 epitope, rTEV cleavage site, and residues 2–734 of the coding sequence of TM1286. This vector was overexpressed in BL21(DE3)Star (Invitrogen) by induction with 0.8 mM IPTG for 8 h in LB media supplemented with zinc sulfate. The histidine-tagged protein was purified by a 10-min 70 °C heat step, which precipitates heat-labile protein, followed by affinity chromatography on Zn(II)-NTA and elution with a 50 mM to 1.5 M glycine gradient. The protein was dialyzed against 50 mM Tris (pH 7.4) and 500 μM Tris(2-carboxylethyl)phosphine (TCEP) and concentrated to 20 mg/ml. Selenomethionine-labeled protein was expressed in M9 medium supplemented with amino acids and zinc sulfate.
Crystallization
TM1286 was crystallized by the vapor batch (microbatch) method under oil utilizing 96-well Douglas vapor batch plates and a 1:1 mixture of silicon:paraffin oil (Hampton Research, Alliso Viejo, California, United States). Orthorhombic crystals of space group P21212 (a = 163.57 Å, b = 158.76 Å, c = 64.16 Å, α = β = γ = 90°) were grown by mixing 20 mg/ml protein 1:1 with 25% poly(ethylene glycol) 4000, 0.2 M ammonium sulfate, and 0.1 M sodium acetate (pH 4.6). Selenomethionine-labeled protein was crystallized by mixing 1:1 with 12% poly(ethylene glycol) 4000, 0.2 M ammonium sulfate, and 0.1 M sodium acetate (pH 4.6). Anapoe detergents (Anatrace, Maumee, Ohio, United States) were used as additives and seen to have a favorable effect on crystal morphology. Crystals in a cryoprotectant solution consisting of 15% poly(ethylene glycol) 4000, 0.12 M ammonium sulfate, 0.1 M sodium acetate (pH 5.2), and 12.6%–14.1% meso-erythritol were flash-cooled in liquid nitrogen.
These crystals were found to be depleted of zinc with a disulfide bond connecting the zinc ligands, Cys620 and Cys704. Zinc-replete crystals were obtained by soaking in cryoprotectant solution containing 500 μM zinc sulfate and 500 μM TCEP for 4–16 h prior to flash cooling. The enzyme:folate binary complex was formed by soaking crystals in cryoprotective solution with added 3.5 mM CH3-H4PteGlu3 (a gift from Rebecca E. Taurog) for several hours. The enzyme:Hcy binary complex was formed by soaking crystals pre-equilibrated with zinc sulfate and TCEP in cryoprotective solution containing 10 mM L-Hcy (a gift from Rebecca E. Taurog) for several hours.
Phasing and refinement
All datasets were collected at the Advanced Photon Source (APS) at Argonne National Laboratory. Data collected at the DND-CAT beamline on a Mar225 detector were processed with XDS [35] whereas those collected at COM-CAT on a Mar165 detector were processed with DENZO/SCALEPACK [36]. Statistics for the datasets appear in Table 1.
Experimental phases to 2.8 Å were derived from selenium SAD measurements at the selenium peak using heavy atom sites located by a three-wavelength selenium MAD experiment at lower resolution. Thirty of the 36 expected selenium sites were found and phases were determined using SOLVE version 2.05 [37], and statistical density modification was performed in RESOLVE [38]. The initial model from RESOLVE was rebuilt in MI-fit [39] and partly refined, and was used with the molecular replacement program EPMR [40] to determine the higher resolution structure of oxidized MetE at 2.00 Å. A search model derived from the refined oxidized structure was subsequently used to solve the substrate complexes with EPMR. All models were developed by refitting and rebuilding in MI-fit alternated with refinement in CNS version 1.1 [41]. Refinement protocols included simulated annealing with torsional dynamics, coordinate minimization, and adjustment of individual B-factors.
In late rounds of refinement of the binary complexes, weak restraints were applied to maintain the geometry at the zinc site. For the MetE·CH3-H4folate complexes (the resting state of the zinc center), restraints were based on ideal tetrahedral geometry; for the Hcy complex, restraints were chosen using bond valence sums analysis [42] to make the bond lengths compatible with the known +2 oxidation state of zinc. The resting-state zinc site (Zn–NHis618, 2.07 Å; Zn–SCys620, 2.31 Å; Zn–SCys704, 2.29 Å; Zn–OGlu642, 2.14 Å) gives a bond valence sum of 1.88, consistent with the known +2 oxidation state of zinc, and a net contraction of Zn–SCys bonds from 2.30 to 2.24 Å upon Hcy binding is enough to maintain the bond valence sum for the five-coordinate state with long axial bonds.
Supporting Information
Coordinates of the structures have been deposited in the Research Collaboratory for Structural Bioinformatics' Protein Data Bank (http://www.rcsb.org/pdb/) with accession codes 1T7L (substrate-free oxidized), 1XDJ (zinc and Hcy complex), 1XPG (zinc and methyltetrahydrofolate complex), and 1XR2 (oxidized methyltetrahydrofolate complex).
Video S1 Video of MetE Showing the Substrate and Metal-Binding Sites
(7.9 MB WMV).
Click here for additional data file.
Accession Numbers
The SwissProt (http://www.ebi.ac.uk/swissprot/) accession numbers for the gene products discussed in this paper are A. thaliana MetE (SwissProt O50008), E. coli MetE (SwissProt P25665), S. cerevisiae MetE (SwissProt P05694), T. maritima MetE/TM1286 (SwissProt Q9X112), and MetH (SwissProt P13009).
This research was supported by grants from the National Institutes of Health (NIH) GM16429 (to MLL) and the Michigan NIH Molecular Biophysics Training Grant GM08270 (to RP). The authors would like to thank Rebecca E. Taurog for gifts of enantiomerically pure CH3-H4PteGlu3 and Hcy for preparation of the binary substrate complexes. Use of the APS was supported by the US Department of Energy, Basic Energy Sciences, Office of Energy Research under contract number W-31–102-Eng-38. Portions of this work were performed at the DuPont-Northwestern-Dow Collaborative Access Team (DND-CAT) Synchrotron Research Center located at Sector 5 of the APS. DND-CAT is supported by E. I. DuPont de Nemours, the Dow Chemical Company, the US National Science Foundation through grant DMR-9304725, and the State of Illinois through the Department of Commerce and the Board of Higher Education grant IBHE HECA NWU 96. Assistance with data collection at the APS synchrotron was provided by Zdzislaw Wawrzak (DND-CAT) and Joseph Brunzelle (LS-CAT). We thank Rowena G. Matthews, Katherine A. Pattridge, and Rebecca E. Taurog for critical evaluation of the manuscript.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. RP and MLL conceived and designed the experiments. RP performed the experiments. RP analyzed the data. RP contributed reagents/materials/analysis tools. RP and MLL wrote the paper.
Citation: Pejchal R, Ludwig ML (2004) Cobalamin-independent methionine synthase (MetE): A face-to-face double barrel that evolved by gene duplication. PLoS Biol 3(2): e31.
Abbreviations
APSAdvanced Photon Source
BHMTbetaine–homocysteine methyltransferase
DMV
467Asp-Met-Val
EXAFSextended X-ray absorption fine structure
Hcy
L-homocysteine
MetEcobalamin-independent methionine synthase
MetHcobalamin-dependent methionine synthase
CH3-H4folateN5-methyl-5,6,7,8-tetrahydrofolate
CH3-H4PteGlu3N5-methyl-5,6,7,8-tetrahydropteroyl-(tri)-γ-L-glutamate
TIMtriose phosphate isomerase
TCEPTris(2-carboxylethyl)phosphine
==== Refs
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030035SynopsisCell BiologyDevelopmentMus (Mouse)Hirsute or Hairless? Two Proteins May Spell the Difference Synopsis1 2005 28 12 2004 28 12 2004 3 1 e35Copyright: © 2005 Public Library of Science.2005This 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 Signaling Pathway Involving TGF-β2 and Snail in Hair Follicle Morphogenesis
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If you're a cat fancier, you're well aware that hair follicles are expendable. The product of a spontaneous mutation that caught a cat breeder's eye, le chat nu, would quickly succumb in the wild—its winter coat consists of little more than a ridge of fur down the midback and tail—and needs special care to thrive as a pet. Hairless animals in the lab, on the other hand, can be very instructive. Understanding how hair develops sheds light on the fundamental processes that generate a wide range of tissues and organs, including the lungs, cornea, and mammary glands.
How complex, three-dimensional structures emerge from single sheets of cells is a fundamental question in developmental biology. The dispensability of hair follicles makes them the perfect model system for studying this question—specifically, how structures and organs develop from buds. In a new study, Elaine Fuchs and colleagues use a three-pronged approach—involving gene expression analysis, transgenic mice, and cell cultures—to study how epithelial buds, the precursors of hair follicles, form. Their experiments point to two key actors in a signaling pathway that molds a targeted cluster of cells into a hair bud.
During the budding process, overlapping signaling pathways from two adjacent embryonic cell layers—the epithelium and the mesenchyme—direct morphogenesis. The mesenchymal cells initiate the cell-to-cell “crosstalk” that controls bud formation by first directing a small cluster of epithelial cells to form a placode, the pouch that forms hair plugs. The placode in turn directs underlying mesenchymal cells to form the base of the hair follicle, called the dermal papilla, and both structures contribute to the mature hair follicle. During development, cells are constantly bombarded with external signals. The trick is figuring out which signals trigger the transcriptional and behavioral properties in cells that spur bud formation.
In previous experiments, Fuchs and colleagues showed that reducing expression of E-cadherin—a membrane protein that forms the adhesive junctions between epidermal cells—is essential for allowing the cell remodeling required for bud formation. Here, the authors analyze the timing of external signals against the response of targeted cells to determine how targeted cells translate signals into changes in cell adhesion and remodeling, proliferation, and differentiation—the agents of most types of organogenesis.
Transgenic epidermis expressing Snail (red) results in expanded keratin 1 expression (green)
Since Snail, a protein that impedes the transcription of a subset of genes, functions in many developmental processes requiring epithelial remodeling, the authors reasoned it might do the same in hair bud formation. Working with developing mouse embryos, they saw a spike in Snail expression on embryonic day 17.5, coinciding with hair bud formation, enhanced cell proliferation, and the down-regulation of E-cadherin. Artificially sustaining Snail expression in the skin of transgenic mice caused abnormal levels of cell proliferation in the epidermis and reduced cell adhesion.
Working with skin keratinocytes, precursors of hair fibers, Fuchs and colleagues explored several signaling proteins known to be involved in bud formation as possible activators of Snail expression. When the authors treated keratinocytes with small amounts of one stimulator, TGF-β2, they saw “rapid and transient induction of Snail.” Snail proteins were absent from 17.5-day-old knockout mice lacking TGF-β2 but not from their nonmutant littermates. Conversely, transgenic mice with elevated TGF-β2 signaling activity displayed ectopic expression of Snail. Knockout mice lacking TGF-β2 also showed higher levels of E-cadherin—normally down-regulated by Snail—than their nonmutant littermates.
Altogether, these findings suggest that TGF-β2 signaling transiently induces Snail, which in turn down-regulates E-cadherin and activates a proliferation pathway in the developing bud. Reduced E-cadherin, the authors conclude, appears to contribute to Snail-mediated enhanced proliferation by allowing proteins normally sequestered at the membrane to operate in a proliferation pathway after the number of cellular junctions diminishes. By identifying which molecules are active in specific cell types at specific developmental stages, this study lays the foundation for dissecting the mechanisms that connect two key processes—intercellular remodeling and proliferation—in epithelial development. And since the consequences of TGF-β2 activity seen here in the hair bud more closely resemble certain types of skin cancer progression than skin development, a mechanistic understanding of hair follicle development promises to shed light on how skin cancer develops as well.
| 0 | PMC539066 | CC BY | 2021-01-05 08:21:19 | no | PLoS Biol. 2005 Jan 28; 3(1):e35 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030035 | oa_comm |
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030054SynopsisCell BiologyMolecular Biology/Structural BiologyBiochemistryEubacteriaUnique Double-Barreled Enzyme Makes Methionine the Hard Way Synopsis2 2005 28 12 2004 28 12 2004 3 2 e54Copyright: © 2005 Public Library of Science.2005This 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.
Cobalamin-Independent Methionine Synthase (MetE): A Face-to-Face Double Barrel That Evolved by Gene Duplication
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If a cell is a complex symphony of chemical reactions, its enzymes are the instruments through which this elemental music is played. Each reaction is catalyzed by a specific enzyme, whose uniquely shaped active site not only binds reactants, but, by forming weak and temporary bonds, coaxes them into new orientations with new partners, thus creating the products. Determining exactly how any individual enzyme accomplishes this task—which amino acids make up the active site, which bonds form where when enzyme meets substrate, which electrons switch partners as new bonds form—is the work of the structural biochemist. In this issue, Martha Ludwig and Robert Pejchal elucidate the structure of cobalamin-independent methionine synthase (MetE) from the bacterium Thermotoga maritima, and describe how it catalyzes the formation of the amino acid methionine.
Methionine synthases actually come in two forms, which use somewhat different mechanisms to accomplish the same task: transfer of a methyl group (CH3) from methyltetrahydrofolate to the terminal sulfur of homocysteine. The cobalamin-dependent form, MetH, relies on the cofactor cobalamin (vitamin B12), which pulls the methyl away at one active site, and then donates it at a second active site. Here, a central zinc atom binds and activates homocysteine, enabling it to attack the incoming methyl group that is attached to cobalamin. MetE, on the other hand, has no cofactor and only one active site, which sits at the junction of two eight-stranded barrels. The structure and sequence of these barrels indicate they arose through duplication of a primordial zinc-bearing, homocysteine-binding protein. This unique duplex now bears only one zinc atom, deep within the cleft separating the two barrels.
As in MetH, the role of the zinc is to bind homocysteine, but in MetE, this event also induces a conformation change around the zinc. The zinc and its coordinating partners form an umbrella; entering from the handle end, the homocysteine sulfur pulls the zinc toward it and turns the umbrella inside out. Methyltetrahydrofolate initially binds along the edge of the cleft, with the methyl group on the folate oriented far from the sulfur on the homocysteine, as can be seen in the research article's Video S1 (DOI: 10.1371/journal.pbio.0030031.sv001). There must be subsequent conformational changes within the active site that serve to bring the two substrates together and promote transfer of the methyl group. Exactly how methyltetrahydrofolate reorients within the cleft to complete the reaction is not yet clear. The reaction catalyzed by MetE proceeds slowly, at only 1%–2% of the speed of that catalyzed by MetH. One reason for this rather sluggish activity is that homocysteine, even when activated by binding to zinc, is much poorer than cobalamin at displacing the methyl group of methyltetrahydrofolate.
While MetE's unique active-site structure was made possible by gene duplication, the two barrels are no longer identical. Through evolution, the second, N-terminal, barrel has lost the ability to bind zinc or homocysteine, and indeed appears to contribute little to the active function of the enzyme. Nonetheless, this barrel may be necessary to temporarily isolate the substrates from solvent and to form the hydrophobic environment in which the reaction is more favorable. Further research may indicate more about the function of this unequal partner, and provide more detail on the exact atomic movements within the cleft at the moment of reaction.
| 0 | PMC539230 | CC BY | 2021-01-05 08:21:18 | no | PLoS Biol. 2005 Feb 28; 3(2):e54 | utf-8 | PLoS Biol | 2,004 | 10.1371/journal.pbio.0030054 | oa_comm |
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1901558505910.1186/1471-2105-5-190SoftwareA two-way interface between limited Systems Biology Markup Language and R Radivoyevitch Tomas [email protected] Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 44106 USA2004 7 12 2004 5 190 190 3 6 2004 7 12 2004 Copyright © 2004 Radivoyevitch; 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
Systems Biology Markup Language (SBML) is gaining broad usage as a standard for representing dynamical systems as data structures. The open source statistical programming environment R is widely used by biostatisticians involved in microarray analyses. An interface between SBML and R does not exist, though one might be useful to R users interested in SBML, and SBML users interested in R.
Results
A model structure that parallels SBML to a limited degree is defined in R. An interface between this structure and SBML is provided through two function definitions: write.SBML() which maps this R model structure to SBML level 2, and read.SBML() which maps a limited range of SBML level 2 files back to R. A published model of purine metabolism is provided in this SBML-like format and used to test the interface. The model reproduces published time course responses before and after its mapping through SBML.
Conclusions
List infrastructure preexisting in R makes it well-suited for manipulating SBML models. Further developments of this SBML-R interface seem to be warranted.
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Background
Systems biology markup language (SBML) is a standard for representing dynamical systems of biological interest [1,2]. Interfaces between SBML and high level computational environments are currently being developed for Mathematica [3] and Matlab [4], but to the author's knowledge, no such efforts are being carried forth for R/S-plus. This brief paper presents the author's initial developments toward a two-way SBML-R interface. The interface is currently limited in the range of SBML input files that it can handle. For example, it only handles SBML level 2 and does not handle "Events" and "FunctionDefinitions." The interface can nevertheless be used for some models, examples [5,6] of which are included under "demo" in the SBMLR package [7]. This paper provides an explicit example of one approach to an SBML-R interface. It is assumed throughout that the reader is already quite familiar with both SBML [8] and R [9].
Implementation
The software exists completely in R. It is comprised of four functions and is currently being distributed as a developmental package called "SBMLR" through Bioconductor [10]. The software was written subject to two constraints: 1) models expressed in SBML-like R must be exchangeable with a range of SBML models; and 2) models must be amenable to simulation in R. The first subsection that follows defines an SBML-like R model structure, the second illustrates how it can be used in simulations, and the third describes its conversions into and out of SBML.
An SBML-Like Model Structure in R
To facilitate mappings between SBML and R, an SBML-like list structure is defined in this subsection using the purine metabolism model of Curto et al. [6] as a specific example (Figure 1). In this figure and elsewhere, ellipses (...) indicate missing code not critical to current discussions; complete source codes are available through the SBMLR package [7]. The essential components of an SBML model, namely, its compartments, species and reactions, are all present in this R analog of an SBML model. In the model of Curto et al. [4], there is one compartment, be it the cell or the entire human body, and 18 species: 2 boundary conditions (bc = True) and 16 state variables (bc = False), each with an initial condition (ic) or value. Each reaction is a list that includes a reaction id, the names of species that are reactants (reacts), the names of species that are reaction rate modulators (mods), the names of species that are produced by the reaction (prods), parameter values (params), and the reaction rate law (law) function definition. In this framework, only state variables need be listed as products, boundary condition reactants can equivalently be listed as modulators, and missing terms (e.g. mods in reactions 1 and 37) are equivalent to a NULL assignment. The rate law function has as its input arguments two vectors, one carrying the concentrations of reactants and modulators (r), the other carrying reaction parameter values (p). If the body of the rate law function contains n statements, the first n-1 trivially convert input vector components into variables with the same names. The nth statement then contains the complete reaction rate law. It can occupy multiple lines, but it must be a single statement, i.e. it cannot depend on substitution variables temporarily defined in preceding statements.
SBML-like Model Execution in R
Model definition codes such as that given in Figure 1, when placed in a separate file (e.g. Curto.r), can be sourced into a parent script to become globally available for simulations. For example, the purine metabolism model of Curto et al. [6] can be simulated using the execution code shown in Figure 2.
This code simulates the response to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time t = 0 and plots the responses of inosine monophosphate (IMP) and hypoxanthine (HX) as shown in Figure 3. Two functions called by this script are defined in the SBMLR package and shown in Figure 4. They are, getIncidenceMatrix(), which computes the incidence/stoichiometry matrix used by the second function, fderiv(), which computes state derivatives for integration by the function lsoda() of the "odesolve" package. In getIncidenceMatrix(), the incidence matrix is generated automatically using an i loop over the rows (i.e. state variables) and a j loop over the columns (i.e. reactions). If a state is a product of a reaction, the corresponding matrix element becomes a positive integer equal to its stoichiometry [factor() converts string names to factors so that summary() can count them], and similarly for reactants, though with negative numbers entering the matrix in this case (or possibly zero, if a reactant of a reaction happens to also be a product of the same reaction).
The function fderiv() creates the current species vector by overriding initial states with current states clipped to positive values, and by overriding any time varying boundary conditions defined by rules (SBML rules are not needed for the purine model, but are needed to implement other models [5]). The function fderiv() then computes the reaction rate flux vector (v) based on the current species vector (St) and multiplies it by the incidence matrix to produce the current state derivative vector (xp). The names of xp and v are reset at the end of each function call to override the problem of variables gaining new composite names from the names of their expression arguments.
A Two-Way Interface between SBML and R
Two functions comprise the SBML-R interface: write.SBML() converts SBML-like R models (e.g. Curto.r) into SBML models (e.g. Curto.xml), and read.SBML() converts SBML models (e.g. Curto.xml) into an SBML-like R model (e.g. CurtoX.r). A key component of these two interface functions is a locally defined recursive function named recurs(). This function converts arbitrary R expressions into arbitrary MathML expressions, and vice-versa; it is defined differently, locally, in each of the two functions. In write.SBML(), shown in Figure 5, recurs() initially takes as its input argument the last component of the body of the kinetic rate law function definition, which is the entire rate law expression (as mentioned above, rate laws involving multiple R statements are not supported). In R, expressions are LISP like in that they contain a first element, the operator, and the remaining elements, the arguments, any of which can be an expression. If the operator is the parentheses operator, the action taken is that of a unary identity operator, and we simply skip it and move on to its argument since parentheses are not needed in MathML. Each nested call to the function recurs() sends "<apply>" and the converted operator to the output file on its way in, and a matching "</apply>" on its way out. Nested calling continues until all nodes of the expression tree are of class "name" or "numeric," i.e. when all found objects are leaves of the tree rather than "expressions" that require further parsing. Leaves are then sent to the output file bracketed by <ci> and </ci>.
The second of the two SBML-R interface functions, read.SBML(), maps a limited range of SBML level 2 files (function definitions and events are not handled) into SBML-like R model files. Portions of read.SBML() are given in Figure 6. The main difference between this function, read.SBML(), and the previous function, write.SBML(), is that here, rather than using parse() to decompose the list-of-lists structure of the model defined in R, the SBML model is instead decomposed as an XML object using xmlTreeParse() of the XML package available to R [11]. In read.SBML(), the locally defined recursive function recurs() uses an overkill of parentheses to avoid operator precedence issues. This recursive function is passed a MathML reaction rate law which it parses recursively until the leaves of the tree (the "ci") are all found. During the recursion a corresponding R expression is built as a vector of character strings which, upon exit from the last of the recursive calls, is collapsed into a single string and sent to the output file as the last line of the current rate law function definition.
Results
The function write.SBML() was applied to Curto.r to generate Curto.xml and the function read.SBML() was then applied to Curto.xml to generate CurtoX.r. Execution of the script given in Figure 2 with line 4 of the execution code changed to act on CurtoX.r instead of Curto.r generated the same plots as before (Figure 3). This shows that the R model was successfully converted into an SBML file that can be reconverted back into a properly functioning R model. The intermediate file Curto.xml was successfully validated as an SBML level 2 file [12]. The SBML file could thus be imported into visualization packages such as JDesigner [13].
Discussion
If the model of Curto et al. [4] were implemented in R without any knowledge of SBML, a form that it might take is that given in Appendix B (Figure 7). Compared to its SBML-like counterparts, this code is more compact and easier to understand, e.g. the system's network connectivity is clearly visible. The disadvantage of such code is that it is not readily converted into SBML. Since the benefits of SBML are compelling, this disadvantage alone warrants the use of SBML-like model structures.
As SBML evolves to handle a broader range of dynamical systems, it will become more and more challenging for simulation packages to handle all possible SBML models. It is envisioned here that the development of this SBML-R interface will be driven by its users, and not by the model representation capabilities of SBML, i.e. it is expected that the users of this interface will be programmers who are capable of modifying it as their needs require.
Conclusions
Compared to Matlab, which may be better equipped than R to simulate arbitrarily complex dynamical systems, R has the advantage of list handling infrastructure in parse() and xmlTreeParse(), and it also has the advantage of indexing by names instead of numbers. A further advantage, though not exploited here, is that R is object-oriented; in future versions of this interface, a print() method might be defined for objects of class SBMLR (i.e. models) to generate more readable renderings of models in R. Another advantage of R over Matlab is that it provides access to a much broader collection of microarray analysis tools, e.g. see Bioconductor [10]. This aspect is important for those individuals who are interested in biochemical systems analyses of microarray data [14,15]. For statisticians already familiar with R, there are also the obvious economies of maintaining system familiarity. Finally, perhaps the biggest advantage of R over Matlab is that it is freely available. On balance, there seems to be ample motivation for further developments of this interface between SBML and R.
Availability and requirements
Project name: SBMLR
Project home page:
Operating system(s): Windows XP
Programming language: R 2.0
Other requirements: R packages: XML and ODESOLVE
License: GNU GPL
Any restrictions to use by non-academics: no restrictions
List of abbreviations
SBML = Systems Biology Markup Language; XML = extensible markup language; MathML = Mathematical Markup Language; ODE = ordinary differential equation.
Authors' contributions
TR is the sole contributor.
Appendix A
The SBMLR package is available through Bioconductor as a developmental package [7]. It has been developed and tested only under Windows XP. To install, do NOT unzip the file SBMLR.zip after downloading to a local directory, rather, within the R GUI, click packages and install from local zip. The XML package installs similarly [11]. Note that an error message from library(XML) can be resolved by copying the *.dll files of the XML package libs directory into the "C:\windows" directory. The ODESOLVE package must be installed before running simulations. This package is installed from the R GUI by clicking packages and install from CRAN.
Appendix B
The implementation of Curto et al.'s model shown in Figure 7 is independent of any knowledge of SBML. It is included here to illustrate what comes "naturally" when implementing a model in R, see Discussion.
Acknowledgements
This research was supported by the Biostatistics Core Facility of the Comprehensive Cancer Center of Case Western Reserve University and University Hospitals of Cleveland (P30 CA43703), by the American Cancer Society (IRG-91-022-09), and by the National Cancer Institute's Integrative Cancer Biology Program (P20 CA112963-01).
Figures and Tables
Figure 1 The model of Curto et al. implemented as an SBMLR structure
Figure 2 The purine metabolism model of Curto et al. represented in SBMLR Figure 1) simulated to respond to a 10-fold increase (5 μM to 50 μM) in phosphoribosylpyrophosphate (PRPP) at time t = 0
Figure 3 The purine metabolism model of Curto et al. responding to a 10-fold increase in phosphoribosylpyrophosphate (PRPP) at time t = 0 (see Figure 2). IMP is inosine monophosphate, HX is hypoxanthine, time is in minutes, and concentration is in μM.
Figure 4 R codes for the functions getIncidenceMatrix() and fderiv().
Figure 5 R code for the function write.SBML().
Figure 6 R code for the function read.SBML().
Figure 7 The purine metabolism model of Curto et al. implemented in "natural R".
==== Refs
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| 15585059 | PMC539231 | CC BY | 2021-01-04 16:02:41 | no | BMC Bioinformatics. 2004 Dec 7; 5:190 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-190 | oa_comm |
==== Front
BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1911558506210.1186/1471-2105-5-191Research ArticleOptimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information Chen Yian A [email protected] David J [email protected] Shuyuan [email protected] Matthew J [email protected] Robert [email protected] Paul S [email protected] Gregory W [email protected] Jonas S [email protected] Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA2 Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC, USA3 Marine Biomedicine and Environmental Science Center, Medical University of South Carolina, Charleston, SC, USA4 South Carolina Department of Natural Resources, Marine Resources Research Institute, Charleston, SC, USA2004 7 12 2004 5 191 191 21 8 2004 7 12 2004 Copyright © 2004 Chen 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
Expression microarrays are increasingly used to characterize environmental responses and host-parasite interactions for many different organisms. Probe selection for cDNA microarrays using expressed sequence tags (ESTs) is challenging due to high sequence redundancy and potential cross-hybridization between paralogous genes. In organisms with limited genomic information, like marine organisms, this challenge is even greater due to annotation uncertainty. No general tool is available for cDNA microarray probe selection for these organisms. Therefore, the goal of the design procedure described here is to select a subset of ESTs that will minimize sequence redundancy and characterize potential cross-hybridization while providing functionally representative probes.
Results
Sequence similarity between ESTs, quantified by the E-value of pair-wise alignment, was used as a surrogate for expected hybridization between corresponding sequences. Using this value as a measure of dissimilarity, sequence redundancy reduction was performed by hierarchical cluster analyses. The choice of how many microarray probes to retain was made based on an index developed for this research: a sequence diversity index (SDI) within a sequence diversity plot (SDP). This index tracked the decreasing within-cluster sequence diversity as the number of clusters increased. For a given stage in the agglomeration procedure, the EST having the highest similarity to all the other sequences within each cluster, the centroid EST, was selected as a microarray probe. A small dataset of ESTs from Atlantic white shrimp (Litopenaeus setiferus) was used to test this algorithm so that the detailed results could be examined. The functional representative level of the selected probes was quantified using Gene Ontology (GO) annotations.
Conclusions
For organisms with limited genomic information, combining hierarchical clustering methods to analyze ESTs can yield an optimal cDNA microarray design. If biomarker discovery is the goal of the microarray experiments, the average linkage method is more effective, while single linkage is more suitable if identification of physiological mechanisms is more of interest. This general design procedure is not limited to designing single-species cDNA microarrays for marine organisms, and it can equally be applied to multiple-species microarrays of any organisms with limited genomic information.
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Background
Expression microarrays are powerful tools for human disease diagnosis, prognosis and treatment [1] offering unparalleled insight into the function of the entire genome and the dynamic interactions among genes. The ability of microarrays to identify gene expression signatures, specific subsets of genes that respond to particular stimuli, make them valuable tools for characterizing organisms' response to environmental conditions and host-parasite interactions. This method relies on organisms as sentinel markers of environmental changes. Since aquaculture marine species are easy to keep in a captive environment, they can be used as convenient sentinels by profiling their physiological responses. An efficient and economic method to quantify their physiological responses is to collect the expressed sequence tags (ESTs) with the purpose of constructing cDNA microarrays, which can be used to screen their transcriptomes. Therefore, several pilot studies have been initiated in economically important marine species to generate genomically enabled tools for the purpose of elucidating the role of biological and environmental factors in ultimately determining the difference between survival, morbidity and mortality [2-4]. The growing need for a marine functional genomics approach using microarrays bespeaks a general-purpose cDNA microarray probe selection tool to identify which ESTs to spot on the microarray from large collections of ESTs with unknown functions and variable redundancies.
The two most widely used expression microarray systems are oligonucleotide and cDNA microarrays. Oligonucleotide microarrays are generated by chemically synthesizing short oligo probes (20–70 bp) onto the slides [5]. In contrast, cDNA microarrays are created by spotting long strands of amplified cDNA sequences (e.g., the expressed sequence tags) [6]. In this paper, the sequences spotted on the arrays are referred to as "probes." Although many algorithms have been developed for selection of oligonucleotide [7-11] or gene-specific probes [12,13], only one application was found by the authors for cDNA microarray probe selection [14]. However, this algorithm was designed specifically for organisms with extensive genomic data, not for the organisms with limited genomic information.
In the absence of cDNA microarray probe selection algorithms, EST selection for spotting on microarrays has been approached using various informal methods. These methods included spotting ESTs without sequencing information, spotting only sequenced ESTs with annotations, or forcing the selection on gene-oriented clusters [15]. The choice of method typically reflects cost/benefit ratios and the stage of development of the EST collection. A comprehensive review of microarray probe selection can be found in Tomiuk and Hofmann [16]. Gene or transcript oriented clusters are generally formed by gene indexing projects, such as TIGR [17,18], Stack [19], or Unigene [20]. Gene indexing projects involve three general steps. First, the quality control step filters out contaminating sequences such as vector or bacterial sequences. Second, ESTs are partitioned into smaller clusters, often using the hierarchical single-linkage method with an arbitrarily chosen cut-off threshold [21,22]. Finally, although not all projects include a assemblage step, sequences are often assembled into contigs using existing software, such as CAP3 [23] or PHRAP [24].
In this study, we propose a probe selection procedure for cDNA microarray that tracks both sequence redundancies and functional representativeness of the selected probes in an integrated sequence diversity plot (SDP). SDP includes a sequence diversity index (SDI) to measure the sequence similarities within EST clusters quantitatively. The issue of how many probes are sufficiently representative for all collected ESTs is approached in a manner similar to the choice of dimensions to retain in principle component analysis (PCA). This approach reflects the fact that there is no definitive right answer to the question [25]; the number of "clusters" of ESTs may vary as the stringency of microarray hybridization condition changes. All collected ESTs are automatically annotated using Gene Ontology [26] terms, and then a unique probe GO index (UPGI), a functional index, was devised to access functionally how representative the selected probes are. This integrated and flexible method using SDP allows users to decide which clustering method and stringency to use when designing a cDNA microarray for organisms with limited genomic information based on their logistical constraint and experimental purposes. A small data set of ESTs was used to test this algorithm so that the detailed results of this algorithm could be examined.
Results
A small data set of 1047 ESTs from Atlantic white shrimp (Litopenaeus setiferus) from the Marine Genomics website [27] was analyzed. After pre-processing, 971 sequences longer than 100 bp were further used in the analysis (details see methods; Figure 1). The ESTs were progressively grouped using different hierarchical linkage methods from 1 to n (n = 971) clusters (details see methods). The sequence diversity plot (SDP) summarizes sequence properties within clusters and the functional representativeness of the selected probes using three indexes: the sequence diversity index [SDI; Eq. (1)], the contiguity index [CI; Eq. (2)], and the unique probe GO index [UPGI; Eq. (3)] (Figure 2).
Sequence diversity index (SDI) measures within-cluster sequence dissimilarity
This index is the ratio of within-cluster sequence dissimilarities to the total sequence dissimilarity when m clusters are formed (m = 1,2,...n):
where dmi is the distance (dissimilarity), the E-value from blast result (details see methods), between the ith pair of sequences for a total km pairs of within-cluster comparisons when m clusters are formed. D is defined as , the average distance of the total N pair-wise distances among all n sequences (where in this data set).
Contiguity index (CI) measures the sequence contiguity within clusters
The within-cluster sequence contiguity is evaluated using CAP3 [23], commonly used sequence assembly software (see methods). The number of putative unique genes, denoted as PGm, is the sum of the number of assembled contigs and singlets (single sequences, which cannot be assembled with any other sequences) when m clusters (m = 1,2...n) are formed. The contiguity index (CI) at a given number of clusters (m) is defined as the inverse of the average number of putative genes per cluster, which equals the number of clusters per gene:
This index reflects how contiguous the sequence members are within a cluster. Maximum value of CI is 1 when all the members are contiguous (one cluster per gene).
Unique probe Gene Ontology (GO) index (UPGI) measures functionally how representative the selected probes are
The unique probe GO index (UPGI) when m clusters of ESTs are formed is defined as the number of unique GO terms associated with all m probes (m = 1, 2...n) divided by the number of the GO terms associated with all n sequences (n = 971).
where ProbeGOmj is the number of unique GO terms associated with the probe representing the jth cluster when m clusters are formed and sequenceGOi is the number of GO terms of the ith sequence. This index measures functionally how representative the selected probes are among all functionally unique sequences in the entire EST collection. Three UPGIs are calculated for three GO domains, respectively: molecular function (UPGI-MF), biological process (UPGI-BP), and cellular components (UPGI-CC) (Figure 3; see more about Gene Ontology in methods).
Sequence diversity plot (SDP) used as an aid to decide how many probes to spot on microarray
The dissimilarities among sequences within a cluster, measured by SDI, decrease as total number of the clusters increases; sequences within a cluster share higher similarity as the number of clusters formed increases (Figure 2).
From the collection of 971 Litopenaeus setiferus ESTs, the first break point of SDI using single linkage method was 442 clusters (Figure 2). An elbow (bend) in SDI, analogous to an elbow of scree plot of the principle component analysis (PCA), indicates that the remaining within-cluster diversity is very low after this number of clusters formed [25]. The selected probes presented 93% unique molecular functions, 94% unique biological processes, and 96% unique cellular components when 442 clusters were formed using single linkage method (Figure 3).
Other amalgamation algorithms produced clusterings with different properties. The average and complete linkage methods reduced the sequence dimensionality more efficiently than that by using the single linkage method (Figure 2). For the complete linkage method, the break point was observed at 289 clusters, at which, the selected probes represented only 50% of unique molecular functions while the selected probes represented 71% of unique molecular functions using single linkage, and 56% using average linkage (Figure 2). The probes selected using single linkage were functionally more unique in all three domains (molecular functions, biological processes, and cellular components) than the ones selected using average or complete linkage methods (Figure 3). Exceptions to this rule were found when very small (<60 clusters) or large (>442 clusters) numbers of probes were selected. The functional representativeness of the probes at very high or low ends (<60 or >442 clusters) was comparable using any of the three linkage methods. When 442 probes were selected, 93 – 95% unique biological process, ~92% within-cluster biological process, and ~96% unique cellular component was represented by the selected probes (Figure 3). Although fewer annotated EST clusters (number of clusters containing at least one annotated sequences) were formed using single linkage method compared to those selected using the other two linkage methods given a fixed number of cluster within the middle range (~60–442 clusters), more functionally unique probes were selected among the formed clusters by single linkage method (Figure 4).
Contig assemblage using CAP3 yields a similar result as that of cluster analysis using the single linkage method (Figure 2). A total of 461 putative genes was generated using sequence assembly software CAP3 without partitioning the sequences into subgroups (by cluster analysis). These putative unique genes included 356 singlets (single ESTs) and 110 assembled contigs. This result followed closely the result of cluster analysis with single linkage method, which indicated 442 clusters. The EST members in each putative gene were in general agreement with the result of single-linkage cluster analysis with some exceptions. For example, sequence 59 (Penaeidin 2), sequence 10 (Penaeidin 3a), and sequences 177 (Penaeidin 3c) were not assembled into any contigs using CAP3, but they were clustered together when 422 clusters were formed using single linkage method. These sequences share high similarities and high percent identities (E-values < 10-37; Table 1), and they are likely to hybridize with each other. Probes selected using clustering methods reflect the hybridization potential compared to the assembly approach. Some sequences, on the other hand, were not clustered into a group although they could be assembled into one putative contig. For instance, sequences 79 and 158 were not clustered in a group because the overlapping segment is marginally short (61 bp/64 bp identical) and this segment is composed of low-complexity sequences (31 pairs of GA repeats, which were masked when using BLAST). The different characteristics of three linkage methods could be further illustrated by local sequence percent identity and the lengths of high scoring pair segments (HSP) (Figure 5). Sequences within a cluster formed using single linkage method do not always have to overlap with each other as long as the distances between some of the "linking sequences" are short (the similarities are high). That is, the fragmented ESTs could be "linked" by fragmented (or incomplete sequenced) ESTs and the average within-cluster percent identity is not necessary high when using the single linkage method (Figure 5). The sequences within same clusters using the average linkage methods, as expected, have the highest average percent identity (before all three methods converge around 545 clusters).
Sequence contiguity assessed by CAP3 (Eq. (2)) has shown similar results observed using the probe functional index, UPGI (Eq. (3); Figure 2). Clusters formed using the single linkage method contained slightly more contiguous EST members while the other two linkage methods generated fewer contiguous sequences in the mid range (Figure 2). Similarly, when the number of clusters was either very low or high, the results were comparable.
ESTs were annotated based on Gene Ontology (GO) terms (details see methods). Three types of functionally unassigned sequences were generated through the GO annotation process: the first type was the sequences having no similar sequences found in the GO database. The majority of ESTs (63%) belonged to this category (607 out of 971 ESTs; Figure 6). The second type was similar sequences found in the GO database with the function of those sequences annotated as "unknown." The last type of "unknown" was similar sequences found in the GO database, but only certain domains of GO annotation were complete. For example, it could only have molecular functional annotation associated with the sequence but biological process and cellular components are unknown. The last two types of sequences were combined into one "unknown category" in that particular functional domain (Figure 6). Twenty five percent of sequences was annotated in molecular function while 12% was unknown; 27% was annotated in biological process while 10% was unknown; and 27% was annotated in cellular components with 11% unknown. Among the annotated sequences, 36%, 49%, and 22% of annotated sequences were associated with unique GO terms in each of the three domains (molecular function, biological process and cellular component), respectively (Figure 6).
Both functional and sequence indexes for the three clustering methods converge around the threshold of 442 clusters. When the user-defined number of probes is fewer than this threshold value (442 clusters), the functional uniqueness of the selected probes using single linkage method is superior than that of the other two methods while average linkage is the most effective method for dimension reduction (Figure 2).
Discussion
cDNA microarray is one of the most common microarray platforms, but it is also known to have cross-hybridization potentials. The hybridization potentials between sequences may also vary as the experimental condition changes. This changing nature and the potential of cross-hybridization could be depicted by the index developed in this study, the sequence diversity index (SDI). The magnitude of SDI decreases as the number of clusters increases; sequences are more similar within clusters as the number of clusters increases. SDI is analogous to the F-statistics. That is, SDI is the "within" variation divided by the "total" variation while the F-statistics is "within" variation divided by "between" variation. Two ancillary indexes (a functional index (UPGI) and a sequence contiguity index (CI)) were designed to evaluate the functional representativeness of the selected probes and identify the numbers of putative genes each probe potentially would cross-hybridize. These indexes aid the probe selection processes by bringing in the functional annotations of ESTs as the main goal of the microarray experiments is generally to interpret the biological significances and interactions of genes of interest. A common goal of microarray experiments is to identify co-regulated genes. This is based on the assumption that if two genes are co-expressed, they are likely to be co-regulated through the same mechanism [28]. It has been shown experimentally, at least in yeast, that combining expression data and sequence functional annotation information results in a better predictive model than using microarray expression data alone [29]. The integrated procedure in our study including both probe sequence and functional annotation allows a user-defined flexibility based on the purpose of experiments and the limitation or experimental conditions, such as different hybridization stringencies, budget limitations for numbers of probes to spot on the array, or physical size constraint of the array.
Different clustering processes mimic different scenarios of cross hybridization between sequences. Sequences from the same transcript will hybridize with each other, and this is reflected in the clusters formed using the single linkage method. In contrast, some of the sequences in the clusters formed by the complete or average linkage methods could be paralogs or alternative splicing variants of the same gene. It might be argued that if a sequence, for example Penaeidin 2, was chosen as a probe from the cluster of sequences containing different subtypes to spot on the microarray, this sequence will likely hybridize to the sequences in the same cluster, for example, Penaeidin 3a and Penaeidin 3c. The contiguity index and probe functional index developed in our study will identify the cross hybridization potential for the users. Potential cross-hybridization has become a more apparent problem for the transcriptomics community. A tool was developed to identify potential cross-hybridized probes lately [30], however, this tool is designed for species with rich genomic information. Our method provides an integrated approach for cDNA microarray design for any organisms, especially for projects with very limited genomic information.
Cross-hybridization potential between long cDNA seqeuences is harder to model than that between short (oligonucleotide) sequences. Although several studies have shown that local sequence percent identity seem to be a reasonable predictor for cross hybridization for cDNA microarray experiments [31-33], the cross-reactivity varies in a wide range (0.6 – 57% signal) even when percent identity is high and within a similar range (80–85% identity) for sequences in different gene families [33]. Currently, there is (are) no good predictor(s) to model the cross-hybridization on cDNA microarray. The similarity measurement between sequences in our study (dmi) could be easily replaced in the future by any good cross-hybridization predicting parameter(s) developed for long cDNA sequence hybridization. The design procedure we described here will work in the exact same fashion. In a similar manner, although the traditional hierarchical clustering algorithm with three linkage methods was used in our study, any bottom-up clustering algorithm (e.g., K-nearest means clustering) or top-down approach (e.g., principle component analysis, single value decomposition) could be easily performed, and the corresponding SDIs, UPGIs and CIs will be generated in the same way and summarized in the SDP. The performances of these different bottom-up or top-down algorithms (to group or partition the sequences) could be compared using the SDP. In brief, other distance matrix and clustering algorithms other than what we used in this study could be easily applied using our algorithm, and their performances could be evaluated quantitatively using the suite of indexes in SDP.
Annotation of functionally unknown sequences is not a trivial task itself. Gene Ontology has become a standard ontology to annotate unknown sequences. Sequence similarity search against the GO database using BLAST was used in this study. The completeness of the GO database and sensitivity/selectivity of the BLAST procedure would dictate the annotation capability. Several different approaches could potentially improve the annotation in the future. One example found in this study was Penaeidin family, a unique family of antimicrobial peptides with both proline and cysteine-rich domains that were first identified and characterized as peptides in the hemolymph of the Pacific white shrimp, Litopenaeus vannamei [34]. No homologous proteins are found in GO database. Future research could emphasize how to integrate other sources of knowledge (database) to enrich the functional annotation process, especially as very limited knowledge is available for marine organisms in the public domain. Different approaches to annotate the EST sequences could also be adapted. For instance, position-dependent method (such as using HMMER [35] to search against Pfam database [36,37]) could be used to search the existing database. This may increase the chance to annotate sequences with lower sequence homologies with the sequences stored in the database. The current functional representativeness of the selected probes was quantified using the unique GO terms associated with the probes among all sequences. The quantification of how representative the probes are could be modified in the future to include the hierarchical nature of the GO terms.
The integration of this cDNA probe selection procedure with the database through Marine Genomics web-based interface [27] is currently in progress, and the marine genomics community will be directly benefited, and it will be equally applicable to any organisms with limited genomic information.
Conclusions
The sequence diversity index (SDI) was developed in this study to select probes using ESTs for designing cDNA microarrays. Two ancillary mathematic indexes (sequence contiguity index [CI] and unique probe GO index [UPGI]) were used to identify potential cross-hybridization between different transcripts (or paralogs) and to quantify biologically how representative the probes were. These three indexes were summarized in a sequence diversity plot (SDP) and were used to assist cDNA microarray probe selections for organisms without any genomic information. This method allows the user-defined number of probes to be selected for the cDNA microarray experiments. Different clustering methods balance the representativeness of the probe functional annotations and minimization of the sequence redundancies. Accordingly, different linkage methods can be used to decide between microarray designs for biomarker discovery or for functional genomics.
It is clear that sequence assembly into contigs is not necessary for microarray probe selection although it is informative to identify the relationship among sequence members within clusters based on the CI. The microarray design procedure described here could also be used for multi-species or cross-species microarray design in a scenario where the sequences with high similarity from different species cross hybridize to each other [32], but not necessary be assembled into contigs.
This method is not limited to the ESTs collected from single or multiple marine organisms. Furthermore, this method can be applied to any organisms without the complete sequenced genomes.
Methods
Sequence availability and pre-processing
Twenty six thousand and six hundred fifty-six (26,656) Expressed Sequence Tags (ESTs) from 14 marine species were generated and stored in a postgresSQL database through a user-friendly interface at the Marine Genomics website [27]. All the sequences are freely available to the public. One thousand and forty seven ESTs from Atlantic white shrimp (L. setiferus) were used in this study. Pre-processing included customized low quality filtering, poly-A tail, vector, adaptor screening, trimming, and low-complexity masking by DUST [38]. After pre-processing, 971 sequences longer than 100 bp were further analyzed (Figure 1).
Sequence similarity comparison
All against all pair-wise BLASTN [39] was performed between these 971 ESTs. In the BLASTN result, with sufficiently large sequence lengths q and n, the statistics of HSP (high-scoring segment pairs) scores are characterized by two parameters, K and lambda. The E-value, the expected number of HSPs with score of at least S, given by the formula E = Kqne-λS, was used as the distance measurement (dmi in Eq (1) in results) between ESTs for cluster analysis to determine sequence redundancies. dmi is the distance between the ith pair of sequences for a total km pairs of within-cluster comparisons when m clusters are formed.
Sequence redundancy reduction by cluster analyses
Hierarchical cluster analyses with three common linkage methods (single linkage, average linkage, complete linkage) were performed to reduce the redundancies among sequences.
Two sequence indexes were used to quantify sequence diversity and contiguity within clusters
Two sequence indexes, the sequence diversity index [SDI; Eq. (1) in results] and the contiguity index [CI; Eq. (2) in results], were used throughout the sequence redundancy reduction. SDI was used to aid the number of probes to select. The within-cluster sequence contiguity (CI) is evaluated using CAP3 assembly software with default parameters [23]. Unweighted average within-cluster percent identity of the HSP segments and HSP length from BLAST results were quantified throughout the process of clusterings.
Probe selection
To maximize the hybridization probability between the selected probe and the sequences within the cluster, the sequence has the highest similarity to all the other sequences within the cluster is selected. That is, the centroid EST, the sequence has the minimum average distance to all the other sequences within each cluster was spotted on the array.
Sequence functional annotation using Gene Ontology (GO) terms
Functions of all 971 sequences were annotated using the functional categorizations of similar sequences stored in Gene Ontology (GO) database [40]. GO terms are commonly used for functional categorization in three domains (biological process, molecular function, and cellular component) for gene products (proteins) or nucleotide sequences. The GO terms and associated protein sequences were downloaded from the GO website [41] in the format of mySQL database [42]. The ESTs were annotated by the top BLASTX hit after blasting them against the proteins with GO terms associated in the database. The sequences with the E-value threshold set at 10-6 for GO annotation are considered as similar, and they potentially share the same molecular functions, cellular components, or biological processes. The GO terms found associated with the EST sequences, if any, were recorded separately for each of the three domains. If there were multiple GO terms in any single domain (e.g., molecular function), the inverse of the number of GO terms in that domain is used for functional quantification (i.e., the traditional pie-chart summary of the functional categories of ESTs). For example, there are three molecular functional annotations (GO:0005515, GO:0004866, GO:0004867) associated with the sequence 1046, then each of them is considered 1/3 in the GO quantification for this particular sequence. Therefore, the quantification for each GO domain will sum up to the original analyzed sequence numbers at the end when we quantify the percentage of each category (n = 971).
A functional index to quantify how representative the selected probes are
The unique probe GO index [UPGI; Eq. (3) in results] was used to quantify functionally how representative the selected probes were within EST clusters.
Number of probes to retain using the sequence diversity plot (SDP)
Two sequence indexes (CI and SDI) and one functional index (UPGI) mentioned above were included in the sequence diversity plot (SDP) (Figure 1). Sequence similarity was measured by SDI (Eq. (1) in results), and within-cluster sequence contiguity was measured by CI (Eq. (2) in results). The unique probe GO index (Eq. (3) in results) was used to quantify functional annotation levels represented by the selected probes. This integrated information will allow user-defined flexibility of probe selection involving both sequence similarity and functional annotation.
List of abbreviations
SDP: sequence diversity plot, SDI: sequence diversity index, CI: sequence contiguity index, and UPGI: unique probe GO index
Acknowledgements
This work was supported by the South Carolina Sea Grant (NA16RG2250, P. S. Gross, PI). We thank Dr. Xinghua Lu for his suggestion on GO annotation process, Dr. Tom Smith for his writing assistance, Javier Robalino for his EST generation and biological insights, and the comments from Marine Genomics Consortium at Charleston, SC.
Figures and Tables
Figure 1 Schematic diagram of the optimal cDNA microarray probe selection from expressed sequence tags (ESTs) for marine organisms. The methods of this study were mainly implemented using Matlab™ and other languages or software (labelled in blue).
Figure 2 Sequence Diversity Plot (SDP) includes both sequence diversity and probe functional representativeness SDP summarized three indexes [the sequence dissimilarities index (SDI; Eq. (1)), the sequence contiguity index (CI; Eq. (2)), and the unique probe GO index (UPGI; Eq. (3)) in molecular function domain] among three clustering linkage methods (single, average and complete linkage methods). All indexes range between zero and one in a linear scale in this figure. Sequence diversity decreases as the number of clusters (number of selected probes) increases; functional representativeness increases as the number of probes selected increase.
Figure 3 unique probe Gene Ontology (GO) index (UPGI) in three GO domains: molecular function, biological process and cellular component. Comparison of the unique probe GO index (UPGI) in three GO domains: molecular function, biological process, and cellular components among three linkage methods (single, average linkage, and complete linkage methods). The probes selected using single linkage were functionally more unique in all three domains than the ones selected using average or complete linkage methods when selecting middle range of number of probes (60 – 442 probes).
Figure 4 Summary of annotated EST clusters and unique representativeness of the selected probes in three GO domains: molecular function, biological process and cellular component. EST clusters contain at least one annotated sequences (noted as "cluster annotated*" in the legend) and unique annotations of selected microarray probes in each of the three Gene Ontology (GO) domains. (A) Molecular Function (MF) (B) Biological Process (BP) and (C) Cellular component (CC). Although fewer annotated EST clusters (number of clusters containing at least one annotated sequences) were formed using single linkage method compared to those selected using the other two linkage methods given a fixed number of cluster within the middle range (~60–442 clusters), more functionally unique probes were selected among the formed clusters by single linkage method
Figure 5 Average within cluster percent identity and the lengths of high scoring pair (HSP) segments throughout clustering process. The percentage identity and lengths of HSP further confirmed the observations in Figure 2 that sequences within a cluster formed using single linkage could potentially be fragments of same gene/transcript, but fragments of sequences might not overlap that average within-cluster percent identity is lower when using single linkage method. The average percent identity is the highest when using average linkage method as expected.
Figure 6 Summary of uniquely annotated and unannotated sequences in three Gene Ontology (GO) domains. The percentage of unique annotated sequences, "redundant" annotated sequences, and sequences have annotations in some of the three GO domains, but not in the particular domain of interest, and sequences have no similar gene products found in the Gene Ontology (GO) database (BlastX E-value was set at 10-6) among 971 L. setiferus ESTs. Three GO domains are (A) molecular function, (B) biological process and (C) cellular component.
Table 1 Sequence similarities between three penaeidin sequences in a group formed by cluster analyses using the single linkage method. Percent identity (%) and sequence length (bp) of the high scoring pair from the pair wise blast results (in parentheses).
Lset10 Lset59 Lset177
Lset10 100 (635) 88 (140) 90 (140)
Lset59 88 (140) 100 (586) 95 (141)
Lset177 90 (140) 95 (141) 100 (456)
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| 15585062 | PMC539232 | CC BY | 2021-01-04 16:02:47 | no | BMC Bioinformatics. 2004 Dec 7; 5:191 | utf-8 | BMC Bioinformatics | 2,004 | 10.1186/1471-2105-5-191 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-451557162810.1186/1471-2180-4-45Methodology ArticleDNA-free RNA preparations from mycobacteria Stephan Joachim [email protected] Johannes G [email protected] Fritz [email protected] Michael [email protected] Lehrstuhl für Mikrobiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Staudtstr. 5, D-91058 Erlangen, Germany2 Department of Microbiology, University of Alabama at Birmingham, 609 Bevill Biomedical Research Building, 845 19th Street South, Birmingham, AL 35294, USA2004 30 11 2004 4 45 45 16 9 2004 30 11 2004 Copyright © 2004 Stephan 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 understand mycobacterial pathogenesis analysis of gene expression by quantification of RNA levels becomes increasingly important. However, current preparation methods yield mycobacterial RNA that is contaminated with chromosomal DNA.
Results
After sonication of RNA samples from Mycobacterium smegmatis genomic DNA is efficiently removed by DNaseI in contrast to untreated samples.
Conclusions
This procedure eliminates one of the most prevalent error sources in quantification of RNA levels in mycobacteria.
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Background
Mycobacterium tuberculosis is the leading cause of death from a single infectious disease with approximately 8.8 million new cases and two million deaths per year. To understand the pathogenesis of M. tuberculosis, analysis of gene expression by relative or absolute quantification of RNA levels using microarrays and RT-PCR (batch- and real-time) becomes increasingly important [1]. Widely used methods to isolate bacterial RNA are acid-phenol extraction or guanidinium isothiocyanate extraction combined with cesium chloride purification or nucleic acid binding resins [2]. However, the cell wall of mycobacteria is very stable and a very effective permeability barrier, and, therefore, rather refractory to lysis by chaotropic agents and detergents, hampering RNA isolation from these microorganisms [3]. Since the average half-life of mycobacterial mRNA is in the range of a few minutes, mycobacteria have to be vigorously treated (e.g. bead-beating, freeze-thawing, nitrogen decompression) to quickly isolate RNA [4]. This causes fragmentation of chromosomal DNA that contaminates RNA preparations, which is one of the most prevalent error sources in quantification of RNA levels in mycobacteria. Several methods have been suggested to circumvent this problem [5,6]. Virtually all RNA isolation protocols use DNaseI, which does not completely remove large amounts of DNA. Our goal was to improve the efficiency of DNaseI digestion by solubilizing chromosomal DNA with sonication prior to DNaseI treatment. Mycobacterium smegmatis is especially refractory to lysis and therefore was chosen as a model organism.
Results and Methods
M. smegmatis SMR5 [7] was grown in 10 ml Middlebrook 7H9 liquid medium (Difco Laboratories; supplemented with 0.2% glycerol, 0.05% Tween 80) to an OD600 of 0.8 and mixed with 5 ml killing buffer (20 mM Tris-HCl, 5 mM MgCl2, 20 mM NaN3) [8]. The cell suspension was incubated on ice for 5 min. Cells were harvested by centrifugation (20 min at 6000 × g and 4°C). 20 mg cells (dry weight) were lysed in FastRNA Blue-Tubes (Bio-101 Inc.) using a FastPrep FP120 bead-beater apparatus (Savant, USA) for 20 sec at level 6.5. The tubes were centrifuged for 10 min at 10000 × g and 4°C. The supernatant was transferred to microcentrifuge tubes containing a nucleic acid binding resin (Nucleospin RNA II; Macherey-Nagel), and further experimental steps were done as described by the manufacturer. A total of 62 μg RNA was eluted in 60 μl of RNase-free water. The RNA was diluted to 50 ng μl-1 into several aliquots. One aliquot containing 10 μg RNA was left untreated. The second aliquot was directly treated with 10U of RNase-free DNaseI (Roche) for 1 h at 37°C, while the third aliquot was sonicated two times for 20 sec with 0.9 sec intervals at 20 % power (Sonopuls HD 2070; Bandelin electronic) prior to DNaseI treatment. Between the two sonication steps the cell suspension was chilled on ice for 5 min. DNaseI was removed by precipitation with polyethylene glycol (PEG) 6000. As a control for the RNA quality, cDNA was synthesized by Omniscript reverse transcriptase and sensiscript reverse transcriptase (OneStep RT-PCR system, QIAGEN) from total RNA (100 ng) for 35 min at 50°C followed by an inactivation step of 15 min at 95°C. The 16Sr RNA was then amplified with the primers 16S-FP (5'-TGCTACAATGGCCGGTACAAA-3') and 16S-RP (5'-GCGATTACTAGCGACGCCGACTT-3') using up to 30 cycles of 1 min at 94°C, 30 sec at 53°C, and 1 min at 72°C before a final extension step of 7 min at 72°C. As a control for DNA contamination, standard PCRs were performed, to which RNA was added after the RT inactivation step. PCR products were analysed at cycles 23, 25, 27 or 30 to check the purity of the RNA. All samples apparently contained 16S rRNA (Fig. 1). However, DNA contamination was detected by PCR at cycle 25 in the RNA sample that was not treated with DNaseI. Conventional DNaseI treatment delayed the appearance of a signal in the sample without the RT step until cycle 27. By contrast, no amplification product was obtained in the control sample even after 30 cycles, when the RNA sample was sonicated before DNaseI treatment (Fig. 1).
Conclusions
This result shows that sonication improved DNA degradation by DNaseI most likely by rendering the chromosomal DNA more accessible to enzymatic action. This work describes a simple and efficient procedure to improve the quality of RNA preparations from M. smegmatis and will be of great value for RNA preparations from other microorganisms, including M. tuberculosis.
Authors' contributions
JS carried out the RNA preparations, the RT-PCR experiments and wrote a draft of the manuscript. JGB performed RNA preparations and RT-PCR experiments. FT participated in coordinating and supervising the study. MN conceived of the study, participated in coordinating and supervising the study, and wrote the final manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft (NI 412, SFB 473). We thank Greer Kaufmann for editing the manuscript.
Figures and Tables
Figure 1 Analysis of DNA contaminations in a RNA preparation from M. smegmatis by RT-PCR . One-tenth of the volume after RT-PCR was loaded on a 1% agarose gel, which was stained with ethidium bromide. Three different samples for RT-PCR were used: an untreated RNA (indicated as w/o), a DNaseI treated preparation (DNaseI) and a sonicated and DNaseI treated extraction (sonic + DNaseI). The four gels show samples taken after cycle 23, 25, 27 and 30 of the PCR. For every RT-PCR a negative control (-) was performed, in which the RNA was added to the sample after the RT step.
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| 15571628 | PMC539233 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Nov 30; 4:45 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-45 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-461557921310.1186/1471-2180-4-46Research ArticleP80, the HinT interacting membrane protein, is a secreted antigen of Mycoplasma hominis Hopfe Miriam [email protected] Ricarda [email protected] Birgit [email protected] Institute of Medical Microbiology and Center for Biological and Medical Research, Heinrich-Heine-University, Moorenstrasse 5, 40225 Duesseldorf, Germany2004 6 12 2004 4 46 46 18 8 2004 6 12 2004 Copyright © 2004 Hopfe 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
Mycoplasmas are cell wall-less bacteria which encode a minimal set of proteins. In Mycoplasma hominis, the genes encoding the surface-localized membrane complex P60/P80 are in an operon with a gene encoding a cytoplasmic, nucleotide-binding protein with a characteristic Histidine triad motif (HinT). HinT is found in both procaryotes and eukaryotes and known to hydrolyze adenosine nucleotides in eukaryotes. Immuno-precipitation and BIACore analysis revealed an interaction between HinT and the P80 domain of the membrane complex. As the membrane anchored P80 carries an N-terminal uncleaved signal peptide we have proposed that the N-terminus extends into the cytoplasm and interacts with the cytosolic HinT.
Results
Further characterization of P80 suggested that the 4.7 kDa signal peptide is protected from cleavage only in the membrane bound form. We found several proteins were released into the supernatant of a logarithmic phase mycoplasma culture, including P80, which was reduced in size by 10 kDa. Western blot analysis of recombinant P80 mutants expressed in E. coli and differing in the N-terminal region revealed that mutation of the +1 position of the mature protein (Asn to Pro) which is important for signal peptidase I recognition resulted in reduced P80 secretion. All other P80 variants were released into the supernatant, in general as a 74 kDa protein encompassing the helical part of P80. Incubation of M. hominis cells in phosphate buffered saline supplemented with divalent cations revealed that the release of mycoplasma proteins into the supernatant was inhibited by high concentrations of calciumions.
Conclusions
Our model for secretion of the P80 protein of M. hominis implies a two-step process. In general the P80 protein is transported across the membrane and remains complexed to P60, surface-exposed and membrane anchored via the uncleaved signal sequence. Loss of the 4.7 kDa signal peptide seems to be a pre-requisite for P80 secretion, which is followed by a proteolytic process leading to a helical 74 kDa product. We propose that this novel form of two-step secretion is one of the solutions to a life with a reduced gene set.
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Background
The contact of a pathogenic bacterium with its eukaryotic host provokes a multitude of reactions. A prerequisite for successful infection with the host is the cytadhesion of the bacterium generally mediated by surface localized proteins [1]. Besides adhesion, pathogens like Listeria, Yersinia and even some of the mycoplasmas are able to invade the host cells [2-4]. An intracellular localization is obviously a privileged niche, as the bacteria are well protected from the immune system. Moreover, bacteria not only remain concealed, but have evolved strategies for an attack on the eukaryotic cell. In secreting virulence factors, such as antigenic or toxic proteins, bacteria can mislead the host immune response or damage the colonized tissue [5-7].
The large majority of exported proteins possess an N-terminal signal sequence [8]. Most signal sequences are recognized by the Sec-dependent protein translocation complex (translocase), which mediates membrane translocation of unfolded precursors [9]. The signal sequences of proteins predicted to be recognized by type I signal peptidases are composed of a short, positively charged amino-terminal region (n-region), a central hydrophobic region (h-region) and a more polar carboxyl region (c-region) containing the cleavage site [10]. The signal peptides present in pre-lipoproteins additionally contain a well-conserved lipobox with an invariant cysteine residue that is lipid-modified prior to precursor cleavage by signal peptidase II [11,12]. Cleavage of the signal peptide is not required for translocation of the proteins through the membrane, but is generally the final step in processing [13]. However, some precursors remain membrane bound because of an uncleaved hydrophobic signal peptide and diffuse laterally from the translocase [14].
In the last few years, computer programs such as PSORT-II, PSORT-B, ExProt and SignalP have been developed to facilitate the identification of putative secreted proteins [15-18]. Comparison of proteomes of Gram-negative bacteria, Gram-positive bacteria and Archaea using ExProt revealed that the fraction of putative secreted proteins ranged from 8% in the archaeal bacterium Methanococcus jannaschii to 37% in the mollicute Mycoplasma pneumoniae [17]. Analysis of the exported proteins of Bacillus subtilis found that only 50% of the secreted proteins were detected by genomic prediction, indicating that proteomic analyses of secreted proteins (the secretome) are necessary for a comprehensive definition of all secreted proteins [19].
Only a handful of mollicute genomes have been decoded, but no analyses of their secretomes have been conducted. A secreted protein, probably processed by the classical mechanism described above, has been characterized in the swine pathogen M. hyopneumoniae. P102 is encoded as a precursor protein carrying a type I signal sequence and is found exclusively in the extra-cellular milieu suggesting cleavage by signal peptidase I. The expression of secreted P102 is coupled to that of the surface-exposed cilium adhesin P97, which seems to represent a new variant of processed surface antigens. P97 is derived from a 126 kDa precursor protein by cleavage at amino acid residue (aa) 195. The cleaved 22 kDa N-terminal fragment, which carries an uncleaved type I signal sequence is found embedded in the membrane, in the cytoplasm and in a soluble form in the supernatant, whereas the mature P97, proposed to be membrane bound, is the target of complex proteolytic cleavage, which leads to the subsequent release of some fragments into the supernatant [20,21]. The lipoprotein MALP-404 of M. fermentans is a further example of a surface-localized protein undergoing proteolysis after reaching its target. Site-specific cleavage leads to the generation of the membrane bound immune-stimulatory lipopeptide MALP-2 and the release of the remainder (the RF fragment) in a soluble form into the supernatant [22].
In M. hominis, which is mainly found as a commensal in the urogenital tract, but has also been associated with human urogenital tract infections [23,24], two variants of a cell-surface protein exist, one a lipoprotein (P120) and a homologous P120' without a lipid anchor, but containing an uncleaved N-terminal signal sequence for type I signal peptidases [25]. Recently we identified a gene locus coding for a surface-localized protein complex composed of a 60 kDa lipoprotein (P60) tightly bound to an 80 kDa precursor protein (P80) with an uncleaved type I signal sequence [26]. We have proposed that the uncleaved N-terminus extends into the cytoplasm and thus mediates an interaction with the concomitantly expressed cytoplasmic HinT protein. HinT is found in prokaryotes and eukaryotes and in the latter is known to function as an adenosine nucleotidyl-hydrolase [27].
The data in this paper suggest that the signal peptidase I recognition site is protected in the membrane bound form of P80, but is accessible as the first step in the release of the helical part of P80 into the cell culture supernatant, a process which is accompanied by a decrease in size of 10 kDa.
Results
To confirm our hypothesis that the P60/P80 membrane complex interacts with the cytoplasmic HinT via the uncleaved N-terminus of P80 that extends into the cytoplasm, we set out to express different fragments of the P80 protein to map the domain of P80 that interacted with HinT. As mycoplasmas use TGA as a tryptophan codon, rather than a stop codon as in E. coli, the TGA codons were mutated to TGG to allow expression. However, even though we mutated all TGA codons to TGG in the hitA gene, which encodes P80, we were not able to express and purify P80 peptides with an intact N-terminus. While P80-2, the C-terminal region of P80 from AA 320 to AA 713, was stably expressed in E. coli, P80-NT, the initial 21AA of the N-terminus, and P80-1, the N-terminal portion of P80 from AA 1 to AA 326, were degraded at the N-terminus during purification (Figure 1). Purification of the 2.6 kDa P80-NT fused to dihydrofolate reductase (DHFR) led to complete loss of the P80 region. As P80 NT did not contain the cleavage site of the signal peptidase I this proteolysis may be due to the presence of further processing signals within the first 21 AA of P80. The isolation of P80-1 revealed rapid degradation of the 40.7 kDa peptide with loss of 5, 6 and 8 kDa portions of the protein and loss of the poly His tag. Expression of the whole rP80 polypeptide chain (AA 1–713) resulted in a protein about 10 kDa smaller than expected, which lacked the poly His tag (depicted as a gray striped box in Figure 1). The conjecture that N-terminal degradation occurs was supported by Western blot analyses with anti-tetra His antibodies in which the purified forms of P80-1 and rP80 were not detectable (data not shown).
As conventional protease inhibitors had no effect on the P80 degradation (data not shown) and as type I signal peptidases share the unusual feature of being resistant to general inhibitors of the four other peptidase classes [10] we speculated that the degradation observed in the expressed rP80 may be in fact the result of physiological processing in E. coli.
Analysis of the mechanism of rP80 processing
In order to test the hypothesis that the degradation of rP80 is mediated by E. coli signal peptidase I, we characterized different mutants of the recombinant P80 protein according to their processing in E. coli. Using PCR, we constructed clones expressing the mature P80 protein without the 44 AA signal peptide (ΔSP) and an 89 AA N-terminal truncated P80 protein, which corresponds to the proposed helical, surface-localized part of P80 (Helic.). As the absence of proline at the +1 position of the mature protein is consistent with the observation that the SPase I of E.coli was inhibited by recombinant precursor proteins having a proline residue at this position [12], we generated a P80 precursor with an Asn to Pro mutation (N/P) at the (+1) position in the mature protein expecting a reduced or complete inhibition of signal peptide cleavage (Figure 2-A). Western-blot analysis of the heterologous expressed P80 variants in whole E. coli lysates revealed that a marked reduction of P80 processing was indeed observed for the (N/P) mutant, while the sizes of secreted P80 variants corresponded to that of the helical variant (Figure 2-B). Small amounts of rP80 precursor were only observed in the lysate after prolonged exposure in immunostaining with the P80-specific mAb LF8 (data not shown). All variants, with the exception of the N/P mutant, were transported across the inner and the outer membranes and were of nearly the same size as the secreted 74 kDa peptide of the P80 protein found in M. hominis (Fig. 2; FBG, supernatant). In the case of P80-(ΔSP) and P80-(Helic.), the two variants lacking signal sequences as translocation signals, this transport may be mediated by an alternative, Sec-independent export mechanism.
P80 secretion in Mycoplasma hominis
The data suggest that the recombinant P80 processing in E. coli is signal peptidase I-mediated and therefore we investigated the protection of the signal peptidase I recognition site of P80 in Mycoplasma hominis [26]. Therefore, we analyzed the cellular proteins and the proteins in the culture supernatant in a mycoplasma culture in early-, mid- and stationary growth phases. To ensure that no viable cells were left in the supernatant we checked the separation efficiency by determining the titres of mycoplasmas in the supernatant and the original culture. The titres in the supernatants were 0.01% to 0.13% of those of the original cultures, demonstrating that the Western blot analysis would not be confounded by inclusion of cellular proteins in the supernatants (data not shown).
As might be expected the stationary phase culture contained several proteins that might be released from lysed cells, including P50, P60, OppA and cytoplasmic proteins such as P55 (Figure 3-A). However, the same membrane proteins were found in the mid-logarithmic phase suggesting release of these proteins from living cells into the supernatant. Indeed, the amount of the cytoplasmic P55 was reduced in this culture suggesting that in mid-logarithmic phase few proteins from lysed cells are present. Lipoproteins, such as P50 and P60 were also found in the supernatant of a mycoplasma culture at the beginning of logarithmic growth (early), whereas OppA was absent. In general, immunostaining of P80 was weak in samples derived from the supernatant of a broth culture. This might be due to a masking of P80 by albumin, which was present in high concentrations in the culture medium and of similar size as the secreted P80 protein.
Interestingly, the P80 protein was the only one of the proteins analyzed that had a decreased size in the supernatant. To exclude the possibility that the reduction in size was simply a result of the large amount of albumin in the culture medium, we purified P80 from the supernatant of a culture in mid logarithmic growth using affinity chromatography. In addition we analyzed P60 as a control. As shown in Figure 3-B, the secreted P80 peptide was decreased in size by approximately 10 kDa in comparison to the cellular form, whereas P60 remained unaltered. A degree of processing of the P80 precursor was detectable, with several distinct P80 staining bands between 80 and 74 kDa, running through distinct steps of degradation as shown for the recombinant P80-1 peptide (see Figure 1).
As the activity of proteases often depends on the presence of divalent cations, we examined the secretion of mycoplasma proteins in the presence or absence of divalent cations. Mycoplasma cells were incubated for 1 h at 37°C in phosphate buffered saline (PBS) supplemented with up to 5 mM magnesium or calcium chloride, or chelating agents as EDTA and EGTA. The addition of divalent cations did not increase the processing of P80 (data not shown). As the presence of 5 mM calcium ions resulted in a complete disappearence of P80 from the supernatant we examined the release of mycoplasma proteins from cells following stepwise addition of calcium ions at concentrations of 0 to 5 mM (Figure 4). Silver staining of the cell and supernatant preparations revealed that only a few proteins were released in the supernatant whereas most proteins remained cell-embedded (Figure 4-A). Increasing the calcium chloride concentration to more that 1.3 mM led to a dramatic reduction in secretion. All the antigens analyzed, with the exception of OppA, were found in the supernatant, even the cytoplasmic proteins P55 and elongation factor Tu (EF-Tu) (Figure 4-B).
The cytoplasmic proteins that were detected in the supernatant were not released from lysed cells as the titres of the cultures remained the same throughout the experiment (data not shown). These findings are in accordance with those of Antelmann and coworkers, who found several cytoplasmic proteins, such as elongation factor G (EF-G) and arginase (RocF) as to be components of the secretome of Bacillus subtilis [19].
Increasing the concentration of calcium ions to more than 0.6 mM CaCl2inhibited a release of most mycoplasma proteins from the cells (Figure 4-B, lane 4). Of the proteins analyzed, only EF-Tu was found in the supernatant of a sample treated with 1.3 mM CaCl2 (Fig. 4-B, lane 3).
Thus the surface-localized P80 protein, normally complexed with the P60 lipoprotein in the membrane, appears to be released into the extra-cellular environment as a 74 kDa cleavage product. Our model of processing describes the initial cleavage of the 4.7 kDa signal peptide by a type I signal peptidase, followed by further degradation by unknown mechanisms, leading to a stable 74 kDa peptide.
Discussion
The data presented here indicate that the membrane protein P80 of Mycoplasma hominis is a representative of a new group of proteins in the Mollicutes. P80 occurs as a precursor protein (complexed with the lipoprotein P60) anchored in the membrane and exposed on the surface, and is also released into the extra-cellular environment. As P80 and P60 are concomitantly expressed and both complexed in the membrane fraction it is likely that the soluble P80 variant is derived from the membrane bound precursor. Interestingly, the findings from the heterologous expression of different P80 mutants in E. coli suggest that release of P80 is initiated by cleavage of the N-terminal type I signal sequence, followed by amino-terminal proteolysis by an unknown mechanism, leading to a stable P80 peptide with a predicted helical structure in the supernatant.
As the genome of M. genitalium, the smallest known self-sufficient organism, lacks a gene for the type I signal peptidase [30] it was initially speculated that this enzyme may be absent in the Mollicutes, despite the fact that several proteins with type I-signal sequences had been identified [10,25]. However signal peptidase I genes have been found in the genomic sequences of Mycoplasma gallisepticum (MGA_1091) and M. pulmonis (MYPU_6300) [31,32].
The release of mycoplasmal proteins into the extra-cellular milieu may follow different pathways to those described previously for other bacteria. While the exclusive detection of P102 of M. hyopneumoniae in the extra-cellular matrix suggests that the intrinsic N-terminal type I signal peptide is immediately cleaved after translocation of the precursor through the membrane, as would be expected from current knowledge of the classical protein secretion pathway, the precursor proteins P80 and P120' of M. hominis are attached to the membrane without being processed or released into the supernatant [21,25,26].
The later release of a surface-localized peptide is described here for the first time. Davis and Wise described site-specific proteolysis of the lipid-anchored MALP-404 of M. fermentans that leads to the generation of the immune-stimulatory lipopeptide MALP-2 and showed that the residual RF peptide was soluble and was released into the supernatant [22]. Processing of the P97 cilium adhesin precursor, P126, of M. hyopneumoniae appears to be quite complex. The precursor protein P126, which carries a type I signal sequence with a cleavage site between AA 31 and AA 32, is predominantly cleaved between AA 194 and AA 195, suggesting that the SP-I signature may be essential only for the translocation of the whole precursor protein across the membrane. The cleavage products P22 and P97 both remain closely associated with the membrane. While P97 is generally subjected to further site-directed proteolysis, leading to the release of peptides into the extra-cellular milieu, further processing of P22 occurs to be strain dependent [21].
Release of proteins into the surroundings of a mycoplasma cell should lead to an immediate alteration of the cell surface architecture and, as most membrane proteins are targets for the host immune response [33,34], may also interfere with the host effector response. Type II secreted proteins appeared to be typically associated with non-invasive organisms colonizing mucosal surfaces, such as Mycoplasma hominis, and are considered to be required for the establishment of an infection at these sites. Recently in Legionella pneumophila a type II protein secretion system was characterized as a virulence factor linked to an intracellular infection [35].
P80 has significant similarity with two protein sequences, a hypothetical protein of M. pulmonis (MYPU_0060; gi15828477) the gene of which is followed by P60 (MYPU_0070) and HinT (MYPU_0080) gene homologues, and a rhoptry protein of Plasmodium yoelii yoelii (gi23481286). Rhoptry proteins are released during host cell invasion by apicomplexans [36].
As P80 interacts with HinT, a cytoplasmic protein found in all kingdoms, which in eukaryotes hydrolyses AMP derivatives and in yeasts functions as a regulator of an RNA polymerase II domain [27], a further scenario is imaginable: We do not know which factors promote the processing of the surface-exposed P80 protein. However, release of the helical P80 peptide is accompanied by the retention of the signal peptide I in the membrane. Some signal peptides have been found to leak back into the cell where they bind to proteins, such as the Ca2+-binding calmodulin [37], or are presented to the immune system [38], where they probably have a secondary signaling function distinct from their role in targeting [39]. Our results indicate that an increase in calcium ions prevents secretion of mycoplasma proteins. Thus, an extra- or intra- cellular stimulus – such as a local variation in the concentration of Ca2+ ions – may activate the signal peptidase induced processing of the membrane bound P80 protein. This would lead to a soluble P80-helix and the membrane anchored signal peptide. After loss of the transmembrane spanning C-terminal amino acids or due to a leakage of the whole signal sequence (as described above) the signal peptide could reach the cytoplasm where an interaction with HinT may be a further step in modulating or promoting cellular processes such as growth or activation of gene expression, a process we are currently investigating.
Conclusions
The data presented here clearly demonstrate that P80 is a secreted antigen of Mycoplasma hominis. This is, to our knowledge, the first description of secreted protein with a type I signal sequence that is stably embedded in the membrane as precursor protein and, as we propose as a model of secretion, is subsequently released from the membrane into the supernatant after signal peptidase I cleavage. As in other mycoplasmas several proteins have been shown to either possess an uncleaved signal sequence or to be further processed after anchoring in the membrane, this may be a common phenomenon in mycoplasmas. Because of their minimal coding capacity, mycoplasmas may have evolved a strategy to use secretory proteins in a dual role, as surface localized proteins in the membrane for cell surface architecture, and, in response to a change in the environment, as a soluble protein released from the cell surface.
Methods
Cloning and expression of rP80 and P80 mutants
The P80 protein encoding region (ACC Z29068) of Mycoplasma hominis strain FBG was amplified by PCR [26] using oligonucleotides (MWG Biotech, Ebersberg, Germany) that change the mycoplasma TGA tryptophan codon to TGG. Fragments were either directly cloned for the expression of distinct P80 regions or fused by SOE (splicing by overlap extension)-PCR [40]. To facilitate cloning of PCR products [41], restriction sites were inserted in the primer sequence without changing the amino acid sequence. P80 variants with mutations in the N-terminal part of P80 were generated by PCR by amplifying the region encoding the helical portion of P80 (Helic., AA 90–713), amplifying a fragment encoding the mature P80 polypeptide (ΔSP, AA 45–713), and mutating the asparagine codon at position +1 of the mature protein to a proline codon (N/P).
Amplicons were cut at the restriction sites within the primers and ligated in-frame into the expression vector pQE30 (Qiagen, Hilden, Germany). To express the P80 N-terminus (AA 1–21) as a fusion protein with dihydrofolate reductase and a poly His-tag, we ligated the respective RcaI/BglII restricted amplicon in frame into the NcoI/BglII restricted plasmid pQE60 and inserted the BamHI/BglII restricted DHFR-fragment of pQE40 in the BglII site of the plasmid pQE60-P80 NT. Plasmids were propagated in DH5αF' for constitutive expression [41].
Cells from a mid-logarithmic phase culture in LB broth (Gibco BRL, Life Technologies Inc., Gaithersburg, Md.) containing ampicillin (100 μg/ml) were harvested by centrifugation (20,000 × g, 30 min, 4°C). In the secretion assay, the pelleted cells and supernatant, which was concentrated by lyophilizing, were subjected to Western blot analysis. The recombinant peptides P80-NT and P80-2 were purified by Ni-NTA chelation according to the manufacturer's protocol, while rP80 and P80-1 were purified with the Sepharose-coated P80-specific antibodies LF8 and NB12 as described by Henrich et al. [34].
Sequence analysis
The analysis of DNA and protein sequences and the design of oligonucleotides were facilitated by use of the software package Lasergene (DNASTAR Inc. 1996, Madison, Wisc.). The integrity of the different plasmid sequences was confirmed by analysis on an ABI sequencer using the method of Sanger [42].
Mycoplasma culture, osmotic lysis and secretion assays
Mycoplasma hominis strain FBG was cultivated in PPLO broth supplemented with arginine from frozen stocks of a mid-logarithmic phase broth culture as described previously [43]. The titer of the broth culture was determined by the measuring the number of color changing units (CCU) [34]. For protein secretion analyses, the mycoplasma cells from early- to mid-logarithmic phase cultures were harvested by centrifugation (20,000 × g, 10 min, 4°C), and the cell pellets and supernatants were subjected to Western blot analysis. Alternatively, cell pellets derived by low speed centrifugation (10,000 × g, 10 min, 4°C) were re-suspended in phosphate-buffered saline (PBS; 120 mM NaCl, 5 mM KCl, 20 mM Tris-HCl, pH 7.5) containing 5.0, 2.5, 1.25, 0.63, 0.31 or 0.00 mM CaCl2 and incubated at 37°C for 60 min. Soluble proteins were then separated from insoluble material by centrifugation (15,000 × g, 15 min, 4°C), and all samples analyzed by Silver staining [44] and Western blotting.
Western blot analysis
Proteins were separated in 9.5% polyacrylamide gels [45], transferred to nitrocellulose (Schleicher and Schüll, Dassel, Germany) with a semidry blotting apparatus (Phase, Mölln, Germany) [46], and immunostained as described by Henrich et al. [34] using the monoclonal antibodies BG11 (anti-OppA), BG2 (anti-P50), LF8 (anti-P80), or CG4 (anti-P60), all of which are directed against membrane proteins, and AH10 (P55) and KD2 (EF-Tu), which are directed against proteins primarily located in the cytoplasm.
Authors' contributions
RH began the characterization of antigens released into the supernatant of a M. hominis culture as part of her thesis. MH carried out the immunoassays and completed the secretion assays. BH carried out the molecular genetic studies, participated in the design of the study and drafted the manuscript. All authors have read and approved of the final manuscript.
Acknowledgments
This work was supported by the Deutsche Forschungsgemeinschaft (DFG- HE 2028/3-2). We thank Marzena Wyschkon for excellent technical assistance and Colin MacKenzie for critically reading the manuscript.
Figures and Tables
Figure 1 Physical map and expression profile of recombinant P80 peptides. The 713 AA polypeptide chain of the P80 precursor protein is schematically represented by an alignment of proposed α helical and surface localized regions. The expressed regions in the different P80 variants are shown below in gray flanked by the amino acid numbers corresponding to the position within the P80 precursor. The striped boxes represent the fused poly His tags, colored in black or gray, respectively, depending on their presence or absence after purification of the recombinant protein. P80-NT was expressed as a fusion with dihydrofolate reductase (DHFR). In the Western-blot analysis shown below, lysates (lane 1) and purified P80 variants (lane 2) have been immunostained with an anti-His4-antibody (P80-NT), or the P80-specific monoclonal antibodies NB12 (P80-1) or LF8 (P80-2, rP80).
Figure 2 Western blot analysis of the P80 variants. A. The P80 variants are represented by alignments of their proposed α helical regions and hydropathy profiles. B. After cultivating the different P80 clones for 2–5 h in LB-Amp medium proteins in the supernatant (after 5 h cultivation), the periplasmic fraction and the cell pellet (after 2 h cultivation) were separated by 9.5% SDS-PAGE and subjected to Western blot analysis using the P80 specific monoclonal antibody LF8. The samples correspond to 10 μl of the recombinant P80 (rP80) culture, 1 ml of P80Asn45Pro (N/P) culture, 10 μl of the helical P80 variant (Helic.) culture and 1 μl culture of the culture of the P80 variant without signal peptide (ΔSP). Samples from the culture supernatant were obtained from 30 μl (rP80), 0.8 ml (N/P), 0.2 ml (Helic.) and 60 μl (ΔSP) of media, and periplasmic proteins from 0.4 ml (rP80), 2 ml (N/P), 0.1 ml (Helic.) and 2 ml (ΔSP) of cultures. Additionally, lysate from 50 μl of culture and proteins from 250 μl of cell culture supernatant of M. hominis (strain FBG) were used. Marker, SeeBlue (Invitrogen, Germany).
Figure 3 Proteins released in the culture supernatant of Mycoplasma hominis. A. Western blot analysis of the proteins from 150 μl of supernatant and cells from 15 μl of culture, immunostained with the membrane protein specific monoclonal antibodies BG11 (OppA), LF8 (P80) and CG4 (P60), BG2 (P50), and the cytoplasmic protein specific monoclonal antibody AH10 (P55). B. Western blot analysis of P60 and P80 of M. hominis lysate (Lys.) from 15 μl of culture and of two different fractions of the purified P60 (P60SN) and P80 (P80SN) from the supernatant of a cell culture using sepharose-coupled antibodies LF8 (P80) or CG4 (P60).
Figure 4 Secreted antigens of Mycoplasma hominis. M. hominis cells from a mid-logarithmic phase culture were incubated for 1 h at 37°C in PBS containing 0 to 5 mM CaCl2 as indicated. Proteins in 0.4 ml of culture supernatants and cell pellets from 0.02 ml of cultures were separated in 9.5 % polyacylamide gels and silver stained (A.) or Western blotted (B.) using the monoclonal antibodies BG11 (OppA), LF8 (P80) and CG4 (P60), AH10 (P55), BG2 (P50) or KD2 (EF-Tu). The predominant band in the supernatant without calcium ions is recognized by the anti-P80 antibody LF8. Molecular Weight Marker: See Blue Plus 2 (Invitrogen, Karlsruhe, Germany).
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| 15579213 | PMC539234 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Dec 6; 4:46 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-46 | oa_comm |
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BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-471558828210.1186/1471-2180-4-47Research ArticleAllele specific synthetic lethality between priC and dnaAts alleles at the permissive temperature of 30°C in E. coli K-12 Hinds Tania [email protected] Steven J [email protected] 203 Morrill Science Center IVN, Department of Microbiology, University of Massachusetts, Amherst, MA 01003, USA2004 8 12 2004 4 47 47 12 10 2004 8 12 2004 Copyright © 2004 Hinds and Sandler; 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
DnaA is an essential protein in the regulation and initiation of DNA replication in many bacteria. It forms a protein-DNA complex at oriC to which DnaC loads DnaB. DNA replication forks initiated at oriC by DnaA can collapse on route to the terminus for a variety of reasons. PriA, PriB, PriC, DnaT, Rep and DnaC form multiple pathways to restart repaired replication forks. DnaC809 and dnaC809,820 are suppressors of priA2::kan mutant phenotypes. The former requires PriC and Rep while the latter is independent of them. RnhA339::cat mutations allow DnaA-independent initiation of DNA replication.
Results
It is shown herein that a priC303::kan mutation is synthetically lethal with either a dnaA46 or dnaA508 temperature sensitive mutation at the permissive temperature of 30°C. The priC-dnaA lethality is specific for the dnaA allele. The priC303::kan mutant was viable when placed in combination with either dnaA5, dnaA167, dnaA204 or dnaA602. The priC-dnaA508 and priC-dnaA46 lethality could be suppressed by rnhA339::cat. The priC-dnaA508 lethality could be suppressed by a dnaC809,820 mutation, but not dnaC809. Neither of the dnaC mutations could suppress the priC-dnaA46 lethality.
Conclusions
A hitherto unknown function for either DnaA in replication restart or PriC in initiation of DNA replication that occurs in certain dnaA temperature sensitive mutant strains at the permissive temperature of 30°C has been documented. Models considering roles for PriC during initiation of DNA replication and roles for DnaA in replication restart were tested and found not to decisively explain the data. Other roles of dnaA in transcription and nucleoid structure are additionally considered.
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Background
The loading of the DnaB replicative helicase at the E. coli origin of DNA replication (oriC) is a highly regulated and is thought to be a key step in the assembly of the replisome. DnaB makes important contacts with the τ-subunit of DNA Polymerase III Holoenzyme and DNA primase [1]. DnaB loading at oriC during initiation of DNA replication is a sequence specific, cell cycle regulated event dependent on the DnaA and DnaC proteins (reviewed in [2-4]). In vitro, in the presence of several other accessory proteins (i.e. RNA polymerase, DNA gyrase, HU protein), multiple DnaA proteins bind to four asymmetric 9 bp DnaA binding sites in the 225 bp oriC region allowing formation of a protein/DNA complex [5-7]. This in turn causes melting of a nearby AT rich sequence. A complex of DnaB6-DnaC6 then interacts with the DnaA/oriC complex to load DnaB at the AT rich sequence.
It is thought that DnaA may have other roles in the cells in addition to initiation. These additional roles stem from the fact that there are many DnaA binding sites in the chromosome outside the oriC region [8,9] and that DnaA binding to these asymmetric 9 bp sites, can bend the DNA [10]. It can be hypothesized based on large number of potential DnaA binding sites in the chromosome and the ability of DnaA to bend DNA that it may influence the structure of nucleoid. It has been shown that if a DnaA binding site falls within a promoter region that mutations in dnaA can affect the level of transcription from that promoter [11-14]. Thus, mutations in dnaA may have global effects in gene expression and nucleoid structure as well as affecting initiation of DNA replication at oriC.
In E. coli, dnaA is an essential gene. Several different dnaA temperature sensitive mutant alleles have been isolated. Many of these share the property that they are double mutants (dnaA5, dnaA46, dnaA508 and dnaA602 – Table 1 and [15]). Several of the double mutants share a mutation: a change at codon 184 that replaces an alanine with a valine. Additionally, a dnaA850::Tn10 mutant has been isolated. This is only viable in strains that have an alternate, oriC-independent method of initiation of DNA replication [16].
The loading of DnaB by DnaC in E. coli can occur away from oriC at a repaired replication fork. This is governed by the Replication Restart Proteins (RRPs): PriA, PriB, PriC, DnaT and Rep. The genes coding for these products form multiple pathways to identify the proper substrate and then help DnaC load DnaB (Figure 1 and [17]). Since replication restart is thought to be an essential process, the poor viability (versus complete inviability) of priA mutants suggested the availability of alternate pathways. The PriA-independent pathway depends on PriC and Rep [17]. Two types of priA suppressor mutations have been found and both map to the dnaC gene. The first typified by dnaC809 (E176G) and is dependent on the genes in the PriA-independent pathway of replication restart, rep and priC [17]. The second type of priA suppressor has an additional mutation (K178N) in dnaC relative to dnaC809 and makes this protein's suppression of priA mutant phenotypes independent of priC and rep. This dnaC allele is called dnaC809,820 [17,18]. The multiplicity of replication restart pathways may be a general property in Bacteria since Bacillus subtilis also has a similar arrangement of PriA-dependent and PriA-independent pathways [19,20].
As mentioned above, DnaA-dependent initiation of DNA replication at oriC and replication restart share several properties. The most important of these is that both strive to make protein-DNA complexes that are recognized by the DnaC protein so that DnaB can be loaded. Previous work by Kogoma and colleagues showed that oriC-DnaA-independent initiation of DNA replication could take place in an rnhA mutant strain [16,21]. This type of initiation of DNA replication was termed Constitutive Stable DNA Replication (cSDR) and is dependent on both recombination and replication restart functions (reviewed in [22] and Sandler, submitted).
To begin testing the roles of priB and priC in cSDR (reviewed in [22]) required the construction of dnaAtsrnhA priB and dnaAtsrnhA priC triple mutant strains. However when trying to construct these strains, we found that priC was required for growth in two different dnaAts strains, dnaA46 and dnaA508, at the permissive temperature of 30°C. When priC303::kan was tested with other dnaAts alleles, the synthetic lethality was found to be allele specific. Two different mutations were found to suppress the priC-dnaA lethality. One was rnhA339::cat, a non-allele specific suppressor of dnaA mutants. The other was dnaC809,820, a suppressor of priC and priA mutations. The first type suppressed both the dnaA508-priC and dnaA46-priC lethality while the latter only suppressed the dnaA508-priC lethality. These studies suggest that priC may have a role in initiation of DNA replication at oriC with certain dnaA alleles and or that dnaA may have an additional role in the cell important for replication restart.
Results
PriC, but not priB, is required for growth in a dnaA508 mutant at the permissive temperature of 30°C
We began this study by asking if priB and priC are required for cSDR. During this study it was found that certain dnaAts could be introduced into a strain containing a priB mutation, but not into a strain containing a priC mutation. The standard P1 transductional cross used for the introduction of the dnaAts mutant alleles is shown in Figure 2. Table 2 shows that the co-transduction frequency between tnaA300::Tn10 and dnaA508 in a wild type strain is 92% (49/53). Using a priB mutant strain as the recipient, the co-transduction frequency between tnaA300::Tn10 and dnaA508 was approximately the same as when the wild type strain was used as the recipient (data not shown). Surprisingly, when a priC mutant was used as a recipient, the co-transduction frequency was 0/72 or 0% (Table 2). This suggested that the priC303::kan and dnaA508 mutation may be synthetically lethal at the permissive temperature of 30°C. It is also formally possible that priC303::kan suppressed the temperature sensitive nature of dnaA508. These two possibilities are tested below.
Since it is known that the absence of other gene products (i.e., rnhA [23] and trxA [24]) can suppress the essentiality of dnaA, it is possible that the absence of priC might also suppress the temperature sensitivity of the dnaAts allele. If so, then one should be able to detect the presence of the dnaA mutation on the chromosome of the 42°C resistant transductants. To test this possibility, TetR transductants selected at 30°C were screened for a 42°C R phenotype. These were then further screened for the presence of a restriction site polymorphism (a Eco NI site) created by the dnaA508 mutation. To do this, the dnaA region from the 42°C R TetR transductants was amplified by standard Polymerase Chain Reaction methods using the primers, prSJS480 and prSJS481 (Table 3). The amplified DNA was then restricted with Eco NI. Examination of eight independent 42°CR TetR transductants, constructed using JC12390 as the donor, revealed no case in which a restriction pattern was consistent with the presence of the temperature sensitive allele (data not shown). From these results, it is concluded that the priC303::kan mutation does not suppress the absence of dnaA508 and is synthetically lethal with it.
PriC303::kan is synthetically lethal with dnaA46 and dnaA508, but not with dnaA5, dnaA167, dnaA204 or dnaA602
It is possible that either the dnaA508-priC303::kan synthetic lethality at the permissive temperature of 30°C is allele specific or occurs with all dnaAts alleles. To distinguish between these two possibilities, several other dnaAts alleles were tested. Selection of a diverse collection of mutations to test was aided by an already large repertoire of characterized dnaAts alleles [15] and the recent elucidation of the crystal structure of DnaA from Aquifex aeolicus [25]. This allowed the selection of several temperature sensitive dnaA alleles that had different amino acids substitutions in different parts of the protein (Table 1). Hence, it was attempted to introduce dnaA5, dnaA46, dnaA167, dnaA204 and dnaA602 into the priC303::kan strain (SS145) using the selectable marker tnaA300::Tn10 as before. Table 2 shows that the synthetic lethality only occurred additionally with dnaA46, but not with dnaA5, dnaA167, dnaA204 or dnaA602. It is concluded that the synthetic lethality between priC303::kan and dnaA508 and dnaA46 at 30°C is allele specific.
The priC303::kan dnaAts synthetic lethality is solely due to the absence of only priC and the presence of the dnaAts mutation
Since both the dnaA and priC genes are in operons, it is possible that the synthetic lethality seen above is due to not just the mutation in priC or dnaA, but also due to that mutation and or polar effects on downstream genes within their respective operons. While the potential polar effects of a priC303::kan insertion mutation are easily envisioned, the potential polar effects of a dnaA missense mutation are less obvious. This is tested here as well because, as introduced above and discussed below, dnaA mutations can have effects on the level of transcription of promoters in which there are DnaA binding sites. Since dnaA binds to its own promoter and autoregulates its own expression [12,26], it is possible that dnaA mutation may effect transcription from its own promoter and subsequently effect dnaN and recF expression.
It was first tested if the synthetic lethality between the dnaA508 and priC303::kan was dependent solely on the priC gene. This was necessary to determine because priC303::kan is an insertion mutation and could be polar on the downstream gene, ybaM. This was tested by cloning priC into a plasmid (pTH1, see Methods) and seeing if the priC plasmid could complement the synthetically lethal phenotype. pTH1, containing just the priC promoter and gene, in the priC303::kan mutant strain (SS145), allowed the dnaA508 allele to be introduced into that strain at the wild type co-transduction frequency of 90% (data not shown).
It was then tested if the synthetic lethality was due to polar effects of dnaA508 or dnaA46 on downstream dnaN-recF expression. This was tested in a similar way. A plasmid, pAB3 [27], that expresses the dnaA gene in trans was introduced into the priC303::kan mutant strain (SS145). This strain was then used as a recipient in a cross with either ALO450 (dnaA46 tnaA300::Tn10) or JC12390 (dnaA508 tnaA300::Tn10). TetR transductants were selected at 30°C. In each case, several transductants were selected and screened for the presence of the dnaAts mutation by backcrosses to JC13509. The temperature sensitive phenotype associated with dnaA508 and dnaA46 was detected in each case (data not shown).
It is concluded that the synthetic lethality seen between priC303::kan and dnaA508 or dnaA46 is due to solely the absence of priC and the presence of the dnaAts mutation.
RnhA mutations suppress the priC-dnaA synthetic lethality
It has been shown that rnhA mutations are non-allele specific suppressors of both dnaAts and dnaA insertion mutations [16]. The mechanism of suppression is thought to be the stabilization of R-loops on the chromosome [22]. To determine if the priC-dnaA synthetic lethality is suppressed by a mutation in rnhA, it was attempted to introduce dnaA508 and dnaA46 into a priC303::kan rnhA339::cat (SS1531) double mutant strain. It was found that this dnaAts rnhA priC triple mutant combination was viable in each case (see SS1543 and SS3032 in Table 4). This suggested that the cause of the priC-dnaA lethality was a defect in the mutant dnaA protein's ability to initiation of DNA replication and that priC has some role in initiation of DNA replication in the dnaA508 and dnaA46 mutant strains.
DnaC809,820, but not dnaC809, suppresses the absence of priC in the dnaA508 mutant at 30°C
The above experiment suggested that PriC has a role in initiation of DNA replication in certain dnaA mutants. If so, then suppressors of priC's role in replication restart should not suppress the priC-dnaA synthetic lethality. Two types of replication restart suppressor mutations are known and were tested [18,28]. DnaC809 suppresses the phenotypes of priA2::kan and dnaT822 [28,29]. In vitro, DnaC809 can suppress the absence of all RRPs on several different substrates [30]. In vivo however, priA2::kan suppression requires priC and rep [17]. DnaC809 can be additionally mutated to make the priA suppression both priC and rep independent [17]. This additionally mutated dnaC allele is called dnaC809,820 [18]. To test the above hypothesis, priC303::kan dnaC809 (SS1099) and priC303::kan dnaC809,820 (SS1100) strains were constructed (Table 4) and used as recipients in crosses with the donor P1 from either JC12390 (dnaA508) or AL0450 (dnaA46). Table 2 shows that when dnaC809,820 was used as the recipient and JC12390 as the donor that 41/48 or 83% of the TetR transductants were also temperature sensitive (they inherited the dnaA508 allele). However, when only dnaC809 was used as the recipient, 0/63 TetR transductants inherited the temperature sensitive phenotype. It is concluded that dnaC809,820 can suppress the absence of priC in the dnaA508 mutant and dnaC809 cannot. The dnaA46 allele was additionally tested and was not suppressed by either dnaC809 or dnaC809,820 (Table 3). From this it is concluded that dnaC809,820 is able to suppress the absence of some dnaAts allele. In contradiction to the suggestion of the above rnhA experiment, this result suggests that dnaA may have some role in replication restart necessary in a priC mutant.
Discussion
This paper shows that priC, a gene involved in both the PriA-dependent and PriA-independent pathways for replication restart, is also required for cell viability in two of six dnaAts mutants at the permissive growth temperature of 30°C. These results were surprising on at least two accounts. The first is that in vitro systems for either replication restart or initiation of DNA replication at oriC posed no requirement for the DnaA or PriC protein respectively. The second is that priC has no known role in vivo in initiation of DNA replication (the only reported role is in replication restart [17]) and that dnaA has no known role in replication restart.
One way to answer the question of why the mutations are synthetically lethal is to see what types of mutations may suppress the lethality. RnhA339::cat, a non-allele specific suppressor of dnaA mutants role in initiation of DNA replication, could suppress the priC-dnaA synthetic lethality for both dnaAts mutant alleles. Such suppression is strong evidence that priC and dnaA may both be missing a function needed during initiation of DNA replication. It was further observed, however, that dnaC809,820 (but not dnaC809) could suppress the absence of priC in the dnaA508 mutant. Neither dnaC809 nor dnaC809,820 could suppress the dnaA46-priC synthetic lethality. DnaC809,820 is a PriC-independent suppressor of several genes required in replication restart. This suppression implicates dnaA in replication restart. Thus the inferences from the two types of suppressors seem to contradict one another.
What function(s) in dnaAts strains are missing for initiation of DNA replication that make the strain dependent on priC at 30°C? A structure-function analysis of DnaA would help to answer this question. Briefly, based on alignments of DnaA proteins, the X-ray crystal structure of the DnaA protein from Aquifex aeolicus and much research on the genetics and biochemistry of DnaA, the DnaA protein can be divided into four domains with four proposed functions: Domain I) DnaB recruitment, Domain II) Linker region, Domain III) ATP binding and Domain IV) DNA binding [25,35,36]. Table 1 shows that the six mutations tested substitute amino acids spread throughout DnaA. The two mutants that show a requirement for priC have mutations in Domains I (DnaB recruitment) and III (ATP hydrolysis). However, several of the mutations not requiring priC also affect Domain III. An interesting aspect to dnaA genetics is that many temperature sensitive mutants have two mutations (Table 1 and [2]). The dnaA5, dnaA46 and dnaA602 all have mutations in Domain III near the ATP binding region. Their second mutations cause amino acid replacements in other domains. Unfortunately, the positions of the second changes yield no clues about what might make dnaA508 and dnaA46 mutants require priC for growth at 30°C and why the other four dnaA mutants do not.
What might PriC be doing to help initiation of DNA replication in a dnaAts strain? One idea is that the dnaAts protein is defective in its ability to create a region of ssDNA at oriC. Since the RRPs are also thought to help create regions of ssDNA (away from oriC) so that DnaC binds and loads DnaB, it is possible that PriC may help the DnaA mutants in this endeavor. Another type of model that is formally possible is that PriC may somehow stabilize the dnaAts protein. This seems unlikely, however, given that dnaC809,820 can rescue the synthetic lethality of priC303::kan and dnaA508. Other models may also be possible.
One needs to consider if DnaA may be involved in replication restart. In considering this, one needs to remember that DnaA has the ability to bind DNA at specific sites and bend it. It has been shown that different dnaAts allele can differentially influence the rate of initiation of transcription in some promoters with DnaA binding sites (see below). This in turn can influence replication restart in two ways. First changes in the level of gene expression of a single gene (or groups of genes) may indirectly influence the replication restart process. Second, the ability of DnaA to bind to many sites on the chromosome may influence structure of the nucleoid and the sites at which replication restart may occur.
There are many examples where several phenotypes had been tested systematically for several dnaA alleles (Table 1 and [31-33]). The only other system that seems to have some similarity to the data here is one in which the ability to replicate λ P+ plasmids was investigated [27,34]. Table 1 shows that dnaA46, dnaA204 and dnaA508 all fail to replicate these plasmids while dnaA5, dnaA167 and dna602 can. The model proposed to explain this phenomenon suggests that DnaA is required to activate transcription at the λ PR promoter and that the dnaA46, dnaA204 and dnaA508 mutations decrease this ability[27]. With the exception of dnaA204, the inability to replicate these plasmids mirrors the ability of the dnaAts mutants to grow in the absence of priC.
The results presented in this paper do not allow one to definitively know whether the synthetic lethality studied here is due to a failure in initiation of replication at oriC or is it due to a failure in replication restart based on the study of the suppressors. Since dnaA mutations can affect more than just initiation of DNA replication, it is tempting to speculate that some other dnaA function: transcription of a particular gene or set of genes or the shape of nucleoid in the priC mutant may contribute or be the cause of the synthetic lethality. Understanding the molecular mechanism underlying the dnaAts-priC synthetic lethality may require appreciation of these other aspects of dnaA biology.
Conclusions
A hitherto unknown function for either DnaA in replication restart or PriC in initiation of DNA replication that occurs in certain dnaA temperature sensitive strains at the permissive temperature of 30°C has been documented. Models considering roles for PriC during initiation of DNA replication and roles for DnaA in replication restart were tested and found not to decisively explain the data. Other roles of dnaA in transcription and nucleoid structure are additionally considered.
Methods
Bacterial strains
All bacterial strains used in this work are derivatives of E. coli K-12 and are described in Table 4. The protocol for P1 transduction has been described elsewhere [37]. All P1 transduction were selected on 2% agar plates containing either minimal or rich media and either tetracycline 10 μg/ml or kanamycin 50 μg/ml final concentration. All transductants were first purified on the same type of media on which they were selected. Tests for temperature sensitivity were then done by replica plating patches of the purified transductants at 30°C and 42°C on solid rich media without any antibiotics. Growth was scored by either the presence or absence of a patch after 24 hours.
Cloning of the priC gene
Wildtype chromosome DNA was used as the template in a standard PCR reaction using prSJS283 and prSJS284 (Table 3) as the priming oligonulceotides. The amplified PCR fragment (that includes the putative promoter) was purified by gel electrophoresis and cloned into the pCR 2.1 using the TOPO-TA cloning system from Invitrogen. The priC containing plasmid was called pTH1.
Authors contributions
TH carried out the initial part of the molecular genetic studies. These were completed by SJS. SJS conceived of the study and wrote the manuscript.
Acknowledgments
This work was supported by grant RPG-99-194-04-GMC from the American Cancer Society. We would like to thank Jon Kaguni, Patrice Polard, Benedicte Michel, Mark Sutton and Kirsten Skarstad for sending strains and discussions during the course of this work and Jesse McCool, Michele Klingbeil and Ching Leang for critically reading the manuscript before submission.
Figures and Tables
Figure 1 This diagram compares the ways in which the replicative helicase can be loaded either from oriC or a repaired replication fork in E. coli. Left side of the diagram indicates the starting substrate to which the replisome is to be loaded. The horizontal arrows indicate the way in which the proteins may interact to load the replisome. The dotted lines represent suppressor pathways.
Figure 2 The tnaA- dnaA region of the E. coli chromosome is diagramed on the lower line. The upper line is symbolic of the DNA introduced by the P1 transduction in the standard cross described in this paper where a tnaA300::Tn10 dnaAts donor is introduced to a dnaA+ recipient. Potential crossover events between the two markers are shown.
Table 1 dnaA alleles used in this work and some phenotypes
Allele Amino Acid Change Domain Affecteda Ability to replicate P+ plasmids at 30°Cb Ability to grow in priC303::kan at 30°C
dnaA46 A184V
H252Y III
III No No
dnaA508 P28L
T80I I
I No No
dnaA5 A184V
G426S III
IV Yes Yes
dnaA167 V157E III Yes Yes
dnaA204 I389N IV No Yes
dnaA602 A184V
A347V III
linker Yes Yes
aDomains are defined according to [25, 35, 36].
b Data from [34]
Table 2 P1 crosses using dnaAts strains as the donors and isogenic priC303::kan and dnaC mutants as the recipientsa
Donor Recipient Strain Number Recipient Genotype Ts/total
Strain Number dnaA priC dnaC
JC12390 508 JC13509 + + 49/53
AL0454 46 JC13509 + + 60/69
JC12390 508 SS145 303 + 0/72
AL0454 46 SS145 303 + 0/72
SS1750 5 SS145 303 + 7/12
SS1751 167 SS145 303 + 11/11
WM433 204 SS145 303 + 4/5
SS1752 602 SS145 303 + 10/12
JC12390 508 SS1099 303 809 0/63
JC12390 508 SS1100 303 809,820 41/48
AL0454 46 SS1099 303 809 0/32
AL0454 46 SS1100 303 809,820 0/24
aThese are the results of either single or multiple transductions. The numbers show the number of TetR transductants that were also sensitive to growth at 42°C. If the transductions yielded temperature sensitive transductants then only a few transductants from one transduction is reported. The recipient cells were grown at 37°C in Luria broth. They were then treated with donor lysate made on the tnaA300::Tn10 dnaAts strain indicated. The transductions were then spread on Luria plates containing 10 μgm/ml tetracycline and incubated 24–48 hours at 30°C. Transductants were then picked, purified and tested for growth at 42°C by replica plating.
Table 3 List of Oligonucleotide Primers
Name 5' to 3' oligonucleotide sequence Position
prSJS283 ATATTGAGTGTTGTCAGC Upstream of priC
prSJS284 TCCTCCAGCAGCACAATC Downstream of priC
prSJS480 CCGCGGTCCCGATCGTTTTG dnaA upstream primer
prSJS481 GCAGGGCGTTGAAGGTGTGG dnaA downstream primer
Table 4 Strain List
Strain priC dnaA dnaC Relevant Genotype Source or Derivation
ALO454 + 46 + tnaA300::Tn10 Kirsten Skarstad
AQ12251 + + + rnhA339::cat T. Kogoma
CAG18442 + + + thr-34::Tn10 [38]
CM740 + 5 + Kirsten Skarstad
CM2556 + 167 + Kirsten Skarstad
CM2733 + 602 + Kirsten Skarstad
JC12390 + 508 + tnaA300::Tn10 Lab stock
JC13509a + + + [18]
JC19008 + + 809 priA2::kan [28]
JC19165 303 + + [18]
JC19257 + + 809,820 priA2::kan [18]
SS145 303 + + JC19165 -> JC13509b
SS1091 + + 809 JC19008 -> SS1213c
SS1092 + + 809,820 JC19257 -> SS1213c
SS1099 303 + 809 JC19165 -> SS1091b
SS1100 303 + 809,820 JC19165 -> SS1092b
SS1201 + 508 + tnaA300::Tn10 JC12390 -> JC13509e
SS1213 + + + thr-34::Tn10 CAG18442 -> JC13509d
SS1504 303 508 809,820 tnaA300::Tn10 JC12390 -> SS1100e
SS1531 303 + + rnhA339::cat AQ12251 -> SS145
SS1543 303 508 + rnhA339::cat JC12390 -> SS1531f
SS1738 + + + tnaA300::Tn10 Lab Stock
SS1750 + 5 + tnaA300::Tn10 SS1738 -> CM740e
SS1751 + 167 + tnaA300::Tn10 SS1738 -> CM2556e
SS1752 + 602 + tnaA300::Tn10 SS1738 -> CM2733e
SS1793 303 167 + tnaA300::Tn10 SS1751 -> SS145e
SS1796 303 602 + tnaA300::Tn10 SS1752 -> SS145e
SS1797 303 5 + tnaA300::Tn10 SS1750 -> SS145e
SS1798 303 204 + tnaA300::Tn10 WM433 -> SS145e
SS3032 303 46 + rnhA339::cat ALO454 -> SS1531f
WM433 + 204 + tnaA300::Tn10 Mark Sutton
a JC13509 has the following genotype: sulB103 lacMS286 Φ 80dIIlacBK1 argE3 his-4 thi-1 xyl-5 mtl-1 SmR T6R. The lacMS286 Φ 80dIIlacBK1 code for two partial copies of the lac operon [39, 40].
bSelect for kanamycin resistance on minimal media.
cSelect for threonine prototrophy on minimal media. Screen for the presence of the dnaC mutation by the method described in [18].
dSelect for tetracycline resistance on rich media and screen for threonine auxotrophy.
eSelect for tetracycline resistance on rich media at 30°C and then screen for temperature sensitive growth at 42°C.
fSelect for tetracycline resistance on rich media at 30°C and then screen for restriction site polymorphism and or backcross to test the presence of the temperature sensitive allele.
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| 15588282 | PMC539235 | CC BY | 2021-01-04 16:03:38 | no | BMC Microbiol. 2004 Dec 8; 4:47 | utf-8 | BMC Microbiol | 2,004 | 10.1186/1471-2180-4-47 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-511557919910.1186/1471-2202-5-51Research ArticleNeurotrophin and Trk expression by cells of the human lamina cribrosa following oxygen-glucose deprivation Lambert Wendi S [email protected] Abbot F [email protected] Robert J [email protected] Department of Cell Biology and Genetics, University of North Texas Health Science Center at Fort Worth, Fort Worth, TX, USA2 Glaucoma Research, Alcon Research, Ltd., Fort Worth, TX, USA2004 3 12 2004 5 51 51 8 7 2004 3 12 2004 Copyright © 2004 Lambert 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
Ischemia within the optic nerve head (ONH) may contribute to retinal ganglion cell (RGC) loss in primary open angle glaucoma (POAG). Ischemia has been reported to increase neurotrophin and high affinity Trk receptor expression by CNS neurons and glial cells. We have previously demonstrated neurotrophin and Trk expression within the lamina cribrosa (LC) region of the ONH. To determine if ischemia alters neurotrophin and Trk protein expression in cells from the human LC, cultured LC cells and ONH astrocytes were exposed to 48 hours of oxygen-glucose deprivation (OGD). Also cells were exposed to 48 hours of OGD followed by 24 hours of recovery in normal growth conditions. Cell number, neurotrophin and Trk receptor protein expression, neurotrophin secretion, and Trk receptor activation were examined.
Results
Cell number was estimated using an assay for cell metabolism following 24, 48 and 72 hours of OGD. A statistically significant decrease in LC and ONH astrocyte cell number did not occur until 72 hours of OGD, therefore cellular protein and conditioned media were collected at 48 hours OGD. Protein expression of NGF, BDNF and NT-3 by LC cells and ONH astrocytes increased following OGD, as did NGF secretion. Recovery from OGD increased BDNF protein expression in LC cells. In ONH astrocytes, recovery from OGD increased NGF protein expression, and decreased BDNF secretion. Trk A expression and activation in LC cells was increased following OGD while expression and activation of all other Trk receptors was decreased. A similar increase in Trk A expression and activation was observed in ONH astrocytes following recovery from OGD.
Conclusions
In vitro conditions that mimic ischemia increase the expression and secretion of neurotrophins by cells from the ONH. Increased Trk A expression and activation in LC cells following OGD and in ONH astrocytes following recovery from OGD suggest autocrine/paracrine neurotrophin signaling could be a response to ONH ischemia in POAG. Also, the increase in NGF, BDNF and NT-3 protein expression and NGF secretion following OGD also suggest LC cells and ONH astrocytes may be a paracrine source of neurotrophins for RGCs.
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Background
Glaucoma is an optic neuropathy defined by characteristic optic nerve head and associated visual field changes. Nearly 67 million people worldwide are believed to have glaucoma, including an estimated 2.2 million in the USA [1,2]. Primary open-angle glaucoma (POAG) is the most common form of glaucoma accounting for virtually half of all cases [3]. The visual field changes associated with POAG are due to the loss of retinal ganglion cells (RGCs), which is proposed to occur via apoptosis [4,5]. There is evidence that ischemia contributes to RGC loss in glaucoma. Abnormal optic nerve head (ONH) and retinal blood flow has been observed in glaucoma, and retinal ischemia results in RGC loss [6-11]. In addition, excitotoxicity due to elevated glutamate levels, which occurs following ischemia, can cause RGC death [12-16]. However, not all cellular responses to ischemia are deleterious. The expression of "protective factors", including neurotrophins (NTs), by neurons and glia within the CNS has been shown to increase following ischemia [17-19].
Neurotrophins are polypeptide growth factors involved in the development and maintenance of neurons, as well as non-neuronal cells. Included in this family of trophic factors are nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and neurotrophin 4 (NT-4). Neurotrophin signaling occurs via two types of receptors including (a) tyrosine kinase high affinity Trk receptors and (b) the low affinity p75 receptor [20]. The Trk receptors include Trk A, Trk B and Trk C that bind NGF, BDNF and NT-4, and NT-3 respectively [21]. Truncated isoforms of Trk B and Trk C that lack the tyrosine kinase domain have been identified and their function at this time is unknown, although there is evidence that these receptors interfere with full-length trk signaling through ligand sequestration [22-24].
In addition to their localization at axon terminals, Trk receptors have been localized at neuronal cell bodies, at dendritic projections, and along axons [25-27]. Discrete signaling pathways can be activated and distinct biological responses elicited in neurons depending on the location of Trk receptor stimulation indicating neurons respond not only to retrograde NT sources, but also to paracrine and autocrine sources [28-31]. The lamina cribrosa (LC) region of the optic nerve head (ONH) is composed of connective tissue plates that align to form a sieve-like structure that guides and protects RGC axons as they exit the eye to form the optic nerve. Two major cell types have been isolated from the human LC and include ONH astrocytes and LC cells [32-35]. We have previously demonstrated the expression of NTs and their Trk receptors by cells isolated from the human LC [36]. We also reported that mRNA and protein for the low affinity p75 receptor are not expressed by cells isolated from the human ONH. In addition we have shown that LC cells and ONH astrocytes can respond to exogenous NTs via Trk phosphorylation resulting in cell proliferation and secretion of NTs [37]. Due to the proximity of LC cells and ONH astrocytes to RGC axons within the LC, these cells could serve as a paracrine source of NTs for RGCs.
The administration of exogenous NTs has been shown to protect neurons from ischemic damage [38,39] suggesting endogenous NT sources could also be neuroprotective. A recent report by Rudzinski et al. demonstrated that ocular hypertension increased NGF and BDNF expression within the retina [40]. Furthermore, Trk A expression in the retina was increased following elevated intraocular pressure (IOP), and this increase was observed in RGCs. Cui et al. observed an increase in Trk A, Trk B and Trk C expression in RGCs following optic nerve injury [41], again suggesting a neuroprotective role for NTs in RGC injury.
In POAG, the laminar plates of the LC are compressed and bow backward from the sclera producing an excavated and exaggerated optic cup [42]. Because capillaries within the LC are located in the connective tissue plates [43], elevated IOP would likely compromise the blood flow to the LC. Ischemic insults during the progression of POAG could increase the expression and/or secretion of NTs from LC cells and ONH astrocytes, thereby increasing paracrine and/or autocrine NT signaling within the LC.
Given that ischemia can be a component of ocular hypertension, we used oxygen-glucose deprivation (OGD) as an acute model of in vitro ischemia and examined the expression of NTs and their receptors by cultured LC cells and ONH astrocytes following OGD. We are aware that the model used (oxygen and glucose deprivation) is not a perfect model for what occurs in the glaucomatous optic nerve head. However, this acute model was an attempt to mimic the end results of a chronic condition, and as a "first step" we felt this model was adequate for examining the response to injury in these cell types.
Results
Cell number following oxygen-glucose deprivation
The LC and ONH astrocyte cell lines used in this study have been previously characterized and described [36]. Preliminary studies examining cell survival following anoxia, hypoxia, hypoglycemia or serum withdrawl demonstrated LC cells and ONH astrocytes were resistant to hypoxia and serum withdrawl alone (data not shown). To approach what is occurring in vivo, we used the more acute oxygen-glucose deprivation (OGD) model to examine NT and trk expression, and NT signaling in cells from the ONH.
To determine an exposure of OGD that would result in cellular changes while allowing a majority of cells to remain viable, we examined cell number at various time points of OGD using an assay that estimates cell number based on cell metabolism. The oxygen level within the anoxic incubator was determined to be below detectable levels as measured using an Oxygen Test Kit (Bacharach Inc., Pittsburgh, PA). Lamina cribrosa and ONH astrocyte cell metabolism/cell number following OGD is shown in Figure 1. Lamina cribrosa cell metabolism/cell number did not decrease significantly until 72 hours OGD exposure. At this time point a 40% decrease in LC cell metabolism/cell number was observed. A 20% decrease in LC cell metabolism/cell number was observed following 24 or 48 hours recovery when compared to the controls. ONH astrocyte cell metabolism/cell number also decreased 20–30% following 24–48 hours OGD. A statistically significant 50% decrease in cell metabolism/cell number was observed after 72 hours of OGD. Recovery increased ONH astrocyte cell metabolism/cell number slightly when compared to OGD. We chose to collect protein and conditioned media following (a) 48 hours exposure to in vitro ischemia and (b) 48 hours in vitro ischemia plus 24 hours recovery. We chose these time points since greater than 80% of LC cells and ONH astrocytes were present indicating that a majority of cells were viable.
Neurotrophin protein expression following oxygen-glucose deprivation
The expression of NT protein in LC cells and ONH astrocytes following OGD is shown in Figures 2 and 3. Multiple isoforms for all four NTs were observed in LC cells and ONH astrocytes. These isoforms most likely represent proneurotrophins, premature forms of the NTs that have recently been shown to possess biological activity [44]. A representative blot for each NT is shown. Band density reported as mean percent of the control ± SEM of three cell lines is shown graphically beneath the blots. Western blots for NTs and trks were stripped and re-probed for β-actin to ensure equal loading. Expression of β-actin in LC cells and ONH astrocytes was not significantly influenced by OGD or recovery from OGD. The overall average β-actin band density reported as a percent of the control ± SEM for LC cells was 108% ± 7 following OGD exposure and 96% ± 3 following recovery from OGD. Similar changes were observed in ONH astrocytes (109% ± 8 following OGD and 107% ± 7 following recovery from OGD).
As seen in Figure 2, the overall trend following 48 hours of OGD was a decrease in NT protein expression by LC cells. The exceptions to this trend were NGF (58 kDa), BDNF (78 kDa), and NT-3 (57 kDa), of which only NT-3 (57 kDa) demonstrated a statistically significant increase in protein expression following OGD. Of the remaining NT isoforms, BDNF (67 kDa) and NT-4 (69 kDa) demonstrated a statistically significant decrease in expression following OGD. A recovery period of 24 hours in growth media and an aerobic environment following 48 hours OGD resulted in elevated NT protein expression by LC cells, however only BDNF (67 kDa) expression levels were statistically significant.
Following 48 hours of OGD, ONH astrocytes (Figure 3) demonstrated increased protein expression of NGF (58 kDa), NT-3 (57 kDa) and BDNF (78 kDa); only BDNF (78 kDa) was statistically significant. In addition, the expression of NGF (71 kDa) and BDNF (67 kDa) was decreased to a statistically significant level following OGD. Increased NT protein expression was the trend in ONH astrocytes allowed to recover from OGD for 24 hours in growth media and an aerobic environment. The exceptions were BDNF (78 kDa) and NT-4 (54 kDa), which were slightly lower than control levels. Of the NT isoforms where protein expression increased during recovery, only NGF (71 kDa) demonstrated a statistically significant increase. Western blot densitometry results for LC cells and ONH astrocytes are summarized in Table 1.
Trk receptor protein expression following oxygen-glucose deprivation
Figure 4 represents the expression of Trk receptor protein in LC cells following OGD or recovery from OGD. Data are presented as described in the previous section. Protein expression of Trk B and Trk C receptors decreased to a statistically significant level in LC cells following 48 hours of OGD. In contrast, a statistically significant increase in the expression of Trk A protein was observed following exposure to OGD. Recovery from OGD resulted in a statistically significant decrease in Trk A and Trk C expression, while Trk B and truncated Trk B protein expression was elevated, but not to a statistically significant level.
The expression of Trk receptor protein by ONH astrocytes following OGD and recovery from OGD is shown in Figure 5. A statistically significant decrease in the expression of Trk A, Trk C and truncated Trk B protein by ONH astrocytes was observed following OGD. Trk B protein expression was also decreased, but not to a significant level. Recovery from OGD increased Trk C and truncated Trk B receptor protein expression in ONH astrocytes compared to OGD alone. However, Trk C expression was still decreased to a statistically significant level. Trk A and Trk B protein expression was increased in ONH astrocytes allowed to recover from OGD, although only Trk A expression was statistically significant.
Phosphorylated Trk receptor protein expression following oxygen-glucose deprivation
The expression of phosphorylated Trk receptor protein following OGD and recovery from OGD is shown in Figure 6. Data are presented as described above. Trk phosphorylation indicates the activation of Trk receptors following NT binding. The antibody used was a phospho-pan Trk antibody that recognizes phosphorylated forms of Trk A, Trk B and Trk C receptors. Four phospho-Trk isoforms (148 kDa, 120 kDa, 80 kDa, and 72 kDa) were detected in LC cell and ONH astrocyte controls. The 120 kDa and 72 kDa phospho-Trk isoforms most likely represent phosphorylated Trk A and Trk B respectively [37]. The remaining phospho-Trk isoforms could represent phosphorylated forms of Trk A, B or C.
Overall, exposure to OGD resulted in decreased phospho-Trk protein expression by LC cells and ONH astrocytes. Expression of the 148 kDa phospho-trk isoform was decreased to a statistically significant level in both LC cells and ONH astrocytes, as was the 120 kDa and 80 kDa isoforms in ONH astrocytes. The only increase in phospho-Trk receptor protein expression following OGD was observed in LC cells. Protein expression of the 120 kDa phospho-Trk receptor isoform was increased 60% over the controls in LC cells, which was statistically significant.
Recovery from OGD resulted in increased protein expression of phospho-Trk receptor isoforms in LC cells and ONH astrocytes when compared to OGD alone, with the exception of the 120 kDa isoform in LC cells, which decreased toward control levels. Although its expression increased compared to OGD alone, expression of the 148 kDa isoform was still decreased to a significant level. Interestingly, the 120 kDa phospho-trk isoform was again the only isoform whose expression was increased above control levels. This 100% increase was observed in ONH astrocytes following recovery from OGD and was statistically significant.
Neurotrophin secretion following oxygen-glucose deprivation
The secretion of NGF and BDNF following OGD and recovery from OGD is shown in Figure 7. Data are presented as mean percent of the control ± SEM of three LC and three ONH astrocyte cell lines. The secretion of NGF by LC cells and ONH astrocytes was increased over 200% and 150% following OGD, respectively. This increase in NGF secretion by cultured cells of the human LC was statistically significant. In contrast, BDNF secretion by both cell types was decreased 60 to 90% following OGD, which was statistically significant. Although recovery from OGD returned NGF and BDNF secretion by LC cells toward controls levels, a statistically significant decrease in BDNF secretion by ONH astrocytes was observed. The secretion of NT-3 or NT-4 was not detected by either cell type under any condition.
Discussion
In this study we examined the protein expression of NTs and Trk receptors by LC cells and ONH astrocytes following OGD, an in vitro model of ischemia. Lamina cribrosa cell and ONH astrocyte responses to OGD and recovery from OGD are summarized in Tables 2 and 3. Lamina cribrosa cells and ONH astrocytes increased NGF, BDNF and NT-3 protein expression following OGD. NGF secretion by these cells was also increased by OGD at the time points tested. Exposure to OGD decreased Trk protein expression and activation in LC cells and ONH astrocytes, with the exception of Trk A in LC cells. Recovery from OGD resulted in most NT and Trk receptor protein expression returning toward control levels in LC cells and ONH astrocytes. However, Trk A expression and activation in ONH astrocytes remained significantly elevated over control levels. Overall, this study suggests that paracrine and/or autocrine NT signaling is stimulated in cells from the ONH following an ischemic insult. Alternatively, NT release by ONH cells may act to protect RGCs from ischemic injury.
Ischemia due to elevated IOP during POAG may cause changes within the ONH and contribute to RGC loss [6-11]. As a response to ischemia, neurons and glia have been shown to increase the expression of NTs [17-19]. Local NT sources for RGCs within the retina and LC could potentially protect these neurons during periods of ONH ischemia in POAG. The recent report of Rudzinski et al. [40] is of interest as they demonstrated an up-regulation of both NGF and Trk A after 7 days of ocular hypertension, and a sustained up-regulation of BDNF after 28 days elevated IOP. We have previously demonstrated NT expression and secretion by LC cells and ONH astrocytes [36]. The LC region of the ONH and the cells that reside there may be subject to ischemic injury during the progression of POAG. Therefore, we examined cell number as determined by cell metabolism following OGD to determine if LC cells and ONH astrocytes could survive ischemic insult. Lamina cribrosa cell metabolism/cell number remained within 15% of the controls until 72 hours of OGD, whereas ONH astrocyte cell metabolism/cell number decreased 20–30% over the same exposure time. Lamina cribrosa and ONH astrocyte cell metabolism/cell number returned to within 20% of the controls following recovery from OGD. These results suggest LC cells and ONH astrocytes can survive ischemic injury and therefore could be a potential source of neuroprotective factors.
Neurons, including RGCs, express Trk receptors not only at the axon terminal and cell body, but also along their axons [25-27] suggesting RGCs could bind NTs provided by cells of the ONH. We examined the expression and secretion of NTs by LC cells and ONH astrocytes following OGD to determine if these cells could potentially protect RGCs from ischemic injury. The expression of NGF, BDNF and NT-3 by LC cells and ONH astrocytes increased after exposure to OGD. Examination of NT mRNA levels in LC cells and ONH astrocytes following OGD would determine whether this is due to an up-regulation in expression or to increased processing of proNTs. Interestingly, only NGF secretion by LC cells and ONH astrocytes was increased by OGD. It is possible that BDNF and NT-3 are secreted by LC cells and ONH astrocytes following OGD, but at time points other than those examined or at lower detection levels. Overall, the responses to OGD by LC cells and ONH astrocytes with respect to the expression of NTs appeared favorable to RGCs. Neurotrophin expression was increased in both cell types, as was NGF secretion. Thus it appears LC cells and ONH astrocytes could provide RGCs with neuroprotective factors following ischemic injury.
The expression of Trk receptors (both full length and truncated) by LC cells, ONH astrocytes or other cells within the LC could limit NT availability to RGCs [22-24]. Therefore, we examined Trk protein expression and activation in LC cells and ONH astrocytes following OGD. Trk receptor expression was decreased in LC cells and ONH astrocytes following OGD, with the significant exception of Trk A in LC cells. As an indication of NT signaling, we examined the expression of phosphorylated Trk receptors in LC cells and ONH astrocytes following OGD. Phospho-Trk A expression in LC cells was increased after OGD, while all other phospho-Trk expression was decreased. The up-regulation of autocrine NGF signaling in LC cells (e.g. NGF secretion and Trk A expression) could explain the differences observed between LC and ONH astrocyte cell number in following OGD. In a previous report we demonstrated that exogenous NGF increased LC cell number [37]. As LC cells do not express p75, this response is most likely due to Trk A activation. Together these data suggest Trk expression by ONH astrocytes would not interfere with NT availability to RGCs during ischemia. In contrast, increased expression of Trk A in LC cells resulted in increased Trk A activation, implying NGF expression in these cells during ischemia may be self protective rather than protective toward RGCs.
There is evidence that reperfusion following ischemia is actually more detrimental to cells than ischemia itself [45,46]. To determine if cells from the LC could provide RGCs with NT support during reperfusion of the ONH in POAG, we examined NT and Trk receptor expression following recovery from OGD. ONH astrocytes could be a source of NTs for RGCs during reperfusion of the ONH as an increase in NGF protein expression was observed in these cells after recovery from OGD. However, recovery from OGD also increased Trk A expression and activation in ONH astrocytes, implying these cells up-regulate autocrine/paracrine NGF signaling during recovery from an ischemic event. In addition, Trk B receptor expression by LC cells and ONH astrocytes increased after recovery from OGD suggesting NT availability to RGC axons within the LC may be compromised during ONH reperfusion. Based on these results, LC cells and ONH astrocytes would be unable to provide RGCs with NTs during reperfusion of the ONH. Increased NT expression by LC cells and ONH astrocytes during ONH ischemia could protect RGCs from injury; however, as blood flow was restored NT expression by LC cells and ONH astrocytes would decrease to normal levels, leaving RGCs vulnerable to injury. Neurotrophin expression by LC cells and ONH astrocytes may be beneficial to RGCs during ischemia, but may be unable to promote the survival of these neurons during reperfusion.
In conclusion, we have demonstrated that LC cells and ONH astrocytes increase the expression of NGF, BDNF and NT-3 protein following OGD, which may be neuroprotective for RGCs. Neurotrophins expressed by LC cells and ONH astrocytes following OGD or recovery from OGD bind and activate Trk receptors expressed by these cells. Increased NT signaling within LC cells and ONH astrocytes could increase cell survival following ischemic injury, but may compromise RGC survival during reperfusion. Further studies examining the expression of NTs and Trk receptors following hypoxia or transient ischemic insults would provide a better model for ischemic injury in POAG. Using this model, RGCs could be co-cultured with LC cells or ONH astrocytes to determine the neuroprotective effects of NTs during ischemic injury. By better understanding NT signaling within the LC under normal and injurious conditions, new strategies involving these factors could be developed to better treat patients with POAG.
Conclusions
Lamina cribrosa cells and ONH astrocytes respond to conditions that mimic ONH ischemia by increasing NGF, BDNF and NT-3 protein expression and NGF secretion.
Increased protein expression of Trk receptors and phosphorylated Trk receptors by LC cells and ONH astrocytes following OGD and recovery from OGD respectively suggest paracrine and/or autocrine NT signaling occurs within the ONH following ischemic injury.
Lamina cribrosa cells and ONH astrocytes may be a paracrine source of NGF, BDNF and/or NT-3 for RGCs, especially during ischemic injury within the ONH throughout POAG progression.
Methods
Materials
DMEM and fetal bovine serum (FBS) were purchased from HyClone Labs, Logan, UT. The following materials were purchased from Gibco BRL Life Technologies, Grand Island, NY; glucose free DMEM, L-glutamine, penicillin/streptomycin and fungizone (amphotericin B). Costar 96-well plates and Nunc ELISA/EIA 96 well Maxisorp plates were purchased from Fisher Scientific, Pittsburgh, PA. Polyclonal antibodies to Trk A, Trk B, Trk C, truncated Trk B (Trk B.T) and phosphorylated Trk were purchased from Santa Cruz Biotechnology Inc, Santa Cruz, CA. CellTiter 96® Aqueous Non-Radioactive Cell Proliferation Assays and Emax™ ImmunoAssay Systems specific for NGF, BDNF, NT-3 and NT-4 were purchased from Promega Corporation, Madison, WI.
Lamina cribrosa and ONH astrocyte cell culture
Lamina cribrosa and ONH astrocyte cell lines were obtained from human LC explants from separate donors as described previously [36]. Cells were cultured in Ham's F-10 Media (LC cells, JRH Biosciences, Lenexa, KS) or DMEM (ONH astrocytes) supplemented with 10% FBS, L-glutamine (0.292 mg/ml), penicillin (100 units/ml)/streptomycin (0.1 mg/ml), and amphotericin B (4 μg/ml) as previously described. Cells were passaged using a 0.25% trypsin solution (Sigma-Aldrich, St. Louis, MO). All cultures were maintained in 5% CO2/95% air at 37°C and media was changed every 2 to 3 days. Characterization of these cells was performed as described previously [36]. Cells expressing α-smooth muscle actin that did not express glial fibrillary acidic protein (GFAP) were characterized as LC cells [32,33,36]. Cells expressing GFAP and neural cell adhesion molecule (N-CAM) were characterized as ONH astrocytes [34-36]. Both cell types expressed extracellular matrix proteins, such as collagen I, collagen III, collagen IV and elastin [32,33,36]. Adult cell lines from donors whose ages ranged from 39 years to 90 years were used in the following experiments.
Oxygen-glucose deprivation
Preliminary studies examining cell survival following anoxia, hypoxia, hypoglycemia or serum withdrawl demonstrated LC cells and ONH astrocytes were resistant to hypoxia and serum withdrawl alone (data not shown). To approach what is occurring in vivo, we used the more acute oxygen-glucose deprivation (OGD) model to examine NT and trk expression and NT signaling in cells from the ONH. Preconfluent, age-matched adult LC cells and ONH astrocytes were treated with serum free media for 24 hours. Oxygen-glucose deprivation (OGD) was achieved by culturing LC cells and ONH astrocytes in glucose free serum free DMEM in an anoxic incubator (95% N2/5% CO2) for 48 hours. The oxygen level within the anoxic incubator was measured using a Oxygen Test Kit (Bacharach Inc., Pittsburgh, PA) and was determined to be below detectable levels. Recovery following OGD was achieved by placing cells in OGD conditions for 48 hours and then allowing them to recover in growth media (Ham's F-10 Media or DMEM plus 10% FBS) and an aerobic environment (95% air/5% CO2) for 24 hours. Cells cultured in growth media and an aerobic environment for served as controls.
Determination of cell number based upon cell metabolism following oxygen-glucose deprivation
Adult LC cells and ONH astrocytes were trypsinized, counted using a hemacytometer and plated into Costar 96-well plates at a density of 1,000 cells/well. Cells were allowed to attach overnight and were then placed in serum free media for 24 hours. Lamina cribrosa cells and ONH astrocyte were exposed to OGD as described above for 24, 48 or 72 hours. Recovery following OGD was achieved by placing cells in OGD conditions for 24 or 48 hours and then allowing them to recover in growth media and an aerobic environment (95% air/5% CO2) for 24 hours. Cells cultured in growth media and an aerobic environment for served as controls.
Cell number was estimated using the CellTiter 96® Aqueous Non-Radioactive Cell Proliferation Assay which measures product from a metabolic process to estimate cell number. Following exposure to OGD or recovery from OGD, media was removed and replaced with 100 μl of serum free media. Twenty microliters of MTS/PMS solution was added to each well. Plates were incubated at 95% air/5% CO2 at 37°C for 1 hour, at which time the absorbance at 490 nm was read using a SpectraMax® 190 microplate reader and Softmax® Pro (Molecular Devices Corporation, Sunnyvale, CA). Metabolically active cells convert MTS into formazan, which is soluble in aqueous solutions. The quantity of the formazan product measured by the amount of absorbance at 490 nm is therefore directly proportional to the number of living cells. Cell metabolism/cell number per well was calculated from a standard curve generated using known amounts of cells per well. A standard curve was generated for each cell line assayed. Three LC cell lines and three ONH astrocyte cell lines were assayed. The entire experiment, including standard curves, was repeated twice. Changes in cell metabolism/cell number following OGD and recovery from OGD were reported as a percent of the control ± SEM.
Protein extraction and western blot analysis
Cellular protein was collected in lysis buffer modified from Watson et al. [47] [20 mM Tris (pH 7.4), 137 mM NaCl, 1% NP40, 10% glycerol, 48 mM sodium fluoride, 16 mM sodium pyrophosphate, 1 mM PMSF, 20 μM leupeptin, 10 μg/ml aprotinin, and 1 mM sodium orthovanadate (10 μl/ml)]. Protein concentration was measured using the Bio-Rad Dc Protein Assay System (Bio-Rad Laboratories, Richmond, CA). Cellular lysate (50 μg) was separated on denaturing polyacrylamide gels and then transferred by electrophoresis to nitrocellulose membranes. Blots were processed using primary antibodies and the Western Breeze Chemiluminescent Immunodetection System (Invitrogen, Carlsbad, CA). Blots were then exposed to Hyperfilm-ECL (Amersham, Arlington Heights, IL) for various times depending on the amount of target protein present. The density (O.D. × mm2) of unsaturated bands was measured using the Discovery Series scanner and the Diversity One program from pdi (Huntington, NY) and a digital Venturis FP466 computer (Compaq, Houston, TX). Western blots for NTs and trks were stripped and re-probed for β-actin to ensure equal loading.
Conditioned media and immunoassays
Conditioned media was collected and concentrated using Millipore Centriplus YM-3 Centrifugal Filter Devices (Millipore Corporation, Bedford, MA). Emax™ ImmunoAssay Systems specific for NGF, BDNF, NT-3 and NT-4 (Promega) were performed according to manufacturer's instructions. Conditioned media was added to Nunc ELISA/EIA 96 well Maxisorp plates coated with anti-NT polyclonal antibodies. Secreted NT was detected by treating the plates with the respective NT monoclonal antibody followed by a horseradish peroxidase conjugated secondary antibody. Enzyme substrate was added to generate a color product whose absorbance was read at 450 nm. A NT standard included in each assay was used to generate a standard curve that was used to calculate the amount of secreted NT per well. The amount of secreted NT per sample was normalized to total protein per sample. Samples were assayed in triplicate. Conditioned media from three LC cell lines and three ONH astrocyte cell lines were assayed. Each immunoassay was repeated twice. Changes in NT secretion following OGD and recovery from OGD were reported as a percent of the control ± SEM.
Statistical analysis
Cell metabolism/cell number, western blot densitometry and immunoassay data were analyzed using one way analysis of variance (ANOVA) followed by validation using Student-Newman-Keuls tests. The MedCalc® statistical package, version 7.4.41 [48] was used for statistical analysis. Significance values were adjusted in accordance with Bonferroni's correction for multiple tests [49].
Authors' contributions
WL carried out tissue culture, cell proliferation assays, Western blotting, and immunoassays. WL, AC, and RW participated in design of the study, interpretation of the results, and in the writing and revision of the manuscript. All authors read and approved the final manuscript. This study is taken in part from a dissertation submitted to the UNT Health Science Center in partial fulfillment of the requirements for the degree Doctor of Philosophy for WL.
Acknowledgements
Research underlying this article was made possible by Alcon Laboratories and National Institute of Health # EY12783. The authors would also like to acknowledge and thank The Glaucoma Foundation for their generous support, The Central Florida Lions Eye and Tissue Bank for ocular tissue, and Sherry English-Wright, Dr. William Howe, and Paula Billman for their technical assistance.
Figures and Tables
Figure 1 Cell metabolism/cell number following exposure to and recovery from Oxygen-Glucose Deprivation. Lamina cribrosa cells and ONH astrocytes were exposed to oxygen-glucose deprivation (OGD), recovery following OGD or control conditions for the time points indicated. Three LC cell lines and three ONH astrocyte cell lines were assayed per experiment. A standard curve was generated for each cell line in order to calculate cell metabolism/cell number. Data shown as percent of the control and represents the mean ± SEM (n = 3). * indicates p < 0.008 (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests).
Figure 2 NT protein expression in LC cells following exposure to and recovery from Oxygen-Glucose Deprivation. Lamina cribrosa cells were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). Cells exposed to growth media and 95% air/5% CO2 for 48 hours served as controls (C). Cell lysate was separated by SDS-PAGE and the density of unsaturated bands was measured for each blot. A representative blot for each NT is shown. Band density reported as mean percent of the control ± SEM (n = 3) is shown graphically beneath the blots. * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests).
Figure 3 NT protein expression in ONH astrocytes following exposure to and recovery from Oxygen-Glucose Deprivation. ONH astrocytes were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). Cells exposed to growth media and 95% air/5% CO2 for 48 hours served as controls (C). Cell lysate was separated by SDS-PAGE and the density of unsaturated bands was measured for each blot. A representative blot for each NT is shown. Band density reported as mean percent of the control ± SEM (n = 3) is shown graphically beneath the blots. * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests).
Figure 4 Trk receptor protein expression in LC cells following exposure to and recovery from Oxygen-Glucose Deprivation. Lamina cribrosa cells were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). Cells exposed to growth media and 95% air/5% CO2 for 48 hours served as controls (C). Cell lysate was separated by SDS-PAGE and the density of unsaturated bands was measured for each blot. A representative blot for each Trk receptor is shown. Band density reported as mean percent of the control ± SEM (n = 3) is shown graphically beneath the blots. * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests). Trk B.T; truncated Trk B.
Figure 5 Trk receptor protein expression in ONH astrocytes following exposure to and recovery from Oxygen-Glucose Deprivation. ONH astrocytes were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). Cells exposed to growth media and 95% air/5% CO2 for 48 hours served as controls (C). Cell lysate was separated by SDS-PAGE and the density of unsaturated bands was measured for each blot. A representative blot for each Trk receptor is shown. Band density reported as mean percent of the control ± SEM (n = 3) is shown graphically beneath the blots. * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests). Trk B.T; truncated Trk B.
Figure 6 Phosphorylated Trk protein expression following exposure to and recovery from Oxygen-Glucose Deprivation. Lamina cribrosa cells and ONH astrocytes were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). Cells exposed to growth media and 95% air/5% CO2 for 48 hours served as controls (C). Cell lysate was separated by SDS-PAGE and the density of unsaturated bands was measured for each blot. A representative blot for each phospho-Trk receptor isoform is shown. Band density reported as mean percent of the control ± SEM (n = 3) is shown graphically beneath the blots. * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests).
Figure 7 NT secretion following exposure to and recovery from Oxygen-Glucose Deprivation. Lamina cribrosa cells and ONH astrocytes were exposed to OGD for 48 hours (OGD) or to 48 hours OGD followed by recovery for 24 hours (OGD+R). NGF and BDNF secretion was determined by immunoassay of concentrated conditioned media from three LC cell lines and three ONH astrocyte cell lines. A NT standard was included in each assay and was used to generate a standard curve. Data are reported as percent of the control ± SEM (n = 3). * indicates p < 0.016 for OGD compared to C and OGD+R; † indicates p < 0.016 for OGD+R compared to C (one way analysis of variance [ANOVA] followed by validation using Student-Newman-Keuls tests).
Table 1 Summary of western blot densitometry data expressed in percent of the control ± SEM of three cell lines.
Lamina cribrosa cells ONH astrocytes
OGD OGD+R OGD OGD+R
NGF (71 kDa) 16 ± 7 162 ± 71 14 ± 4 118 ± 7
NGF (58 kDa) 264 ± 67 106 ± 3 157 ± 18 122 ± 39
BDNF (78 kDa) 1244 ± 414 265 ± 152 288 ± 75 78 ± 20
BDNF (67 kDa) 22 ± 6 151 ± 22 22 ± 9 121 ± 13
NT-3 (78 kDa) 57 ± 12 120 ± 52 145 ± 13 129 ± 32
NT-3 (57 kDa) 213 ± 37 77 ± 20 188 ± 22 126 ± 32
NT-4 (69 kDa) 8 ± 7 119 ± 30 4 ± 3 117 ± 30
NT-4 (54 kDa) 32 ± 13 98 ± 35 44 ± 12 85 ± 13
Trk A 175 ± 15 54 ± 12 53 ± 7 178 ± 21
Trk B 25 ± 9 150 ± 24 57 ± 38 157 ± 33
Trk C 46 ± 9 63 ± 7 25 ± 7 73 ± 3
Trk B.T 65 ± 16 132 ± 26 40 ± 8 99 ± 7
pTrk (148 kDa) 23 ± 9 37 ± 10 12 ± 10 59 ± 16
pTrk (120 kDa) 162 ± 16 93 ± 8 19 ± 14 204 ± 19
pTrk (80 kDa) 80 ± 14 92 ± 12 6 ± 4 104 ± 18
pTrk (72 kDa) 13 ± 7 70 ± 18 4 ± 3 77 ± 13
Table 2 Lamina cribrosa cell and ONH astrocyte responses to oxygen-glucose deprivation.
Lamina cribrosa cells ONH astrocytes
Cell Number 95 % survival at 24 hrs, 85% at 48 hrs 70 % survival at 24 hrs, 80% at 48 hrs
NT Expression ↑ NGF, ↑ BDNF, ↑ NT-3; ↓ NT-4 ↑ BDNF, ↑ NT-3; ↓ NGF, ↓ NT-4
Trk Expression ↑ Trk A; ↓ Trk C, ↓ Trk B ↓ Trk A, ↓ Trk C, ↓ Trk B.T
Phospho-Trk Expression ↑ 120 kDa isoform (Trk A); ↓ 148 kDa isoform ↓ all phospho-trk receptors
isoform
Secretion of NTs ↑ NGF; ↓ BDNF ↑ NGF; ↓ BDNF
Table 3 Lamina cribrosa cell and ONH astrocyte responses to recovery following oxygen-glucose deprivation.
Lamina cribrosa cells ONH astrocytes
Cell Number 80 % survival 80 % survival
NT Expression ↑ BDNF; others near control levels ↑ NGF
Trk Expression ↓ Trk A, ↓ Trk C ↑ Trk A
Phospho-Trk Expression ↓ 148 kDa isoform; others near control levels ↑ 120 kDa isoform (Trk A); ↓ 148 kDa isoform
Secretion of NTs near control levels ↓ BDNF
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| 15579199 | PMC539236 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Dec 3; 5:51 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-51 | oa_comm |
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-561558827710.1186/1471-2202-5-56Research ArticleAge-dependent plasticity in the superior temporal sulcus in deaf humans: a functional MRI study Sadato Norihiro [email protected] Hiroki [email protected] Tomohisa [email protected] Masaki [email protected] Takehiro [email protected] Ken-Ichi [email protected] Yoshiharu [email protected] Harumi [email protected] National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan2 JST/RISTEX, 2-5-1, Atago, Minato-ku, Tokyo, 105-6218, Japan3 Fukui University School of Medicine, Fukui, 910-1193, Japan4 Fukui University School of Education, Fukui, 910-1193, Japan5 Biomedical Imaging Research Center, Fukui University School of Medicine, Fukui, 910-1193, Japan2004 8 12 2004 5 56 56 25 7 2004 8 12 2004 Copyright © 2004 Sadato 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
Sign-language comprehension activates the auditory cortex in deaf subjects. It is not known whether this functional plasticity in the temporal cortex is age dependent. We conducted functional magnetic-resonance imaging in six deaf signers who lost their hearing before the age of 2 years, five deaf signers who were >5 years of age at the time of hearing loss and six signers with normal hearing. The task was sentence comprehension in Japanese sign language.
Results
The sign-comprehension tasks activated the planum temporale of both early- and late-deaf subjects, but not that of hearing signers. In early-deaf subjects, the middle superior temporal sulcus was more prominently activated than in late-deaf subjects.
Conclusions
As the middle superior temporal sulcus is known to respond selectively to human voices, our findings suggest that this subregion of the auditory-association cortex, when deprived of its proper input, might make a functional shift from human voice processing to visual processing in an age-dependent manner.
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Background
There is evidence that cross-modal plasticity induced by auditory deprivation is apparent during sign-language perception. Sign languages involve the use of the hands and face, and are perceived visually [1-3]. Using functional MRI (fMRI), Neville et al. [1] observed increased activity in the superior temporal sulcus (STS) during the comprehension of American Sign Language (ASL) in both congenital deaf subjects and hearing native signers. The authors therefore suggested that the STS is related to the linguistic analysis of sign language. Nishimura et al. [2] found that activity was increased in the auditory-association cortex but not the primary auditory cortex of a prelingual-deaf individual during the comprehension of Japanese sign language (JSL). After this patient received a cochlear implant, the primary auditory cortex was activated by the sound of spoken words, but the auditory association cortex was not. The authors suggested that audio-visual cross-modal plasticity is confined to the auditory-association cortex and that cognitive functions (such as sign language) might trigger functional plasticity in the under-utilized auditory-association cortex. In addition, Pettito et al. [3] observed increased activity in the superior temporal gyrus (STG) in native deaf signers compared with hearing non-signers. These findings suggest that the changes associated with audio-visual cross-modal plasticity occur in the auditory-association cortex. However, the age dependency of this plasticity is not known. To depict the age dependency of the cross-modal plasticity, we conducted a functional MRI study of deaf signers with both early and late deafness, as well as hearing signers, performing a sign-comprehension task. 'Early deaf' subjects were defined as those who lost their ability to hear before the age of 2 years, whereas 'late deaf' subjects lost their hearing after the age of 5 years.
Results
Performance on the JSL comprehension task was similar across the groups (F(2, 14) = 1.279, P = 0.309, one-way ANOVA). The patterns of activity evoked during the sign-comprehension task in the hearing signers and the deaf groups are shown in Figure 1. Within the temporal cortex, all groups showed activation in the occipito-temporal junction extending to the portion of the STG posterior to the Vpc line (an imaginary vertical line in the mid-sagittal plane passing through the anterior margin of the posterior commissure). In the early- and late-deaf subjects, the activation of the posterior STG extended anteriorly to the Vpc line to reach the Vac line (an imaginary vertical line in the mid-sagittal plane passing through the posterior margin of the anterior commissure). The activation was confined to the STG, extending into the superior temporal sulcus, and was more prominent on the left side. A direct comparison between early- and late-deaf subjects revealed significantly more prominent activation of the bilateral middle STS in the early-deaf subjects (Figure 1).
Discussion
The onset of deafness is related to language acquisition. Prelingual deafness occurs before spoken language is learned. Hearing people generally learn their first language before 5 years of age; hence, prelingual deaf individuals are either deaf at birth or became deaf prior to developing the grammatical basis of their native language, which is usually before the age of 5 years. Postlingual deafness is the loss of acoustic senses, either suddenly due to an accident or as a gradual progression after native-language acquisition [4]. Hence, the early-deaf subjects in the present study are categorized as 'prelingual deaf' and the late-deaf subjects are categorized as 'postlingual deaf'. More than 90% of children with prelingual hearing loss have parents with normal hearing [5]. Furthermore, in Japan, the traditional teaching method for deaf children includes aural/oral methods, such as lipreading. Native signers are usually limited to those who were brought up by deaf parents. Because of this, the majority of prelingual deaf subjects learn spoken language (Japanese) in artificial ways, such as aural/oral methods. In the present study, the parents of the deaf subjects all had normal hearing. Five out of six of the early-deaf subjects started JSL training after the age of 6 years. Thus, JSL is not the first language for any of the groups in the present study.
The posterior STS was activated in all groups during sign comprehension, which is consistent with the proposed neural substrates that subserve human movement perception [6]. The posterior STS region is adjacent to MT/V5, which is consistently activated during the perception of human body movement [7-9]. Hence, the activation of the posterior STS in both hearing and deaf subjects is related to the perception of the movement of the hands and mouth.
Both the early- and late-deaf groups showed activation in the planum temporale, whereas hearing signers did not. Anatomically, the anterior border of the PT is the sulcus behind Heschl's gyrus and the medial border is the point where the PT fades into the insula. The posterior border of the PT involves the ascending and descending rami of the Sylvian fissure [10]. Functionally, the left PT is involved in word detection and generation, due to its ability to process rapid frequency changes [11,12]. The right homologue is specialized for the discrimination of melody, pitch and sound intensity [13,14].
It has been shown that non-linguistic visual stimuli (moving stimuli) activate the auditory cortex in deaf individuals, but not in hearing subjects [15,16]. McSweeney et al. [17] showed that the planum temporale is activated in deaf native signers in response to visual sign-language images and this activation is larger for native deaf signers compared to hearing signers. Our previous study [18] revealed that cross-modal activation in the temporal cortex of the deaf subjects was triggered not only by signs but also by non-linguistic biological motion (lip movement) and non-biological motion (moving dots). Signs did not activate the temporal cortex of either the hearing signers or the hearing non-signers. Thus, in the present study, the activation of the planum temporale in the early- and late-deaf subjects is probably due to the effects of auditory deprivation, rather than linguistic processes. This theory is also supported by the fact that the hearing signers in the present study did not show temporal-lobe activity during JSL comprehension, whereas the PT was more prominently activated in the deaf subjects irrespective of the timing of the onset of deafness. These findings indicate that auditory deprivation plays a significant role in mediating visual responses in the auditory cortex of deaf subjects. This is analogous with findings related to visual deprivation: irrespective of the onset of blindness, the visual-association cortex of blind subjects was activated by tactile-discrimination tasks [19,20] that were unrelated to learning Braille [20]. These results suggest that the processing of visual and tactile stimuli is competitively balanced in the occipital cortex. A similar competitive mechanism might occur in the PT following auditory deprivation. Activation of the STG in hearing subjects during lipreading [21] indicates which cortico-cortical circuits might be involved in the competitive balance between the modalities. In fact, we found that the cross-modal plasticity in the deaf subjects occurred within the neural substrates that are involved in lipreading in hearing subjects [18].
The middle STS, anterior to the Vpc line, was activated more prominently in the early- than the late-deaf subjects. This difference is probably not related to linguistic processes, as both early- and late-deaf subjects are equally capable of learning JSL with the same amount of training. The middle STS region is presumably the area that is selective to human voice processing [22]. This area is known to receive predominantly auditory input, being involved in the high-level analysis of complex acoustic information, such as the extraction of speaker-related cues, as well as the transmission of this information to other areas for multimodal integration and long-term memory storage [22]. This implies that early auditory deprivation (at <2 years of age) might shift the role of the middle STS from human voice processing to the processing of biological motion, such as hand and face movements (cross-modal plasticity). It has been suggested that once cross-modal plasticity occurs in the auditory cortex, the restoration of auditory function by means of cochlear implants is ineffective [23]. Hence, the first 2 years of life might be the sensitive period for the processing of human voices.
Considering that the STS voice-selective area is not sensitive to speech per se but rather to vocal features that carry nonlinguistic information [22], the functional role of this region in early-deaf subjects with regard to the paralinguistic aspects of sign language is of particular interest and further investigation will be necessary.
Conclusions
The results of the present study suggest that in early-deaf subjects, non-auditory processing, such as that involved in the perception and comprehension of sign language, involves the under-utilized area of the cortex that is thought to be selective to the human voice (middle STS). This indicates that the sensitive period for the establishment of human voice processing in the STS might be during the first 2 years of life.
Methods
The subjects comprised six early-deaf signers (mean age: 22.8 ± 3.1 years), five late-deaf signers (mean age: 34.4 ± 16.2 years) and six hearing signers (mean age: 33.7 ± 12.1 years; Table 1). The early-deaf subjects lost their hearing before 2 years of age, whereas the late-deaf subjects became deaf after the age of 5 years. The parents of all subjects had normal hearing. None of the subjects exhibited any neurological abnormalities and all had normal MRI scans. None of the cases of deafness were due to a progressive neurological disorder. All deaf and hearing subjects were strongly right handed, except for one late-deaf subject who was ambidextrous, according to the Edinburgh handedness inventory [24]. The study protocol was approved by the Ethical Committee of Fukui University School of Medicine, Japan, and all subjects gave their written informed consent.
The tasks involved the passive perception of JSL sentences that are frequently used in the deaf community. JSL, which has its own grammar, morphemes and phonemes, is different from spoken Japanese at all levels. JSL utilizes facial expressions as obligatory grammatical markers, as does ASL [25]. The fMRI session with JSL consisted of two rest and two task periods, each of 30 seconds duration, with alternating rest and task periods. During the 30-second task period, the subjects were instructed to observe a JSL sentence presented every 5 seconds by a male deaf signer in a video, which was projected onto a screen at the foot of the scanner bed and viewed through a mirror. The sentences were relatively short and straightforward; for example, "I cut a piece of paper with scissors". During the 30-second rest period, the subjects fixed their eyes on the face of a still image of the same person. Each session started with a rest period and two fMRI sessions were conducted. The procedure was identical for all hearing and deaf subjects. After the fMRI session, outside of the scanner, the subjects were presented the JSL sentences used during the session. These were shown one by one on the video screen and the subjects were required to write down the presented sentences in Japanese. On each presentation, the subjects were asked if they had seen the JSL sentence in the scanner, in order to confirm that they had been engaged in the task during the session. The percentage of correct responses was calculated as the number of correctly written sentences divided by the number of presented sentences.
A time-course series of 43 volumes was produced using T2*-weighted gradient-echo EPI sequences with a 1.5 Tesla MR imager (Signa Horizon, General Electric, Milwaukee, Wisc., USA) and a standard birdcage head coil. Each volume consisted of 11 slices, with a slice thickness of 8 mm and a 1-mm gap, which covered the entire cerebral cortex. The time interval between two successive acquisitions of the same image was 3,000 ms, the echo time was 50 ms and the flip angle was 90 degrees. The field of view was 22 cm. The digital in-plane resolution was 64 × 64 pixels. For anatomical reference, T1-weighted images were also obtained for each subject.
The first three volumes of each fMRI session were discarded because of unstable magnetization. The remaining 40 volumes per session were used for statistical parametric mapping (SPM99, Wellcome Department of Cognitive Neurology, London, UK) implemented in Matlab (Mathworks, Sherborn, Mass., USA) [26,27]. Following realignment and anatomical normalization, all images were filtered with a Gaussian kernel of 10 mm (full width at half maximum) in the x, y and z axes.
Statistical analysis was conducted at two levels. First, the individual task-related activation was evaluated. Second, the summary data for each individual were incorporated into the second-level analysis using a random-effects model to make inferences at a population level. The signal was proportionally scaled by setting the whole-brain mean value to 100 arbitrary units. The signal time course for each subject was modeled using a box-car function convolved with a hemodynamic-response function and temporally high-pass filtered. Session effects were also included in the model. The explanatory variables were centered at zero. To test hypotheses about regionally-specific condition effects (that is, sentence comprehension compared with rest), estimates for each model parameter were compared using the linear contrasts. The resulting set of voxel values for each contrast constituted a statistical parametric map (SPM) of the t statistic (SPM{t}).
The weighted sum of the parameter estimates in the individual analyses constituted 'contrast' images that were used for the group analysis. Contrast images obtained via individual analyses represent the normalized task-related increment of the MR signal of each subject. To examine group differences (prelingual deaf, postlingual deaf and hearing signers) in activation due to the sign-comprehension task, a random-effect model was performed with the contrast images (1 per subject) for every voxel. Using the a priori hypothesis that there would be more prominent activation in the early- than late-deaf subjects, we focused on the temporal cortex, which was anatomically defined in standard stereotaxic space [28]. The threshold for SPM{t} was set at P < .001 without a correction for multiple comparisons.
Authors' contributions
NS carried out the fMRI studies, data analysis and drafted the manuscript. HY and TO conducted the MR imaging. MY, TH and KM prepared the task materials. YY and HI participated in the task design and coordination. All authors read and approved the final manuscript.
Acknowledgements
This study was supported by a Grant-in Aid for Scientific Research B#14380380 (NS) from the Japan Society for the Promotion of Science, and by Special Coordination Funds for Promoting Science and Technology from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government.
Figures and Tables
Figure 1 The results of group analysis. Statistical parametric maps of the average neural activity during JSL comprehension compared with rest are shown in standard anatomical space, combining hearing signers (left column), early-deaf signers (Early Deaf; second column) and late-deaf signers (Late Deaf; third column). The region of interest was confined to the temporal cortex bilaterally. The three-dimensional information was collapsed into two-dimensional sagittal and transverse images (that is, maximum-intensity projections viewed from the right and top of the brain). A direct comparison between the early- and late-deaf groups is also shown (E – L, right column). The statistical threshold is P < 0.001 (uncorrected). Right bottom, the group difference of the task-related activation (E – L) was superimposed on sagittal and coronal sections of T1-weighted high-resolution MRIs unrelated to the subjects of the present study. fMRI data were normalized in stereotaxic space. The blue lines indicate the projections of each section that cross at (-52, -22, -2). The black arrowhead indicates the STS. Bottom middle, the percent MR signal increase during JSL comprehension compared with the rest condition in the STS (-52, -22, -2) in hearing signers (H), early-deaf (E) and late-deaf signers (L). There was a significant group effect (F(2, 14) = 23.5, P < 0.001). * indicates P < 0.001, + indicates P = 0.001 (Scheffe's post hoc test). Bottom left, task-related activation in the deaf (early + late) groups. The blue lines indicate the projections of each section that cross at (-56, -26, 4). In the deaf subjects, the superior temporal cortices are extensively activated bilaterally.
Table 1 Subject profiles
Lost audition (dB)
Age (years) Sex Age of deafness onset (years) Age of beginning JSL training (years) Duration of JSL training Right ear Left ear Performance (% correct)
Early-deaf signers
1 27 M 0 20 7 95 95 83.3
2 20 M 0 3.5 16.5 90 90 66.7
3 24 M 0 14 10 71 86 75.0
4 19 M 0 6 13 100 100 58.3
5 22 M 0 7 15 95 95 66.7
6 25 M 2 19 6 90 90 66.7
Average 22.8 0.3 11.58 11.3 90.2 92.7 69.4
Late-deaf signers
7 21 F 9 11 10 100 100 83.3
8 22 M 5 6 16 120 120 66.7
9 35 F 10 20 15 120 120 100.0
10 61 F 11 55 6 90 120 58.3
11 33 F 6 27 6 90 90 91.7
Average 34.4 8.2 23.8 10.6 104 110 80.0
Hearing signers
12 46 M 25 21 75.0
13 40 F 32 8 66.7
14 26 F 24 2 83.3
15 20 F 16 4 75.0
16 23 F 21 2 83.3
17 47 F 27 20 75.0
Average 33.7 24.2 9.5 76.4
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| 15588277 | PMC539237 | CC BY | 2021-01-04 16:03:46 | no | BMC Neurosci. 2004 Dec 8; 5:56 | utf-8 | BMC Neurosci | 2,004 | 10.1186/1471-2202-5-56 | oa_comm |
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